Design Thinking Research: Achieving Real Innovation - Meinel Ch., Leifer L.

A Humanistic and Creative Philosophy of Design
1 Introduction

2 Humanistic and Creative Qualities in Design

2.2 Attitudes and Human Values
2.3 Attributes

Interpersonal Trust Activity to Increase Team Creativity Outcome

2 Multidimensional Basis of Design Thinking Team Interaction

3.5 Level of Team Creativity Outcome
3.6 Post Experimental Assessments

3.7 Functional NIRS Data Acquisition
3.8 Functional NIRS Analysis

3.9 Functional NIRS Dynamic Inter-Brain Synchrony (IBS) States Analyses

3.10 Experimental Hypothesis
4 Planned Analyses and Conclusion

Dancing with Ambiguity Online
1 Introduction

2 Related Works
2.1 Online Actions on Social Media

2.3 Ambiguity of Content on Social Media

3 Methods
3.1 Survey Instrument
3.2 Qualitative Coding

4 Results
4.1 Relating to Certain Actions

4.3 Relating to Certain Stakeholders
5 Discussion

5.1 Sometimes Nuance Does Not Matter, Negativity Simply Makes us Wonder Why

5.2 Ambiguity Is Amplified by the Visibility of Actions

5.3 Actions Are Especially Ambiguous when there Are Opposing Sentiments

5.4 Design Implications
5.4.1 People May Not Be Able to Improve, Even If They Want To

5.4.2 People May Be Unaware of Their Individual Impact

Design Thinking for Digital Transformation
1 Introduction

1.1 How Can Design Thinking Contribute to an Organization´s Digital Transformation?
2 Research Approach
3 Practical Perspectives

3.2 Depth and Diffusion of Design Thinking Elements in Organizations

3.3 An Integrative Conceptualization of Design Thinking in Organizations

4.1 A Holistic Perspective on Digital Transformation

4.2 A Dynamic Capabilities View on Digital Transformation

4.3 Design Thinking and Dynamic Capabilities

5 Reconciling Theory and Practice
5.1 Modes of Influence

Experiences of Facilitating Virtual Design Thinking
1 Introduction

3.2.1 Atmosphere in Learners´ Physical Space
3.2.2 Atmosphere in the Virtual Space

3.2.3 Linking the Atmosphere in Physical and Virtual Spaces
4 Process
4.1 Random Inspiration

4.2.1 Technology Usage and Training
4.2.2 Communication with Various Stakeholders
4.2.3 Timing

4.2.4 Avoid Over-Structuring the Learning Experience
5 Discussion

Accessibility of Linked-Node Diagrams on Collaborative Whiteboards for Screen Reader Users
1 Introduction

2 Background and Related Work
2.1 Collaborative Design Tools
2.2 Accessible Spatial User Interfaces for the Blind

3 User Study Design
3.1 Informal Interview
3.2 Online Whiteboard Evaluation

3.3 Exploration of Other Access Methods
3.4 Sequence of Exploration

4 User Study Results
4.1 Informal Interview
4.2 Online Whiteboard Evaluation

A Pattern Language of an Exploratory Programming Workspace
1 Introduction

2 Pattern Form, Intents, and Agents
2.1 The Pattern Form

2.4 The Environment
2.5 The Interaction Context

3 Conversation in Context
3.1 A Desire for Exploration

4 Elaborate Inquiry
4.1 A Desire for Exploration

4.5 Related Patterns
5 Coach Your Environment
5.1 A Desire for Exploration

5.4 Consequences
5.5 Related Patterns
6 Concept in Shards
6.1 A Desire for Exploration

6.5 Related Patterns
7 Proxy Transport
7.1 A Desire for Exploration

7.4 Consequences
7.5 Related Patterns
8 Simple Response

9 Tangible Response
9.1 A Desire for Exploration

9.4 Consequences
9.5 Related Patterns
10 Pause and Explore
10.1 A Desire for Exploration

1 Introduction
2 Conversations and Narratives

6.1 Choosing Robust Scales
6.2 Final Survey Design

Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems
1 Introduction

5 Generating Explanations
6 User Interface Design

10.1 How Can We Continuously Evaluate User Mental Models Over Time?
10.2 How Can We Predict Mental Model Breakdowns?

10.3 How Can We Detect System Failures?
10.4 How Should We Present State Exposures?

Designing for Value Creation
1 Introduction

2 Mapping Design, Design Thinking, and Digital Health Interface

2.1 Bibliometric Review Query
2.2 Zooming in on Digital Health Design-Oriented Education

2.3 The Gap/Opportunity in Digital Health Design Education

3 Action Research Cases
3.1 Embedding Designing-as-Performance (D-a-P) Principles and Methods into Health Contexts

3.1.1 D-a-P Principles (3)
3.1.2 D-a-P Method A

4.1.1 Learning Goals
4.1.2 Conveyed Competencies

4.2 Zooming in on Digital Health Design-Oriented Education
4.2.1 StudyU
Project Goal

4.2.2 Sleepfull Project (DHDL Winter Semester 2021 in Collaboration with StudyU)
Project Goal

5 Reflections and Insights for Design Educators and Practitioners in Digital Health Contexts

Different Concepts of Human Needs and Their Relation to Innovation Outcomes

2 Does a Focus on Needs Foster Incremental Innovation Only?

3 John Arnold´s Need-Based Innovation Theory

5 Robert H. McKim´s Need-Based Design Theory

1 Introduction
2 Experimental Psychology in Design

2.1 Ergonomics and Human Factors
2.2 Human-Computer Interaction

2.3 Interaction Design
3 Medicine and Rehabilitation in Design

3.1 Design for People with Disabilities
3.2 Transgenerational Design

3.3 Universal Design
3.4 Design for All
3.5 Inclusive Design

4 Humanistic Psychology in Design
4.1 Humanistic and Creative Design
4.2 Design Thinking

5 Communal Practices in Design
5.1 Design Participation
5.2 Cooperative Design
5.3 Participatory Design

6 Social Psychology in Design
6.1 Social Design

6.2 Design as Social Collaboration
7 Conclusion
References

3 Study Design and Methods
3.1 Overview of the Research Design

3.2 ME310 and ME218 Alumni Survey Instruments and Respondents

4 Results
4.1 Overall Picture of ME310 and ME218 Alumni

5 Discussion and Next Steps
5.1 Discussion

Different Types of Productive Thinking in Design
1 Introduction

2.1 Psychological Position in Productive Thinking

3 Different Types of Productive Thinking in Design
3.1 Rational Productive Thinking

3.3 Experimental Productive Thinking
3.3.1 Anticipation

The Cultural Construction of Creative Problem-Solving

2 Critical Reflection on Design Thinking Based on Designers´ Cultural Needs

3.1 Historically Derived Ideas of Creative Problem-Solving
3.1.1 Historically Derived Ideas in the USA

3.1.2 Historically Derived Ideas in East Asia

3.1.3 Cultural Ideas Between the USA and East Asia

3.2 Policies, Norms, and Practices Around Creative Problem-Solving
3.2.1 Policies, Norms, and Practices in the USA

3.2.2 Policies, Norms, and Practices in East Asia

3.3 Psychological Tendencies of Creative Problem-Solving

4.1 Cultural Variations in Perceptions of the Agency of Context

4.2 Relation Between Perceived Agency of Context and Other Factors and Its Manipulation

4.3 Engineering Students´ Motivation for Problem-Solving
4.4 Which Makes an Idea Better

5 Discussion
5.1 Embracing Diversity Inherent in the Human Processes of Creative Problem-Solving

5.2 Critical Next Steps in Design Thinking, Teaching, and Learning

5.3 Reflection on Studies and Future Work
5.3.1 Study Limitations
5.3.2 Future Work

5.3.3 Other Potential Empirical Approaches

Design Thinking as a Catalyst and Support for Sustainability Solutions

3 Design Thinking Characteristics That Support Sustainability Solutions

3.3 Encourages and Inspires Diversity in Thought and Action
3.4 Adopts a Holistic, Systems-Thinking Mindset

3.5 Offers a Streamlined, Action-Oriented Process

3.6 Connection Between Design Thinking and Collective Action

Author: Meinel Ch. Leifer L.

Tags: business design innovation

ISBN: 978-3-031-09296-1

Year: 2022

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Text

Understanding Innovation

Christoph Meinel
Larry Leifer Editors

Design Thinking
Research
Achieving Real Innovation

Understanding Innovation
Series Editors
Christoph Meinel, Potsdam, Germany
Larry Leifer, Stanford, USA

“Everyone loves an innovation, an idea that sells.” Few deﬁnitions of innovation are
more succinct. It cuts to the core. Yet in doing so, it lays bare the reality that selling
depends on factors outside the innovation envelope. The “let’s get creative” imperative does not control its own destiny. Expressed another way, in how many ways
can we deﬁne innovation? A corollary lies in asking, in how many ways can the
innovative enterprise be organized? For a third iteration, in how many ways can the
innovation process be structured? Now we have a question worth addressing.
“Understanding Innovation” is a book series designed to expose the reader to the
breadth and depth of design thinking modalities in pursuit of innovations that sell. It
is not our intent to give the reader a deﬁnitive protocol or paradigm. In fact, the very
expectation of “one right answer” would be misguided. Instead we offer a journey of
discovery, one that is radical, relevant, and rigorous.

Christoph Meinel • Larry Leifer
Editors

Design Thinking Research
Achieving Real Innovation

Editors
Christoph Meinel
Hasso Plattner Institute and Digital
Engineering Faculty
University of Potsdam
Potsdam, Germany

Larry Leifer
Stanford University
Stanford, CA, USA

ISSN 2197-5752
ISSN 2197-5760 (electronic)
Understanding Innovation
ISBN 978-3-031-09296-1
ISBN 978-3-031-09297-8 (eBook)
https://doi.org/10.1007/978-3-031-09297-8
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland
AG 2022
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether
the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of
illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and
transmission or information storage and retrieval, electronic adaptation, computer software, or by
similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors, and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or
the editors give a warranty, expressed or implied, with respect to the material contained herein or for any
errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional
claims in published maps and institutional afﬁliations.
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Foreword

As year 13 of the Hasso Plattner Design Thinking Research Program comes to a
close, we can take this opportunity to reﬂect on over a decade of close cooperation
between the d.school at Stanford University in California and the School of Design
Thinking at the Hasso Plattner Institute for Digital Engineering in Potsdam. Humancentered design is an integral part of the digital engineering curriculum. At the same
time, students from other ﬁelds also have the possibility to learn Design Thinking
methods and create impact through human-centered design.
There is an increasing interest in Design Thinking. In universities around the
world, Design Thinking programs have been established. The aim has been to
complement the education of students through a practical, user-centered approach
focused on real-life projects. However, the methodology has not just appeared in
academic settings. Practitioners have also adopted Design Thinking in business and
industry. Design Thinking offers a powerful framework to foster innovation in
products, services, and operations. There is no blueprint to develop an innovation
culture. It is a step-by-step process that evolves with the individual contributions of
team members. Researchers and practitioners alike identify, iterate, and test ideas as
part of a Design Thinking process.
In parallel, the Hasso Plattner Design Thinking Research Program, a research
initiative conducted jointly by Stanford University in California and the Hasso
Plattner Institute for Digital Engineering in Potsdam, Germany, has been working
diligently to learn more about the approach. Afﬁliated researchers in North America
and Europe have meanwhile conducted more than 160 research projects investigating, illuminating, and making sense of Design Thinking. Our understanding of the
methodology has increased exponentially and we have a solid body of new knowledge on the characteristics and mechanisms of effective Design Thinking tools, team
dynamics, and its application in various contexts. Cutting-edge research, of course,
includes data on interactions in virtual and hybrid environments—topics that have
gained increasing importance in the last year.
Collaboration between different university faculties has increased. Levers for
value generation based on human-centric principles can be found in many academic
v

Foreword

disciplines. Concrete outputs are captured in this volume. One excellent example is a
cooperation with neuroscientists. Design Thinking researchers have leveraged newly
developed research methodologies, approaches, and knowledge to foster new ideas
and research in neurodesign, emerging in both Stanford and Potsdam. In 2019, the
Hasso Plattner Institute in Potsdam was the ﬁrst to offer a neurodesign curriculum
for its IT students. In the last three years, the Hasso Plattner Institute has held several
neuroscience symposiums for researchers and established formal and informal
agreements for collaborative work with internationally recognized researchers
from several countries and continents. This work illuminates the biological basis
of creativity, collaboration, innovation, and human-centered design.
In each of the projects highlighted in this volume, co-creation helps teams harness
Design Thinking for value capture and provides value to the (research) community.
Tools and mindsets are calibrated and recalibrated as researchers make strides to
integrate new datasets into established models and to develop new models as
necessary. These developments demonstrate once again the fruitful research
conducted by multi-disciplinary teams in the area of design thinking research. One
way of bringing the ﬁndings to innovators everywhere is through the book series
“Design Thinking Research” published by Springer. This series presents a comprehensive collection of research studies carried out by scholars at both the Hasso
Plattner Institute in Potsdam and Stanford University. In addition to providing the
ﬁndings of the most recent projects, the 13th volume of the series pushes the
boundaries and searches for insights on achieving “real innovation.”
The Design Thinking Research Program includes a growing community of
researchers in a transatlantic context and has inﬂuenced many others around the
world. The program enables a rich exchange between current doctoral candidates,
alumni, researchers, and practitioners from diverse disciplines. Through collaboration and partnerships of many kinds, the Design Thinking Research Program brings
new perspectives, insights, and lasting value not only to the program and its related
researchers, but also to Design Thinking itself and the growing community of
Design Thinkers. We urge you to make a contribution to the already rich exchange
between innovators and researchers. New actors and established stakeholders are
invited to reach out and encourage cooperation and experimentation in their personal
ecosystems. In so doing, the practice and understanding of Design Thinking continues to broaden and deepen, as it carries on its course of deﬁning solutions for the
challenges facing our world.
Palo Alto, CA, USA

Hasso Plattner

Introduction: “The Impact of REAL Prototyping
and Thinking”

Is there any chance of removing “THINKING” and replacing it with
“QUESTIONING”? John Arnold, the founder of the Design Program at Stanford,
famously asserted that “DESIGN is all about QUESTIONS” in a class shortly after
he came to Stanford. Notably, he had been more or less forced out of the mechanical
engineering faculty at another university because of his insistence on teaching
engineering students to be creative, whereby the focus of the engineering mainstream was all about being a good physicist or mathematician. In order to make
strides forward in this ﬁeld, we needed to be creative and to value creativity. This is
still the best strategy.
Creativity must be then applied to BUILDING real prototypes. Only in this way
are we able to move beyond “thinking” to “testing.” Real IMPACT comes when we
test with prospective HUMAN users of the product, the prototype, or the service.
The Design Thinking process enables researchers to think outside the box about
future solutions.
REAL innovation demands REAL divergent thinking and building. Where might
we look to measure divergence? How about looking at the brains of creative thinkers
versus trend thinkers. This volume of Design Thinking Research presents us with
dramatic examples of just this view. One research team, for instance, measures
differences in brain activity and thereby compares the behavior of divergent with
trend thinkers. While we have to be aware of the context in which we are working, at
the same time we must be ready to rethink our preconceived ideas. Design Thinking
is a learning experience. As such, each learning experience should consider new
methods, new capabilities, and new ways of thinking, and yield convergence in the
Design Thinking mindset.
Since 2008, research teams from the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany. and from Stanford University, USA, have engaged in
the joint Design Thinking Research Program, which is ﬁnanced and supported by the
Hasso Plattner Foundation.
In this volume, multidisciplinary research teams from the HPI-Stanford Design
Thinking Research Program scientiﬁcally investigate Design Thinking, digital
vii

viii

Introduction: “The Impact of REAL Prototyping and Thinking”

transformation, and innovation in their various dimensions. Researchers are continually engaged in ideating, advancing, and appraising. Creative collaboration is a
hallmark of the teams. In regularly scheduled community building workshops,
researchers share insights from current investigations and obtain constructive feedback. This process is all part of the multidisciplinary approach to which researchers
aspire. Scientiﬁc evidence supports all design activities undertaken by our research
teams.
For many years, members of our Design Thinking research teams have asked and
continue to ask why subjects do what they do. For impact, we test prototypes with
real users (including each other). Design Thinking behavior can be quantiﬁed, and
there are tools and frameworks in place that help us advance our knowledge of
Design Thinking.
Let us consider the novel use of haptic information (gestures) versus the spoken
and/or written word. From our various political situations, we have discovered that
gestures often reveal more about the speaker’s emotions and deep beliefs than their
words. The role of emotions in technical work and innovation has long been
regarded with suspicion. The PhD research of some of our doctoral candidates has
now revealed that emotions play a signiﬁcantly greater role in technical decision
making than was previously thought. Similar ﬁndings have been produced for
business decision makers, for example, by psychology Professor Brian Knutson at
Stanford University.
Throughout the history of our book series, we have looked critically at the issues
and values that yield innovation—or fail to do so. Our design research has made
many different measures of team innovation a reality: its causes, limits, and implementation. Very importantly, we have gathered evidence to measure what does and
does not make a difference in the ability of a culture or organization to innovate, as
well as the impact of these innovations (real or imagined) on the company culture.
We have measured the emotions and the logic of innovation teams. No one
variable is predictive of a corporate design team’s performance. All the variables
and their context need to be understood and measured. The chapters in this volume
address the complex relationships between Design Thinking, business models, and
entrepreneurship. Creative solutions and continuous development are the goals
needed to redesign work practices. Following these goals allows teams to evolve
in response to internal and external factors. To achieve these solutions and this
development we need creative thought and communication.
Our teams measure the impact of Design Thinking education on young professionals’ predisposition to entrepreneurial outcomes in the short term and long
term. The entrepreneurial outcomes of former Design Thinking students have
increased signiﬁcantly over the last decade. Relevant coursework contributes to
the creation of an ecosystem, which propels these entrepreneurs forward, as it
transcends its original context. Other teams theorize about how to build an appropriate framework to assess the impact of Design Thinking in organizations. However, before we can build a framework we have to understand what challenges can be
addressed by practitioners in the organizational context.

Introduction: “The Impact of REAL Prototyping and Thinking”

Engage. Collaborate. Interact. Join us in exploring the hidden world of team
innovation. Read our books. Get in the driver’s seat and steer innovation. Design
your own research program. Then, share your ideas with us. It is our intention that
this series volume will be a starting point for dialogue in many realms.
Please join us in our quest for noteworthy hypotheses, and in understanding why
these hypotheses are critical to the implementation of Design Thinking and Design
Thinking Research in academia and in industry.

Road Map Through This Book
Over the past year, researchers from HPI and Stanford University have immersed
themselves in a wide range of research projects on Design Thinking. Volume 13 in
the series Design Thinking Research highlights the ﬁndings of afﬁliated
researchers for program year 13. The breadth of topics is impressive. We have
been able to arrange the contributions into three parts, each addressing a different
aspect of Design Thinking research.

Part 1: Value Creation in Virtual Innovation Spaces
The demand for virtual solutions has increased dramatically over the last two years.
In the virtual realm, interaction takes many new directions. Researchers in the
HPDTRP tackle the provocative questions of value creation in virtual innovation
spaces. They do this by way of nuanced information, which they have obtained, on
how individuals interact, and then develop cooperative scenarios, and negotiate
ambiguity. Teams develop appropriate measurement tools for leveraging success
in outcomes, examine existing barriers for technology users, and stake out programmatic changes for facilitating virtual Design Thinking.
In the ﬁrst text, Stephanie Balters, Theresa J. Weinstein, Grace Hawthorne, and
Allan Reiss proceed with the goal of designing better (i.e., new and more efﬁcient)
team performance. The team sets out to show to what extent participation in an
interpersonal trust activity prior to teamwork in an in-person or virtual setting
increases the interpersonal trust and creativity level of the collaborative outcome.
In order to test this hypothesis, functional near-infrared spectroscopy (fNIRS)
hyperscanning is used. The desired outcome is for DT educators and practitioners
to design novel and effective DT activities that correlate positively to team trust and
collaboration.
The research team consisting of So Yeon Park, Mark E. Whiting, and Michael
Shanks raise the question of why ambiguity is so common in online interactions. By
design, ambiguity is possible, with online actions curated and designed by platforms.
Platforms in turn are the mediators. Park, Whiting, and Shanks address the desired
goal of removing ambiguity in online actions through targeted implementations

Introduction: “The Impact of REAL Prototyping and Thinking”

while also improving clarity. The team provides complete design implications for
this purpose that target technical functioning and context. This chapter explores how
social media is designed, and how the scientiﬁc community and public react when
using it.
The following chapter prepared by Carolin Marx examines links between Design
Thinking and digital transformation. Carolin Marx presents a study that deﬁnes
context-dependent and cause-effect relationships to investigate the role dynamic
capabilities can play within this network of effects. By studying these relationships,
practitioners can test hypotheses for speciﬁc contributions of Design Thinking on
digital transformation. The chapter offers results from two empirical studies.
Selina Mayer, Martin Schwemmle, Claudia Nicolai, and Ulrich Weinberg introduce theoretical reﬂections for the process of providing optimized virtual education
experiences. This chapter provides theoretical foundations for evolving conceptions.
In reviewing various theories, the reader is inspired to use ideas to improve Design
Thinking education in the virtual realm.
Danyang Fan, Kate Glatzko, and Sean Follmer underscore the importance of
reﬂecting the diagram structure with the navigation scheme of online whiteboards to
provide a spatial overview for users to reference. These tools are often exclusionary
to people who are blind and visually impaired. This work provides a number of
recommendations and endeavors to improve the accessibility of online whiteboards.
The authors elaborate upon an accessibility gap when interacting with linked-node
diagrams within online white boards and relate the results of user studies with
university students to better understand the accessibility of current tools. Researchers
and students then collaboratively evaluated several existing audio and haptic
approaches. They describe design guidelines and future directions that might
improve user access and understanding of linked-node diagrams.

Part 2: Fostering Innovation Behavior and Co-evolution
Contributions in the second part of this book tackle the question of how innovation
behavior can be fostered and augmented. The researchers identify perceived problems or target the creation of novel approaches to augment existing practice. The DT
project teams are engaged in formulating the right questions and sharing key insights
for DT practitioners and society.
The practice of programming changes rapidly. Marcel Taeumel, Jens Lincke,
Patrick Rein, and Robert Hirschfeld begin with the assumption that newcomers to
the programming ﬁeld face signiﬁcant challenges at the beginning in understanding
mindsets and tools. The team applies the idea of patterns to stake out traditional and
modern practices of exploratory programming. The workspace tool is evaluated. The
team extracted data into a novel pattern language that focuses on the typical
question/response cycle that programmers follow. Their ﬁndings will beneﬁt project
team efﬁciency.

Introduction: “The Impact of REAL Prototyping and Thinking”

In the next chapter, the lead author’s dissertation project with an industry partner
is front and center. Lena Mayer, Katharina Hölzle, Karen von Schmieden, Reem
Refaie, Hanadi Traifeh, and Christoph Meinel contribute to the publication. Narratives from innovation practitioners are translated into a quantitative study. The
empirical study examines the links between employee job insecurity and innovation
behavior. The team further discusses the organizational factors that can foster
employee innovation behavior.
Parastoo Abtahi, Sidney Q. Hough, Jackie Yang, Sean Follmer, and James
A. Landay grapple with how next-generation computing systems challenge traditional ideas of good design and user-centered processes. The authors explore state
exposure timing strategies that enable users to develop up-to-date mental models of
systems that are in a constant process of learning and evolving. When user expectations are not met, according to the research teams, systems should allow for the
co-evolution of mental models and dynamic systems.
Jonathan Antonio Edelman, Babajide Alamu Owoyele, Joaquin Santuber, and
Stefan Konigorski argue that there is a need for more education and training on value
creation in developing better healthcare systems. The authors discuss two
approaches (MEDGI and PretoVids) that they have developed within the past
three years. They have achieved signiﬁcant inroads in increased customer engagement, reduced risk, and improved proﬁt by encouraging medical practitioners and
industry actors to leverage Design for Value Creation (D4VC) in healthcare. After
reviewing design literature in the ﬁeld of healthcare, the authors show how design
can mediate and facilitate the creation of value for many stakeholders.

Part 3: Problematizing Design Thinking as a Concept
This volume’s third part is concerned with building comparative frameworks in the
process of problematizing DT as a concept. The chapters in this section help the
reader to visualize existing models, stake out relationships between DT education
and societal change, and gain a better understanding of DT and its evolution over the
past 50 years.
Julia von Thienen, Constantin Hartmann, and Christoph Meinel deﬁne the variable “human needs” as part of DT—but how does this relate to the variable (radical)
innovation? The authors examine need theories of three authors: John Arnold,
Abraham Maslow, and Robert McKim. Many questions are raised, such as: What
is the future of DT? Is DT only implemented to provide incremental innovation—in
other words, improvement within existing solutions? The researchers conclude with
a discussion on the diversity of human needs concepts in DT. The outlook shows that
new development provides a comprehensive framework of human needs to analyze
product risks and create systemic beneﬁts.
Jan Auernhammer, Matteo Zallio, Lawrence Domingo, and Larry Leifer stake out
the evolution of multiple human-centered design approaches over the preceding
50 years. It is noted that these approaches were developed and adopted in response to

xii

Introduction: “The Impact of REAL Prototyping and Thinking”

socio-material and socio-economic challenges. In this work, emphasis is put on the
need to continue exploring creative design approaches to new challenges.
The author team, composed of Sheri D. Sheppard, Helen L. Chen, George Toye,
Timo Bunk, Nada Elﬁki, Felix Kempf, Johannes Lamprecht, and Micah Lande,
focuses on alumni of speciﬁc mechanical engineering courses (ME310 and ME218)
at Stanford University. The authors examine the long-term impact of speciﬁc
engineering design courses on graduates’ career paths, as well as the subjects’
practical utilization of design after graduation. This research helps the team assess
to what extent enrollment in speciﬁc coursework leads to innovation and entrepreneurship among alumni. In addition, the team is able to pinpoint speciﬁc improvements for current engineering design education.
Jan Auernhammer and Bernard Roth outline the theory of productive thinking in
design. In the process, they identify ﬁve different types of productive thinking.
These strands of thinking contribute to an evolving type of productive culture that
gives individuals a diverse toolbox with which to design in many realms. The
authors emphasize the goal of cultivating a harmonious Productive Culture.
Xiao Ge, Chunchen Xu, Nanami Furue, Daigo Misaki, Cinoo Lee, and Hazel
Rose Markus address a gap in research and practice: that is, the role of culture in
understanding and promoting creativity in design education. Creative behavior can
connect, preserve, or offer new ideas and change. The authors examine how creativity and creative problem solving are shaped in the cultural contexts of the USA
and East Asia using the culture cycle framework. The author’s pilot studies challenge the popular view that only agentic change-makers are to be seen as creative
problem solvers. The research results should be encouraging to designers from
non-Western societies who wish to incorporate culturally varied ideas about creative
problem solving into design processes.
Nicole M. Ardoin, Alison W. Bowers, Veronica Lin, and Indira Phukan,
researchers at the Social Ecology Lab at Stanford University, argue that Design
Thinking is a useful, appropriate, and necessary approach to support collective
action in addressing sustainability problems. The authors argue for greater alignment
between Design Thinking and sustainability action. The Social Ecology Lab has
consistently found Design Thinking useful in generating, iterating, reﬂecting, and
adapting, especially when working with a diverse group of individuals. The
researchers seek to understand the relationship between Design Thinking and sustainability. In doing so, they discuss propositions as to whether sustainability issues
can be reframed as broader societal considerations.

Outlook
Creativity and innovation are the building blocks of Design Thinking. Understanding how researchers can bridge the gap between theory and practice is essential to the
work currently being done in the ﬁeld of Design Thinking. With this objective in
mind, research teams assess how creativity is manifested in many different

Introduction: “The Impact of REAL Prototyping and Thinking”

xiii

situations. We deﬁne three areas of investigation in this year’s volume. In the ﬁrst
area, attempts are made to bring about value creation in virtual innovation spaces. In
a second line of research, project teams show how to foster innovation behavior and
implement co-evolution. A third group of researchers builds comparative frameworks and problematizes Design Thinking as a concept. The ongoing research,
collaboration, and discovery in the ﬁeld of Design Thinking generated by our
afﬁliated researchers is laid out in this volume to inspire readers and to capture a
snapshot of work in progress that has a broad impact on academia and industry.
Included in this year’s volume, immediately following the introduction, the
editors and several authors have come together to discuss and stake out a philosophy
for the ﬁeld. This chapter outlines a humanistic and creative philosophy of design
that has evolved over several decades at Stanford University. This philosophy of
design is often referred to as human-centered design and Design Thinking. It is
design that incorporates humanistic and creative qualities, including creative thinking modes, attitudes and human values, creative attributes, visual and collaborative
abilities, activities and practices, useful techniques, and a supportive environment
while also addressing blocks to creativity. Developing and cultivating these qualities
aims to encourage creative design in individuals and teams that not only satisﬁes
people’s profound needs, but also resolves and harmonizes societal and ecological
tensions. The aim is to develop innovators. A critical number of creative individuals
who collaboratively support each other in the challenges inherent in designing
innovation and entrepreneurial activities have the ability to spark an era of
innovation.
For over a decade, the HPI-Stanford Design Thinking Research Program has
transmitted valuable insights on how and why Design Thinking works. In this
volume, concrete examples serve as guides to others active in the ﬁeld. On the
way to new frameworks, tools, and systems, our scholars from their various academic backgrounds work in interdisciplinary teams and share their successes and
failures with us in this volume.
We invite you to visit www.hpi.de/dtrp. This website provides an overview of the
research projects included in the HPI-Stanford Design Thinking Research Program,
from 2008 to the present. Here you can also ﬁnd the names and afﬁliations of
participating team members.
Another good resource is the website: https://thisisdesignthinking.net. This site
offers a comprehensive look at current research projects in the ﬁeld of Design
Thinking. Contributors to the site offer unparalleled access to the strengths and
weaknesses of particular strategies. The challenges facing our world today are also
problematized and examined through the lens of Design Thinking. Educators and
design practitioners can obtain timely information on challenges in the ﬁeld of
Design Thinking and join in the conversation. Please send experiences, stories,
and inquiries to thisisdesignthinking@hpi.de.
Achieving real innovation is possible. You have the building blocks in your
hands. Live Design Thinking—ask the question “why?”, but don’t stop there. Keep
on asking questions. We wish you success in your endeavors and encourage you to
contact us directly. It would be our pleasure to hear from you.

Acknowledgments

We thank all the authors for sharing their research results in this publication. Our
special thanks go to Dr. Sharon Nemeth for her constant support in reviewing the
contributions and to Jill Grinager for the publication’s project management.

Contents

A Humanistic and Creative Philosophy of Design . . . . . . . . . . . . . . . . . .
Jan Auernhammer, Larry Leifer, Christoph Meinel, and Bernard Roth
Part I

Value Creation in Virtual Innovation Spaces

Interpersonal Trust Activity to Increase Team Creativity
Outcome: An fNIRS Hyperscanning Approach . . . . . . . . . . . . . . . . . . .
Stephanie Balters, Theresa J. Weinstein, Grace Hawthorne,
and Allan L. Reiss

Dancing with Ambiguity Online: When Our Online Actions Cause
Confusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
So Yeon Park, Mark E. Whiting, and Michael Shanks

Design Thinking for Digital Transformation: Reconciling Theory
and Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Carolin Marx

Experiences of Facilitating Virtual Design Thinking: Theoretical
Reﬂections and Practical Implications . . . . . . . . . . . . . . . . . . . . . . . . . .
Selina Mayer, Martin Schwemmle, Claudia Nicolai, and Ulrich Weinberg

Accessibility of Linked-Node Diagrams on Collaborative Whiteboards
for Screen Reader Users: Challenges and Opportunities . . . . . . . . . . . . .
Danyang Fan, Kate Glazko, and Sean Follmer

Part II

Fostering Innovation Behavior and Co-evolution

A Pattern Language of an Exploratory Programming Workspace . . . . . 111
Marcel Taeumel, Jens Lincke, Patrick Rein, and Robert Hirschfeld

xvii

xviii

Contents

Practice-to-Research: Translating Company Phenomena into
Empirical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
Lena Mayer, Katharina Hölzle, Karen von Schmieden, Reem Refaie,
Hanadi Traifeh, and Christoph Meinel
Timely State Exposure for the Coevolution of Mental Models
and Dynamic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Parastoo Abtahi, Sidney Q. Hough, Jackie Junrui Yang, Sean Follmer,
and James A. Landay
Designing for Value Creation: Principles, Methods, and Case
Insights from Embedding Designing-as-Performance in Digital
Health Education and Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Jonathan Antonio Edelman, Babajide Alamu Owoyele, Joaquin Santuber,
and Stefan Konigorski
Part III

Problematizing Design Thinking as a Concept

Different Concepts of Human Needs and Their Relation
to Innovation Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Julia von Thienen, Constantin Hartmann, and Christoph Meinel
Facets of Human-Centered Design: The Evolution of Designing by,
with, and for People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Jan Auernhammer, Matteo Zallio, Lawrence Domingo, and Larry Leifer
Decades of Alumni: Designing a Study on the Long-Term Impact
of Design Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Sheri D. Sheppard, Helen L. Chen, George Toye, Timo Bunk, Nada Elﬁki,
Felix Kempf, J. L. Lamprecht, and Micah Lande
Different Types of Productive Thinking in Design: From
Rational to Social Design Thinking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
Jan Auernhammer and Bernard Roth
The Cultural Construction of Creative Problem-Solving:
A Critical Reﬂection on Creative Design Thinking, Teaching,
and Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
Xiao Ge, Chunchen Xu, Nanami Furue, Daigo Misaki, Cinoo Lee,
and Hazel Rose Markus
Design Thinking as a Catalyst and Support for Sustainability
Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
Nicole M. Ardoin, Alison W. Bowers, Veronica Lin, and Indira Phukan

Contributors

Parastoo Abtahi Department of Mechanical Engineering, Stanford School of
Engineering, Stanford University, Stanford, CA, USA
Nicole M. Ardoin Graduate School of Education, Stanford University, Stanford,
CA, USA
Stanford Woods Institute for the Environment, Stanford University, Stanford, CA,
USA
Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA, USA
Jan Auernhammer ME Design Group, Center for Design Research, Stanford
University, Stanford, CA, USA
Stephanie Balters Department of Psychiatry and Behavioral Sciences, Center for
Interdisciplinary Brain Sciences Research, Stanford University School of Medicine,
Stanford, CA, USA
Alison W. Bowers Graduate School of Education, Stanford University, Stanford,
CA, USA
Timo Bunk Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
Helen L. Chen Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
Lawrence Domingo ME Design Group, Center for Design Research, Stanford
University, Stanford, CA, USA
Jonathan Antonio Edelman Stanford, California, USA
Nada Elﬁki Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA

xix

Contributors

Danyang Fan Department Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
Sean Follmer Department Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
Nanami Furue School of Management, Tokyo University of Science, Tokyo,
Japan
Xiao Ge Center for Design Research, Stanford University, Stanford, CA, USA
Kate Glatzko Department Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
Constantin Hartmann Hasso Plattner Institute, Potsdam University, Potsdam,
Germany
Grace Hawthorne Hasso Plattner Institute of Design (d.school), CA, USA
Robert Hirschfeld Hasso Platter Institute, Potsdam, Germany
Katharina Hölzle HPI School of Design Thinking, Hasso Plattner Institute for
Digital Engineering, Potsdam, Germany
Sidney Q. Hough Department of Mechanical Engineering, Stanford School of
Engineering, Stanford University, Stanford, CA, USA
Felix Kempf Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
Stefan Konigorski Hasso Plattner Institute for Digital Engineering, Potsdam,
Germany
J. L. Lamprecht Department of Mechanical Engineering, Stanford School of
Engineering, Stanford, CA, USA
James A. Landay Department of Mechanical Engineering, Stanford School of
Engineering, Stanford University, Stanford, CA, USA
Micah Lande Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
Cinoo Lee Stanford SPARQ, Stanford University, Stanford, CA, USA
Larry Leifer ME Design Group, Center for Design Research, Stanford University,
Stanford, CA, USA
Jens Lincke Hasso Platter Institute, Potsdam, Germany
Veronica Lin Graduate School of Education, Stanford University, Stanford, CA,
USA
Hazel Rose Markus Stanford SPARQ, Stanford University, Stanford, CA, USA
Carolin Marx Hasso Plattner Institute, University of Potsdam, Potsdam, Germany

Contributors

xxi

Lena Mayer HPI School of Design Thinking, Hasso Plattner Institute for Digital
Engineering, Potsdam, Germany
Selina Mayer HPI School of Design Thinking, Hasso Plattner Institute for Digital
Engineering, Potsdam, Germany
Christoph Meinel Hasso Plattner Institute and Digital Engineering Faculty,
University of Potsdam, Potsdam, Germany
Daigo Misaki Faculty of Engineering, Kogakuin University, Tokyo, Japan
Claudia Nicolai HPI School of Design Thinking, Hasso Plattner Institute for
Digital Engineering, Potsdam, Germany
Babajide Alamu Owoyele Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
So Yeon Park Stanford University, Stanford, CA, USA
Indira Phukan Graduate School of Education, Stanford University, Stanford, CA,
USA
Reem Refaie HPI School of Design Thinking, Hasso Plattner Institute for Digital
Engineering, Potsdam, Germany
Patrick Rein Hasso Platter Institute, Potsdam, Germany
Allan L. Reiss Department of Psychiatry and Behavioral Sciences, Center for
Interdisciplinary Brain Sciences Research, Stanford University School of Medicine,
Stanford, CA, USA
Bernard Roth ME Design Group and Hasso Plattner Institute of Design, Stanford
University, Stanford, CA, USA
Joaquin Santuber Hasso Plattner Institute for Digital Engineering, Potsdam,
Germany
Martin Schwemmle HPI School of Design Thinking, Hasso Plattner Institute for
Digital Engineering, Potsdam, Germany
Michael Shanks Stanford University, Stanford, CA, USA
Sheri D. Sheppard Department of Mechanical Engineering, Stanford School of
Engineering, Stanford, CA, USA
Marcel Taeumel Hasso Platter Institute, Potsdam, Germany
George Toye Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
Hanadi Traifeh HPI School of Design Thinking, Hasso Plattner Institute for
Digital Engineering, Potsdam, Germany
Karen von Schmieden HPI School of Design Thinking, Hasso Plattner Institute for
Digital Engineering, Potsdam, Germany

xxii

Contributors

Julia von Thienen Hasso Plattner Institute, Potsdam University, Potsdam,
Germany
Ulrich Weinberg HPI School of Design Thinking, Hasso Plattner Institute for
Digital Engineering, Potsdam, Germany
Theresa J. Weinstein Hasso-Plattner-Institute, Potsdam University, Potsdam,
Germany
Mark E. Whiting University of Pennsylvania, Philadelphia, PA, USA
Chunchen Xu Stanford SPARQ, Stanford University, Stanford, CA, USA
Jackie Yang Department of Mechanical Engineering, Stanford School of Engineering, Stanford University, Stanford, CA, USA
Matteo Zallio Inclusive Design Group, Engineering Design Center, University of
Cambridge, Cambridge, UK

A Humanistic and Creative Philosophy
of Design
Jan Auernhammer, Larry Leifer, Christoph Meinel, and Bernard Roth

Abstract This chapter outlines a humanistic and creative Philosophy of Design that
evolved over several decades at Stanford University. This Philosophy of Design is
often referred to as Human-centered Design and Design Thinking. It incorporates
humanistic and creative qualities, including creative thinking modes, attitudes and
human values, creative attributes, visual and collaborative abilities, blocks to creativity, activities and practices, useful techniques, and a supportive environment.
Developing and cultivating these qualities aims to encourage creative design in
individuals and teams that satisﬁes people’s profound needs and resolves and
harmonizes societal and ecological tensions. The intention is to develop innovators.
A critical number of creative individuals who collaboratively support and help each
other in the challenges inherent in designing innovation and entrepreneurial activities can spark an era of innovation.

1 Introduction
Over the last seven decades, Design has evolved at Stanford University from creative
product design to design thinking, tackling many diverse challenges. In the beginning, the integration of arts and engineering education established the Joined Product
Design Program. Over time, many different disciplines, such as biology, computer
science, electronics, management, marketing, and medicine, combined into a

J. Auernhammer (*) · L. Leifer
ME Design Group and Center for Design Research, Stanford University, Stanford, CA, USA
e-mail: jan.auernhammer@stanford.edu
C. Meinel
Hasso Plattner Institute and Digital Engineering Faculty, University of Potsdam, Potsdam,
Germany
e-mail: Christoph.Meinel@hpi.de
B. Roth
ME Design Group and Hasso Plattner Institute of Design, Stanford University, Stanford, CA,
USA
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_1

J. Auernhammer et al.

comprehensive design practice. The focus shifted from designing artifacts for people
to interactions and experiences design, biodesign, software design, life design,
design for extreme affordability, entrepreneurship, and organizational design, to
name a few (Auernhammer & Leifer, 2019; Buchenau & Fulton Suri, 2000; Burnett
& Evans, 2016; Miller et al., 2001; Moggridge, 2007; Srinivasan et al., 1997;
Winograd, 1996).
Underneath the different pan-disciplinary design practices is a shared Philosophy
of Design often referred to as Human-centered Design and Design Thinking. This
philosophy is deeply embedded in the humanistic psychology of creativity (Adams,
1974; Arnold, 1959; Guilford, 1959; Hartman, 1959; Maslow, 1959; McKim, 1959,
1972; Roth, 1973). It emphasizes designing for the whole person and people in need
(Arnold, 1959; McKim, 1959). It also centers on enabling creativity in designers.
John Arnold, Bob McKim, and many members within the wider design community
developed educational programs and practices to encourage the creative potential in
people through perceptual, imaginative, expressive, and collaborative exercises and
project-based learning (Adams, 1974; Arnold, 1959, 1962a; Kelley & Kelley, 2013;
Leifer, 1998; McKim, 1972, 1980a, 1980b; Roth, 1973, 2015; Stein-Greenberg,
2021). This Philosophy of Design incorporates several humanistic and creative
qualities. These qualities are (1) perceptual, imaginative, and expressive thinking
modes, (2) attitudes and human values, (3) attributes, (4) abilities, (5) blocks, (6) activities and practices, (7) techniques, and (8) environment (Auernhammer & Roth,
2021). These qualities are a holistic view of human creativity in design practices.
This chapter outlines the humanistic and creative qualities in design that have
been cultivated over decades. This Philosophy of Design expanded globally into
academic programs, such as the Hasso Plattner Design Thinking Research program
and companies, such as IDEO, Apple, Concept2, Edge Innovation, and Wet Design,
contributing to society with various innovations. Many companies, such as P&G,
SAP, IBM, Intuit, and Samsung, and numerous Universities, through global collaboration, such as ME310, developed a design approach inspired by this humanistic
and creative Philosophy of Design.

2 Humanistic and Creative Qualities in Design
The qualities inherent in the Philosophy of Design are based on humanistic psychology on creativity (Auernhammer & Roth, 2021). Creativity incorporates a dynamic
interplay of (1) thinking modes, including perceptual, imaginative, and expressive
modes (Arnold, 1959; McKim, 1980a). Designing for people requires (2) attitudes
and human values to recenter to understand people’s needs and experiences, going
beyond stereotypes (Arnold, 1959; McKim, 1959, 1980a; Wertheimer, 1945).
Developing creative behavior is associated with nurturing creative (3) attributes,
such as problem and need-sensitivity, openness to experience, and tolerance for
ambiguity (Arnold, 1959; Guilford, 1950; Rogers, 1954). Making a tangible design
that others can react to requires (4) abilities, such as visualizing, making, and
communicating (Arnold, 1959; McKim, 1980a). Developing tangible design

A Humanistic and Creative Philosophy of Design

solutions requires diverse abilities, such as collaborating with many diverse people.
In such collaborations, (5) blocks to creativity prevent people from creating novel
and valuable design solutions (Adams, 2019; Arnold, 1959; Maslow, 1962;
Papanek, 1973). Actively overcoming these creativity blocks in everyday interactions in teams and organizations encourages creative behavior and a culture conducive to creativity. Various (6) activities and practices, such as need-ﬁnding,
visualization, mock-up building, and evaluation, enable collaboration among diverse
stakeholders, resolve structural tensions, and satisfy people’s needs (McKim, 1980a,
1982). Various (7) techniques are useful in these activities to stimulate, e.g., divergent thinking and group creativity (Arnold, 1959, 1962b). A conducive (8) environment for creativity is imperative for the various activities and practices to be
productive. Such an environment provides psychological safety and freedom to
play unreservedly with materials and learn from unsuccessful attempts without
fear of making a mistake (Arnold, 1959; Arnold et al., 1960; McKim, 1980a).

2.1

Thinking Modes

The creative accomplishment of innovative design is primarily an embodied mental
activity (Arnold, 1959; McKim, 1980a). It consists of mental activities, such as
perceptual, imaginative, and expressive activities (Faste, 1994; McKim, 1980a). The
perceptual processes of organizing, identifying, and interpreting sensory input are
imperative when designing (Arnheim, 1967; McKim, 1980a). Perceiving a situation
from different centers makes it possible to overcome stereotypes, grasp people’s
needs, understand the human-made environment, and perceive the whole situation in
new ways (McKim, 1980a; Nelson, 2003; Wertheimer, 1945). Recentering and
redesigning situations is an imaginative act, which incorporates mental activities,
such as envisioning mental imagery, visual recall, and directed fantasy (McKim,
1980a). Communicating these envisioned situations requires expressive embodied
mental activities, including visualization, idea sketching, mock-up building, and
enacting (McKim, 1980a). These mental activities are imperative in ﬁnding needs,
envisioning situations, and experiencing and evaluating the design within the situation. Perceptual, imaginative, and expressive mental activities occur dynamically
as, for example, a sketch is perceived in a new way triggering a new mental image
(Bamberger & Schön, 1983; Koffka, 1927; McKim, 1980a; Schön, 1992). Moving
dynamically between perceiving, imagining, and expressing makes it possible to
explore diverse design solutions. In such creative acts, designing for people and
resolving structural tensions to provide value requires certain attitudes and human
values.

J. Auernhammer et al.

2.2

Attitudes and Human Values

Positive attitudes towards other living organisms are essential in recentering, overcoming one-sided, superﬁcial views (McKim, 1980a; Wertheimer, 1945). Such
attitudes are essential in perceiving how others experience the situation and
designers’ own role in the situation. Human values and attitudes, such as questioning
and keen observation, are imperative in perceiving the situations as a whole to grasp
people’s needs and tensions (Arnold, 1959). McKim (1959) emphasizes the importance of human values in design as follows:
[. . .] knowledge of and concern for human values and needs is of prime importance to the
designer. Design is, after all, a response to human needs; needs which are all too often lost
sight of in this age of intense technology.

Human values and attitudes are essential in recentering from self to other people,
enabling perceiving the situation as a new whole, overcoming ego-perspective and
stereotypes, grasping people’s needs, and enabling designing new human experiences (Arnheim, 2009; McKim, 1980a; Wertheimer, 1945). In contrast, negative
assumptions and self-centric attitudes often prevent designers from seeing the whole
situation from diverse points of view. Designers’ values and needs inﬂuence the
design at each step (Rittel, 1987).
This Philosophy of Design focuses on creatively designing for people to create
value by resolving structural tensions and satisfying human needs. Technology has
no value by itself. Designs create value and meaning in everyday life situations when
they empower people, satisfy profound needs, harmonize tensions, and reinterpret
existing designs (Krippendorff, 2006; McKim, 1959; Wertheimer, 1945). Without
human values and attitudes (i.e., mindsets), designers do not recognize people’s
needs and tensions, and their designs have little value in the real world, satisfying
merely designers’ interests (McKim, 1959; Papanek, 1973).

2.3

Attributes

Discovering or ﬁnding a profound need or problem requires attributes of openness to
experiences and problem or need-sensitivity. This means designers’ attributes are
imperative in innovation (Arnold, 1959). Sensitivity to human needs and problems
and openness to experiences are qualities attributed to creative individuals (Guilford,
1950; Rogers, 1954). Finding needs and problems identify opportunities for innovation and often sets creative individuals apart from experts (Getzels, 1980). In
everyday life, people ignore or do not notice other people’s needs and wider societal
and ecological tensions. Developing the attributes of need and problem-sensitivity
and curiosity through openness to experience allows uncovering opportunities for
new tangible designs that satisfy needs and resolve tensions. Exercising practices,
such as engaging with people in their life situations, develops an understanding of
the profound need and tension.

A Humanistic and Creative Philosophy of Design

For resolving tensions and satisfying needs through design, ﬂuency in thinking
modes and ﬂexibility in creation and use of techniques are essential attributes. The
emerging need and problem from the situation demands different “thinking languages,” such as symbolic, visual, kinesthetic, or emotional languages (Adams,
2019; Faste, 1994). It requires attributes of ﬂexibility to dynamically respond
appropriately to situations and inherent needs. Similarly, ﬂuency in perception,
recentering, imagination, expression, and communication is imperative in grasping
situations and inherent needs to redesign them in more meaningful and valuable
ways. Attitudes to consider one group over another and functional ﬁxedness inhibit
recentering and grasping the whole situation, preventing an understanding of the
actual needs, tensions, and opportunities (Duncker, 1945; McKim, 1980a;
Wertheimer, 1945).
Engaging in a situation that is unclear and ambiguous, and thereby preventing a
clear path to action, is often unbearable for people (Wertheimer, 1945). Therefore,
the attribute of tolerance for ambiguity enables designers to explore situations
without hasty conclusions and premature closure (Rogers, 1954). Such tolerance
for ambiguity increases in importance when not merely ﬁnding a suitable solution,
but instead what is a truly valuable solution for many people. Such value creation
incorporates the attribute of originality. Originality is the attribute inherent in
creative individuals to produce novelty (Guilford, 1950). An existing solution may
resolve the tension. However, developing the attribute of originality, which is the
curiosity and desire to contribute with a novel design, may provide a more profound
experience for people by designing for the whole person and pluralistic society
(McKim, 1959; Rittel, 1987). Original designs can be created through collaborative
dialogues of reinterpreting existing solutions through new product semantics
(Krippendorff, 2006). Originality is highly dependent on Zeitgeist, as one design
solution resolves tension and, over time, creates another, making design a neverending temporal-dependent effort (Rittel & Webber, 1973).
Any undisputable creative contribution to society requires attributes of drive,
motivation, and conﬁdence (Amabile, 1996; Arnold, 1959). Motivation often
emerges in the thinker from the felt need and structural tension, determining the
direction towards harmonizing it (Wertheimer, 1945). Designing and developing
tangible solutions demands attributes of drive to increase motivation over longer
periods, which is required to overcome the many obstacles and pitfalls involved in
innovation (Arnold, 1959). Such creative contribution demands building, testing,
breaking, and rebuilding the design repeatedly until a working design solution is
determined for many potential structural tensions, such as environmentally friendly
manufacturability and satisfying diverse people’s needs. Overcoming these pitfalls
requires conﬁdence in creative ability. Part of this ability is to ﬁnd the necessary
resources and people with essential skills. Collaboratively developing countless
projects, in which people help each other, develops creative conﬁdence, i.e. selfefﬁcacy in design (Arnold, 1962a; Kelley & Kelley, 2013). Problem and needsensitivity, openness to experience, tolerance for ambiguity, ﬂuency, ﬂexibility,
originality, drive, motivation, and conﬁdence are imperative creative attributes in

J. Auernhammer et al.

design. These attributes in design team members are ultimately the essential attributes for entrepreneurship and innovation.

2.4

Abilities

Any truly creative design task requires many diverse abilities. Creative abilities are
distinct from expertise. Expertise is knowing what and how from past experience,
while creative ability is ﬁguring out who has the tension, why this tension emerges,
when it occurs, how it occurs, and what (i.e., solution-methods) the structural tension
in society is and how it can be resolved. Diverse abilities are required to develop,
build, and enact a tangible design that resolves the tension. In such creative collaboration, team members require the ability to express and communicate often incomplete situations, needs, problems, ideas, and concepts. Visual thinking abilities
support perceiving, imagining, expressing, and communicating these various aspects
(McKim, 1980a). Such visual abilities incorporate sketching, mock-up building, and
enacting situations (e.g., role-playing) to directly experience the designed situation
from various centers (Arnheim, 2009; McKim, 1980a; Simsarian, 2003). Developing a shared language between diverse groups is essential for creative collaboration,
including communicating, debating, experiencing, and evaluating designs. In collaboration with non-designers, a frequent challenge is that designers notice people’s
naiveté about design and lack of articulable knowledge, resulting in designers
creating what they want, rather than for people’s needs and experiences (Gilmore
et al., 1999). Abilities and attitudes are required to develop a shared visual language
to enable creative collaboration and to help non-designers take part in the collaborative activity of conveying and translating need-situations into concepts and tangible designs. Developing abilities of collaboration, visual thinking, communication is
imperative in creative accomplishments and innovation (Arnheim, 2004; McKim,
1980a).

2.5

Blocks

Inherent in any collaborative and creative practice are blocks (Adams, 2019; Arnold,
1959; Duncker, 1945; Maslow, 1962; Papanek, 1973). Perceptual blocks, such as
functional ﬁxedness, prevent designers from perceiving critical aspects of and new
means for the situational tension (Adams, 2019; Arnold, 1962a; Duncker, 1945;
Wertheimer, 1945). The perceptual block of functional ﬁxedness is the reason why
one cannot simply ask people what they need (Duncker, 1945). It is impossible to
know what to look for in the case of innovation (Arnold, 1962a). In such cases,
strategies, such as recentering, reframing, experimenting, and learning from mistakes make it possible to see the problem in a new way and lead to new directions
that resolve structural tension creatively (Duncker, 1945; McKim, 1980a; Roth,

A Humanistic and Creative Philosophy of Design

2015; Schön, 1983; Wertheimer, 1945). Similarly, stereotype vision is a selective
mechanism in perceiving objects and people based on familiarity and socially
conditioned images (McKim, 1980a). Visual clues that are surprising and “unacceptable” images allow designers to recognize their own cultural stereotypes.
Designing for stereotypes rather than for actual people prevents designers from
satisfying needs and produces tensions for people. Such a stereotype vision of the
“default user” is a discriminatory bias in design (Perez, 2019). Overcoming perceptual blocks to recognize the situation as a new whole (i.e., recentering) is imperative
in creative design (Arnold, 1959; Wertheimer, 1945).
Similarly, cultural and environmental blocks prevent individuals from collaborating creatively and designing for people’s actual needs (Adams, 2019; Arnold,
1959). Such blocks are taboos, no humor is permitted, and there is an overemphasis
on technical-rationality, unsupportive environments, and unproductive criticism
(Adams, 2019; Arnold, 1959). Such cultural blocks can kill creativity in organizations (Amabile, 1998). Developing a space of psychological safety and freedom in
collaborations encourages debating taboos, productive criticisms, and incorporates
diverse points of view (Auernhammer, 2012; Edmondson, 1999; Lewin, 1936;
Rogers, 1954; Sutton & Hargadon, 1996). Such psychological safety and freedom
are particularly relevant for keeping motivation high over longer periods (Arnold
et al., 1960). For example, the culture and coaching practices in the Product
Realization Lab enable students to receive help and support to ﬁgure out how to
build, break, and rebuild a tangible design. This culture is essential for creativity and
encourages creative behavior in educational settings. Without the necessary support,
creative accomplishments are unlikely to happen as people are occupied with
navigating cultural pitfalls rather than engaging in and focusing on creative tasks.
In creative collaborations, emotional blocks prevent people from exploring new
directions and learning. Inherent fears of making mistakes, taking risks, personal
ambiguities and inabilities to move consciously between idea production and evaluation and reality and fantasy are strong blocks to creativity (Adams, 2019; Arnold,
1959; Rogers, 1954). Practicing the ability to move ﬂuently between different
thinking modes and ﬂexibly between different thinking languages, engaging in
ambiguous situations for longer periods, and building many design projects can
develop conﬁdence in creative ability (Adams, 2019; Arnold, 1959; Kelley &
Kelley, 2013). An environment in which students can explore and develop projects
freely and safely, while earning a degree, allows engaging in creative projects and
helps overcome blocks to creativity. This Philosophy of Design focuses on developing innovators (i.e., people) by overcoming their blocks to creativity, enabling
their creativity potential.

2.6

Activities and Practices

Various activities, exercises, and practices are essential in collaboratively
uncovering needs and tensions within the living world and visually imagining new

J. Auernhammer et al.

situations and experiences. Collaborative practices, such as need-ﬁnding, visualizing, mock-up building, enacting, and evaluating through direct experience, are vital
for understanding the situation as new wholes and resolving tensions through new
designs.
People’s needs and behavior emerge in relation to the designed, cultural, and
natural environment (Gibson, 2014; Maslow, 1954; McKim, 1959). Finding and
grasping moments of people’s needs and problems is supported by diverse practices,
as problems are envisaged, posed, formulated, and created in various ways (Getzels
& Csikszentmihalyi, 1976). For example, need-ﬁnding practices enable designers to
engage with people and situations to directly experience tensions from different
centers. Environmental problems are felt when, for example, the pollution in drinking water and trash ﬂoating in oceans are experienced directly. Such pollution exists
as people do not grasp and experience the structural tension their everyday behavior
produces. Therefore, practices that enable designers to experience structural tensions
and recenter to grasp the whole, result in new understanding and motivation towards
changing the situation (Wertheimer, 1945). Visualizing practices support grasping
the situation from different centers, uncovering hidden interrelations, and
envisioning new valuable whole situations (McKim, 1980a). Building mock-ups
and various prototyping practices allow exploring and evaluating various aspects of
the interventions that change the whole towards resolving the structural tensions
(Barkan & Iansiti, 1993; Buchenau & Fulton Suri, 2000; Fulton Suri, 2000, 2003;
Gilmore et al., 1999; Houde & Hill, 1997; McKim, 1980a; Simsarian, 2003;
Verplank et al., 1993).
These are dynamic practices and not a cookbook pattern of step-by-step processes
(McKim, 1980a, 1980b). Recentering changes the entire meaning of the situation,
resulting in completely new questions and perspectives. Prototyping and evaluating
validates and informs the grasped need. These are not separate process ends.
Practices are intertwined and are required depending on the emergent understanding
of the situation. Creative design requires responding dynamically with the necessary
activities and practices rather than following a cookbook pattern. Such cookbook
patterns or machine models disregard human experience and emerging changes
inherent in the whole situation (Koffka, 1927; McKim, 1980b). The Philosophy of
Design emphasizes designers as people with qualities, such as attitudes, attributes,
and abilities, and not as mechanistic information processors.

2.7

Techniques

Techniques and tools can be useful in diverse design activities and practices (Arnold,
1962b). Techniques such as Brainstorming and Synectics aim to enable teams to
develop psychological safety and freedom and to produce a collaborative environment conducive to creativity (Arnold, 1959; Gordon, 1961; Osborn, 1957; Prince,
1970). Morphological analysis and attribute listing help create many combinations
and increase divergent thinking (Crawford, 1954; Zwicky, 1948). Over the decades,

A Humanistic and Creative Philosophy of Design

many useful techniques have been developed for various purposes and uses (e.g.,
Buchenau & Fulton Suri, 2000; Fulton Suri & Marsh, 2000; Ideo., 2015; Jones,
1992; Kumar, 2012; Roozenburg & Eekels, 1995; Wasson, 2000).
However, developments in the movements of design methods and design thinking/cognition resulted in method-centricity rather than problem and peoplecentricity. This mechanistic doctrine of human thinking emerged in debates in the
early developments of experimental psychology (Koffka, 1927; Selz, 1922; Wallas,
1926). It developed further into the information processing theory advocated by
Simon and colleagues (Newell et al., 1958; Newell & Simon, 1956, 1972). In design,
the design process incorporates mechanistic models and related methods (Archer,
1965; Hubka & Eder, 1996; Liedtka & Ogilvie, 2011; Pahl et al., 1996). It dehumanizes creative thinking and practice. In response to the design method movement,
one of the leading personalities, Chris Jones (1977), expressed that he dislikes the
machine language, behaviorism, and continual attempt to ﬁx the whole of life into a
logical framework. Similarly, Alexander (Alexander, 1964 revised edition; Alexander, 1971) stated that he publicly rejects the whole idea of design methods as a
subject of study and suggested to “forget the whole thing.” The mechanistic and
information processing theory has also been criticized and questioned for various
reasons in psychology over the century (Benary, 1923; Csikszentmihalyi, 1988;
Greeno & Moore, 1993; Koffka, 1927; Neisser, 1963; Varela & Thompson, 1990).
Techniques are not deterministic tools to make people think and act to accomplish
the next process step. They are useful tools that help to produce a conducive situation
to creativity and accomplish required tasks inherent in the challenge. For example,
prototyping techniques help explore and evaluate various design aspects, while
brainstorming helps to encourage psychological safety and freedom through “defer
judgment” (Arnold, 1959; Houde & Hill, 1997; Osborn, 1957). Creative accomplishments that contribute to society with a novel, valuable, and tangible design
require developing the ability to create and respond with new solution-methods to
situations and not merely utilize existing ones. Exercising and practicing various
techniques can help strengthen abilities conducive to creativity. However, it is not
the techniques that result in creative acts. It is the ability to creatively create and
utilize various techniques that are useful depending on the situation. For example, in
environments of constant and unproductive critique, consciously deferring judgment
can help to encourage psychological safety and freedom. It is often the social
environment that either encourages or hinders creativity.

2.8

Environment

The environmental context inﬂuences people’s thought processes (Wertheimer,
1945). A mind occupied by various environmental distractions will not be able to
focus on a creative challenge over longer periods. Cultural and physical environments inﬂuence designers thought processes, motivational attitudes, and abilities to
express ideas freely and collaborate creatively (Adams, 2019; Amabile, 1996;

J. Auernhammer et al.

Auernhammer & Hall, 2014). Physical environments, such as a Design Loft, allow
for developing a collaborative culture in which designers help each other while
working on different challenges and can play freely with materials without fear of
making a mistake or being penalized (Arnold, 1959; Arnold et al., 1960; McKim,
1980a). A supportive culture in the Product Realization Lab enables designers to
build and explore different design directions rapidly. The combination of designers’
willingness and freedom to use their resources, machines, and tools creates an
environment conducive to creativity, allowing rapid prototyping and exploring
various tangible solutions. Such a culture of help and support, encouraged by
anchored physical spaces and enacted by motivated and driven designers, enables
creative activities and practices. Similarly, a wider network of people with diverse
abilities and skills who support design teams with advice, time, and other resources
is conducive to creativity and innovation. In design education, a network of alumni
who engage in courses by sponsoring projects, providing real-world problems and
practices, and mentoring teams is an essential part of a supportive environment for
innovation.
Entrepreneurial and innovation ecosystems require a critical mass of people who
support and help motivated individuals drive their projects forward. For example,
making connections and the support provided by people like Nolan Bushnell, Don
Valentine, and Mike Markkula was essential in helping Steve Jobs and Steve
Wozniak, who were in their twenties at the time, to start Apple Computer. Such an
environment of helping and supporting others is part of the humanistic and creative
Philosophy of Design, enabling a culture of design, entrepreneurship, and
innovation.

3 Cultivating the Philosophy of Design
Since the 1950s, this humanistic and creative Philosophy of Design integrated
humanistic psychology (e.g., satisfying people’s needs), engineering (e.g., solving
technical problems), arts (e.g., creating esthetic product meaning), and business
(e.g., establishing a scalable and sustainable operation). Design is often reduced to
merely visual product languages reinterpreting existing designs or technical aspects
of optimizing structures and functions with little regard to addressing the fundamental tensions and profound needs existing in the world. Such designs result from selfcentered attitudes, in which designers’ needs and interests determine thinking and
activities (Arnheim, 2009; Maslow, 1954).
The humanistic and creative Philosophy of Design focuses on designing for the
whole person, including physical, intellectual, emotional needs and tensions in
society and wider ecology (Arnold, 1959; Fuller, 1969; McKim, 1959). Developing
these attitudes and human values in designers by engaging in need-ﬁnding activities
is essential in order for them to be able to grasp the situation from diverse centers,
beyond stereotype visions (McKim, 1959, 1980a). Such design activities are a
comprehensive design philosophy of cultivating pan-disciplinary collaboration to

A Humanistic and Creative Philosophy of Design

respond ﬂexibly to the emergent situation and inherent tensions and needs. The
inherent tension of the situation should determine the solution and not the designers’
disciplinary practice. This pan-disciplinary collaboration requires developing the
abilities to collaborate creatively, such as ﬁnding and developing shared languages
for visualizing and developing incomplete mental imagery into tangible designs.
Visual abilities are developed through various exercises of visual perception, imagination, and expression (Arnheim, 2004; McKim, 1980a). These abilities facilitate
creative dialogues and collaboration, which is imperative for resolving profound
social tensions inherent in a pluralistic society. An important task in cultivating the
humanistic and creative Philosophy of Design is helping design teams to overcome
emerging blocks to creativity. Perceptual blocks, such as personas based on stereotype vision, require keen observation, and deep engagement with people, as well as
recentering to understand their experience and need-tension (Adams, 2019; McKim,
1980a). Exercises that generate self-awareness of stereotype vision help designers
overcome such socially conditioned blocks to creativity. Cultural blocks, such as
taboos, prevent teams from exploring and debating the actual tension inherent in the
problem-situation (Adams, 2019). Practices from theater and improv, such as roleplaying, enable designers to enact and experience tensions within a psychologically
safe and free space (Faste, 1993; Simsarian, 2003). Emotional blocks, such as the
fear of making mistakes, are strong blocks to creativity (Adams, 2019; Arnold,
1959). Developing a supportive environment in which designers can freely engage
in unconventional projects is conducive to overcoming emotional blocks to creativity. Exercising and practicing activities and related techniques that support ﬁnding
and grasping needs, recentering and envisioning whole situations, making and
enacting new situations, and experiencing and evaluating these situations can stimulate creative solutions in design (Adams, 2019; Arnold, 1962a, 1962b; McKim,
1980a). Developing an environment, including a Design Loft and Product Realization Lab, in which a culture of experimental learning, collaboration, and help
ﬂourish, supports designers in freely playing with materials to creatively explore
new directions for innovation (McKim, 1980a).
The humanistic and creative Philosophy of Design aims to enable and develop
people’s humanistic and creative qualities. A critical mass of people that creatively,
collaboratively, and dynamically approach needs in society and tensions in wider
ecology by designing solutions towards harmonizing them has the potential to spark
an era of innovation and sustainability.

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Part I

Value Creation in Virtual Innovation
Spaces

Interpersonal Trust Activity to Increase
Team Creativity Outcome: An fNIRS
Hyperscanning Approach
Stephanie Balters, Theresa J. Weinstein, Grace Hawthorne,
and Allan L. Reiss

Abstract Organizational research demonstrates that team interpersonal trust
enhances team performance and creativity. Design thinking offers many interactive
team events such as warm-up games that are aimed at increasing team trust and
collaboration. While effective in practice, little is understood about the underlying
brain mechanisms that facilitate interpersonal trust during these activities. In this
chapter, we present a novel interpersonal trust activity centered around human
emotions that is designed to enhance interpersonal trust in both in-person and virtual
(zoom) team interactions. We hypothesize that participating in an interpersonal trust
activity prior to a collaborative design task will increase interpersonal trust and the
creativity level of the collaborative outcome (i.e., creative product/innovation). We
present our scientiﬁc approach for testing this hypothesis by applying the methods of
functional near-infrared spectroscopy (fNIRS) hyperscanning in both in-person and
virtual (video conferencing) team interactions. A better understanding of the neural
signatures underlying an interpersonal trust activity will allow design thinking
practitioners and educators to design novel and effective design thinking activities
and interactions that can positively impact team trust and collaboration.

S. Balters (*) · A. L. Reiss
Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences
Research, Stanford University School of Medicine, Stanford, CA, USA
e-mail: balters@stanford.edu; reiss@stanford.edu
T. J. Weinstein
Hasso-Plattner-Institute, Potsdam University, Potsdam, Germany
e-mail: theresa.weinstein@hpi.de
G. Hawthorne
Hasso Plattner Institute of Design (d.school), Stanford, CA, USA
e-mail: grace@dschool.stanford.edu
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_2

S. Balters et al.

1 Introduction
There are a myriad of empirical studies demonstrating that team emotional intelligence (i.e., “the ability of a group to develop a set of norms that manage emotional
processes so as to cultivate trust, group identity, and group efﬁcacy” (Druskat &
Wolff, 2001a)) enhances team performance and creativity (Akgün et al., 2015;
Barczak et al., 2010; Brattström et al., 2012; Chang et al., 2012; De Jong et al.,
2016; Lee & Wong, 2019; Parke et al., 2015; Rezvani et al., 2019; Rezvani &
Khosravi, 2019). Empirical results reveal that a team’s emotional intelligence promotes interpersonal trust, which, in turn fosters a collaborative culture that enhances
team creativity (Akgün et al., 2015; Barczak et al., 2010; De Jong et al., 2016;
Rezvani & Khosravi, 2019). Interpersonal trust can be deﬁned as an individual’s
willingness to accept vulnerability based upon positive expectations of the intentions
or behavior of another (Rousseau et al., 1998). Organizational research further
differentiates between affective trust (i.e., the conﬁdence one places in a team
member based on one’s feelings of caring and concern illustrated by a co-worker)
and cognitive trust (i.e., one’s willingness to rely on a team member’s expertise and
reliability) (McAllister, 1995). We follow the latter deﬁnition in connection with the
aim of this chapter.
Design thinking practice and education have adopted the concepts of emotional
intelligence and interpersonal trust to improve team performance and creativity
(Druskat & Wolff, 2001b; Uebernickel & Thong, 2021). Many activities used in
design thinking explicitly or implicitly help improve interpersonal (team) trust. The
activities include various team rituals such as team “check-ins” and “check-outs”
before and after each workday, during which team members share their momentary
personal sensitivities, opinions, and feelings with the team (Ney & Meinel, 2019). If
a team or interpersonal conﬂict needs to be resolved, design thinking encourages the
use of structured formats for communicating and receiving feedback (Ney & Meinel,
2019). Other commonly used activities are the so-called warm-up games which are
inspired by improvisation theater (Rothouse, 2020; Talbot, 2021). While there are
many different warm-up game varieties serving different purposes in the design
thinking process, they all help create a positive and open atmosphere. Since the
games usually involve stepping out of one’s comfort zone and often result in some
form of playful body movement or verbal interaction, they contribute to establishing
a work culture in which one is permitted to make mistakes and show vulnerabilities
in front of colleagues without fearing rejection (West et al., 2017). The activities
usually serve as primers for certain collaborative work modes and are typically
applied when initiating a new design thinking work phase. Alternatively, these
activities function as breaks whenever the overall team mood and collaborative
atmosphere need special attention. In essence, the example activities aid in team
collaboration by encouraging empathetic behavior toward the other team members,
offering tools to avoid or resolve team conﬂict though open and respectful communication, and contribute to a psychologically safe culture.

Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . .

Research shows that after completing a design thinking training, participants have
not only gained “creative conﬁdence” (Jobst et al., 2012; Kelley & Kelley, 2013;
Rauth et al., 2010; Royalty et al., 2012, 2014), but they have also become better and
more conﬁdent collaborators through practicing their empathy and social interaction
skills in a design thinking team setting (Noweski et al., 2012; Plank & Meinel, 2021;
Traifeh et al., 2020; von Thienen et al., 2017). Furthermore, many trained design
thinkers are equipped with practical activities and “hacks” to enhance collaboration
within any team (Kerguenne, 2021; Koch, 2021; Ney & Meinel, 2019). Although
design thinking methodology offers many practical activities, little is understood
about the underlying social mechanisms that make these activities work. Social
neuroscience now offers the tools to systematically examine how such activities
affect our brains and can provide evidence for their effectiveness in increasing
empathy, trust, and creativity in teams (Balters et al., 2021a, 2021b; Balters et al.,
2020; Balters et al., 2021a, 2021b; Li et al., 2021; Mayseless et al., 2019). Here, we
propose an emergent technology in brain-imaging—hyperscanning (i.e., measuring
two brains simultaneously to derive measures of inter-brain synchrony) with functional near-infrared spectroscopy (fNIRS)—as an ideal brain-imaging technique to
gain understanding in the underlying social brain mechanisms of interpersonal trust
activities.
In this chapter, we present an interpersonal activity that is designed to enhance
interpersonal trust in in-person and virtual (zoom) design thinking teams. The
activity is derived from Nonviolent Communication Practices, a communication
method to increase interpersonal trust between individuals (Rosenberg & Chopra,
2015). Due to its focus on human needs, the activity we utilize in our research is
strongly in line with design thinking’s human-centered approach. We hypothesize
that the interpersonal trust activity prior to a creativity exercise will increase interpersonal trust among participants in the same group and enhance the originality and
effectiveness of their collaborative outcome (i.e., creative product/innovation). In
this chapter, we describe our neuroscientiﬁc approach for applying the methods of
fNIRS hyperscanning to assess whether and how an interpersonal trust activity prior
to an ideation session increases collaborative outcome (i.e., creative product/innovation) in both in-person and virtual (video conferencing) team interactions. A better
understanding of the neural signatures underlying an interpersonal trust activity will
allow design thinking practitioners and educators to design novel and effective
design thinking activities and interactions that can positively impact team trust and
collaboration.

S. Balters et al.

2 Multidimensional Basis of Design Thinking Team
Interaction
Studying design thinking team interaction is a complex undertaking. It requires the
consideration of many measurable constructs that can and have been quantiﬁed in
the context of a design thinking team interaction. Our approach to better understanding the impact of an interpersonal trust activity on the collaborative outcome (i.e.,
creative product/innovation) is the quantiﬁcation of multiple measurement constructs. For that matter, we cluster a variety of potentially regulating constructs
broadly based within ﬁve categories (Fig. 1): (1) Behavioral constructs such as eye
gaze, body movements, verbal and nonverbal interactions (Cannon & Edelman,
2019; Lasecki et al., 2014; Sonalkar et al., 2013); (2) Physiological constructs
such as heart rate, blink rate, skin conductance, or hormonal changes (Balters &
Steinert, 2015; Mønster et al., 2016); (3) Individual moderators such as age, culture,
ethnicity, sex/gender, socioeconomic status, personality traits, creative ability, interpersonal trust (Ancona & Caldwell, 1992; Baker et al., 2016; Caldwell & O’Reilly
III, 2003); (4) Outcome constructs such as levels of cooperation/collaboration based
on subjective or objective measures and collaborative outcome such as creative
innovation (Dong et al., 2004; Kress et al., 2012; Sjöman et al., 2015); and
(5) Neuroscientiﬁc constructs such as inter-brain synchrony (IBS) between
interacting partners engaged in a design thinking task (Balters et al., 2020). While
we focus on examining IBS with fNIRS hyperscanning in this chapter, we suggest
that a consideration of all of the constructs listed above is needed to generate a
holistic model of a design thinking team’s interaction and interpersonal team trust.

Understanding the multidimensional basis of design thinking team
interaction: measurable constructs
• Behavior: eye gaze, body movements, verbal and nonverbal interactions
• Physiology: heart rate, blink rate, skin conductance or hormonal
changes
• Moderators: age, culture, ethnicity, sex/gender, socioeconomic status,
personality traits, creative ability, interpersonal trust
• Outcome: Level of cooperation (objective and subjective), collaborative
outcome (i.e., creative innovation)
• Inter-brain synchrony (IBS): Simultaneous scanning of two or more
brains (i.e., hyperscanning) with neuroimaging modalities such as with
portable functional near-infrared spectroscopy (fNIRS)

Fig. 1 Potentially regulating constructs of design thinking team interactions

Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . .

2.1

Functional NIRS Hyperscanning

Here, we propose functional near-infrared spectroscopy (fNIRS) hyperscanning (i.e.,
measuring two or more brains simultaneously as they interact dynamically) as a tool
to quantify and understand the effects of an interpersonal trust activity on team
creativity outcome. Functional NIRS is a non-invasive neuroimaging technology
that assesses cortical brain activity with relatively high spatial resolution (~1 cm)
compared to electroencephalography (EEG) (Scholkmann et al., 2014) and relatively
high temporal resolution (~10 Hz) compared to functional magnetic resonance
imaging (fMRI) (Cui et al., 2010). The technology uses near-infrared light to
measure the hemodynamic response (i.e., blood oxygenation level) of the cerebral
cortex as a proxy for neural activity (Cui et al., 2010; Scholkmann et al., 2014). In
recent years, fNIRS systems have become increasingly portable and affordable,
allowing researchers to investigate neurocognitive behavior in real-world settings
(Baker et al., 2017). Researchers have extended fNIRS measurements from single
brain to hyperscanning applications to investigate shared brain functions related to
social interactions in a laboratory (Cui et al., 2012; Funane et al., 2011) and
increasingly naturalistic settings (N. Liu et al., 2016; Mayseless et al., 2019; Miller
et al., 2019). For a more comprehensive introduction to fNIRS hyperscanning, we
refer the reader to Balters et al. (2020).
Results from fNIRS hyperscanning studies have shown increased inter-brain
synchrony (IBS) to be related to enhanced levels of interaction between team
members (ref). Researchers also identiﬁed signiﬁcant differences in inter-brain
synchrony values between cooperation and competition tasks (Cui et al., 2012;
Kruse et al., 2021; T. Liu et al., 2017). Cui et al. (2012) demonstrated that coherence
between brain activation patterns in participants’ superior frontal cortices increased
signiﬁcantly during cooperation, but not during competition. Relatedly, T. Liu et al.
(2017) found increased IBS in the right posterior superior temporal sulcus in the
cooperation and competition conditions; however, only the competition condition
involved signiﬁcant IBS in the right inferior parietal lobule (thought to be due to
additional requirements of mentalizing resources in competing contexts). Via a
machine learning approach (i.e., convolutional neural networks), Kruse et al.
(2021) could classify the sex composition of a dyad (i.e., female–female versus
male–male dyad) in both cooperation and competition tasks, with prediction accuracy of >80%. These ﬁndings suggest that female–female and male–male dyads
exhibit different brain behavior when cooperating and competing with one another.
Nuanced inter-brain synchrony patterns also emerged depending on task objectives
of the interacting dyad such as face-to-face deception (Zhang et al., 2017a, 2017b)
and risky decision-making during gambling games (Zhang et al., 2017a, 2017b).
Notably, fNIRS hyperscanning studies have emerged which focused particularly on
team creativity (Lu et al., 2019, 2020; Mayseless et al., 2019). Mayseless et al.
(2019) applied fNIRS hyperscanning to observe differences in IBS between partners
engaging in ideation versus problem-solving activities. Lu et al. (2019) applied
fNIRS hyperscanning to investigate how cooperative and competitive interaction

S. Balters et al.

modes affect the group creative performance; and whether the sex composition of the
dyad impact these interactions (and related performance) (Baker et al., 2017; Lu
et al., 2020).

3 Methodology
In this work, we describe the methods and procedures that we are planning to use to
determine and understand the impact of an interpersonal trust activity on the
collaborative outcome (i.e., creative product/innovation) and inter-brain synchrony
in both virtual and in-person interactions. Actual data analysis and related results are
not part of this book chapter.

3.1

Participants

A total of 96 adults will participate in the study (48 females, 48 males, age range:
18–45 years). All participants will be right-handed, healthy with normal or corrected
to normal hearing and vision. Participants will be randomly assigned to a previously
unacquainted dyad partner. The study will follow a 2 2 between-subject design
(Fig. 2). The dyads will be randomly assigned to either of the four groups (i.e.,
Group 1: in-person with interpersonal trust activity; Group 2: in-person without
interpersonal trust activity; Group 3: virtual with interpersonal trust activity; and
Group 4: virtual without interpersonal trust activity) to meet equal sex distributions
and inter-dyad age differences across all groups. Sex distribution will be four
female–female, four male–male, and four female–male dyads for each group. Consent will be obtained for all participants. Recruitment will be done through local
advertisement via ﬂyers, email lists, and social media. Participants will be compensated with an Amazon gift card ($25 USD per hour). The experimental procedure
will last about 2 h.

Fig. 2 Study design will follow a 2 2 between-subject design with 12 dyads per group

Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . .

3.2

Procedure

Dyad partners of the in-person groups will be seated in front of each other on
opposite sides of a square table. To obey COVID-related regulations, participants
will wear clear medical-grade face masks (FDA approved) and keep a distance of
9 feet to one another (Fig. 3a). Participants of the virtual groups will interact with
their dyad partner over zoom via two identical laptops (Lenovo Yoga 730-15IKB,
15.600 ) placed in two separate and auditorily disconnected rooms (Fig. 3b). Zoom
settings will be set to full screen without self-view windows. Despite interacting in
two different rooms, participants will wear clear medical-grade face masks to avoid
bias between the conditions. Functional NIRS caps will be attached while participants are in the same room or while zoom connection is already established.
Participants will be instructed to not talk to one another during the setup. Before
starting the experiment, participants will be given 3 min to introduce themselves to
one another. After the experiment, each participant will complete additional assessments in separate rooms to assess personality traits, creative ability, and prior
proﬁciency with virtual (zoom) interactions.

3.3

Experimental Tasks

The study is designed to assess whether and how an interpersonal trust activity prior
to an ideation session increases collaborative outcome (i.e., creative product/innovation) in design thinking teams. Given the recent digitalization of team interactions,
the aim is to incorporate both in-person and virtual (zoom) team interactions into our
methodology (i.e., between-subject design). Our goal is to create experimental
paradigms that require participants to collaborate in settings that are of signiﬁcance
to design thinking practices. In addition to our interpersonal trust activity, we chose
to integrate a naturalistic creative innovation task as well a consensus decisionmaking task as a control condition (Fig. 4). During each of the three tasks, dyads will
collaborate on the task challenge for a continuous time of 8 min with little instruction
and no intervention. Participants will ﬁll out a questionnaire after each task (see

Fig. 3 Experimental setup for the in-person group (a) and the virtual group (b)

S. Balters et al.

Fig. 4 Experimental procedure for the between-subject study in both in-person and virtual
condition

Needs we all have
acceptance
affection
appreciation
belonging
cooperation
communication
closeness
community
companionship
compassion
consideration
consistency
empathy
inclusion

intimacy
love
mutuality
nurturing
respect/self-respect
safety
security
stability
support
to know and to be known
to see and be seen
to understand and be understood
trust
warmth

Introduction: For the next eight minutes collaborate with your partner. Together, identify four
needs from the list above that are most meaningful to both of you. To emphasize the
importance of each need, describe a situation from your own life when that need was not met
and how it made you feel. As a partner, listen actively, acknowledge the feelings of the one
who shared, and describe why the need is also meaningful to you.

Fig. 5 Interpersonal trust activity

below). Dyads will be randomly assigned to task order 1 (i.e., interpersonal trust
activity prior to creative innovation task) or task order 2 (i.e., no interpersonal trust
activity prior to the creative innovation task) in both in-person and virtual conditions,
resulting in four different groups.
During the interpersonal trust task, participants will be asked to engage in a
modiﬁed version of a Nonviolent Communication (NVC) exercise that is used to
increase interpersonal trust between individuals (Rosenberg & Chopra, 2015).
Participants will be provided with a NVC list of “Needs We All Have”
(we selected the needs within the “connectedness” section, please see Fig. 5).
Participants will be asked to collaborate and identify four needs from the list that
are most meaningful to them. To emphasize the importance of each need, they will
be asked to describe a situation from their life when that need was not met and how it
made them feel. The partner will be instructed to actively listen, to acknowledge the
feelings of the one who shared and to describe why the need is also meaningful to

Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . .

them. Dyads will be told that they will describe their list of needs to an experimenter
after completion of the task.
During the creative innovation task, participants will be asked to collaborate and
design a solution that can increase water conservation in the highest possible number
of households. The solution can take any form (i.e., product, process, campaign,
etc.). Dyads will be told that they will have to describe their solution to an experimenter after completion of the task. To avoid time effects, we decided to include a
control task prior to the creative innovation task in the control condition. The task
will have the same length as the interpersonal trust activity. During the consensus
decision-making control task, participants will be asked to collaborate and identify
the four most important trafﬁc rules that enhance safety on US highways. To
emphasize the importance of each rule, participants will be asked to describe how
the rule enhances safety on US highways and why the rule is more important than
other rules. Dyads will be told that they will have to describe their rules to an
experimenter after completion of the task.

3.4

Assessments during the Experiment

Questionnaires during the experiment will capture subjective levels of cooperation
and level of interpersonal trust (Fig. 4). Video and audio recordings will be captured
to derive objective levels of cooperation.
Subjective Cooperation Index Participants will be asked to rate the overall cooperation of the team, the cooperation rating of themselves and their partner each on a
scale of 1 (not at all) to 9 (extremely).
Level of Interpersonal Trust Our study explores two kinds of interpersonal trust
important in team dynamics: affective and cognitive trust. “Affective trust” is the
conﬁdence one places in a team member based on one’s feelings of caring and
concern illustrated by that co-worker (McAllister, 1995). “Cognitive trust” is based
on one’s willingness to rely on a team member’s expertise and reliability (Johnson &
Grayson, 2005; McAllister, 1995). Participants will rate affective and cognitive trust
based on the McAllister’s interpersonal trust questionnaire with Likert-type 7-point
scales with 1 indicating total disagreement and 7 indicating complete agreement with
the statements (McAllister, 1995). Affective and cognitive trust items will be
averaged to a combined interpersonal trust index (Barczak et al., 2010).
Objective Cooperation Index Audio data will be used to code the level of cooperation and level of leadership for each task and dyad. The level of cooperation will be
coded as the number of turn-taking while speaking and the level of leadership will be
coded as the ratio of speaking time for each participant.

3.5

S. Balters et al.

Level of Team Creativity Outcome

After completion of the creative innovation tasks, participants will individually write
down the solution idea generated by their team. Ideas will be checked for congruency between dyad partners. Two trained raters will independently assess the quality
of product ideas on a 5-point Likert scale from 1 (low) to 7 (high). Raters will view
all videos before scoring. Product ideas will be scored for originality (how original
and infrequent) and effectiveness (efﬁciency of the solution, whether it improved/
incentivized water conservation) following previous work (Mayseless et al., 2019).
Inter-rater reliability index (as measured by Intra Class Correlation Coefﬁcient
(ICC)) will be assessed for both measures, and average score of originality and
efﬁcacy will be used as team creativity outcome.

3.6

Post Experimental Assessments

To control for potential bias between the four interaction groups, participants’
personality traits, creative ability, and prior zoom experience will be assessed after
the experiment (in two separate rooms).
Personality Traits To capture personality traits, participants will be asked to complete the NEO-FFI-3 survey (McCrae & Costa Jr., 2007) and Adult Attachment
Scale survey (Collins & Read, 1990).
Creative Ability The Alternate Uses Task (AUT) will be applied to assess individual levels of divergent thinking and creativity (Guilford, 1967). Participants will be
presented with pictures of four common objects. The name of each object as well as
each object’s most common everyday use will be indicated in parenthesis next to the
pictures. Participants will be asked to list as many alternative uses as possible for
each object within a total duration of 8 min. Participants are instructed to be as
creative as possible. The items will be a pillow, key, toothbrush, and ceramic plate.
Before performing the task, participants will be presented with an example of
alternate uses for a paper cup to familiarize them with the task. Only responses
that do not replicate the common uses given will be counted and included. Scoring
will include ﬂuency (number of responses) and originality (rarity of the response).
Final scores will be calculated based on the average score of all items. Scoring of
originality will follow the procedures described in Torrance (1974). Original
responses will be deﬁned as statistically infrequent responses within the population
of the study.
Zoom Experience Participants will be asked about their prior experience and
proﬁciency with zoom video conferencing.

Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . .

3.7

Functional NIRS Data Acquisition

The cortical hemodynamic activity of each participant will be recorded using a
continuous wave fNIRS system (NIRSport2 System, NIRX, Germany) with two
wavelengths (760 and 850 mm) and a sampling frequency of 10.2 Hz. A total of
128 optodes (64 sources 64 detectors) will be divided between the two participants
resulting in 100 measurement channels per participant. Optodes will be placed over
the entire cortex according to the international 10–20 EEG placement system
(Fig. 6a). Additionally, 16 short channels per participant will be placed across the
cortex to capture and correct for background physiological noise (e.g., cardiac,
respiratory, and blood pressure ﬂuctuations; pink dots in Fig. 6a). Plastic connectors
will be utilized between each source/detector channel pair to maintain an estimated
3 cm distance.

3.8

Functional NIRS Analysis

The raw fNIRS data will be analyzed using the NIRS Brain AnalyzIR Toolbox
(Santosa et al., 2018). Raw data will be converted to optical density data and further
corrected for motion artifacts by applying a wavelet motion correction procedure
(Molavi & Dumont, 2012). Short separation preﬁltering will be applied (Santosa
et al., 2020, ‘ShortDistanceFilter’ function) to reduce systemic physiological noises
(i.e., respiration and cardiac oscillations). Data will then be transformed into concentration changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin
(HbR) according to the Modiﬁed Beer–Lambert Law (Wyatt et al., 1986). Noisy
channels will be manually identiﬁed and removed from further analysis using a
correlation-based method (Cui et al., 2010). Since NIRS data are relative values,
HbO and HbR data will be converted to z-scores and regions of interest (ROI) will be

B
BA-4 left
BA-7 left
BA-9 left
BA-10 left
BA-17 left
BA-19 left
BA-21 left
BA-22 left
BA-47 left
BA-4 right
BA-7 right
BA-9 right
BA-10 right
BA-17 right
BA-19 right
BA-21 right
BA-22 right
BA-47 right

Fig. 6 (a) We use 100 measurement channels (blue lines) and 16 short channels (pink lines). (b)
Source localization analysis identiﬁed 18 ROI across the cortex

S. Balters et al.

created based on source localizations. All channels that share a common fNIRS
source will be averaged together resulting in 18 ROIs centered on source locations
(Fig. 6b for ROI localizations).

3.9

Functional NIRS Dynamic Inter-Brain Synchrony (IBS)
States Analyses

Wavelet Transform Coherence (WTC) will be used to assess similarity between
NIRS signals of partners. WTC can identify locally phase locked behavior between
two time series by measuring cross-correlation between the time series as a function
of frequency and time (Baker et al., 2016; Cui et al., 2012). For a more in-depth
explanation of WTC, please see (Grinsted et al., 2004). The wavelet transform
coherence (WTC) package in Matlab (Grinsted et al., 2004) will be used. WTC
will be calculated between each ROI and the rest of ROIs on the converted HbO time
series (a total of 324 combinations: 18 ROIs 18 ROIs). The inter-brain synchrony
(IBS) between same ROI pairings will then be averaged. This will result in 171 IBS
pairings. The average coherence value between 0.15 and 0.015 Hz will be calculated.
This frequency band allows us to exclude noise associated with cardiac pulsation
(about 1 Hz) and respiration (0.2–0.3 Hz). Finally, averaged coherence values will
be converted to Fisher z-statistics (Cui et al., 2012).
“For the dynamic IBS network analysis, the interpersonal trust activity, control
task and creative innovation tasks will be analyzed separately following the procedure described in Li et al. (2021). The IBS calculated by WTC across the entire task
duration for each dyad will be segmented by using a sliding window approach. The
window size will be set to 15 s and shifted in an increment of 1 s along the entire
task. Within each time window, WTC values between the same pair of ROIs will be
averaged, resulting in an IBS matrix for each time window. The 8-min measurement
duration will then be segmented into a series of windowed IBS matrices (18 ROIs
18 ROIs 451 windows) for each dyad. To characterize the dynamic IBS (dIBS)
states during cooperation tasks, k-means clustering will be applied to the
concatenated windowed IBS matrices averaged across the group. The k-means
clustering approach can estimate the similarity between the windowed IBS matrices
and will identify various clusters representing distinct IBS states derived from these
windowed IBS matrices. The number of clusters will be determined using the elbow
criterion of the cluster cost, computed as the ratio between within-cluster distance to
between-cluster distance. We will compute the cost for different k values (e.g., from
1 to 15), and then plot these cost values as a function of the cluster number. With this
approach, we will seek to minimize the within-cluster distance and maximize the
between-cluster distance, while controlling the number of the clusters. The appropriate number of k is generally selected at the elbow of the curve, optimally
balancing the cluster cost and cluster number. In each iteration of cluster estimation,
we will estimate the similarity between the windowed IBS matrices using the L1

Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . .

distance function (Manhattan distance) and will repeat 1000 times to decrease
chances of escaping local minima, with randomly initialized centroid positions.
Cluster centroids obtained from the group-averaged IBS matrices will then be used
as the initial centroids for the individual dyad-level clustering analysis, resulting in
ﬁnal dIBS states for each dyad and each cooperation task. To identify the true taskrelated regional connections in each clustered dIBS state, we will construct a series
of permuted IBS matrices as a ‘baseline’ for each dIBS state (i.e., an unrelatedpaired-participants’ IBS matrix as compared to the real-paired-participants’ dIBS
matrices). This will be calculated by using the time series of two unrelated players
from the whole participant group (i.e., two players from different dyads and cooperation tasks). Out of a large number of possible pairings, permuted IBS matrices
will be formed by randomly selecting a set of 400 unrelated pairs (permuted pairs).
We will then perform a series of two-sample t-tests on single regional connection
level between individual dIBS matrices and the permuted IBS matrices to identify
signiﬁcant task-related regional connection in the dIBS matrices.” (Li et al., 2021).

3.10

Experimental Hypothesis

Through our experimental methodology, we intend to address the following experimental hypothesis:
H1: Participating in an interpersonal trust activity prior to an innovation event
increases a team’s creativity outcome.
By means of fNIRS hyperscanning, we will further explore whether and how an
interpersonal trust activity prior to an ideation session increases collaborative outcome (i.e., creative product/innovation) in both in-person and virtual team interactions. The aim is to distill neural inter-brain signatures of effective interpersonal trust
activities to understand the underlying brain function of effective activities.

4 Planned Analyses and Conclusion
To test our hypothesis, we will ﬁrst execute a stimulus check and test whether
interpersonal trust levels are higher after the interpersonal trust activity in contrast
to the control task in both the in-person and virtual interaction condition (i.e.,
repeated measures ANOVA across all four groups). We expect that interpersonal
trust is higher after the interpersonal trust activity in both interaction conditions in
contrast to the control task. Next, we will test our experimental hypothesis H1. We
will use the team creativity outcome scores and run repeated measures ANOVA
across all four groups. We expect higher creative outcomes for the two groups who
have immersed themselves in the interpersonal trust activity in contrast to those
groups who have engaged in the control task prior to the creative innovation task.
We also do not expect differences in creative outcome between the two interaction

S. Balters et al.

conditions (i.e., in-person versus virtual) for those who engaged in either interpersonal trust activity or control task. As a third analysis, we will assess the differences
of properties of dIBS states, which will be characterized by three metrics (Li et al.,
2021): (1) Fractional windows of each state (i.e., percentage of total windows a dyad
spent in each dIBS state); (2) Number of transitions between states (i.e., total number
of switches between any two states during the task); and (3) Global efﬁciency of
each state (i.e., elementary graph-based metric that represents the integration of the
entire network, reﬂecting how active and efﬁcient a network can share information
between regions). Repeated analysis of variance (ANOVA) will be used to assess
whether there are signiﬁcant differences between these dIBS states in terms of their
properties when comparing (i) interpersonal trust activity and control task as well as
(ii) creative innovation task with and without prior interpersonal trust activity. A
better understanding of the neural signatures underlying the interpersonal trust
activity will allow design thinking practitioners and educators to design novel and
effective design thinking activities and interactions in the future.
Following the success of the present investigation, we plan to extend the methods
and measures presented in this chapter. In future work, we are speciﬁcally interested
in examining the impact of design experience (e.g., design thinking experts versus
novices) on the effectiveness of this interpersonal trust activity. Furthermore, we aim
to extend two-person hyperscanning to hyperscanning of three persons simultaneously. It is our hope that this work will provide new and valuable information
on human social interaction within working teams in the design thinking and related
areas.

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Dancing with Ambiguity Online: When Our
Online Actions Cause Confusion
So Yeon Park, Mark E. Whiting, and Michael Shanks

Abstract Online social actions are often ambiguous, leading us to wonder: Why did
this person unfollow me? Why did my friend like this negative content? Such
ambiguity is common and perceived as a natural part of our ubiquitous online
interactions. However, as online actions are curated and designed by platforms,
this ambiguity is, at least in part, something platforms can control—for example,
some platforms provide explicit dislike functionality, while others do not provide
features to clearly signal such sentiment. Our understanding of this ambiguity
around online actions is limited. We are unaware of the wide spectrum of situations
in which people are confused by others’ online actions and how widespread such
confusion might be. We conducted a survey study to identify when such ambiguity
occurs—when people wonder why online actions are taken. We found that ambiguity of online actions occurs in non-nuanced situations. Speciﬁcally, some people
wondered why online actions were taken when simply certain actions, content, or
stakeholders were involved. For example, malicious content caused ambiguity,
regardless of whether others posted or interacted with such content. Our ﬁndings
suggest that more platform features may help to improve the clarity of people’s
actions as well as the extent of the impact of these actions, which may help to avoid
such uncertainty.

1 Introduction
Our use of social platforms hinges on the assumption that other people understand
the actions we take. We may like something because of our legitimate excitement
about the content, or for virtue signaling and moral grandstanding [Tosi & Warmke,
2016]. We may share a link to a community sincerely or ironically, or for any one of

S. Y. Park (*) · M. Shanks
Stanford University, Stanford, CA, USA
M. E. Whiting
University of Pennsylvania, Philadelphia, PA, USA
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_3

S. Y. Park et al.

numerous other reasons. Despite the ubiquity of the actions (e.g., posting, liking,
sharing) that we take and receive on social media platforms, being heavily mediated
by the platforms (i.e., designing how they look, feel, and function) often hinder clear
interpretation of these online actions. As a result, most people, most of the time, have
uncertainty about the posts and digital traces they are seeing on platforms [Hayes
et al., 2016; Lee et al., 2016; Park et al., 2021; Sumner et al., 2018]. This causes
ambiguity in online actions that often leaves people feeling surprised or bewildered
and can lead to misinterpretations and misunderstandings. Yet to address this
phenomenon, we need to ﬁrst understand the sources of ambiguity, not only technically (i.e., what design choices regarding the interaction cause this), but also
contextually; what situations lead to uncertainty?
The advent of the Facebook papers [Accountable Tech, 2021] and related revelations—namely, that social platform companies likely understand a lot more about
the nuanced impacts of their products than they let on—has struck a chord with the
public. These revelations have generated an uproar surrounding the topic of how
platforms should act for our mental health and our society. However, as deeper
analysis of these efforts revealed, the truth is much more complicated and the data
much more nuanced than ﬁrst impressions may indicate. In a world designed for
one-click simplicity, it may appear that social platforms have simpliﬁed their
products to provide a fundamental reduction of certain social interactions. What is
simpler than 280 characters—a rule enforced for tweets?1 But in practice, this quest
for simplicity may remove cues that, if present, make clear to others what we are
actually thinking when we use the Internet. An underlying challenge with this
problem context is that misunderstanding is largely a latent negative outcome that
can have devastating consequences much later, including imprisonment.2 Users
performing online actions may rarely realize they have been misunderstood, platforms may not have clear ways to estimate when such misunderstandings occur, and
viewers often have no immediate way to ask why something has happened (other
than to comment on the post). This last challenge is exempliﬁed when a famous
person takes an online action that becomes a point of debate in the media, such as
former President Trump’s tweet regarding termination of sanctions against North
Korea on a day when no sanctions had been issued,3 or the oddities fans found in
Britney Spears’ social media posts (now found as having been controlled under her
conservatorship).4

https://developer.twitter.com/en/docs/counting-characters,%20Accessed%20November%201,%
202021.
2
https://evangelicalfocus.com/world/2559/christian-in-pakistan-sentenced-to-life-imprisonmentfor-blasphemy, https://www.smh.com.au/national/sydneyman-unlawfully-imprisoned-in-egyptianprison-since-january-20200920-p55xff.html, Accessed November 16, 2020.
3
https://www.vox.com/world/2019/3/26/18282408/trump-north-korea-tweet-bloomberg-sanctions,
Accessed November 1, 2021.
4
https://www.newyorker.com/news/american-chronicles/britney-spears-conservatorshipnightmaregt, https://www.reﬁnery29.com/en-us/2021/07/ 10555863/who-runs-britney-spearsinstagram-real-fake, Accessed November 20, 2021.

Dancing with Ambiguity Online: When Our Online Actions Cause Confusion

In an effort to understand situations that lead to uncertainty, we surveyed thousands of Twitter users and asked them when they had wondered why someone took
an online action. We conducted inductive thematic analysis on 1042 free-text
responses, and we found that ambiguity was perceived in instances where only
certain actions, content, and stakeholders were involved. We found that people’s bewilderment or confusion by others’ online actions do not only occur in
situations that are nuanced. This is also the case for some generalizable contexts that
are dependent upon one of three factors related to taking online actions: How an
action was carried out (action), what the action was taken on (content), and who was
involved in the action (stakeholder). With these ﬁndings, we contribute to an
improved understanding of when online actions are ambiguous and cause users to
wonder why they were taken, as well as design implications for social platforms to
help reduce the ambiguity inherent in their design of interactions.
This work gives voice to the unspoken uncertainty that plagues our experiences
online. We hope that by investigating how individual experiences of the social
Internet manifest and addressing these issues, a broader discussion of sociodynamics
in online platforms, and platform design decisions in general, can be initiated. While
platforms conduct extensive internal research that has undoubtedly elucidated many
of these aspects [Accountable Tech, 2021], the possibility for the broader scientiﬁc
community and general public to understand and act on insights about how the
design of social media affects us remains elusive.

2 Related Works
2.1

Online Actions on Social Media

Social media is an integral part of our lives. Statistics have shown that Facebook
users spend on average 58 minutes daily on the platform, and in October 2020, for
example, made “an average of 5 comments, 12 post likes, 1 share, and 1 Page like.”5
Our engagement on social media comes in the form of diverse online actions that
vary in the amount of cognitive effort they require—the lowest effort required, after
consuming, is contributing. This is often operationalized as mere button clicks, such
as liking and retweeting [Kim & Yang, 2017; Muntinga et al., 2011]. Among these
contributions, however, there are varying levels of cognitive effort. While clicking
the Like button has been found to be the “default reaction” that accounts for 78.9%
of such low-effort contributions [Tian et al., 2017], other Facebook Reactions6 have
5

https://www.socialpilot.co/blog/social-media-statistics#fb-usage-stats, Accessed August 25, 2021.
Reactions are “an extension of the Like button, to give you more ways to share your reaction to a
post in a quick and easy way,” and these are made up of six emojis including original “like” (thumbs
up) button as well as “love” (heart) and four facial expressions of “happy,” “wow,” “angry,” and
“sad.” https://about.fb.com/news/2016/02/reactions-now-available-globally, Accessed September
4, 2021.

S. Y. Park et al.

been found to be more “thoughtful behaviors” and “signiﬁcantly more deliberate
than Likes in their use” [Sumner et al., 2020]. An extreme example of a relative lack
of deliberation behind a Like in social media was labeled in previous research as
“mindless” liking [Park et al., 2021].
Online actions on social media are perceived to express not only different levels
of deliberation, but also to have different intentions associated with them. The user’s
intent in clicking the Like button—in addition to analogous buttons of Favorite and
Upvote—has been classiﬁed into six categories: (1) to indicate that they actually
“like” the content as the label of the emoticon connotes, (2) to acknowledge one’s
viewing of the content to others, (3) to indicate social support to others, (4) to virtue
signal7 for social grooming purposes, (5) for utilitarian reasons (e.g., clicking Like
with the intention of returning to the liked content later or of affecting the platform’s
algorithm to receive more content similar to the one that was liked), and (6) for a
mindless reason (e.g., by accident) [Park et al., 2021; Sumner et al., 2018]. Analyzing
Likes, researchers found that “52% were meant to convey content-related thoughts,
23% were meant to communicate relational-based sentiments, and 24% were said to
contain both types of meaning” [Sumner et al., 2018], similar to ﬁndings in earlier
work [Lee et al., 2016]. People receiving the action of liking were well aware of the
variety of meanings behind the action [Sumner et al., 2018], and thus, despite the
positive connotation of the word “like” itself, the action was not always received
positively (e.g., it was used self-servingly to create certain impressions [LoweCalverley & Grieve, 2018]).
Other low-effort “lightweight acts of communication” [Hayes et al., 2016]
include retweeting on Twitter. Similar to liking, retweeting is yet another “speech
act that we can only perform online, and whose linguistic meaning is not entirely
obvious” [Marsili, 2020]. Retweeting can be seen as copying, rebroadcasting, and
endorsing others among many other interpretations [Boyd et al., 2010; Weller,
2016]; in fact, Boyd et al. have provided ten reasons for retweeting with a greater
degree of nuance [Boyd et al., 2010] than the aforementioned six reasons for three
actions [Park et al., 2021; Sumner et al., 2018]. Notably, some were found to value
retweeting while others “lament[ed] users’ selﬁsh motivations” of attention seeking
[Boyd et al., 2010], similar to that of the social grooming purpose for liking.

2.2

Ambiguity of Online Actions

Despite being aware of these various intents behind taking online actions on social
media, users are still confused by them. Ambiguity is prevalent and is felt by users
on both sides of online actions alike—actors and recipients/observers [LoweCalverley & Grieve, 2018; Park et al., 2021; Sumner et al., 2020]. Due to these
online actions—also termed paralinguistic digital affordances [Hayes et al., 2016]—

https://www.spectator.co.uk/article/easy-virtue, Accessed January 6, 2022.

Dancing with Ambiguity Online: When Our Online Actions Cause Confusion

being “somewhat ambiguous” in nature, reasons attributed to others taking such
actions are not reliably inferred [Sumner et al., 2020]. This can lead to failure in
achieving the desired interpersonal functions behind the online action [Hayes et al.,
2016], for example, when an online action is perceived as being antisocial when it is
in fact intended for social connection. Research has found that how people felt about
these online actions, operationalized as having a positive and negative affect, is
signiﬁcantly predicted by the reason they “most readily associated with the action”
[Park et al., 2021]. Further, what recipients of these actions feel may be completely
different from what was expected or intended. Perhaps due to this, many users also
feel “pressure regarding how to interpret and provide these cues in an appropriate
way” [Sumner et al., 2020]. This may have also been the reason for the huge success
of a system enabling Twitter users to share links with greater visibility [Gilbert,
2012].
While research ﬁnds that there are cases in which this ambiguity is advantageous—when carrying out “unsocial behaviors” (e.g., unresponsiveness) [Lopez &
Ovaska, 2013] or other scenarios where users wish to mitigate social difﬁculties and
costs (e.g., saving face) [Aoki & Woodruff, 2005]—the overall desire among users is
to ﬁnd clarity in ambiguity with more social information. Social information—of
“who’s interested in what we say” and “whether our audience is bored and looking
out the window or hanging on our every word”—helps us to “act appropriately”
[Gilbert, 2012]. By seeking such information about others, uncertainty, which causes
cognitive stress and uncomfortable feelings, can be reduced and lead to relationship
development [Berger & Calabrese, 1974; Ramirez Jr et al., 2016]. Social anxiety
stemming from this uncertainty was also found to be mediated by actions for
reducing anxiety (e.g., actively inquiring others, passively observing) [Courtois
et al., 2012]. However, this social information may not be readily acquired, leading
to social media users’ asymmetric experiences online [Fetchenhauer & Dunning,
2010]. This information asymmetry was found to cause failure in “learn[ing] about
others’ true level of trustworthiness” and in people seeing “others as driven more by
self-interest than they really are” [Fetchenhauer & Dunning, 2010]. This consequently presents an example of people’s tendency to assume negative intent in
others’ actions [Nasby et al., 1980].

2.3

Ambiguity of Content on Social Media

There is also ambiguity in the content shared on social media, especially when it
comes to the veracity of shared news. It is unclear to some users whether a piece of
news is true or false, and making sense of this ambiguity depends heavily on
contextual factors such as who shared the content, how it was shared, and who is
receiving it [Nekmat, 2020; Sterrett et al., 2019; Vraga & Tully, 2021]. Research has
found that people are more likely to trust social media content if it is shared by
people who they trust as a public ﬁgure, who is within their network, or who is
perceived as being like-minded [Metzger et al., 2010; Sterrett et al., 2019]. Social

S. Y. Park et al.

diversity “among people who share a story about a scientiﬁc discovery” was also
found to “enhance the perceived credibility” of shared content [Hayat et al.,
2019]. How the information is endorsed by others was also found to be important
in evaluating credibility. People are disposed to attribute credibility to information
and sources if others do so “without much scrutiny of the site content or source
itself” [Metzger et al., 2010] as per the “consensus heuristic” [Chaiken, 1987]. Such
ﬁndings can be alarming in light of research such as that which found people more
inclined to accept information than to reject it [Lewandowsky et al., 2012] and that
only those having as well as valuing greater news literacy “are more skeptical of
information quality on social media” [Vraga & Tully, 2021]. All these studies show
that while people resort to various cues to ascertain the stance they should take, more
often than not they lean toward the interpretation of what is available, yet these
strategies may not lead to the correct clariﬁcation of ambiguity.

3 Methods
To evaluate when people desire more understanding around others’ online actions,
we conducted a survey of Twitter users in which they answered questions about
their past experiences on Twitter and described situations that motivated them to
wonder what others meant online. In this section we outline the design of the survey
instrument, our data coding approach, and the participant recruitment process.

3.1

Survey Instrument

The survey evaluated when users felt a sense of ambiguity around online actions. We
asked participants to answer the following question as a free-text response: “In what
kinds of situations do you wonder why someone has taken an online action (e.g.,
like, dislike, share, retweet)?” This was part of a larger survey experiment that
investigated how comfortable people felt about these online actions.8

3.2

Qualitative Coding

We ﬁrst assigned participant numbers with the numbering protocol of “P” followed
by a four-digit number. We then coded participants’ free-text responses about when
they wonder about the reasons for an online action with inductive thematic analysis
[Braun & Clarke, 2006]. For this, one researcher reviewed 100 responses and from

Detailed further in forthcoming work.

Dancing with Ambiguity Online: When Our Online Actions Cause Confusion

these generated initial codes. The initial codes that emerged were discussed among
the researchers and ﬁnalized into themes that are independent but can be concurrently implicated (e.g., one response can implicate multiple themes). The full set of
responses was coded by one researcher except for 123 text responses that the
researcher deemed as requiring discussion; two researchers reached consensus for
these responses that 43 of them were unclear (i.e., to be excluded from our thematic
analysis as they could not be coded) and then decided which codes would apply for
the remaining 80 responses.9 Of these themes, we report on the three themes that
were speciﬁcally dependent upon action, content, and stakeholders in this chapter.
Participant responses with spelling corrections are included with participant numbers
as per the protocol used by the researchers.10

3.3

Recruitment and Compensation

We recruited Amazon Mechanical Turk participants located in the USA who had
completed at least 1000 tasks on the platform with a 96% acceptance rate—a
common recruiting protocol [Whiting et al., 2019, 2020]. The ﬁrst page of the
survey checked if participants met our eligibility criteria, including their usage of
Twitter and having had the experience of their content being liked within the past
30 days and having been knowingly unfollowed in the past. These ﬁltering questions
were asked amid other similar questions to avoid desirability effect confounds.
Participants who did not meet the requirements were rewarded $0.40 and did not
follow through with the rest of the survey.
Those who met our eligibility criteria moved on to complete the rest of the survey.
The ﬁnal question included in the survey asked whether respondents had answered
any of our questions in the survey randomly. We ﬁltered out responses from
participants who answered “yes” to this question—these participants’ compensation
was not penalized. In addition, we reviewed four free-text responses (not reported
here) to eliminate data from participants who provided responses that were irrelevant
from our analysis. Those who provided relevant responses to all four free-text
responses received $4.20 according to the median time taken to complete the survey
at a rate of $15 per hour [Whiting et al. Whiting et al., 2019]; those who did not
received either $2.20 or $1.00 depending on the relevance of their responses. Upon
ﬁltering of participant data, we resulted in a total of N ¼ 1042 responses that could
be thematically coded for this chapter. Our participants were aged 19–78 (μ ¼ 36.9,

Unclear responses included “If I see something that I need to speak up about, then I will take
action” (P0042), “To make something better” (P0314), and “when Black Lives Matter tweeted
many of them has taken an online action” (P0773).
10
Spelling corrections have been made for readability and they include “NSTOP” to “STOP”
(P0947), “embarrasing” to “embarrassing” (P0321), “twitted” to “tweeted” (P0126), and “ot” to
“it” (P0087).
9

S. Y. Park et al.

σ ¼ 10.8), 42% female, 71% with Bachelor’s degree or above, and 83% with Twitter
experience of a few years.

4 Results
We unpacked free-text responses to the question of when online actions were
ambiguous—in other words, what kinds of situations cause people to wonder why
someone has taken an online action—using inductive thematic analysis. We found
that 84.4% of responses indicated that the situations in which they wondered why
people took online actions depended upon speciﬁc action, content, and/or stakeholders. Of these, most responses implicated certain actions (68.5%; e.g., liking,
retweeting, following). Despite asking about online actions, we found that certain
content (43.0%; e.g., offensive or controversial content) and stakeholders (27.5%;
e.g., self, friends) also caused people to wonder why others were taking online
actions. Below, we present the results of those who mentioned only certain actions,
content, or stakeholders as being the cause for ambiguity.

4.1

Relating to Certain Actions

Situations involving only speciﬁc online actions caused many to wonder why they
were taken; these actions included negative and positive ones, as well as actions that
could be seen as being neutral or both negative and positive. Of these, negative
actions (e.g., dislike, unfollow, block) were brought up most frequently as being the
valence of action that caused people to wonder the most about others’ reasons why.
•
•
•
•
•

“I WONDER WHEN THEY DISLIKE OR STOP FOLLOWING” (P0947)
“mTurk blocks from requestor, Facebook/Twitter unfollows” (P0489)
“Unfollow or unfriend” (P0315)
“Usually in negative situations, like when someone dislikes a post” (P0337)
“Why they revoke a friend request” (P0594)

People also wondered in situations when a certain action was discontinued or
undone: “Someone who has been following for a long time suddenly stops” (P0246),
“I wonder why someone dislikes a post after liking it” (P0118), “When there is a
sudden unfollow” (P0989). Some showed the kinds of interpretations they had for
others taking such negative actions: “I wonder if someone dislikes a video just to be
mean” (P0207).
Discussing, commenting, and retweeting emerged as actions that could be perceived either way in terms of their valence, i.e., positive or negative. For such
actions, participants wondered most often when these were carried out with negative
intent or sentiment.

Dancing with Ambiguity Online: When Our Online Actions Cause Confusion

•
•
•
•
•
•

“When they comment something mean” (P0774)
“When someone starts an argument in a comment section” (P0745)
“making a statement I don’t agree with” (P0592)
“If someone quote retweets with a negative connotation” (P0302)
“When someone retweets a post to make fun of it” (P0327)
“When someone comments something unnecessarily rude or mean-spirited or
just nit-picking a post instead of understanding the underlying message of the
post” (P0716)

Neutral actions taken by others without any positive or negative connotations
could also be a source of confusion and misinterpretation of their actions.
• “When I know they don’t really have a strong opinion about a social issue but
they decide to chime in with their opinion anyway. I think they want to make
themselves out to be more of an advocate than they are” (P0029)
• “I wonder why someone comments” (P0127)
• “Situations where people express their opinion” (P0815)
• “When someone retweets a post, [I wonder if] it is because they ﬁnd it interesting
information or to help someone requesting a particular service, medicine, or
help” (P0145)
The disproportionate effort in writing comments could also be the reason for
wondering why: “When they spend time writing a really long and drawn out opinion
based comment” (P0824). Deciding to engage in such “drawn out” discussions could also
cause people to wonder: “When they comment when on a situation that they know will start a
back and forth” (P1006).

Positive actions (e.g., like, follow) could also be confusing and cause people to want
to know (and even attribute) the reasons for them, as they felt these actions could be
motivated by a multitude of reasons.
• “If something they liked or retweet[ed] was something they actually support,
endorse, or just want to share” (P0189)
• “liking and re-sharing a post” (P0006)
• “when he or she has retweeted on a particular post” (P0200)
• “If they like something when they actually don’t like it” (P0962)
• “If I feel like they are trying to garner attention for likes/follows” (P0628)
The lack or absence of such a positive action also produced confusion: “when
they don’t like something” (P0326), “I also wonder why people do NOT like things,
sometimes” (P0381).
Sometimes, the type or nature of action (i.e., an action and its counteraction) caused people to wonder why:
• “When I see someone unfollowed or when I see new followers” (P0993)
• “They didn’t like something, or they liked something” (P0144)
• “I sometimes wonder why someone ‘likes’ or ‘dislikes’ something when they do
so” (P0277)

S. Y. Park et al.

Finally, for some, all of the above reasons caused them to wonder more broadly
about people’s interactions in general, as in the case of P0883: “I wonder what cogs
in the brain ﬁre to generate that response. It is less-so when I agree with the like/
dislike/retweet and more-so when I don’t agree with it. Why/how did humans end up
becoming so divided?”

4.2

Relating to Certain Content

Divisive (e.g., controversial, political) and offensive (e.g., racist, hateful) content—
and any interaction with such content—caused people to speculate about others and
their motives for engaging in any way with the content.
• “When they are engaging with a controversial post” (P0240)
• “When it’s about a controversial topic which shows they are a hypocrite and a
sheep” (P0032)
• “If it is controversial or taboo” (P0354)
• “if it’s really angry or controversial” (P0186)
• “If it was extremely hateful or hurtful. I wonder what is going on in there lives to
make them so malevolent” (P0243)
• “Typically in scenarios where it’s political related” (P0037)
• “Usually related to politics and sports” (P0134)
• “if they say something cruel or racist” (P0319)
• “If the content is inappropriate, controversial, or offensive” (P0551)
A few participants expanded the list with more humanitarian content: “When it
comes to political issues, or humanity issues” (P0020), “political views, healthcare,
immigration, homelessness” (P0014). Another type of content added to divisive and
offensive content related to content with low general interest: “I wonder why
someone has taken an online action when: (1) the message is controversial.
(2) not many people express interest on the subject. (3) the tweet contains inappropriate materials such as porn, vulgar language, etc.” (P0375). People also wondered why others interacted with such content that lacks interest from the general
public (e.g., “when the content is not important” (P0574)) or “irrelevant” as
expressed by P0454: “When people put personal business out there or information
about others that is irrelevant and just put out there to stir up online drama. or when
“Karens” take to social media to try to make a point about something petty, like
McDonald’s left their fries out of their meal or something.” Informational content,
whether it is truthful or questionable, were also content that made people wonder
why online actions were taken.
• “When it is information that is obviously wrong” (P0378)
• “When what is said doesn’t make sense, seems intentionally confrontation
[al] and/or aggressive, involves questionable content and/or information, anything that seems out of the ordinary for no reason” (P0063)

Dancing with Ambiguity Online: When Our Online Actions Cause Confusion

Few also mentioned wondering why an online action was taken when related to
content that did not make sense to them: “When the tweet is just complete nonsense.
Like a few words or anything that just says ‘among us.’ I don’t get memes these
days” (P0173) and “when the tweet is some incomprehensible nonsense” (P0293).
Additional types of content that emerged included the following:
• “when it is something embarrassing” (P0321)
• “When someone takes an online action for content related to race, language and
other things” (P0632)
• “When the content is appropriate or not appropriate” (P0164)

4.3

Relating to Certain Stakeholders

Stakeholders involved in online actions—whether they were the actor, recipient, or
observer of an action—could also be the reason for wondering why actions were
taken. Many of the responses in which the participants’ wondering depended only on
stakeholders were in relation to others who were close to them.
•
•
•
•

“i only care if it is like my family or boyfriend” (P0441)
“If I know them personally” (P0128)
“When I have a close personal relationship with the person” (P0753)
“If it’s someone I have a personal relationship with in real life (friend, acquaintance)” (P0157)

People also wondered about others’ online actions when it involved themselves:
“When it directly affect[s] me” (P0087) and “I said something they did not like”
(P0643). To others, it depended on their state: “Times I’m not in a good mood or sad
about something” (P0787). Some implicated both themselves as well as others who
were perceived to be close to them: “When it happens to me on my proﬁle and when
it’s someone I know personally” (P0748).

5 Discussion
We found that in addition to how the actions themselves were taken, who took the
action and regarding what could also be reasons for wondering why online actions
are taken. That each of these independent factors alone could simply cause people to
wonder why and possibly to be confused by an online action taken shows that
speculation does not always occur in nuanced situations.

5.1

S. Y. Park et al.

Sometimes Nuance Does Not Matter, Negativity Simply
Makes us Wonder Why

Speciﬁc actions could alone make people wonder why they were taken, and for
some, this was especially the case for the “negative action” of unfollowing. This
could be due to their unawareness of differences between reasons for online vs
ofﬂine relationships ending [Quercia et al., 2012; Sibona & Walczak, 2011]. Sibona
and Walczak found that disliked behavior and changes in the relationship were two
factors that contributed to ofﬂine unfriending decisions, while there were four types
of posts that caused online unfriending on Facebook: Unimportant/frequent, polarizing, inappropriate, and everyday life posts [Sibona & Walczak, 2011]. These
motivations for unfollowing online were similarly found in the context of Twitter:
Kwak et al. found that “those who left many tweets within a short time, created
tweets about uninteresting topics, or tweeted about the mundane details of their
lives,” with whom they lacked homophily, were unfollowed [Kwak et al., 2011]. Perhaps those participants who pinpointed to merely the action of unfollowing or
unfriending, and even blocking, as being questionable or inviting speculation
show that such ﬁndings from a decade ago are still true today—that they may not
be aware of why people end relationships online, in addition to the lack of awareness
around differences of reasons between online vs ofﬂine unfriending. Another plausible explanation may be that people are not be sure which of the many reasons above
triggered the action of unfollowing and would like clarity. Participant responses
showed that ambiguity around unfollowing occurred when the action was perceived
as being taken out of the blue (i.e., when they were least expecting it), which
evidences their lack of awareness of which particular reason led to the outcome.
For others, actions taken with some negativity were also cause for wondering.
One of the most common occurrences of such was retweeting with negative connotation or intent, e.g., with “arguments” and “hatred” or in a “mean” way. This may
be caused by the online disinhibition effect, which is the effect of people “loosen
[ing] up, feel[ing] less restrained, and express[ing] themselves more openly” online
[Suler, 2004]. Of such an effect, there is toxic disinhibition, which is characterized
by “rude language, harsh criticisms, anger, hatred, even threats” [Suler, 2004]. People are prone to taking on or displaying disinhibitive behaviors on social media
platforms that enable “dissociative anonymity, invisibility, asynchronicity, solipsistic introjection, dissociative imagination, and minimization of authority” [Suler,
2004]. As aversive communication often engenders responding to them in kind
[Chen, 2015], those on the receiving or witnessing end of such online actions may
be taken aback by them. While toxic disinhibition has been present online for a
while, and quite commonplace according to some responses, people may still be
wondering for the most part why this occurs as it does not reﬂect the majority of the
social media users’ behavior. Moreover, less toxic disinhibition displayed ofﬂine
may also cause people to be surprised by or unsure of its occurrence online.
Not only were negative actions mentioned, but also negative content was brought
up as a reason for why people wondered why online actions were taken. Negative

Dancing with Ambiguity Online: When Our Online Actions Cause Confusion

content mentioned by the participants included content that was seen as being
controversial, political, taboo, angry, hateful, offensive, wrong, etc. Participant
responses seemed to connote a lack of understanding of why anyone would post
or engage with such content. The propagation of such negative content could be the
result of a lack of thoughtfulness and gatekeeping on social media, as Rainie et al.
posits: “Armies of arbiters as democratic shapers of the deﬁning climate of social and
political discourse have fallen out of favor, replaced by creators of clickbait headlines read and shared by short-attention-span social sharers” [Rainie et al.,
2017]. This does not only apply to news content, but also to online content in
general as users arguably post content on social media to gain visibility. Moreover,
engaging with content—whether it is liking, commenting, or retweeting—helps the
post gain greater visibility. While this may not have been the intent behind one’s
engagement with negative content (e.g., simply a knee-jerk reaction), some users
may have felt that propagation was the outcome and therefore wondered why others
would help enable an inappropriate outcome for questionable content.

5.2

Ambiguity Is Ampliﬁed by the Visibility of Actions

When describing ambiguity around certain actions, we found a few situations in
which participants raised the possibility of someone taking an online action for the
visibility of it, as mentioned in § 5.1. Examples include: “I wonder if someone
dislikes a video just to be mean” (P0207) and “when I know they don’t really have a
strong opinion about a social issue but they decide to chime in with their opinion
anyway. I think they want to make themselves out to be more of an advocate than
they are” (P0029). In these quotes, people are raising the issue of taking online
actions for a performative effect, and therefore alluding to the self-interest of social
grooming, which is one resulting interpretation caused by information asymmetry
[Fetchenhauer & Dunning, 2010]. As found in prior literature in the collaborative
editing context, visibility of edits can help to improve the knowledge of collaborators
but at the same time cause conﬂicts [Birnholtz & Ibara, 2012]. Similarly, these
actions, simply taken for utilitarian purposes, could have deleterious consequences
from misinterpretation as shown by these participants. As such, critically, a lack of
visibility undermines understanding and awareness. A unique property of online
actions, in stark contrast with those ofﬂine, is that the visibility of each online action
is implicitly designed by the platform, whereas the visibility of ofﬂine actions is
often something the action performer can choose to manage. For instance, in the
ofﬂine world one might merely verbally express disgust about a TV show to
someone else and then move on, but doing so online (e.g., disliking a video on
YouTube) starts a chain of evidence (sometimes permanent) of the action, which is
visible in various ways to other stakeholders on the platform. Intriguingly, YouTube
is now experimenting with reducing the visibility of the speciﬁc property of
disliking—making dislike counts invisible to the public, while keeping them visible

S. Y. Park et al.

to creators.11 While this measure has only recently been implemented, the response
to it has been dramatically negative.12 Key criticisms include issues such as reducing
information for stakeholders due to the removal of signals and using the feature in
different ways for different videos.13 This serves as a striking example, reinforcing a
core idea of our work: Designed decisions about online interactions have widereaching consequences. In addition, decisions about the visibility of an action often
serve as the battle ground on which those consequences are understood.

5.3

Actions Are Especially Ambiguous when there Are
Opposing Sentiments

Online actions could be especially ambiguous when the recipient or observer of the
action disagreed with the action taken. For example, P0592 stated wondering about
the reason for actions when others make statements they “don’t agree with.” This
was the case with P0883 who stated that they wondered “more-so when I don’t
agree with [the like/dislike/retweet action].” This is a case in which people on social
media are not able to understand the perspective or “read the minds of others,” which
is considered important to “their mental states that determine their actions” [Frith &
Frith, 2006]. Limited cues are communicated through the predominantly nonverbal
interactions designed by social platforms—interactions that lack kinesic, oculesic,
vocalic, haptic, and facial cues among many others [Antonijević, 2013; Park et al.,
2021]. Due to a lack of such cues, others’ emotions, desires, and intentions are
difﬁcult to read from these pervasive online actions [Adolphs, 2002; Langton et al.,
2000]. Without information to help understand others’ perspectives, the way online
actions on social media are currently designed ampliﬁes our cognitive biases such as
the curse of knowledge, by which those who are informed ﬁnd it extremely difﬁcult
to consider perspectives of the lesser-informed [Newton, 1990]. Ambiguity was also
felt when the actors themselves seemed to be at odds with what their actions
represented, as in the case of why someone would “like something when they
actually don’t like it” (P0962). P0029 similarly wondered why someone decided
to get involved in a conversation “when I know they don’t really have a strong
opinion” and had their own interpretation for the others’ actions: “I think they want
to make themselves out to be more of an advocate than they are.” As such, in giving
their responses to this question, many others also provided their interpretations of
questionable actions. While what P0029 surmises may be one plausible reason,
another could be that due to the perceived dissociative anonymity and/or minimization of authority online, others who did not seem to have strong opinions may have

https://blog.youtube/news-and-events/update-to-youtube/, Accessed November 12, 2021.
https://www.theverge.com/2021/11/17/22787080/youtube-dislikes-criticism-cofounder-jawedkarim-ﬁrst-video-description-zoo, Accessed November 20, 2021.
13
https://youtu.be/CaaJyRvvaq8, Accessed November 12, 2021.
12

Dancing with Ambiguity Online: When Our Online Actions Cause Confusion

felt more comfortable to do so, leading to online disinhibition effect [Suler,
2004]. Yet another reason may be that the other person became increasingly aware
of and knowledgeable about the social issue. This could have led them to form strong
opinions about it, thus creating a subtle occurrence that the participant (as an
observer) may not have been privy to. Given that this negative interpretation of
others was assumed out of the many other interpretations could be attributed to the
fact that such ambiguous online actions conﬁrms users’ hostile attribution bias—of
readily attributing ill intention in others [Nasby et al., 1980; Park & Whiting, 2020;
Park et al., 2021].

5.4
5.4.1

Design Implications
People May Not Be Able to Improve, Even If They Want To

Responses suggest that people are not aware of why negative actions are taken, as
discussed in § 5.1. Ambiguity may be desirable for those carrying out “unsocial
behaviors” [Lopez & Ovaska, 2013]; however, in so doing, this encourages people
to take more of these actions which may not only cause others to wonder but also to
feel upset. This perpetuates greater ambiguity for those at the receiving end of such
behaviors. This perpetuation of ambiguity leads to missed opportunities for users to
learn from and even respond to such actions; this is especially the case for the users
who are unaware of why such “unsocial behaviors” are being directed at them
and why they have happened. Hence, in the current design of online actions, this
ambiguity only serves the actors, who are arguably fewer than the recipients and
observers combined. Similarly, there may be some good reason for posting and
engaging with what some perceive as negative content on social media, such as
putting the spotlight on the negative aspects of life or misinformation that people
wish to bring to others’ attention. Yet such intentions neither “come through” nor are
made clear. The outcomes of taking online actions are by design more visible (§ 5.2)
and salient (e.g., how many retweets or likes a tweet has received) and have therefore
become a critical part of how people perceive content and actions, when the original
intent may have nothing to do with the outcome. Therefore, more cues ought to be
communicated with the content and actions taken, in addition to the recently
implemented labels for misinformation.14
Design implementations could be short labels that cue in others as to why certain
content has been shared (e.g., “concerned about this,” “this requires attention”, “this
is funny (but not true)”) or why certain actions have been taken (e.g., “too frequent
posts” for unfollowing, “violent” for disliking content). This may be uncomfortable
for some users (e.g., difﬁcult to accept, invasion of privacy to have such information
14

https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-infor
mation, Accessed November 16, 2020. https://developer.twitter.com/en/docs/twitter-api/enterprise/
engagement-api/guides/interpreting-metrics, Accessed October 30, 2021.

S. Y. Park et al.

public), and in such cases, an aggregated breakdown mediated by the platform to
inform users as to why the majority of users decided to take an action, such as a piece
of content retweeted mainly across multiple users to raise awareness, could be
helpful.

5.4.2

People May Be Unaware of Their Individual Impact

As Bernstein et al. stated, “users have scarce information about who actually sees
their content, making their audience seem invisible and difﬁcult to estimate”
[Bernstein et al., 2013]. This still seems to hold true today, as people are not
aware of the extent to which their actions impact others on social media—it is
difﬁcult to quantify who will be seeing one’s likes or retweets and be inﬂuenced
by them at the time these actions are taken. This helps explain why some actions that
do not seem to ﬁt people’s understanding are taken (§ 5.3); speciﬁcally, that some
may not understand the adverse consequences their actions or content could have.
Currently, Twitter provides impression and engagement metrics, which are the
number of views and the number of engagement such as retweets, favorites, replies,
etc., on a given tweet.14 Yet these metrics are provided after an action has been
taken, not prior. Given that propagation of information is not completely up to
chance (i.e., by algorithms designed by platforms), and given the wealth of data that
can enable platforms to make somewhat accurate predictions of how a given action
could have repercussions, platforms can better inform users as to how their online
actions may inﬂuence others and have consequences. Predictions could be instantiated with varying granularity as the designers see ﬁt. For instance, predictions could
be provided as rough estimates of how many people a piece of content or an action
will be seen by and affect others, as levels of impact, or even as a binary of whether it
will have a big impact or not. Such an implementation could have similar effects as
the ﬁndings by Pennycook and Rand—that “a simple nudge or prompt that shifts
attention to accuracy increases the quality of news that people share (typically by
decreasing the sharing of false content)” [Pennycook & Rand, 2021]. This may not
be in the platform’s favor, as it could lead to greater mindfulness of users and
subsequently less interaction. However, in addition to enabling platforms to provide
more responsible design, such an implementation could nudge users to opt more for
nuanced comments in response to such mindfulness, instead of low-effort contributions of likes and retweets, which would lead to more thoughtful engagements
overall.
While a quantiﬁed approach that predicts the impact of a piece of content or
action might be one way to help users better estimate their inﬂuence, another is to
show users how their online actions could be questionable or unclear to others. By
implementing clariﬁcation features, such as having a button to indicate the uncertainty people feel about having a piece of content posted or action taken, users can
get a better understanding of when their own actions are not obvious to others. With
a clariﬁcation feature, a user may come to understand that posting personal content
causes others to wonder why they were posted in the ﬁrst place, and, in response,

Dancing with Ambiguity Online: When Our Online Actions Cause Confusion

provide more commentary when sharing content that is personal. Rather than a
content-speciﬁc or action-speciﬁc clariﬁcation feature, a more generalized approach
can be taken. A feature based on an accumulation of such statistics could also be
shown to users; for example, platforms could inform users that 70% of the time
others questioned when the user took a negative online action and 30% of the time
when they posted or interacted with questionable content.
Such implementations of informing users could come at a cost, however. While
these features could help increase mindfulness and awareness, users may feel more
self-conscious and censor themselves more. This could curb users from taking
actions as freely as they would otherwise and encourage more social grooming
practices. In the latter case, this would mean taking actions that are primarily
motivated by how these users feel they come across to others. Therefore, in
implementing such features, platforms ought to consider the resolution in which
informing occurs so that a good balance can be reached between being informative
while at the same time discouraging self-censorship.

6 Limitations & Future Work
The free-text responses have given us much insight into when people feel ambiguity
around online actions. Yet due to recency bias, participants may have provided only
the examples that are most top of mind or salient for them, leading to
incomprehensive responses from any given individual or leading to biases away
from subtle or nuanced effects. In an effort to address this, we used speciﬁc wording
to evoke a thoughtful response that reﬂected speciﬁc experiences; we also provided
an incentive to the participants based on the quality of their responses. In our
qualitative analysis, we excluded responses from participants who did not answer
the question we had posed and therefore could not be coded. This may have been a
lost opportunity as we were not able to capture 4.0% of the responses we had
collected. However, in capturing more than 1000 responses from participants, we
have reached saturation of information and that all the nuances we are interested in
are contained within the data set that we have thematically coded. We have uncovered only a subset of themes and will further conduct thematic analysis to uncover
independent factors different from action, content, and stakeholders that we derived
in our current analysis.
As future work, we also aim to bring insights of the ambiguity felt by users into
contexts in which their outcomes impact the interactants more directly, such as the
bases of collaborative contexts.

S. Y. Park et al.

7 Conclusion
Ambiguity is “baked into” the design of online actions (e.g., liking, commenting,
retweeting) on social media platforms today. Whether by intention or not, users
dance, or even skirt, around the ambiguity surrounding these online actions. We
conducted a survey-based study and analyzed 1042 responses to why people wonder
why online actions are taken on Twitter. From inductive thematic analysis, we found
that it is not clear cut when people are confused by online actions: Different actions
matter to different people in different ways. In particular, and more surprisingly,
simply involving certain actions, content, and stakeholders can cause ambiguity for
the recipients and observers. In looking closely at the responses, we ﬁnd that
negativity online in general—whether it is negative actions or any interaction with
negative content—as well as the visibility of these actions cause people to wonder
why actions are taken. We posit that current designs do not help in clarifying this
ambiguity around online actions and further hinder people from being able to gauge
the impact of their actions and improve upon them; we provide design suggestions
for platforms to provide users strategies in navigating the current ambiguity of
people’s online actions.

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Design Thinking for Digital
Transformation: Reconciling Theory
and Practice
Carolin Marx

Abstract While scholars have made valuable steps towards claiming legitimacy for
deploying Design Thinking in organizations, the underlying practices, effects, and
links between Design Thinking and Digital Transformation still seem an
underexplored area. This conceptual study aims to shed light on how Design
Thinking can contribute to an organization’s Digital Transformation by linking
explicit Design Thinking elements to dimensions of Digital Transformation and
investigating the role Dynamic Capabilities might play within this effect nexus. In
particular, this study proposes three modes of design-enabled Digital Transformation
and integrates theoretical with practical perspectives in a conceptual framework. The
holistic nature of the framework, including the variety of possible combinations,
permits a nuanced investigation of speciﬁc, context-dependent cause–effect relationships. This can be used by researchers as a foundation for deriving and testing
hypotheses on speciﬁc contributions Design Thinking can make to Digital Transformation and by practitioners to analyze, communicate, and manage how they
utilize Design Thinking for Digital Transformation.

1 Introduction
The proliferation of digital technologies and changing market dynamics increase the
complexity for organizations to navigate towards successful adaptation (Bharadwaj
et al., 2013; Vial, 2019). Since no industry or organization is immune to these effects
(Matt et al., 2016) gaining a competitive advantage through Digital Transformation
has become a strategic imperative and a top managerial priority (Verhoef et al.,
2021; Vial, 2019; Warner & Wäger, 2019). Similarly, the concept of Design
Thinking, described as a novel problem-solving capability is increasingly receiving
attention from both researchers and practitioners—especially regarding uses that
reach beyond its form-giving roots (Kolko, 2015; Micheli et al., 2019).

C. Marx (*)
Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
e-mail: carolin.marx@hpi.de
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_4

C. Marx

Given that the majority of Digital Transformation activities fail under increasing
pressure for change (Forth et al., 2020) while Design Thinking is heralded to become
the foundation of competitive advantage (Micheli et al., 2019) doing Design Thinking for Digital Transformation holds promising potential. Within this chapter, we
want to contribute to a better understanding of how organizations can make use of
adopting Design Thinking practices to foster their Digital Transformation.
While scholars have made valuable steps towards claiming legitimacy for
deploying Design Thinking in organizations, the underlying practices, effects, and
links between Design Thinking and Digital Transformation still seem to be an area
that is underexplored (Magistretti, Pham, et al., 2021; Micheli et al., 2019). Only a
few attempts have been made by scholars to unpack the role of Design Thinking in
the context of digital technology (Verganti et al., 2020). While those scholars have
made valuable contributions towards claiming legitimacy for deploying Design
Thinking in organizations, the underlying practices, effects, and links between
Design Thinking and Digital Transformation still seem an underexplored area
(Micheli et al., 2019). Addressing this research gap, Magistretti, Ardito, et al.
(2021) attempted to close the link between Design Thinking and Digital Transformation by explaining how the Dynamic Capabilities of Design Thinking can facilitate Digital Transformation in the consulting industry.
We build on the Dynamic Capability perspective (Barreto, 2010; Teece et al.,
1997) in this chapter and extend Magistretti, Pham et al.’s (2021) recent approach by
linking Design Thinking to Hanelt et al.’s (2020) holistic Digital Transformation
perspective of continuous change instead of a usually taken simpliﬁed and industryspeciﬁc view. Further, we propose that there exist multiple modes of how Design
Thinking can inﬂuence Digital Transformation when applying a holistic lens.
Researchers found that Design Thinking is linked to building Dynamic Capabilities
(Liedtka, 2020; Magistretti, Ardito, et al., 2021) and that Dynamic Capabilities are
useful for Digital Transformation (Warner & Wäger, 2019). The examination of
existing research shows that, on the one hand, there is a lack of evidence for the
often-emphasized strategic inﬂuence of Design Thinking beyond assumptions,
anecdotes, and success stories (Liedtka, 2015; Micheli et al., 2019). On the other
hand, it is still unclear how Dynamic Capabilities can be developed and how this
contributes to the Digital Transformation of organizations (Helfat et al., 2009; Vial,
2019).
Hence, our goal is to connect those perspectives and shed light on how Design
Thinking can contribute to an organization’s Digital Transformation by linking
explicit Design Thinking elements to dimensions of Digital Transformation and
investigating the role Dynamic Capabilities might play within this network of
effects. Therefore, we pose the following overarching question of interest:

Design Thinking for Digital Transformation: Reconciling Theory and Practice

1.1

How Can Design Thinking Contribute to an
Organization’s Digital Transformation?

We will approach this question using a reconciliation of theory and practice. In a ﬁrst
step, we conceptualize the phenomenon of interest—Design Thinking in organizations—based on existing research and two empirical studies we conducted on the
integration of Design Thinking in practice. In a second step, theoretical perspectives
on Digital Transformation and Dynamic Capabilities are analyzed and ﬁnally integrated as a conceptual framework on the role of Design Thinking in organizational
Digital Transformation. We build on and extend Magistretti, Pham et al.’s (2021)
valuable contribution by linking Design Thinking to Hanelt et al.’s (2020) holistic
Digital Transformation perspective of continuous change and by proposing that
there exist multiple modes of how Design Thinking can inﬂuence Digital Transformation. This study thereby extends existing research on Design Thinking for Digital
Transformation by proposing a uniﬁed perspective and overarching framework that
can inform future theoretical and empirical studies and guide practitioners.

2 Research Approach
The general research approach for this conceptual work relies on the reconciliation
of theory and practice. We approach our goal of creating new knowledge on the role
of Design Thinking for Digital Transformation from both sides within this chapter
using a combination of inductive and deductive reasoning.
In a ﬁrst step, to better understand the phenomenon of interest we will empirically
investigate how Design Thinking is used by organizations in practice. From a largescale empirical assessment, we obtain a conceptualization of Design Thinking in
organizations, develop patterns of usage, and propose antecedent conditions for the
resulting network of effects.
In a second step, we approach our goal of creating new knowledge on the role of
Design Thinking for Digital Transformation via a theory-driven deduction including
a holistic perspective of continuous change and the dynamic capability view of
the ﬁrm.

3 Practical Perspectives: Design Thinking in Organizations
In this sub-chapter, we will provide a conceptualization of the phenomenon of
interest—Design Thinking in organizations—by describing of its elements and
identifying different types of Design Thinking diffusion (application and localization within an organization) and depth (ﬁrm-level capabilities). Taken together, these

C. Marx

perspectives facilitate a nuanced conceptualization of the usage of Design Thinking
in practice that accounts for real-world application scenarios.

3.1

Design Thinking Elements

The concept of Design Thinking, described as a novel problem-solving capability,
has increasingly received attention both from researchers and practitioners (Kolko,
2015; Micheli et al., 2019). Design Thinking can be understood as an iterative
problem-solving approach characterized by an emphasis on empathy, usercentricity, integrative thinking, collaboration, and the active use of ideation and
visualization tools (Brown, 2008; Liedtka, 2015). A recent review of Design Thinking (Micheli et al., 2019) has identiﬁed ten principal attributes that shape the
concept. These attributes include among others human-centeredness and a focus
on empathy, the interdisciplinary approach to collaboration, and an iterative and
experimental approach, which make DT particularly suitable for uncertain and
ambiguous situations. Moreover, Design Thinking is problem- and solution-oriented
and relies on hypothesis-driven practices that stress the role of problems before
focusing on solutions (Brown, 2008; Liedtka, 2015). Finally, Design Thinking
connects the abstract and the concrete world to generate innovative ideas, involving
analysis and synthesis as well as rational and intuitive modes of thought (Beckman
& Barry, 2007; Micheli et al., 2019).
As individuals, processes, interactions, and structures are strongly intertwined
when looking at Design Thinking in an organizational context we applied an
integrative view for the conceptualization. Making use of existing conceptualizations on the topic of Design Thinking (Liedtka, 2020; Magistretti, Ardito, et al.,
2021; Micheli et al., 2019), we developed a comprehensive list of describing
elements and then aggregated the elements to overarching areas. Table 1 presents
this list of elements. The overarching areas identiﬁed by aggregating the reviews
consist of user-centeredness and involvement, problem-solving, iteration and experimentation, interdisciplinary collaboration, tolerance of ambiguity and failure, and
blending rationality and intuition. Elements describing the area of problem-solving,
for instance, include encouraging reframing the problem, building local capabilities
to solve new problems, assessing and prioritizing needed capabilities or problemsolving, and allowing emergent solutions. The example shows that each area incorporates the integration of individual, interactional, structural, and process-related
elements.
While most of Design Thinking’s implementations are rooted in product design,
“the subject matter of design is potentially universal in scope, because Design
Thinking may be applied to any area of human experience” (Buchanan, 1992,
p. 16). Design Thinking has emerged as a set of formal methods for addressing
uncertain and ill-deﬁned problems (Buchanan, 1992) while aiming to combine
viability, feasibility, and desirability. As a response to the increasing external
complexity that organizations face in dynamic and potentially disrupted markets,

Design Thinking for Digital Transformation: Reconciling Theory and Practice

Table 1 Elements of organizational Design Thinking (Liedtka, 2020; Magistretti, Ardito, et al.,
2021; Micheli et al., 2019)
Area
User-centeredness and
involvement

Problem-solving

Iteration and
experimentation

Interdisciplinary
collaboration

Tolerance of ambiguity and
failure

Blending rationality and
intuition

Design thinking element
• Deep understanding of user needs
• Inclusion of diverse perspectives
• Providing user-driven criteria for evaluation
• Building emotional engagement
• Inviting ownership and engagement
• Empathizing with users
• Looking at sociocultural trends
• Encouraging reframing of the problem
• Building local capabilities to solve new problems
• Assessing and prioritizing needed capabilities for problem-solving
• Allowing emergent solutions
• Multiple solutions coexisting alternatives
• Creating an action orientation and execution
• Prototyping and experimentation
• Iteration between discovery ideation and experimentation
• Dialogue-based conversations
• Expanding the repertoire of teams
• Involving key stakeholders beyond the core team
• Fostering alignment across diverse perspectives
• Broadening access to networks and resources and enhancing
willingness to co-create
• Attracting champions
• Decentralizing responsibility
• Brokering knowledge
• Encouraging a possibility inspired mindset
• Reducing visibilities of failures
• Increasing psychological safety
• Unleashing and embracing ambiguity
• Learning by doing
• Creative conﬁdence
• Logical and rational reasoning
• Mitigating decision biases
• Deductive thinking
• Inductive thinking
• Abductive thinking
• Naive mind

Design Thinking is applied to more complex experiences and systems such as
services, business models, business strategies, and social policies (Brown & Martin,
2015; Kolko, 2015). Such Design Thinking applications may have far-reaching
effects on organizations and societies, given that the users of Design Thinking are
capable of changing established mindsets and behaviors. In the same vein, scholars
have shown that Design Thinking can be the foundation of competitive advantage
(Martin & Martin, 2009; Micheli et al., 2019) and an enabler for an organization’s
paradigmatic shift in strategic vision (Liedtka, 2015).

C. Marx

This fundamental impact on an organization is most likely an indirect one. For
instance, past studies have argued that Design Thinking can contribute to an
organization’s innovation capabilities (Carlgren et al., 2014) and may help to
shape the organization’s culture (Elsbach & Stigliani, 2018). This transpires through
an unveiling of latent customer needs over time, balancing exploratory and exploitative innovation activities (Martin & Martin, 2009), and fostering the assimilation
and reconﬁguration of internal know-how from innovation systems (Acklin, 2010).
An academic focus of interest underlines the shift in broadening the usage and
impact of Design Thinking for an organization and the individuals involved.

3.2

Depth and Diffusion of Design Thinking Elements
in Organizations

As outlined previously, Design Thinking, even when described via the same elements, can have different manifestations in the mode of organizational integration.
In other words, organizations use Design Thinking for different purposes and with
different levels of capabilities. Therefore, in order to approach a comprehensive
conceptualization, we will provide a description of diffusion, described by application and localization, and depth, described by Design Thinking capabilities, as
relevant factors shaping the actual Design Thinking integration. We thereby aim to
identify relevant integration conditions that when applied to the organization of
interest help to conceptualize the usage of Design Thinking. For both conditions, a
conceptual model has been developed and applied via an online survey to organizations practicing Design Thinking.
When looking at the depth of the integration of Design Thinking elements within
organizations one can identify capability levels as the suitable comparative lens.
Grounded in existing research (Nakata & Hwang, 2020; Wrigley et al., 2020) and six
qualitative interviews with Design Thinking experts from academia and practice we
developed a multidimensional model that conceptualizes the depth of organizational
Design Thinking via ﬁrm-level capabilities, derived items, and applied the model to
547 organizations via an online survey. See (Marx et al., 2022) for details on the
model development and the empirical application.
Table 2 shows the corresponding data structure including dimensions and
sub-dimensions of ﬁrm-level Design Thinking capabilities that we developed.
Firm-level Design Thinking capabilities are formed by Design Thinking related
actions and processes, strategy, organizational resources, and mindset. These
dimensions are each formed by multiple sub-dimensions which are each reﬂected
in a set of two to four items.
Besides depth, we propose that the second crucial factor when conceptualizing
Design Thinking in an organizational context is the diffusion of Design Thinking
elements, hence where (localization) and for what (application) Design Thinking is
used (see Marx, Haskamp, et al., 2021 for a detailed overview of the model

Design Thinking for Digital Transformation: Reconciling Theory and Practice

Table 2 Depth of Design Thinking integration (Marx et al., 2022)
Firm-level
Design
Thinking capabilities

Dimension
Actions and processes

Strategy

Organizational resources

Mindset

Sub-dimensions
• Discovery
• Ideation
• Experimentation
• Performance measurement
• Funding
• Leadership and decision-making
• Organizational structure
• Link to strategy
• Work environment
• Access to resources
• Learning and development
• Human-centeredness
• Abductive reasoning
• Learning by failure

developed for assessing Design Thinking diffusion in organizations). Based on
Kootstra (2009 and Wrigley et al. (2020), we identiﬁed traditional design problems
and products, services and experiences, business model and strategy, and culture
and ecosystem as diffusion stages when looking at for what organizations use Design
Thinking, hence the application. Additionally, we suggested combining the identiﬁcation of the Design Thinking application stage with an assessment of the localization of Design Thinking practices within the ﬁrm. For this part, we adapted
Junginger’s model (Junginger, 2009) including peripheral, somewhere, central,
and everywhere as potential localizations of Design Thinking in organizations.
Table 3 gives an overview of how we conceptualized the diffusion of Design
Thinking integration in organizations.
The results of the empirical application in both studies indicate that our models
are a suitable means to assess ﬁrm-level Design Thinking depth and diffusion across
industries and ﬁrm-size categories. Further, the heterogeneity observed in the data
underlines the relevance of including depth and diffusion as inﬂuencing conditions
on the conceptualization of Design Thinking elements in organizations. It is therefore crucial to not only look at the Design Thinking elements but ﬁrst to identify
which level of Design Thinking capabilities is currently reached by the organization
of interest and which combination of application and localization describes it best.

3.3

An Integrative Conceptualization of Design Thinking
in Organizations

Figure 1 gives an overview of the synthesized empirical ﬁndings on the current use
of Design Thinking in practice and the existing research on the conceptualization of

C. Marx

Table 3 Diffusion of Design Thinking integration (Marx, Haskamp, et al., 2021)
Diffusion
type
Application

Stage
Traditional design
problems and products
Services and
experiences
Business model and
strategy
Culture and ecosystem

Localization

Peripheral

Somewhere

Central

Everywhere

Explanation
Design Thinking is used to solve traditional design
problems of form and to develop speciﬁc products.
Design Thinking is applied to the development of
internal and external services and experiences.
Design Thinking shapes strategy and is applied to new
business model development and strategic decisionmaking.
Design Thinking is part of the organizational culture
and is applied beyond products, services, and strategy
to an organization’s ecosystem.
Design Thinking has no continuous presence in the
organization but is booked as a resource on demand. It
takes place separately from operational activities.
Design Thinking is practiced as a part of one or two
organizational functions, such as the marketing or R&D
department.
Design Thinking has a central position in strategic
decision-making. It is linked to leadership and an
overall strategy. Design Thinking shapes aspects of the
organization and has the potential to transform it.
Design Thinking is integral to all parts of the organization. The organization is questioned, formed, and
shaped by ongoing Design Thinking.

the general phenomenon from the previous sub-chapters including deﬁning elements, diffusion, and depth.
We suggest, that when investigating the role of Design Thinking for Digital
Transformation it is crucial to concretize the diffusion of Design Thinking in the
organization of interest reﬂected by application and localization and the depth of
Design Thinking integration indicated by ﬁrm-level Design Thinking capabilities. In
combination with the consolidated list of Design Thinking elements, the abovementioned contextual factors permit a nuanced description of the actual use of
Design Thinking in practice and build the foundation for our further conceptual
analysis.

4 Theoretical Perspectives: Digital Transformation
and Dynamic Capabilities
In this sub-chapter, we build on the above-developed practical-oriented conceptualization of our phenomenon of interest—Design Thinking in organizations—and set
the focus on how it relates to Digital Transformation activities. We do this by
introducing Digital Transformation from a holistic perspective, and connecting it

Design Thinking for Digital Transformation: Reconciling Theory and Practice
Diffusion (Localization & Application

Depth (Capabilities)

ORGANIZATIONAL DESIGN THINKING ELEMENTS
User centeredness and involvement

Problem solving

• Deep understanding of user
needs
• Inclusion of diverse perspectives
• Providing user driven criteria for
evaluation
• Building emotional engagement
• Inviting ownership and
engagement
• Emphasizing with users
• Looking at sociocultural trends

• Encouraging reframing of the
problem
• Building local capabilities to
solve new problems
• Assessing and prioritizing
needed capabilities for problem
solving
• Allowing emergent solutions

Interdisciplinary collaboration
• Dialogue-based conversations
• Expanding repertoire of teams
• Involving key stakeholders not
part of core team
• Fostering alignment across
diverse perspectives
• Broadening access to networks
and resources and enhancing
willingness to co-create
• Attracting champions
• Decentralize responsibility
• Brokering knowledge

Iteration and experimentation
•
•
•
•

Tolerance of ambiguity and failure
• Encouraging a possibility
inspired mindset
• Reducing visibilities of failures
• Increasing psychological safety
• Unleashing and embracing
ambiguity
• Learning by doing
• Creative confidence

Multiple solutions coexisting
alternatives
Creating an action orientation &
execution
Prototyping & experimentation
Iteration between discovery
ideation and experimentation

Blending rationality and intuition
•
•
•
•
•
•

Logical and rational reasoning
Mitigating decision biases
Deductive thinking
Inductive thinking
Abductive thinking
Naive mind

Fig. 1 Integrative conceptualization of Design Thinking in organizations via diffusion, depth, and
elements

to the dynamic capability view of the ﬁrm. The aim of this sub-chapter is to connect
different theoretical perspectives that help to shed light on how Design Thinking
might contribute to an organization’s Digital Transformation.

4.1

A Holistic Perspective on Digital Transformation

The proliferation of digital technologies such as artiﬁcial intelligence, digital platforms, big data analytics, shifts in customer expectations, and changing market
dynamics increase the complexity for organizations seeking successful strategic
adaptation (Bharadwaj et al., 2013; Vial, 2019). Fueled by the need to adapt,
organizations fundamentally alter and create capabilities, resources, operational
processes, end-user experiences, and business models (Bharadwaj et al., 2013;
Correani et al., 2020; Fitzgerald et al., 2014; Rogers, 2016; Verhoef et al., 2021).
These demonstrate a process of Digital Transformation. Since no industry or organization is immune to the need to adapt (Matt et al., 2016) gaining competitive
advantage through Digital Transformation has become a top managerial priority
(Verhoef et al., 2021; Vial, 2019; Warner & Wäger, 2019).

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In practice, however, many companies do not recognize the potential of Digital
Transformation, and many of those that do still struggle to change their habits and
ways of working sufﬁciently to reap the maximum beneﬁts from digital efforts
(Fitzgerald et al., 2014; Parviainen et al., 2017; Vial, 2019). Recent reports underline
this apprehension showing that 87% of companies at a global level believe that
Digital Transformation will disrupt their industry, while only 44% state that they are
prepared to respond or lead this process of disruption and only 30% of enterprises
actually achieve their initial digital goals (Forth et al., 2020).
While transforming mechanisms have the potential to fundamentally change the
organizational setup, as well as the ﬁrm’s competitive positioning, and the core
organizational design, they are extremely challenging to successfully execute. This
is especially the case for incumbent ﬁrms in traditional industries facing severe
barriers to transformation (Forth et al., 2020; Linderoth et al., 2018; Vial, 2019;
Warner & Wäger, 2019). We see that even when organizations are increasingly alert
and prioritize investments, much remains to be done in terms of getting prepared to
embrace and leverage Digital Transformation’s potential for enabling innovation and
value creation.
Given this tension between desirability and the challenge of implementation,
recent scholars argue that a more holistic conceptualization of Digital Transformation would hold academic and practical advantages. For instance, Matt et al. (2016)
claim that while the majority in academia have been largely concerned with providing guidance on certain aspects of Digital Transformation, the application of a
holistic approach to the phenomenon of interest is both valuable and lacking.
In line with such a holistic perspective it is important to stress that organizational
Digital Transformation goes beyond the use of technology. It is in fact deeply
intertwined with fundamental changes affecting every aspect of the business, the
ecosystem it operates in, and the people involved with it (Hanelt et al., 2020; Kane,
2019; Wessel et al., 2021). The necessity for organizations to better integrate digital
technologies with their overall strategy requires a rethink of how to view and
implement technologies in a way that empowers the business on a holistic level
(Ellström et al., 2021; Hanelt et al., 2020). Based on this holistic perspective, we
follow (Hanelt et al., 2020, p. 1187) describing Digital Transformation as “organizational change triggered and shaped by [episodic bursts, rooted in] the widespread
diffusion of digital technology.” This continuous change involves and indicates a
general shift “towards malleable organizational designs that are embedded in and
driven by digital business ecosystems” (Hanelt et al., 2020, p. 1168). Past conceptual
and empirical research (Chanias et al., 2019; Soh et al., 2019) reinforces this
understanding of Digital Transformation as an ongoing and ever-changing process.
Firms that undergo Digital Transformation have to constantly assess their current
organizational setup against emerging opportunities and threats and adapt it to the
new environment (Gannon, 2013; Westerman et al., 2014).
In this regard, core to accelerating the shift towards malleable organizational
design and adaptation is rapid scaling generated by data-driven operation through
monitoring and user proﬁling followed by swift transformation and instant,
feedback-based release (Hanelt et al., 2020; Huang, The University of Warwick,

Design Thinking for Digital Transformation: Reconciling Theory and Practice

Fig. 2 Mechanisms generating rapid adaptation and supporting changes in the organizational setup
(Hanelt et al., 2020; Huang, Henfridsson, et al., 2017)

et al., 2017). Digital ﬁrms have been found to adapt rapidly through those mechanisms (Huang, The University of Warwick, et al., 2017) that are related to observable
changes in the organizational setup on multiple levels (Hanelt et al., 2020). Firstly,
changes towards data-driven processes and empirical insight-based managerial
decision-making can be linked to data-driven operation, for instance, user proﬁling,
decision-hedging, and monitoring. Secondly, changes in organizational structures
towards permeable agility support the redeﬁnition of organizational identity and the
contextualization of technology as part of swift transformation mechanisms. Third,
the instant feedback-based release mechanism is fueled by changes in organizations’
value creation setup including a shift towards customer-centered business models
and smart, customized products. Figure 2 gives an overview of the connection
between the described mechanisms for rapid adaptation and supporting changes in
the organizational setup.
Building on the observation that organizations strive for rapid adaptation through
data-driven operation, swift transformation and instant release in speciﬁc cases, and
a malleable organizational design in general, Hanelt et al. (2020) provide a framework that embodies multiple dimensions and levels of Digital Transformation along
with contextual conditions, mechanisms, and outcomes. We will use an adapted
version of this framework as a holistic representation of organizational Digital
Transformation for analyzing the network of effects related to Design Thinking
and Dynamic Capabilities.

4.2

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A Dynamic Capabilities View on Digital Transformation

The second theoretical lens we apply in order to understand how Design Thinking
might contribute to an organization’s Digital Transformation is the dynamic capability perspective of the ﬁrm.
One can distinguish between two classes of capabilities: ordinary and dynamic
(Eisenhardt & Martin, 2000; Teece, 2007). Ordinary capabilities foster efﬁciency
(doing things right) in well-delineated tasks, are usually imitable, and do not vary
much in environments open to global competition. Dynamic Capabilities, on the
other hand, can be described as doing the right things at the right time by creating,
extending, and revising capabilities and resources (Drnevich & Kriauciunas, 2011).
Teece et al. (Teece et al., 1997, p. 516) deﬁne Dynamic Capabilities in their
landmark article as “the ﬁrm’s ability to integrate, build and reconﬁgure internal and
external competencies to address rapidly changing environments.” Speciﬁcally, the
value of Dynamic Capabilities lies in the “potential for helping the organization do
this repeatedly, thereby helping to create a durable competitive advantage” (Teece,
2014, p. 335) especially in high-velocity, competitive markets. Barreto (2010,
p. 257) suggests a new conceptualization of Dynamic Capabilities as an aggregated
multidimensional construct: Here, Dynamic Capabilities are described as a “ﬁrm’s
potential to systematically solve problems, formed by its propensity to sense opportunities and threats, to make timely and market-oriented decisions, and to change its
resource base.”
As pointed out by Vial (2019, p. 133), “there is an interesting ﬁt between
Dynamic Capabilities as a conceptual foundation and Digital Transformation as a
phenomenon of interest.” Several researchers have found that Dynamic Capabilities
as a theory-grounded conceptual foundation could be valuable to foster Digital
Transformation and to gain a competitive advantage (Pezeshkan et al., 2016; Warner
& Wäger, 2019).
To explain this link in more detail we refer back to our understanding of Digital
Transformation as a challenging process of continuous change whereby digital
technologies create episodic bursts triggering a general shift towards malleable
organizational design embedded in and driven by digital business ecosystems
(Hanelt et al., 2020). To successfully drive this process and overcome barriers to
continuous adaptation, it is crucial for organizations to
(A)
(B)
(C)
(D)

sense disruption at the right time,
assess the opportunities and threats attached to it,
seize them, and
reconﬁgure their organizational setup towards integrating digital in their strategy, business model, processes, culture, and in the use of technologies.

Hence, Dynamic Capabilities can be seen as equipping ﬁrms to respond to digital
disruption in a dynamic manner and in return contribute to their Digital Transformation. Helfat and Peteraf (2015) explained that attention and perception relate to
sensing activities that enable ﬁrms to recognize opportunities and create them.

Design Thinking for Digital Transformation: Reconciling Theory and Practice

Similarly, problem-solving and reasoning relate to seizing activities that foster
strategic investments and business model design. And ﬁnally, communication and
social aspects inﬂuence a ﬁrm’s reconﬁguring or transforming activities, which
might lead to strategic asset alignment and overcoming barriers to change. The
described chain of effects nicely shows the deep connection between Dynamic
Capabilities and activities directly inﬂuencing the organizational setup and performance in a Digital Transformation context.
Numerous empirical studies have underlined the connection between Dynamic
Capabilities and elements of Digital Transformation activities like technological
capabilities, digital platform capabilities, technological innovation capabilities
(Karimi & Walter, 2015; Protogerou et al., 2011; Zhou et al., 2019), and the concept
of digital maturity (Marx, de Paula, & Uebernickel, 2021). Other studies have
broadened the view towards the role of Dynamic Capabilities within the Digital
Transformation process. Warner and Wäger (2019) and Velu (2017), for instance,
found that ﬁrms must build a system of Dynamic Capabilities for Digital
Transformation.

4.3

Design Thinking and Dynamic Capabilities

Not only have Dynamic Capabilities been linked to elements of Digital Transformation as direct or indirect outcome variables. As Dynamic Capabilities can help to
“identify latent customer needs and the most promising technological opportunities
[and] then orchestrate the resources needed to innovate, or co-innovate” (Teece,
2014, p. 332), also innovation studies have increasingly relied on the Dynamic
Capabilities perspective. Accordingly, there is a growing body of research applying
a Dynamic Capability lens to Design Thinking in organizations showing how
Design Thinking activities can contribute to building Dynamic Capabilities. As
ﬁrms struggle with increasingly broad and complex innovation challenges in the
rapidly changing environment, the connection intuitively seems plausible.
Within this academic discourse, special emphasis is given to Design Thinking
elements as potential micro-foundations of Dynamic Capabilities (Felin & Foss,
2005; Winter, 2003). Those micro-foundations reveal why certain ﬁrms excel, while
others fail in increasingly complex and dynamic business environments. For this
reason, they are of special interest to researchers and practitioners.
Like Dynamic Capabilities, Design Thinking is rooted in the role and characteristics of the organizational members, as well as in the ways and structures characterizing their interaction modes. The linkage, however, has been demonstrated on
multiple levels including processes, structures, and organizational design. Dynamic
Capabilities rely on environmental scanning and real-time information, prototyping,
experimentation, cross-functional collaboration, and brainstorming (Eisenhardt &
Martin, 2000)—similar tools and methods that constitute Design Thinking. Regarding the speciﬁc dimensions of Dynamic Capabilities, Design Thinking favors a
better understanding of customers, their contexts, and latent needs, which is directly

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connecting empathy to sensing as part of the dynamic capability perspective
(Carlgren et al., 2016). Moreover, adopting tools and methods such as visualization,
storytelling, and prototyping to support rapid testing and innovation development
combined with logical reasoning helps organizations to seize the identiﬁed opportunities. Reconﬁguring or transforming is fostered by Design Thinking elements that
steadily stimulate novel and innovative ideas, different approaches to problemsolving, and idea management to cope with changing market needs and technological dynamism as well as to embrace ambiguity (Beverland et al., 2015). Hence, a
strong link emerges between Design Thinking and Teece’s (2007) Dynamic Capabilities of sensing and seizing opportunities as well as reconﬁguring the innovation
approach (Carlgren et al., 2014; Liedtka, 2020).
Several empirical studies have supported this connection. For instance,
Kurtmollaiev et al. (2018) showed that Design Thinking training in organizations
had a positive effect on the participants’ sensing and seizing capabilities, which had
a further positive effect on their transforming capability, team innovation output, and
team operational capability. Similarly, Nagaraj et al. (2020) showed that team
Design Thinking as a Dynamic Capability can overcome inertia in routines and
cognition in organizations.

5 Reconciling Theory and Practice
In this sub-chapter, we will build on the conceptualization of Design Thinking in
organizations and the theoretical integration outlined in the previous sub-chapters. In
the ﬁrst step, three modes of inﬂuence are identiﬁed and tied to existing research on
the impact of Design Thinking on organizational Digital Transformation. In a second
step, we incorporate the practical and theoretical perspectives into one integrative
framework of effects.

5.1

Modes of Inﬂuence

Based on our conceptual analysis around Digital Transformation and Dynamic
Capabilities we identiﬁed three potential modes of how the organizational integration of Design Thinking can contribute to a ﬁrm’s Digital Transformation. It is most
plausible that within one organization all three modes happen simultaneously and
each occurs on an individual, structural, and procedural level (Felin et al., 2012). It
is, therefore, crucial to consider them jointly as formative and interdependent.
We deﬁne Mode 1 as a direct, short-term-oriented mode. For instance, Design
Thinking can be directly used in digital innovation projects as a method for the
development and implementation of elements of a Digital Transformation strategy.
Resulting product or service innovations have the potential to directly translate into
business model change or the creation of new value direction as part of a ﬁrm’s

Design Thinking for Digital Transformation: Reconciling Theory and Practice

Digital Transformation. Nagaraj et al. (2020) gave an example of this direct mode by
empirically linking Design Thinking to product usefulness and novelty. Similarly,
Przybilla et al. (2020) showed that Design Thinking can lead to higher quality
prototyping, and more innovative business models, thereby directly inﬂuencing the
creation of digital solutions as crucial outcome elements of Digital Transformation.
In most cases, however, stronger connections can be assumed between the
integration of Design Thinking and intermediate outcomes which then affect Digital
Transformation elements. Mode 2 is described as an indirect, mid-term-oriented
mode where Design Thinking is linked to Digital Transformation via changes in the
organizational setup and transformation mechanisms. From an individual perspective, the use of Design Thinking can foster a shift of mental models and pre-digital
mindsets towards overcoming biases in decision-making (Liedtka, 2015). Practicing
Design Thinking also impacts individuals’ mindsets and attitudes towards their
profession and has the potential to foster motivation, ownership, and engagement—thereby indirectly contributing to elements of Digital Transformation within
organizations. For instance, Design Thinking has been linked to higher levels of
empowerment in teams. One can also identify indirect connections on the group and
process level, where the integration of Design Thinking has the potential to shape
corporate culture via fostering experimentation, iteration, failure, and facilitating
knowledge through collaboration. Another example from research for this mode is
given by Appleyard et al. (2020), who linked Design Thinking to a higher level of
trust and collaboration in cross-functional teams. Team trust and collaboration have,
on the other hand, been identiﬁed as crucial elements affected by and affecting
Digital Transformation (Hanelt et al., 2020; Vial, 2019). On a structural level, an
example for Design Thinking to indirectly contribute to a ﬁrm’s Digital Transformation is via the facilitation of structural agility, for instance, in the course of Digital
Innovation Units (Nambisan et al., 2017). Structural effects can also be identiﬁed
when looking at inertia and resistance to transformation endeavors that are assumed
to be limited by the effects of integrating Design Thinking (Magistretti, Pham, et al.,
2021).
Building on the connection between Design Thinking and Dynamic Capabilities
presented in the previous sub-chapter, Mode 3 describes an indirect, long-termoriented effect of integrating Design Thinking that contributes to a ﬁrm’s Digital
Transformation via building and sustaining Dynamic Capabilities. A strong link can
be observed between Design Thinking and sensing and seizing opportunities as well
as reconﬁguring the innovation approach (Carlgren et al., 2014; Liedtka, 2020;
Magistretti, Pham, et al., 2021).

5.2

Integrative Framework

The primary goal of this conceptual work was to analyze how Design Thinking can
contribute to an organization’s Digital Transformation. Hence, in this sub-chapter,
we present our attempt of integrating the previously analyzed perspectives into a

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single framework that can guide researchers and practitioners. Beyond looking at
possible modes in which Design Thinking might contribute to an organization’s
Digital Transformation, the integration of a holistic perspective on Digital Transformation in combination with the powerful link with Dynamic Capabilities and the
practice-oriented conceptualization of Design Thinking elements holds promising
potential.
Figure 3 shows our conceptualization of Design Thinking in organizations via its
elements dependent on the diffusion and depth of its integration, and how it is linked
via Dynamic Capabilities to Digital Transformation dimensions including contextual
conditions, mechanisms, and outcomes. The lower part of the framework is adapted
from Hanelt et al. (2020) and shows those dimensions of Digital Transformation at
the corresponding level that are affected directly or indirectly by Design Thinking
elements.
Design Thinking affects sensing contextual conditions of Digital Transformations: Elements of Design Thinking such as empathy with the user and the inclusion
of diverse perspectives as part of user-centeredness and involvement can be linked to
sensing contextual conditions inside and outside the organization. Affected dimensions of Digital Transformation include awareness of the top management team
towards topics, trends, opportunities, and challenges associated with Digital Transformation and sensing and interpreting digital consumer demands.
Design Thinking affects acceleration and alignment mechanisms of Digital
Transformation: Within the area of Digital Transformation mechanisms, innovating
and integrating have been presented as relevant types of agentic behavior that have
the potential to develop and align the required new organizational elements via
constant unfreezing (Hanelt et al., 2020, p. 1178)). Our analysis proposes that
Design Thinking elements have the potential to affect both types. First, it can be
directly linked to creating digital innovations and more indirectly to mobilize the
workforce towards welcoming the Digital Transformation of the organization.
Further, we can see a linkage between Design Thinking and mindset shifts which
fosters overcoming biases and potential inertial forces regarding the transformation.
Secondly, one can connect elements of Design Thinking, such as interdisciplinary
collaboration and the tolerance of ambiguity and failure with the mechanisms of
unlocking and learning—hence integration. Agile and collaborative structures as
embraced by Design Thinking further promote physical-digital harmonization and
technological ﬂexibility as part of the general alignment mechanism. With a view to
this framework, we propose that Design Thinking affects the acceleration and
alignment mechanisms of Digital Transformation.
Design Thinking affects the outcomes of Digital Transformation within and
outside the organization: Elements of Design Thinking can also be linked to changes
in the organizational setup, spillovers of those changes to the individual and market
level as well as changes in the economics of the ﬁrm. Agile organizational structures,
customer-focused business models, and smart customized products are examples of
Digital Transformation outcomes that are closely related to all elements of Design
Thinking. Depending on the depth and diffusion of the Design Thinking integration
also ecosystem-oriented and embedded organizational design can be partially

Fig. 3 An integrative framework of how Design Thinking contributes to Digital Transformation

Design Thinking for Digital Transformation: Reconciling Theory and Practice
73

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fostered by the use of Design Thinking. Additionally, we propose a linkage between
Design Thinking elements and spillovers as Digital Transformation outcomes. One
of those spillover outcomes is the digitalization of the individual. Design Thinking
can be used to identify or anticipate these effects and potential changes in expectations. Similarly, Design Thinking is closely related to general paradigms of customer
centricity and connected markets, which have been identiﬁed as relevant spillover
outcomes of Digital Transformation. Also on the economic level, one can draw a line
between Design Thinking elements and Digital Transformation outcomes as
reﬂected in improved ﬁrm performance via new forms of value creation or improved
service quality.

6 Conclusion
Organizations are increasingly confronted both internally and externally with novel
challenges, deeply entangled with Digital Transformation. In this chapter, we shed
light on how the integration of Design Thinking can enhance the value of digital
technologies towards a more human-centric Digital Transformation. In particular,
this study proposes three modes of design-enabled Digital Transformation and
integrates theoretical with practical perspectives in the presented conceptual
framework.
The given examples of how to interpret the framework show that while Dynamic
Capabilities can be seen as a generally strong link between Design Thinking and
Digital Transformation, a variety of Design Thinking elements have the potential to
affect every dimension of Digital Transformation in a multitude of ways. The
holistic nature of the framework including the variety of possible combinations
permits a nuanced investigation of speciﬁc, context-dependent cause–effect relationships. It can be used by researchers as a foundation for deriving and testing
hypotheses on the speciﬁc contributions Design Thinking makes to Digital Transformation dimensions or to analyze empirically observed relationships from realworld cases. Concerning academic debate, this chapter enriches the understanding of
Digital Transformation by unshadowing the value that Design Thinking might play
in it. Practitioners can utilize this framework for analyzing, communicating, and
steering how their organizations aim to contribute to their Digital Transformation
with the aid of Design Thinking.

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Experiences of Facilitating Virtual Design
Thinking: Theoretical Reﬂections
and Practical Implications
Selina Mayer, Martin Schwemmle, Claudia Nicolai, and Ulrich Weinberg

Abstract To deliver excellent virtual education experiences, design thinking educators adapt along all three “P”s: of People, Place, and Process. This book chapter
provides the theoretical foundations for delivering virtual education experiences and,
relying on both relevant streams of research and the authors’ own expertise, derives
six areas of action—(Digital) Engagement, Embodied Cognition, Safe Space, Atmosphere, Random Inspiration, and Managing Workshops. The theories linked to these
areas also inspire questions for further research and, together with the speciﬁc
suggestions provided, give practitioners a rich resource for enhancing their virtual
DT education.

1 Introduction
Over the last years, Design Thinking (DT) has been on the rise at multiple institutions and universities to teach an innovative approach to problem solving to students
and professionals (Dunne & Martin, 2006; Withell & Haigh, 2013). The Global
Design Thinking Alliance (GDTA), a network of 22 internationally recognized
academic institutions with the mission of developing DT education worldwide,
derives elements of DT’s core principles and the mindset behind DT from what
they call the big three “P”s: People, Place, and Process (GDTA, n.d.). “People”
refers to the focus on multidisciplinary teams, fostering collaboration with broad
perspectives. “Place” addresses the ﬂexible innovation spaces, referring to materials
as well as communication, collaboration, prototyping, and sharing opportunities
through visualizations and other means. And ﬁnally, the “Process” offers guiding
Selina Mayer and Martin Schwemmle contributed equally.
S. Mayer (*) · M. Schwemmle · C. Nicolai · U. Weinberg
HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam,
Germany
e-mail: Selina.Mayer@hpi.de; martin.schwemmle@hpi.de; claudia.nicolai@hpi.de;
uli.weinberg@hpi.de
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_5

S. Mayer et al.

steps to tackle complex problems in an iterative manner. In spring 2020, the Covid
epidemic disrupted DT education, which had to be massively transformed from
physical spaces to virtual settings. In this chapter, we explore how the three core
elements of the DT, People, Place, and Process, should be adopted to the virtual
setting.
First, we describe the Education Experience Model, a model that provides
insights on experiences that should be considered when teaching over distance.
We also give an overview about the three Ps and how they might differ in different
teaching settings, such as teaching face-to-face or virtually. Second, drawing on this
model and our own experience within student and professional education of facilitating a large spectrum of different DT programs virtually in the last 18 month, we
identiﬁed six ﬁelds of action—two for each of the three Ps—that help fostering DT
education in a virtual environment. We describe these ﬁelds of action in detail and
rely on a diverse set of research streams from psychology, business administration,
or economics to explain their effects. Last, we provide examples from our facilitation
experience, giving practitioners speciﬁc actions that can help them leverage the
opportunities given by these six ﬁelds of action. As the future of DT education
should include physical and virtual spaces as well as potential hybrid combinations
(HyFlex; Beatty, 2014), it is important to understand the challenges and opportunities in the different contexts.
Our chapter systematically uses two models to analyze the differences between
ofﬂine and virtual DT education. For researchers, we provide an overview of the
diverse set of research streams that might be of interest to deeper understand the
speciﬁcs of designing DT offers and facilitating DT activities in virtual settings. For
DT practitioners, we offer six speciﬁc areas of action, giving an understanding of
how these areas come into play and we provide speciﬁc behaviors or actions that
practitioners can engage in to foster the virtual facilitation of DT.

1.1

The Education Experience Model

To understand the transformational changes induced by virtual DT facilitation, we
rely on the “Education Experience Model” by Garrison et al., (2000), which was
developed in the context of (early) distant education. As shown in Fig. 1, this
framework postulates three main components of an online learning experience:
cognitive presence, social presence, and teaching presence. To deliver these three
presences and, as a result, achieve a good online education experience, DT education
needs to adapt all three Ps. We therefore begin by introducing the elements of the
Education Experience Model and the changes they undergo through digital education. Afterwards, we introduce the theories and strategies along the three Ps that
subsequently make it possible to achieve such changes.
Cognitive presence denotes the most basic element necessary for educational
success and refers to learners’ ability to create meaning through communication.
While the communication between members of an analog DT team usually occurs

Experiences of Facilitating Virtual Design Thinking:. . .

Fig. 1 Education Experience Model (Garrison et al., 2000, p. 88)

naturally and is also fostered by physical closeness between learners and educators’
interventions, a virtual environment needs new ways to ensure cognitive presence.
Furthermore, it is more difﬁcult for participants to be present in online than in ofﬂine
trainings, since looking at a screen creates fatigue and technology offers manifold
distractions, such as trying out new software functions or answering e-mails.
Social presence refers to the component of feeling comfortable and is deﬁned as
the “ability of participants [. . .] to project themselves socially and emotionally” into
the community, in other words, to bring themselves into the learning environment as
human beings with needs and desires and not only as learners. The absence of social
presence can easily turn collaborative online learning into a simple mode of lecturing
or ex-cathedra teaching (Schrage, 1995). In addition, emotions are more difﬁcult to
read as the perception of others is reduced to their faces in a virtual setting and facial
expression is quite limited in range as many people focus on content on their screens
in parallel to trying to look at other people. Hence, facilitating DT in a virtual context
requires, in particular, embedding human elements and emotions that contrast with
the more technology-driven virtual teaching environment.
Teaching presence involves the two functions: (1) designing the educational
experience, including the selection of content, the design of learning activities, and
the development of an assessment, and (2) facilitating the learning experience.
Building on social and cognitive presence, it is teaching presence that provides

S. Mayer et al.

and delivers the actual learning experience. While virtual DT education might not
tremendously change the teaching content, it has severe implications for its
e-pedagogical design and facilitation.
Together, cognitive, social, and teaching presence create the educational experience. Transitioning from ofﬂine to online education, hence, requires the
virtualization of the entire experience, which means its three main components
need to be carefully aligned. In particular, adapting the teaching presence without
reconsidering cognitive and social presence can only deliver an incomplete online
education experiences. In the next sections, we will therefore discuss strategies that
ensure such an overall virtual DT education experience across a social, cognitive,
and teaching presence.

1.2

Facilitating in Various Settings: People, Place, Process

Many institutions around the world use the three Ps, People, Place, and Process, as
grounding pillars for facilitating DT, for example, at the HPI School of Design
Thinking in Potsdam (HPI D-School, n.d.) or the Hasso Plattner School of Design
Thinking at the University of Cape Town (UCT d-school, n.d.).
The ﬁrst P, People, is deeply anchored in the core of DT. Research identiﬁes
interdisciplinary collaboration as one of the key attributes to characterize DT
(Micheli et al., 2019). DT education therefore emphasizes bringing together people
from various backgrounds and with a broad set of expertise, to form diverse,
multidisciplinary teams that allow for the emergence of different perspectives on
complex challenges. Fostering collaboration between team members is central to
teaching DT. Setting up heterogeneous teams is equally possible in the face-to-face
setting as in a virtual setting—or, in fact, might be even easier in the virtual context.
Since international participation is not restricted by physical attendance, a larger
audience can be reached with virtual learning programs. Yet, we have experienced
differences in the communication, interactions and therefore interpersonal relationships when it comes to virtual facilitation. While virtual facilitation might allow
more diversity and interdisciplinarity, it raises certain challenges concerning the
social presence as described in Sect. 1.1. For instance, the following key questions
arose: How do learners experience empathy? And How do people engage with each
other in a virtual setting? In Sect. 2, we focus on literature of team engagement and
embodiment to offer theoretical grounding for these two ﬁelds of action when
fostering DT facilitation in a virtual setting.
The second P, Place, refers to the creative spatial conditions DT often relies on to
foster collaboration and creativity in teams. This includes movable furniture or
material for quick prototyping as well as spaces designed for team work and for
gathering to share learnings in larger groups. Investigating the inﬂuence of space on
DT has found a wide interest in the research community (Overmyer & Carlson,
2019; Schwemmle et al., 2018; Schwemmle et al., 2021). As the physical space is
clearly different in a virtual context, we were curious as to how DT facilitation can

Experiences of Facilitating Virtual Design Thinking:. . .

create a positive learning context in the virtual setting by offering a “safe space” as
well as the right atmosphere—both online and ofﬂine. The Education Experience
Model emphasizes the importance of setting climate, and incorporating the social
and teaching presence. In Sect. 3, we rely on research on psychological safety as well
as creativity research as a theoretical grounding for these two ﬁelds of action.
The last P, Process, refers to the underlying structure DT, i.e. the DT process
(Liedtka, 2011). While there is no clear deﬁnition or agreement on what DT exactly
is (Micheli et al., 2019), most academic and professional teaching institutions rely on
process steps. For instance, d.school Stanford uses a ﬁve-step process and the HPI
School of Design Thinking uses a six-step process (What, n.d.b). While the process
steps remain the same in a virtual facilitating context, Process also refers to surrounding conditions and activities such as engaging in warm-ups to break the ice
between team members (Carlgren et al., 2016), using timeboxing to allow for speed
and multiple iteration cycles (Efeoglu et al., 2013), or supporting small teams via
expert coachings to enable learning in novices (Liedtka, 2020). We therefore wanted
to understand how much process and structure is needed in virtual facilitation and
how this structure might be different from a physical setting. In Sect. 3, we
investigate how random encounters also ﬁt a structured process as to what is
important for managing workshops online.
To sum it up, the next three sections follow the structure of People, Place, and
Process and, for each P, present two ﬁelds of action that we derived from our
experience in facilitating DT in a virtual setting. As displayed in Fig. 2, the six
ﬁelds of action are (Digital) Engagement, Embodiment, Safe Space, Atmosphere,
Random Inspiration, and Managing Workshops. While they are not part of the
chapter structure, cognitive, social, and teaching presence underlie all of these ﬁelds
of action, as they are necessary for the creation of successful educational
experiences.

2 People
2.1

(Digital) Engagement

One foundation of DT is the collaboration in diverse teams. Yet, to leverage the
different perspectives, participants need to actively engage in the teamwork. In a
virtual setting, devices and screens can act as a natural barrier to a feeling of
involvement with the other people “present,” making the engagement of learners
an important ﬁeld of action in virtual DT education.
Prior research identiﬁed ﬁve main drivers of engagement in virtual teams: trust,
cultural intelligence, formal and informal communication, individual maturity, and
technology (Shaik & Makhecha, 2019). These drivers cover a broad spectrum of
topics, creating an overlap with the following sections. Trust, for example, also plays
a role in creating psychological safety and a safe space for interaction and learning,
which we elaborate on in Sect. 3.1. Similarly, informal communication and the

S. Mayer et al.

Fig. 2 Six ﬁelds of action to foster Design Thinking facilitation in a virtual context

creation of networks through random encounters are detailed in Sect. 4.1. These
overlaps, however, mirror the strong interwovenness of the different elements of
virtual education, as we have emphasized in the introduction.
In this section, we want to elaborate on aspects of technology and its consequences for communication. As mentioned in the beginning, technology can hinder
aspects of our normal communication, but it also allows new channels to be created.
For example, being “alone” in front of a screen and muting the microphone often
creates a high threshold to engage in discussions in a plenum setting. Therefore,
addressing participants more directly than in an ofﬂine setting can help bridge these
barriers in a virtual setting. One easy trick can be simply naming participants and
asking directly for their opinion. While this might feel like being called on by the
teacher in school, it raises cognitive presence, as people tend to be more attentive if
there is a chance of being called on spontaneously. Furthermore, in a face-to-face
setting, we often rely on non-verbal cues to engage others in a discussion, for
example, by relying on eye contact. Due to the virtual setting, it is not possible to

Experiences of Facilitating Virtual Design Thinking:. . .

address individuals simply through eye contact; we therefore have to resort to verbal
contact.
Another hurdle to engagement for the facilitator is the lack of feedback, especially
when faced with a larger group of learners. With larger groups, all the learners are
often not visible at the same time due to technological limitations. One way to get
easy feedback, for example, if participants have completed a certain task or template,
is to ask for low threshold reactions. Most online communication tools allow some
type of reactions, referring to icons demonstrating applause, thumbs up, or smiles,
therefore offering a low threshold to engage with this new channel of communication. In DT, we often plan for sharing rounds, where each team presents their current
results in a short time frame. In a physical space, these are often followed by
applause, while in a virtual environment presentations are followed by silence.
Introducing means of engagement explicitly (e.g., using the emojicon reactions,
waving your real hands, or unmuting all together and then clapping loudly) can help
to keep large audiences engaged. It can even be a source of fun for the group to try
out different emoticons or clapping rhythms together.

2.2

Embodied Cognition

Embodied cognition is a relatively new stream in research. It posits that individuals
acquire, interpret, remember, and express valuable information with their entire
body, and not only with their brains (Barsalou, 2008; Harquail & Wilcox King,
2010). As a consequence, they are also able to recall and reenact perceptual
experiences. For example, that eating a piece of cake leads to a variety of sensual
perceptions, including the visual appearance, texture, smell, and feel in the mouth.
These perceptions can be mentally simulated, later, for instance, when a person
simply sees the image of a cake (Shen et al., 2016). Hence, especially referring to this
element of recalling, embodied cognition also plays an important role in learning and
education (Shapiro & Stolz, 2019).
DT relies on a lot of elements that support embodied cognition, such as working
with visuals, becoming aware of one’s own body in warm-ups, and working with
material to create haptic experiences during prototyping. Because a lot of these
elements no longer work in virtual settings, facilitating DT virtually should in
particular focus on recreating such embodied experiences. On the one hand, visual
expression should be emphasized; on the other hand, new ways of individual
embodied experience need to be created.
To make use of embodied cognition, virtual expression should be a particular
focus of virtual education. To this end, establishing a set-up where all participants
have their cameras switched on is a basic requirement, which also fosters the
cognitive and social presence. A very simple way of increasing embodied cognition
is using gestures to underline the spoken word. Moreover, a more metaphorical
language and the use of symbols and images in presentations or on boards fosters
embodied cognition.

S. Mayer et al.

There are many ways how educators can bring in learners’ own bodies into the
learning experience. To begin with, warm-ups are a fantastic way of activating the
entire body. Hence, virtual facilitators should refrain from the temptation of using
technological-heavy warm-ups and create experiences that make learners aware of
and feel their bodies. Being a passionate singer, I (Martin) have made great experiences with warm-up exercises from singing, as they involve body relaxation as well
as breathing and feeling vibrations on the skin while humming. Used as interventions, such exercises can also re-activate learners during longer sessions in front of
the screen. Dancing, stretching, and guided relaxation activities provide similar
effects and also help learners reconnect with their senses. Second, not all virtual
education needs to take place in front of the screen. Seeing fellow learners as heads
in black tiles makes us easily forget that they (and we) all have an entire body.
Hence, encouraging learners to get up, move, or even go outside during a virtual
education experience will help rediscover the entire body. This can easily be done
with individual reﬂection or ideation sessions, but is also possible for many other
activities during a DT workshop. Third, online education usually reduces the tactile
experience to mouse and keyboard. Thus, facilitators should include elements, where
learners work with pen, paper, and other materials and then upload pictures to virtual
whiteboards. Examples include prototyping (certainly), but also the understand or
synthesis phase. Not only will such activities inhibit fatigue in front of the screen, but
they also make it easier for learners to be visual since hand drawings are much easier
made on paper than graphical objects on a board. Finally, physical props can
enhance embodied cognition in virtual teaching. For instance, educators can ask
students to prepare objects that refer to the challenge they are working on (like a
three-dimensional mood board) and actively involve them in the workshop. For a
challenge in the ﬁeld of healthy nutrition, they might prepare a plate of fruit that
students (/participants) smell, touch, and eat during the workshop.

3 Place
3.1

Safe Space

Psychological safety refers to the individual’s belief, that a certain context, such as a
workplace or a classroom, is safe for taking risks without interpersonal consequences
(Edmondson, 1999). It is one key factor to fostering teamwork and team learning
(Edmondson, 1999; Edmondson & Lei, 2014). One meta-analytic review noted
factors that were inﬂuential antecedents for psychological safety. These included
peer support, learning orientation, interdependence, and role clarity (Frazier et al.,
2017). If a space feels psychologically safe, individuals experience a higher satisfaction and engage more actively in learning behaviors, such as seeking and sharing
information, and reﬂecting and experimenting (Frazier et al., 2017).
One famous example for the inﬂuence of psychological safety is a study from
Google, which sought to determine factors that predict high performance teams. The

Experiences of Facilitating Virtual Design Thinking:. . .

number one distinguishing factor identiﬁed was psychological safety, identifying
“high performance” teams which are less likely to leave the organization, more
likely to build on the ideas of others, generate more revenue, and are evaluated as
more effective by their leaders (Rozovsky, 2015).
Establishing rituals can support an open failure culture and encourage a constructive way to deal with mistakes and negative feedback. Organizations, for example,
establish regular meetings such as failure Fridays in order to share learnings
generated by mistakes (Mayer et al., 2021). As DT embraces uncertainty and
encourages making mistakes early on, following the “normal” DT program naturally
already fosters the creation of psychological safety. However, we have experienced
that it is far more difﬁcult to establish psychological safety in a virtual environment
than in a physical DT studio space. Nevertheless, in order to leverage peer support as
one inﬂuencing factor for psychological safety, it is important to provide “space” for
shorter peer exchanges. While DT already works with team check-ins and team
check-outs, it can be helpful in virtual education to allow time for smaller exchanges
in groups of twos and threes. Furthermore, facilitators can engage in one-on-one
settings, so that the individual learner has a direct contact to the teacher. Often, these
settings allow to emphasize cultural aspects, such as being open to making mistakes
and not seeing that as failure, but rather as learning opportunities.

3.2

Atmosphere

Creative climate research has investigated a variety of organizational factors that
foster (or inhibit) creativity (Amabile et al., 1996; Newman et al., 2020). In a similar
vein, a rich stream of literature has identiﬁed elements that create a positive atmosphere in service environments, such as stores and restaurants (Bitner, 1992; Brocato
et al., 2014). Among these factors are leadership, experience, and education, but also
very speciﬁc elements, including temperature, air quality, music, and personal
artifacts. While the former elements are more general and overlap with other ﬁelds
of action in this chapter, in this section on atmosphere, we especially focus on the
latter ones. Applied to educational settings, educators need to ensure an atmosphere
that is conducive to learning and that especially fosters concentration and well-being,
which, in turn, fosters learners’ social and cognitive presence.
In ofﬂine teaching, most elements can be determined or inﬂuenced by the
educator, for instance, choosing the right space or preparing the space accordingly.
Virtual DT facilitation, however, turns the creation of a good atmosphere for
learning into a more complex task, as there are two different levels of space: The
ﬁrst space is the common virtual space, where educators and learners meet, such as
the video conferencing tool, the virtual whiteboard, or a combination of both
simultaneously. The second space is the physical space where each learner (and
the educator herself/himself) takes part in online education. Successfully creating a
good learning atmosphere thus means to inﬂuence the atmosphere in both the virtual
and the physical spaces, as well as their integration.

3.2.1

S. Mayer et al.

Atmosphere in Learners’ Physical Space

Educators have only a very limited inﬂuence on their learners’ actual physical
surrounding. However, there are various ways of how they can make the most out
of the limited inﬂuence they have. First, and especially in longer formats, educators
can invest some time to talk about the physical learning environment and encourage
learners to try out different spots. For instance, learners could attend inputs sitting at
their desks, then move to the dining table during group work, and, ﬁnally, move to
the couch for reﬂections or feedback sessions. In doing so, they increase learners’
awareness for their spatial environment. Second, and extending this awareness
building, educators can include the physical environment and bring it to the virtual
space. For instance, learners can be asked to avoid using artiﬁcial backgrounds or
show a 360-degree view of where they are as part of an introduction or check-in.
Such activities allow all learners at least to see where their fellow students are, which
helps to foster social presence. Third, educators can encourage students to use breaks
to get some fresh air, by opening windows or even going outside. We have also had
good experiences with asking learners to go outside for a speciﬁc part of the
workshop. For instance, they might get inspiration when taking a walk during an
ideation session or while discussing team issues. Fourth, especially with longer
projects, learners could prepare their space with speciﬁc elements that relate to
their DT experience. For instance, they might choose an iconic object that refers to
the challenge they are working on. Or, every team member has the same object on
their desk that creates and communicates the teams' identity (e.g., imagine a team
that calls itself “Team Cactus” and each member putting a cactus on their desk to not
only remind themselves of their team mates, but also to show the other learners that
they belong to this team).

3.2.2

Atmosphere in the Virtual Space

The two major atmospherical elements educators should inﬂuence in the virtual
space are music and esthetics. Music has a huge effect on individuals. For instance,
students who listened to classical music during a lecture showed better academic
performance compared to a group that listened to no music (Dosseville et al., 2012).
Other research shows that music inﬂuences the perception of the atmosphere in
banks and bars (North et al., 2000). Moreover, playing music can give spaces with a
negative atmosphere, such as basement room without windows, a more pleasant
atmosphere (Ehret et al., 2021). Taken together, using music deliberately during
workshops can advance both the overall atmosphere and learning outcomes. A
detailed analysis of which kind of music best suits which DT phase would go
beyond the boundaries of this book chapter. However, overall, more reﬂective
phases will beneﬁt from calmer music, ideation and prototyping from more energizing music, and easy listening music similar to the background music in cafés can
create a welcoming atmosphere in breaks or before the workshop starts. Educators

Experiences of Facilitating Virtual Design Thinking:. . .

Fig. 3 Digital whiteboard design for a workshop with three teams (designed by Félix Deraed)

should align the choice of music to both their own and their target groups' preferences. The majority of videoconferencing tools allows playing background music
directly from the computer, so that no complex technological set-ups are required.
The second atmospherical factor refers to the esthetics and design of virtual
elements. For most DT education experiences, this might in particular refer to the
design of digital whiteboards and other collaboration tools. On the one hand,
prestructured boards with headlines, colors, and other visuals create a positive
atmosphere, as they symbolize that somebody has prepared for the education
experience. On the other hand, other than physical whiteboards, virtual whiteboards
make it possible to create whole roadmaps. For instance, the structure of the entire
workshop can be visualized along different whiteboards. Figure 3 shows an example
for such a board design that especially illustrates plenary phases and team work in
three parallel teams (design by Félix Deraed). Figure 4 shows the whiteboard

S. Mayer et al.

Fig. 4 Digital whiteboard design for a Design Thinking education program at the HPI School of
Design Thinking (designed by Anja Harnisch and Maria Aragon)

structure of a Design Thinking Program at the HPI School of Design Thinking. It not
only contains boards where teams work together, but also provides an interactive
(i.e., clickable) structure for the entire semester (design by Anja Harnisch and Maria
Aragon). Preparing such whiteboard ecosystems almost creates an own virtual DT
space with its speciﬁc atmosphere.

3.2.3

Linking the Atmosphere in Physical and Virtual Spaces

As already mentioned, successful virtual facilitation integrates the atmosphere of
both physical and virtual spaces. As an illustration, the most beautiful digital
whiteboard alone will not be able to create a good atmosphere, if the learner sits in
a dark, small room. To link these two spheres, educators can encourage participants
to upload pictures of their real physical environment into the online space or try to
have certain visual elements from the virtual space also present in their physical
environment. This could be an object, such as a plant, or a little paper artifact that is
sent to learners in advance.

4 Process
4.1

Random Inspiration

DT is often labeled as a creative approach to problem solving that relies on
inspiration. Inspiration can be deﬁned as a “motivational state that compels individuals to bring ideas into fruition” (Oleynick et al., 2014, p. 1). Facilitating DT often
follows the six phases of the DT process, offering a natural structure to teaching DT
in a rather linear sequence of methods within the phases. We observed that the

Experiences of Facilitating Virtual Design Thinking:. . .

transfer of this structure to the virtual world can result in a meticulous structuring,
focusing purely on the content of the phases, decreasing learners’ internal motivation
and therefore reducing inspiration. During face-to-face programs, learners often
draw inspiration from random encounters with peers, for example, the classic
meeting at the coffee machine during a break. In a virtual setting, these chances
are limited or non-existent. We can draw from research to understand how meeting
people by chance or allowing some free time to exchange thoughts can be beneﬁcial
in two main ways.
First, drawing from the economics of social networks, meeting people at random
is the starting point for creating (social) networks (Jackson & Rogers, 2007).
Researchers found the random meeting factor to be signiﬁcant for building friendships and lasting relationships, aside from an online community (Jackson & Rogers,
2007). Therefore, in order to allow participants in online education formats to
leverage the social contacts, it is important to provide them with opportunities for
random encounters. Second, research from cognitive psychology shows that taskand goal-related thinking is different to mind-wandering, dreaming, and spontaneous
thoughts. Yet the latter three are signiﬁcantly related to creativity, for example,
mind-wandering being positively correlated to creative achievement (Agnoli et al.,
2018; Tan et al., 2015). Therefore, allowing random encounters and times with no
speciﬁc task can be very beneﬁcial to online facilitation, fostering the development
of social networks as well as creativity. From our experience, a little bit of process is
still helpful here, since a random meeting in front of the coffee machine, the
building, or at reception does not happen in a virtual context. We therefore set up
random “blind dates,” bringing random groups of two or three participants together
for coffee breaks or just for networking sessions, in particular at the beginning and
during program activities. These can be accompanied by an open question to start
conversions in the groups, but participants are explicitly allowed to turn to whatever
topic of interest they like, to allow spontaneous thoughts and mind-wandering for the
individual. As food and drinks play an important role in the physical setting to bring
people together in a more relaxed context, it is also possible to use food-delivery
services during online education. Doing so, participants get an individual meal, but
have the chance to share the “same” lunch or dinner experience.

4.2

Managing Workshops

DT Education not only includes teaching and coaching, but also a lot of organizational tasks to create an optimal educational experience. While virtual education
makes some aspects easier, such as the preparation of physical spaces, there are a
variety of new challenges that need to be managed. As the Education Experience
Model implies, communication, the tools allowing it, and the facilitation of the
education experience play important roles. Focusing on these elements, we share
our experiences in four ﬁelds.

4.2.1

S. Mayer et al.

Technology Usage and Training

Unstable Internet connections, old software versions, and non-working technology
can be severe barriers or obstacles in DT online education. To this end, educators
should try to reduce or eliminate such obstacles early on. In our experience, already
the invitation to workshops should clearly indicate the tools used (including the
readiness to switch on the camera). This allows participants to prepare accordingly
and, for instance, update their software in advance. Depending on the speciﬁc
workshop, we explicitly recommend not dialing in from mobile devices, as this
might cause issues with breakout sessions or other features. Moreover, we provide
the data to dial into meetings via phone in addition to the link, so that learners who
have trouble with their Internet connection have an easy workaround. Doing so is
especially relevant if learners dial-in from regions or countries with a low bandwidth.
For learners who are not experienced in virtual education, using videoconferencing
tools and digital whiteboards might be a barrier for participation. To this end, we
offer tech checks before the ofﬁcial workshop starts and provide a 15-minute
introduction to the digital whiteboard so that every learner knows basic skills,
such as navigating on the board as well as writing on and moving post-its. In longer
academic programs and also in professional contexts, a digital hotline or concierge
can be always approachable for learners in solving technical issues.

4.2.2

Communication with Various Stakeholders

In workshop settings with various stakeholders, such as coaches or project partners,
communicating with and integrating these partners needs some extra effort in virtual
settings. In our experience, opening up a communication channel with all coaches
that is independent from videoconferencing is beneﬁcial, as group chat functions
become problematic during breakout rooms. We usually use Slack or a mobile
messenger. Concerning project partners or other external guests, such as interview
partners or testers, educators need to ensure a proper welcome and introduction.
While they can rather easily observe what is going on in physical workshops, an
introduction to the technology and, maybe, a cheat sheet with the relevant links and
functions might be beneﬁcial in digital education.

4.2.3

Timing

One huge advantage of virtual workshops is timing. Educators can centrally time the
entire workshop using timers on boards or closing breakout rooms at a certain time.
However, especially in professional education, learners are also more time sensitive,
as they usually schedule follow-up meeting in breaks or directly after the workshop.
Hence, educators should communicate the timing of beginning, ending, and breaks,

Experiences of Facilitating Virtual Design Thinking:. . .

and stick to these schedules. Doing so provides learners the opportunities to include
their other work-related tasks and be more present during workshop times.

4.2.4

Avoid Over-Structuring the Learning Experience

The majority of suggestions made in this section and the entire book chapter refers to
clear structure and procedures. For instance, boards are designed in advance, making
spontaneous changes harder than in physical workshops. Similarly, random encounters must be planned and facilitated (please refer to the previous Sect. 4.1). In light of
all these highly structured elements and the boundaries given by technology, educators should be aware of structure “overkill,” getting in the way of the playfulness
and creativity in DT education. For instance, planning buffer times in agendas allows
for spontaneous interventions or preparing two alternative board structures gives
coaches and teams the freedom to select what is best suited for a particular application. Not only will such room for creativity and ﬂexibility beneﬁt the participants, it
will also make educating more fun for educators, as it goes beyond cooking after the
recipe.

5 Discussion
This chapter introduced the Education Experience model to highlight the changes
that occur when moving from ofﬂine to online education. Using the three Ps of DT,
People, Place, and Process, as a structure, we introduced six areas of action for DT
educators in the virtual world. The theories linked to these areas inspire further
research questions and give practitioners a rich resource for further reading.
Further research regarding “People” might, for instance, deal with the engagement concept and analyze potential additional factors that foster engagement online.
In this regard, relying on online tools and gamiﬁcation could offer a variety of new
opportunities for increasing learners’ engagement. Similarly, further research might
transfer the concept of embodied cognition to online settings and develop a taxonomy of how to include its advantages in online education. An assessment of different
ways of digital embodies cognition could indicate superior approaches and context
factors that need to be considered. In the ﬁeld of “Place,” researchers might combine
extant work about psychological safety, trust, and data security to better understand
the additional opportunities and obstacles for creating psychological safety online.
Moreover, a deeper investigation of hybrid spaces and how to best combine digital
and physical education, is a promising ﬁeld for further research. Lastly, in the ﬁeld of
“Process” the question of how to plan for random encounters in online education, the
outcomes and intervening factors provides a ﬁeld for many theoretical and empirical
investigations.
In general, we expect that in the near future, researchers and practitioners will
need to ﬁnd criteria that help suggest if certain education experiences are better

S. Mayer et al.

created ofﬂine or online or how a proper mix might look like. The theories and tricks
suggested in this chapter can serve as a ﬁrst step into this direction.
Acknowledgements The authors want to thank Sascha Hoever for his support with the creation of
the displayed illustrations. Additional thanks to Dr. Sharon Nemeth for her copyediting support.

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Accessibility of Linked-Node Diagrams
on Collaborative Whiteboards for Screen
Reader Users: Challenges
and Opportunities
Danyang Fan, Kate Glazko, and Sean Follmer

Abstract Online whiteboards leverage our spatial thinking abilities to support rich,
collaborative, and interactive design. However, these tools are often exclusionary to
people who are blind. We ran a series of user studies with university students to
better understand the accessibility of current tools and evaluated several existing
audio and haptic approaches to inform design guidelines and future directions. We
observed how current interfaces do not provide access to the graph generation
process, provide incomplete access connections and spatial relationships, and can
lead to users feeling uncertain or misinterpreting information in the graph. By
exploring existing solutions, we identify and discuss the importance of reﬂecting
the diagram structure within the navigation scheme and provide a spatial overview
whereby users can reference their exploration. We summarize a series of recommendations to inform the investigation of future interactions to improve the accessibility of online whiteboards.

1 Introduction
Designers collaborate using a variety of media. Increasingly, we see the use of online
whiteboard software to allow users to collaboratively organize, and share, and reason
through information. Many of these tools allow for rich remote collaboration and
leverage our spatial thinking abilities. A large challenge is accessibility, and these
tools often exclude people who are blind and visually impaired (Coombs, 2010). As
Design moves to focus on the importance of inclusion and accessibility, we need
Blind Designers.
COVID-19 encouraged institutions to explore online methods of learning. Online
whiteboard platforms have often been adopted by instructors to engage students in
collaborative and interactive design (Metscher et al., 2021). These tools can be used
to construct a variety of spatial diagrams, such as mind maps, ﬂow charts,
D. Fan · K. Glazko · S. Follmer (*)
Department Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
e-mail: danfan17@stanford.edu; glazko@stanford.edu; sfollmer@stanford.edu
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_6

D. Fan et al.

hierarchical trees (Zaqoot & Oh, 2018). Such diagrams belong to a class of diagrams
called “linked-node diagrams” (Burch et al., 2021), which are both spatial, and show
connections between different pieces of information presented.
In this work, we have sought to understand some of the accessibility gap when
interacting with linked-node diagrams within online whiteboards. We ran a series of
user studies with university students to better understand the accessibility of current
tools, and we observed many challenges to understanding the information conveyed
on them. These challenges include lack of access to the graph generation process,
incomplete access to connections and spatial relationships, and uncertain feelings or
misinterpretation of information on the graph.
Researchers and students then collaboratively evaluated several existing audio
and haptic approaches to inform design guidelines and future directions that might
improve user’s access and understanding of linked-node diagrams. We discuss the
importance of elucidating the construction process, reﬂecting the diagram structure
within the navigation structure, and providing a spatial overview to which users can
reference their exploration. We conclude with a summary of challenges as well as
design guidelines and research opportunities to address these challenges.

2 Background and Related Work
2.1

Collaborative Design Tools

Designers often engage in co-located spatial collaboration, traditionally using paper,
post-it notes, and whiteboards. Researchers in the ﬁeld of Computer Supported
Collaborative Work have investigated techniques to support spatial collaboration
at a distance, often drawing on collaborative design as a task domain. Some
researchers have sought to replicate the affordances of these traditional collaboration
tools for remote collaboration by investigating the use of synchronized digital
whiteboards (Everitt et al., 2003; Tang & Minneman, 1991a). Alternatively, another
line of research has investigated synchronous collaborative drawing and diagramming through video conferencing tools (Ishii, 1990; Ishii & Kobayashi, 1992; Tang
& Minneman, 1991b; Wellner & Freeman, 1993). More recently, collaborative tools
such as Miro (n.d.) and Mural (n.d.) have gained popularity, providing pervasive
remote collaboration for spatial layout and design tasks, such as brainstorming or
diagramming. However, such research and tools have not thoroughly considered
accessibility for blind and visually impaired designers and participants.

2.2

Accessible Spatial User Interfaces for the Blind

Touch-based interfaces, compared to other non-visual interfaces, often provide
direct access to spatial information (O’Modhrain et al., 2015). Particularly, tactile

Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . .

graphics, which are images with raised surfaces, are commonly used to convey
spatial information to people who are blind and visually impaired (Sheppard &
Aldrich, 2001), and are well suited for technical users to comprehend graphical and
spatial images (Gardner, 2002). However, these graphics are static and are not
practical in collaborative environments in which spatial information is constantly
changing.
There are many efforts to produce refreshable touch-based interfaces to support
dynamically changing content (O’Modhrain et al., 2015). Studies have explored
electrovibration (Xu et al., 2011), modulating electrostatic force (Winﬁeld et al.,
2007), lateral skin vibration (Lévesque et al., 2005), vibrotactile feedback (Giudice
et al., 2012; Linvill & Bliss, 1966; Palani, 2013), actuated pin arrays (Siu et al.,
2019), and force displays (Magnusson & Rassmus-Gröhn, 2005), which provide
haptic feedback as users actively move their hands and/or ﬁngers to spatially interact
with presented information.
While these dynamically refreshable interfaces provide compelling ways to
convey spatial information, they require specialized hardware, are limited in spatial
resolution, and are often prohibitively expensive (Gorlewicz et al., 2018;
O’Modhrain et al., 2015). Audio-based solutions that convey information through
natural language or through sound can deliver high levels of detail and are often
supported in ubiquitous computing devices such as smartphones and laptops (Billah
et al., 2017; Sawe et al., 2020). However, they are often not perceived spatially and
constrain the perception of information in less ﬂexible sequences.
Several works have investigated speech-based interfaces to support linked-node
diagrams. These works explored hierarchical and connection-oriented navigation
schemes (Bennett, 2002; Brown et al., 2003), but were not able to determine how
well participants were able to internalize and form mental models of explore
graphics. Metatla et al. developed a series of interactions that support the modiﬁcation construction of hierarchical diagrams using an audio-keyboard interface
(Metatla et al., 2008). Horstmann et. al. provided additional options for users to
interact with hierarchical diagrams spatially through the use of joysticks and spatial
audio to support spatial understanding (Horstmann et al., 2004). They found that
participants were able to build mental representations using added spatial features.
These studies provided several recommendations, which include supporting both
hierarchical and connection-based browsing (Brown et al., 2003), helping users keep
track of orientation (Horstmann et al., 2004) and providing summary overviews
(Brown et al., 2004). How these ﬁndings might translate into collaborative whiteboard contexts, and how to best leverage spatial interactions, are still research gaps
to be explored.

100

D. Fan et al.

3 User Study Design
To better understand how haptic and audio systems might improve a user’s access to
spatial information in the online whiteboard context, we ran a user study consisting
of three parts: (1) an informal interview, (2) an evaluation of online whiteboards, and
(3), an exploration of other spatial access methods. Three university students who
are blind and use screen readers participated in these studies. The studies took
roughly 60 min and participants were compensated $35 for their time. Participants
were between 18 and 20 years old, and all reported to have high understandings of
Braille, tactile graphics and familiarity with quantitative data (4–5 on a 5-point
Likert scale).

3.1

Informal Interview

The informal interview was designed to better understand the problem setting.
Researchers asked participants to share contexts in which they may have encountered online whiteboards, if there were any challenges with their encounter, and if
participants relied on any strategies to better understand any information presented.

3.2

Online Whiteboard Evaluation

The goal of the evaluation was to identify speciﬁc gaps and challenges that participants come across when consuming information through online whiteboards. Participants were provided a sample whiteboard of a ﬂow chart and hierarchical diagram
to explore using a screen reader with which they were familiar. The sample ﬂow
chart depicted a product development workﬂow, while the hierarchical diagram
depicted the business objectives of a makeshift online video-streaming business
(Fig. 1), and was explored through Miro (n.d.) and Mural (n.d.), two commercial
online whiteboard platforms. Both platforms have accessibility statements indicating
efforts to improve the accessibility of their interfaces. Before participants began
exploring, researchers gave an overview of the graph context. As participants
explored the graph, researchers asked participants to try to gain as much information
as they could from the exploration, and to discuss their thoughts and strategies
through a think-aloud protocol.

Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . .

101

Fig. 1 Study participants explored a sample ﬂow chart and hierarchical diagram hosted on
collaborative whiteboard services Miro (a), and Mural (b)

3.3

Exploration of Other Access Methods

Participants explored an additional audio-keyboard representation and a tactile
graphic depiction of the same linked-node diagrams to identify and discuss potential
methods for improving online whiteboards. In the audio-keyboard representation,
the navigation of the diagram was meant to reﬂect the structure of the diagram,
which was similar to the connection-based scheme used by Bennett (2002). Using
this scheme, participants were able to transition from node to node based on their
connections and retrieve spoken information using their keyboard. A list of keyboard
interactions is provided in Table 1. The tactile graphic was constructed based on
BANA guidelines (B. A. of North America, 2010) and is spatially similar to the
visual graphic (Fig. 2). For this part of the study as well, researchers asked participants to try to gain as much information as they could and discuss their thoughts and
strategies through a think-aloud.

3.4

Sequence of Exploration

Mural, Miro, the audio-keyboard interface, and the tactile graphics were presented in
a sequence through which the participants progressively gained information as they
explored. Mural did not provide screen-reader access to the graphical Canvas, while
textual elements in Miro were conveyed as a sequential list and did not show any

102

D. Fan et al.

Table 1 Participants used the keyboard commands to navigate and retrieve information from the
audio-keyboard interface
Keyboard
command
“s”
“d”
“Down
arrow”
“Up
arrow”
“Enter”

Interaction
Articulates current node
Articulates current node number out of
total number of nodes
Previews nodes that the current node is
connected to. Repeated presses cycle
through those connections
Previews nodes that the current node is
connected from. Repeated presses cycle
through those connections
Moves into previously previewed node

Spoken sample
“Currently at <node text>”
“node id is <current node id> of <total
number of nodes>”
“Connects to <child node text>, <child
node number> of <total number of
children of current node>”
“Connects to <parent node>, <parent
node number> of <total number of
parents of current node>”
“Navigating to <previewed node’s
text>”

Fig. 2 Study participants explored tactile graphics which depicted the ﬂow chart (a) and hierarchical diagram (b) represented by Fig. 1.

connections. The audio-keyboard provided access to the textual elements as well as
the connections, but did not provide any spatial information. The tactile graphic
provided both the textual information, connections, and spatial context. As participants moved from one interface to the next, researchers also asked participants to
highlight if they learned any additional information that they did not learn from their
prior explorations to understand how having access to connections and spatial
relationships might affect their comprehension of the diagrams.

Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . .

103

4 User Study Results
4.1

Informal Interview

Participants described how they had heard of online whiteboards in educational
settings, especially as courses migrated online during the COVID-19 pandemic (p1,
p2), but they were never accessible. As a workaround, participants often asked
course instructors and peers to provide verbal descriptions of the information (p3),
which were described as difﬁcult to understand if the diagrams were spatially
complicated, and especially if the concepts were new and the purpose of the diagram
was to illustrate novel concepts. One participant also requested accessible versions
of graphics through their school’s local accessibility ofﬁce, which were sometimes
provided as a tactile graphic. While tactile graphics convey spatial concepts well,
they show only the end-result, often several days after the lecture or activity. They do
not capture the process in which the graphic was generated for in-class activities or
explained for lectures (p1). As a result, participants often were not equally included
in the generative or design processes involved with collaborative whiteboards.

4.2

Online Whiteboard Evaluation

With Mural, participants were only able to toggle through the interactive menu
items, but could not access the diagram canvas itself. Through Miro, the participants
were able to explore nodes of the diagrams through their screen readers. However,
while individual pieces of information were accessible, we observed many challenges with using the interface to understand the nodes. First, participants were
unable to tell whether a spoken piece of information was a part of the diagram, a
detail about the diagram, or a menu item, which led to uncertainty and confusion (p2,
p3). One participant recommended using tags to identify the type of information that
is described (p3), similar to when accessing the web. For example, nodes in the
graph should be tagged as a graph, and menu items can have a different type of tag.
Second, participants only had access to the nodes as a sequential list, but did not
reﬂect connections and spatial relationships of those nodes. While participants were
able to make loose inferences about connections based on the node content, such an
exercise often causes confusion (p3), and is mentally taxing (p1). Participant
3 recommended that the navigation structure reﬂects the connected structure of the
diagram. Third, additional efforts that were required to gain little additional information about the graph made exploration cumbersome. For example, users had the
option to open additional pop-up windows that provide detailed information about
each node, such as its color and shape. While one participant appreciated efforts to
make this information accessible (p3), the execution of a series of keyboard events or
minimal information—necessitating user memorization—made quick exploration to
understand broader pictures difﬁcult (p1).

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Exploration of Other Access Methods

The audio-keyboard interface was an example of an implementation in which the
navigation structure reﬂected the connections of the diagram. All participants were
able to learn the keyboard instructions within 10 min and to explore all parts of the
sample diagrams. Participants using the interface were able to articulate features
such as connections to and from each node (p1, p2, p3), as well as broader structural
features, such as cycles within the ﬂow chart (p3), the number of layers in the
hierarchical diagram (p1), and the complicated nature of a graph (p3). For understanding broader structures, one participant appreciated information about the total
number of nodes and the number of connections each node had that was provided by
the interface (p1). While the hierarchy did not convey any spatial relationships,
participants also described forming spatial mental models based on information they
explored (p1, p2, p3). For example, p2 describes how they assumed that the nodes
are conveyed top-down, while p1 and p3 used ﬁnger-drawing to internalize the
spatial structure. Interestingly, two participants, while later exploring the tactile
graphics, articulated having formed hypothesized spatial mental models that pretty
well resembled the tactile graphic (p2, p3).
One signiﬁcant challenge participants described when interacting with the audiokeyboard interface was keeping track of the large structure and where they were
within that structure. Participants described having to dedicate effort keeping track
of their mental mapping, which relied on a lot of memorization (p1). For the
interface, participants recommended providing information about the spatial structure in addition to the connections. This was suggested both in the form of a general
structure, as if they were exploring a tree or a circle, as well as in the form of
particular locations they were at in space if the graph were to be laid out onto a piece
of paper (p3). Contributing to this challenge of keeping track of explored information was the necessity to explore the hierarchy sequentially. This took time (p3) and
made revisiting information difﬁcult (p1). Participants used systematic approaches
to understand the entire structure (p1, p2, p3), which often involved going down
each branch in the order the items were presented. The interface, which enumerated
the connections to and from each node provided a way to keep track.
The tactile graphic, providing information about the connections, was spatially
displayed, and enabled multi-hand strategies that all participants leveraged. Participants stated that they were able to quickly gain information about the broader
structure (p1, p2, p3), as well as how individual nodes were situated within that
structure (p1, p2). All participants alternated between reading speciﬁc pieces of
information on the Braille labels, and referencing the connective structures in the
spatial vicinity.

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105

5 Discussion and Recommendations
Based on the user studies, we identiﬁed several categories of design recommendations to inform the investigation of future interactions to improve the accessibility of
online whiteboards.
Improving the Accessibility of the Graph Generation Process Can Promote
Participation and Understanding
Prior efforts focus around improving the accessibility of published or ﬁnal-form
graphics. For generative and collaborative design activities, understanding the
generation process is important for not only understanding the ﬁnal graphic but
being an active participant. From the informal interview, we discussed not just the
importance of making the constructing of spatial diagrams accessible, but also how
we perceived the diagrams as they are being constructed by others. Because the latter
has not been as explored in research, we advocate increased research efforts to
explore interactions to elucidate the process of gradual transformations spatial
graphics undergo as it gradually becomes formed.
Tagging Articulated Information Can Reduce Misunderstandings
and Confusion
With novel spoken interfaces, users can confuse articulated graphical nodes with
textual nodes or menu options. Tagging all spoken components by its content type
and relationship to the spatial graphic can clarify its purpose and provide a way for
users to infer its function. Providing tags of Study participants recommended tagging
schemes and interactions to follow html and pdf best practices (Reid & SnowWeaver, 2008) so that screen reader users can leverage their familiarity to more
effectively navigate and explore the graph.
Reducing Navigation Effort Can Improve Exploration Experience and Focus
Attention on Understanding Broader Relationships
Pop-up windows can be used to elaborate on nodes of the graphics without adding
clutter. However, requiring screen reader users to perform several keyboard operations to access information introduces additional cognitive load and slows down an
already sequential exploration process. Even in cases where little to no useful
information is provided through them, screen reader users may still open up these
windows in order not to miss out on potentially useful information. One participant
recommended reducing the steps of operation for accessing useful information. This
means not providing the possibility to open up additional content if little to no useful
information is present (p1). Additionally, with the tactile graphic, all participants
appreciated how quickly they could access and re-reference information which
effectively ofﬂoads having to hold and retrieve information from memory to their
perceptual system. Another participant recommended adding hotkeys and shortcuts
that enable more ﬂexible navigation (p3), though added features should be careful to
not substantially increase the base level of complexity required to explore the graph.

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Navigation Schemes That Reﬂect the Structure of the Spatial Graphic Provide
Ways for Users to Form Structural and Spatial Understandings of the Graph
Navigation schemes that reﬂect the connective structure of linked-node diagrams
enable screen reader users to listen to details that resemble the information ﬂow
communicated by the graph. While prior studies on hierarchy-based audio-keyboard
schemes did not indicate whether blind users could form accurate spatial models
without spatial information (Bennett, 2002; Brown et al., 2003), our preliminary
observations suggest this was possible for the types of diagrams used in the study
and with participants who self-reported having high familiarity with tactile graphics
and data manipulation. Consistent with previous studies are indications that understanding graphics through these interfaces takes signiﬁcant cognitive load (Brown
et al., 2003). While this work focused on the accessibility of linked-node diagrams,
different types of navigation schemes may be well suited to different forms of spatial
graphics, and there is opportunity to explore how different schemes might work best
in different collaborative whiteboard contexts.
Anchoring Exploration to a Spatial Overview Can Potentially Reduce Cognitive Load and Inform Exploration
Whether exploring the nodes as a linear list in the form of Miro or as an
interconnected hierarchy in the form of the audio-keyboard interface, participants
described trying to form spatial mental models to internalize explored nodes (p1, p2,
p3). Participants appreciated how the tactile graphic conveyed the overall structure
right away, reducing the need to piece together details from a part-to-whole
approach, and to potentially reform mental models as more information is explored.
Anchoring exploration spatially to an overview can also help users better track how
much there is to explore, and areas they have explored already. Using the tactile
graphic, participants quickly alternated between reading speciﬁc pieces of information on the Braille labels, referencing the connective structures in the spatial vicinity,
and exploring broader spatial structures within the graph, highlighting advantages of
spatial haptic representation that enable ﬂexible spatial exploration. For web-based
keyboard access methods, we think there might also be an opportunity for naturallanguage summaries or soniﬁed spatial summaries not just to provide broad spatial
overviews before exploration, but also smaller overviews to anchor users’ exploration to spatial relationships within the exploration vicinity.

6 Conclusion
Through our user studies, we explored the accessibility of online whiteboards with
university students that use screen readers to access the web. We observed how
current interfaces do not provide access to the graph generation process, provide
incomplete access connections and spatial relationships, and can lead to users feeling
uncertain or to misinterpreting information in the graph. By exploring several
existing solutions, we identiﬁed potential recommendations on how the interfaces

Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . .

107

may be improved. Our initial results suggest that connection-based navigation
schemes explored by prior work and web-exploration best practices could improve
these collaborative whiteboard interfaces. Additionally, we identiﬁed the need and
opportunity to investigate and better understand ways to elucidate the construction
process and provide smaller overviews to spatially anchor users’ exploration to
spatial relationships within the exploration vicinity.

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Part II

Fostering Innovation Behavior
and Co-evolution

A Pattern Language of an Exploratory
Programming Workspace
Marcel Taeumel, Jens Lincke, Patrick Rein, and Robert Hirschfeld

Abstract Software design and the underlying programming activities entail a great
portion of exploration to better understand problem and solution spaces. There are
programming tools and environments that support such exploratory programming
practices exceptionally well. However, inexperienced programmers typically face a
steep learning curve until they can reach the promised efﬁciency in such tools. They
need a long time to study best practices ﬁrsthand in real projects. The tools in use
might also need adjustments, given that modern programming languages are continually introducing new features or redesigning old ones. We want to apply the idea
of patterns to capture traditional and modern practices of exploratory programming.
In this chapter, we focus on the workspace tool, whose core ideas transcend many
different programming communities such as the Smalltalk workspace, the Unix
shell, and data-analysis notebooks. We extracted the essence into a novel pattern
language around the conversations that programmers have with their environment.
We believe that our work can help programmers to quickly understand and apply the
idea of workspaces, as well as tool builders to increase the efﬁciency of their project
team when facing exploratory challenges.

1 Introduction
The exploratory mindset is an intensive form of user-to-software mediation where
programmers are especially motivated to ﬁnd a design that both works and inspires:
“I know it when I see it.” Programming languages are expressive enough to unfold
anything from unsurprising complexity to surprisingly beautiful simplicity. The
latter is key.
Programming tools play a crucial part in the exploratory journey to reach highquality software. All programming activities have complementary tool support:

M. Taeumel (*) · J. Lincke · P. Rein · R. Hirschfeld
Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
e-mail: marcel.taeumel@hpi.uni-potsdam.de; jens.lincke@hpi.uni-potsdam.de;
patrick.rein@hpi.uni-potsdam.de; robert.hirschfeld@hpi.uni-potsdam.de
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_7

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Fig. 1 Workspace-like tools support effective and efﬁcient conversations between programmers
and their environments. They can be found in many different domains such as (f.l.t.r.) Squeak/
Smalltalk (workspace), Ubuntu/Linux (shell), and Jupyter/IPython (notebooks)

reading, writing, debugging, testing, deploying, and so on. Programmers can thrive
within environments that offer a sense of liveness (Rein et al., 2018; Tanimoto,
2013) and short feedback cycles through tools. Mistakes can be corrected on short
notice—with the next iteration likely more productive than the previous one. While
an exploratory mindset expects certain things from tools and their user interfaces,
good tools for exploration can even foster such an exploratory mindset. As illustrated
in Fig. 1, exploratory tools have been around for a long time such as the Smalltalk
workspace (Goldberg, 1983), the Unix shell (Raymond, 2004), and notebooks for
scientiﬁc computing (Pimentel et al., 2015).
However, it is not easy for inexperienced programmers to learn about and grow
an exploratory mindset. Even if the appropriate tools are at hand, it can be challenging to use them effectively and efﬁciently. Many best practices are supported
(or even promoted) through tools but not easily discovered during normal use.
Without proper guidance, you may never leave the traditional path, that is, remaining
pleased with the habits you acquired by chance, completing your tasks in a “good
enough” fashion. Provided that you are a tool builder in a foreign environment, it can
be challenging to pinpoint speciﬁc issues but rather to criticize everything that does
not feel “like home.” For example, if you prefer the programming experience in a
Smalltalk environment, you might be tempted to recreate its entirety for your
JavaScript workﬂow. While there is the Lively system for modern web development
(Ingalls et al., 2008; Lincke et al., 2017) in the spirit of Smalltalk, it took a great
effort and many resources to reach its productivity level as of today. Instead of
speciﬁc tool implementations, we are looking for a way to capture the essence of
exploratory programming practices—covering both mindset and tools—and that is
easily accessible for learners and practitioners—for tool users and tool builders.
We apply the idea of patterns to capture the core of best practices for exploratory
programming. Originally promoted for architectural design (Alexander, 1979),
patterns have proven useful for software design (Gamma et al., 1995) as well as
for guiding human behavior (Iba & Isaku, 2016). We like the generative aspects of
the pattern form, which allows for variation while clearly separating the repetitive
elements. For example, there is clearly an overlap between Smalltalk workspaces

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and Unix shells, but it is not easy to see for a non-expert. The patterns in this chapter
build upon our previous work. Our experience with tools for exploration stems from
the Squeak/Smalltalk programming system (Ingalls et al., 1997), which we have
been using in teaching and research for more than a decade. Our ﬁrst take on patterns
approached the concepts of enabling and controlling exploration (Taeumel et al.,
2021). Our second take presented actions around direct window manipulation
(Taeumel & Hirschfeld, 2021), which we collected during our work on the Vivide
tool-building platform (Taeumel, 2020).
In this chapter, we contribute a pattern language around the conversation style
that happens between programmers and their environments using workspace-like
tools: the exploratory programming workspace. The central pattern “Conversation in
Context” describes the typical question/response cycle that programmers follow to
learn more during their programming task such as the location of a bug or the best
way to implement a new feature. The three main components of that interaction lead
to the other patterns: “Elaborate Inquiry” and “Coach Your Environment” dive into
questions, “Concept in Shards” and “Proxy Transport” talk about the context,
“Simple Response” and “Tangible Response” address responses. The last pattern
“Pause and Explore” embeds a single conversation into the bigger picture, because
programmers must often manage multiple conversations at the same time.
In Sect. 2, we describe the pattern form and agents we use in each pattern. We also
give an overview of all pattern’s intents. In Sects. 3–10, we present the eight patterns
in this pattern language. Finally in Sect. 11, we conclude this chapter with a
discussion on pattern quality and possible next steps.

2 Pattern Form, Intents, and Agents
In this section, we describe the elements (or form) of each pattern, give an overview
of all intents in our pattern language, and elaborate on the agents whose characteristics and actions we try to capture in each pattern. It is all about you working on a
project in an environment while continually switching between different interaction
contexts.

2.1

The Pattern Form

Building upon the original triad of context-problem-solution by Christopher Alexander (Alexander et al., 1977), we took inspiration from both patterns of objectoriented design (Gamma et al., 1995) and human behavior (Iba & Sakamoto, 2011)
to shape the pattern form for this chapter:
• Intent is a quick summary of the actions that are typically part of the pattern’s
solution in the manner of “Do this, then that.”

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• A Desire for Exploration motivates the pattern through a hands-on story. We
typically refer to our experiences from the Squeak/Smalltalk system (Ingalls
et al., 1997).
• The Proﬁle is a brief summary of the pattern’s context, problem, and solution. As
in patterns on human actions (Iba & Sakamoto, 2011), it includes a list of forces
that the pattern tries to resolve.
• The Structure is a more detailed listing of what the programmer and its tools
(or environment) must achieve to resolve the conﬂict (i.e., the forces). Less
technical than object-oriented design patterns (Gamma et al., 1995), it, however,
includes an abstract visual model as in Iba’s patterns (Iba & Sakamoto, 2011).
• Consequences outline possible trade-offs that tool builders face when
implementing tools for the particular pattern, which might eventually also affect
programmers as tool users.
• Related Patterns are the backbone in our pattern language. Without meaningful
connections between our patterns, we would have a list of unrelated scenarios.
Yet, our patterns all deal with the conversational style between programmers and
their workspace-like tools.
Note that we will use the term “context” differently throughout this chapter. We
will not address a “problem context” but an “interaction context,” which is the
knowledge base full of technical artifacts leading toward the solution. See below.

2.2

Overview of All Patterns

Our pattern language includes eight patterns. The ﬁrst and the last pattern frame the
middle ones, which are organized in pairs dealing with questions, context, and
responses, respectively. Each pattern’s intent is as follows:
Conversation in Context (Sect. 3) Ask the environment one or more questions to
better understand the context you are currently working with. Iteratively revise
each question until you are satisﬁed with the environment’s response. In followup questions, refer to prior responses to make your wording (and the responses)
more expressive and precise.
Elaborate Inquiry (questions, Sect. 4) Decompose the topic of your conversation
into parts. Then compose your central question from multiple sub-questions
according to those parts. Let the environment manage the topic’s complexity to
reduce your cognitive load and hence the risk of making mistakes.
Coach Your Environment (questions, Sect. 5) Extend the environment’s knowledge by adding information to the current interaction context. Extend its language
vocabulary by adding tools for reference in follow-up questions. Both kinds of
additions can either come from your memory (and experience) or be loaded from
external resources.

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Concept in Shards (context, Sect. 6) Relevant information might be scattered all
over the environment. Do not be satisﬁed with the ﬁrst useful response. Keep
asking questions to reduce guesswork and to gain more substantial insight. If
necessary, import external knowledge from outside the environment.
Proxy Transport (context, Sect. 7) Design a ﬂexible proxy to integrate external
information into your interaction context. Map the external format (e.g., ﬁle
contents) onto the internal one (e.g., object ﬁelds). Use lazy loading and caching
to avoid high memory consumption and slow access times.
Simple Response (responses, Sect. 8) Every question has a simple response. Do not
hesitate but ask away; embrace trial-and-error. A text will appear, a picture will
render, a sound will play—tackle complexity with a combination of these.
Tangible Response (responses, Sect. 9) Directly engage (e.g., click on) a response
as it is represented on screen. The environment will respond to the “What is this?”
question with more details. Return to the particular conversation later on or spawn
new ones.
Pause and Explore (Sect. 10) Suspend an ongoing conversation if you need to
explore and understand details of a prior response. Start a new conversation to
learn more about these details. Later, resume the original response and make use
of your freshly gained knowledge to pose better questions.

2.3

You and Your Project

We are aware that readers of this chapter might not necessarily identify themselves
as programmers. Even though non-professional, end-user programming is widespread (Burnett & Myers, 2014; Ko et al., 2011), we think that exploratory programming affects devoted programmers more than hobbyists. The exploratory
mindset covers more than just “getting things done;” it entails an intensive relationship with the computational medium. Therefore, each pattern’s “You” addresses the
reader as if they were programmers who really want to dive into source code to make
their software project not just working but simple and inspiring.
The exploratory programmer—meaning you—might be working on a multitude
of programming tasks where conversations—and thus our pattern language—can
provide guidance. Besides hunting down bugs or designing new features, programmers also add commentary to source code or write tests for untested modules. They
might also teach new team members about existing system components directly
through the live system, which is more credible than talking about (dead) documentation. In all these cases, programmers engage with the programming environment
and its tools to investigate and learn.
Tool builders are programmers, too. While your project’s goal is mainly about
new software for your customers, there is other software to be built or adapted on
the side: the programming tools. In our pattern language, you will notice requirements for speciﬁc services that the programming environment has to provide. It is
the job of tool builders to ensure that there are tools in the environment that comply.

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If not, tool-using programmers cannot follow the particular pattern’s guidance. So,
when a problem force claims that “It is easy to ...” or the pattern structure dictates
that “The environment should ...,” tool builders have to take action. Note that every
programmer can take on the role of a builder, even ad-hoc during a task.

2.4

The Environment

This is your conversational partner on your exploratory journey:

The environment is an active entity because it offers various ways of interaction.
As a programmer, it helps you author program source code. As an explorer, it helps
you ﬁnd and connect relevant information. It is your tool shed because it offers many
services through interactive tools. You can use keyboard, mouse, or touch input to
express your goals. It will acknowledge your input and try to fulﬁll your expectations
through text, graphics, or sound.
The environment has one or more languages, which you must use during conversations. A language is not necessarily a programming language but sometimes
just a graphical interface with clickable, labeled buttons. Then, you must click the
right buttons to express your goals. In general, a language’s vocabulary is extensible
so that you can teach new phrases for a more efﬁcient communication.
There are other environments, each with its own boundaries. For one thing, your
environment might be embedded within a (parent) environment. Then again, that
parent might embed more environments besides yours, which makes them siblings.
Practical examples include operating systems, which host applications that may
complement each other’s use cases. Being aware of other environments helps you
overcome limitations in your knowledge and that of your environment. You can
always learn from things outside your own boundaries.

2.5

The Interaction Context

The environment has a knowledge base, which you will constantly probe and expand
during conversations: the interaction context. The context is the passive counterpart
to the active environment. It is separate from the language vocabulary and contains

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all kinds of domain-speciﬁc information. Within the environment’s boundaries,
information is represented in a common (transport) format. In a Smalltalk system,
everything is an object. In a Unix-based system, everything is a ﬁle. Thus, Smalltalk
workspaces talk about objects (and structured ﬁelds) while Unix shells talk about
ﬁles (and byte streams).
Accessing information from the interaction context is not as ﬂexible as using your
brain to think of something. The context is typically scoped to a particular tool. That
is, the information from one conversation is not necessarily accessible in another
one. Yet, there are means of combining such local scopes or even promoting them to
being globally available in the entire environment. Note that you can typically refer
to pieces of information in a (scoped) context by name. Examples include the
“environment” in a Unix shell or the “bindings dictionary” in a Smalltalk workspace.
To sum it up, your environment might have all the knowledge, but you have to ﬁnd
an appropriate context to access the information you are looking for.

3 Conversation in Context
Ask the environment one or more questions to better understand the context you are
currently working with. Iteratively revise each question until you are satisﬁed with
the environment’s response. In follow-up questions, refer to prior responses to make
your wording (and the responses) more expressive and precise.

3.1

A Desire for Exploration

In exploratory programming, conversations with the computational medium are
paramount. The environment represents a gateway to all digitally available information, which includes all kinds of software-related artifacts. Starting with a simple
question, the environment can help you learn more about your current opportunities
within your current task context. Even if your overall knowledge feels satisfactory,
exploring responses to concrete questions can reduce the risks of relying on false
hypotheses. If you are lucky, responses are provided without noticeable delay so that
you can follow up and establish a conversation that feels live and direct. At the end
of such an interaction, you will know more than before, and the environment might
also have learned something about your context-speciﬁc inquiries.
So, instead of asking yourself, you ask the environment. Provided that you are
working within a task-speciﬁc context, you might want to ask something about a
module’s interface or explore the structure of an artifact: Does the property X hold in
this context? How many kinds of X objects are there? What are the ﬁelds of X? How
does the service X work? Is this module X still functional? Responses can range
from simple Booleans, numbers, or texts to deeply structured information.

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Note that the actual representations of questions and responses are not captured in
this pattern. We assume that you want to engage with the environment in a direct
conversation to help you move forward in your programming task. There can be
many reasons for why you would want to keep up a conversation, such as unclear
wording in your prior question or confusing details in a prior response. Representations might be textual, visual, or even audible, which depends on your human–
computer interface.
Imagine that you read about the notion of DateAndTime in a piece of documentation. Now you want to learn more about it. By example, you reason like this:
“Today is Friday. The environment should know that. Let’s ask it.” Visually, the
conversation might have happened as follows:

First, you found a space on screen where the environment could listen to your
questions. Second, you posed several questions using the (programming) language
that the environment could understand. In this case, simple text-based representations of both questions and responses drove the conversation successfully. Finally,
you learned more about the interface of DateAndTime, which is knowledge that you
can now apply in your task (and program).

3.2

The Proﬁle

You want to ask the environment a question about something you have on your
mind, that is, within a context. You also know about the (textual or visual) language
that the environment can understand, that is, evaluate to present a notable response.
However, you cannot directly express your thoughts even though you have the
feeling that the environment has all the information it needs to answer your questions, to help you move forward.

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• It is easy to type, draw, or drop something using your input devices (e.g.,
keyboard, mouse, or touch screen).
• It is difﬁcult to ﬁnd a space where (1) you can focus on phrasing the question and
(2) the environment will (try to) respond.
• It is difﬁcult to ﬁnd the right words for a question the ﬁrst time.
• The environment’s responses can be too technical or verbose, thus not comprehensible immediately.
Therefore, you engage with the environment and establish a conversation to
learn more about the current context. First, you locate a space where the environment
waits for your direct input. Then you type, draw, or drop something that complies
with the environment’s language. After posing your question, you make a gesture to
let the environment evaluate your words and present a response. After your initial
interaction, you can establish a conversation through (revised) follow-up questions
to clarify misunderstandings or unpack complexity. Consequently, you can rely on
the environment to maintain a history of prior questions and responses. On the one
hand, you can easily revise your prior wording. On the other hand, you can precisely
refer to selected pieces of information you just acquired. Your goal is to continue this
conversation until you fully understand the environment’s response(s).

3.3

The Structure

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The environment should provide the following services:
• Screen space that is dedicated to direct user input (i.e., the questions) and the
presentation of results (i.e., the responses).
• Context that is accessible through the environment’s known language (and
vocabulary) such as identiﬁers (or names) of relevant software artifacts.
• History of prior questions that is automatically written with every new question
posed. The history’s contents are directly accessible for the user in a way that
allows for effortless revisions.
• History of prior responses that is automatically written with every new response
delivered. The history’s contents are directly accessible for the user in a way that
allows for references selected pieces in follow-up questions.
You should adhere to the following interaction (loop):
(A) Pose a new question by typing, drawing, or dropping something into the
dedicated space that the environment can understand.
(B) Optionally, access one of the prior questions to repeat or revise its wording.
(C) Optionally, access one of the prior responses to integrate references to its
contents into your next question.
(D) Finish your input with a dedicated gesture, let the environment evaluate your
question and present a response, and then explore that response.

3.4

Consequences

Direct conversations with the environment reduce the need for taking ofﬂine notes.
Both histories of questions and responses document your thoughts and can thus help
you remember them later in the process. Plus, the notes you would take outside the
environment would be detached from what actually goes on in your project, challenging to synchronize. Being immersed within your environment, instead, you can
easily build up trust in your knowledge and your decisions.
A conversation can only be as effective as its representations for questions and
responses. For example, if you cannot express your thoughts in text, you might wish
to draw a shape instead. Yet, the environment’s language might not support a visual
syntax. At worst, inappropriate representations can be misleading and thus timeconsuming.
The environment’s response might be “rich” and interactive in the sense that it
promotes you to a different tool (window) and experience. In a similar fashion, your
questions might also be formed through an elaborate interaction using keyboard,
mouse, or touch. That is, even though there are many text-based command-line
interfaces following the “Conversation in Context” pattern, you might also be able to
establish conversations through a graphical tool’s interactive widgets.

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The environment beneﬁts from an efﬁcient window manager to help you organize
your questions and responses on the screen. Screen space is limited, and you might
struggle ﬁnding a “quiet place” where you can focus on your next conversation.
Multiple other conversations might also be in progress at the same time. The
integration of multiple tools (and windows) might become an issue. Otherwise,
you have to repeat your query in every new conversation, risking mistakes on
the way.
The environment should respond as fast as possible; it should create a feeling of
liveness during conversations. Otherwise, there is a chance of you losing interest in
engaging with the environment in the ﬁrst place. To avoid unnecessary workload in
the environment, you ﬁnish your question with an extra gesture. Yet, unforeseen
complications might increase the time before you can receive and read a response.
Maybe you even want a pause button to be able to further explore the environment’s
current progress to then cancel and revise your question.
The history of prior questions and responses might consume many resources, thus
possibly interfering with your other tasks. You might not be able to decide when to
discard older information. You might not even know about the actual footprint of a
question or response. So, the environment should probably ﬁgure out a way to
discard deprecated information and compress redundant details to save resources.
It might be able to inform you about the most demanding artifacts to help you make
critical decisions.

3.5

Related Patterns

This pattern is the umbrella for all other patterns in this chapter:
• The questions in a conversation are covered in “Elaborate Inquiry” and “Coach
Your Environment.”
• The context that the environment needs to evaluate each question is covered in
“Concept in Shards” and “Proxy Transport.”
• The responses that the environment produces is covered in “Simple Response”
and “Tangible Response.”
The ﬁnal pattern “Pause and Explore” complements the liveness and ﬂow (that
we strive for in each conversation) with the importance to manage conversations at a
higher level and be able to reﬂect on every little detail.

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4 Elaborate Inquiry
Decompose the topic of your conversation into parts. Then compose your central
question from multiple sub-questions according to those parts. Let the environment
manage the topic’s complexity to reduce your cognitive load and hence the risk of
making mistakes.

4.1

A Desire for Exploration

Simple questions are good conversation starters. You learn about the environment’s
vocabulary and can easily browse the current interaction context. Soon you begin to
wonder whether it is possible to combine selected pieces of information, that is, prior
questions or responses. There are some operations that you could do in your head or
on a piece of paper such as picking the longest entry from a longer list, sorting a
shorter list alphabetically, or removing all odd numbers from a list. However, all of
these operations are tedious and error-prone, especially if done repeatedly and
manually. And your exploratory journey has just begun. Many interesting artifacts
might lie ahead, waiting to be explored. Computers are generally good at sorting,
ﬁltering, and transforming millions of data points repeatedly and efﬁciently. You just
have to apply the environment’s means of combination and abstraction.
We assume that the environment’s language is either a general-purpose or
domain-speciﬁc programming language that allows for combining simple expressions. Such a language does not have to be textual but usually is. Provided that you
are working in a Unix shell, you can easily combine small (ﬁlter) programs through
pipes:
curl https://en.wikipedia.org/wiki/Unix \
| grep -o -P ’href="/wiki/.*?"’ | sort | uniq \
| sed -r ’s/href="(.+)"/http:\/\/en.wikipedia.org\1/g’ \
> urls.txt

This program fetches a website, extracts URLs from its contents in a sorted and
unique fashion, and ﬁnally writes everything into the urls.txt ﬁle. You can now use
this “Elaborate Inquiry” to continue exploration at a higher level, that is, for
extracting URLs from arbitrary websites.
A language’s means of abstraction can help you wrap complex questions (i.e., the
combinations) into modules (e.g., functions) which you can then refer to by name in
follow-up questions. For example, think about the beginning of the Fibonacci
sequence: 1, 1, 2, 3. You want to know the tenth number but not calculate it by
hand. So, you begin a conversation and describe the underlying rule of the sequence,
which is “Add the two prior numbers to get the next one.” Visually, the conversation
might have happened as follows:

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First, you wrote a little reminder about what you want to discuss here. Then you
designed the underlying rule as a recursive function, which you labeled “ﬁb.” You
tried out the function multiple times until you got it right. Finally, you collected the
sequence up to ten and found your deﬁnitive answer: 55. Given that function, you
asked for the 100th number, but after waiting for a while you decided to cancel the
computation without getting a response.

4.2

The Proﬁle

You are in the middle of a “Conversation in Context.” It turns out that the topic you
are interested in has several aspects with rather complex relationships. You notice a
substantial risk of making mistakes, relying on guesswork.
However, you struggle with comprehending the environment’s responses
because their complexity outranks your questions’ simplicity. Follow-up questions
only increase your cognitive load because you start to combine facts in your head
rather than letting the environment combine the facts for you.
•
•
•
•
•

It is easy to ask follow-up questions that refer to prior responses.
Such follow-up questions beneﬁt from a good understanding of prior responses.
It is difﬁcult to manually interpret complex responses.
Mistakes in understanding can infect follow-up questions.
It is difﬁcult to express your complex thoughts using primitive vocabulary.

Therefore, you adhere to a modular approach when (com-)posing your (followup) questions. At best, the ﬁnal revision of your central question will satisfy your
entire informational need. First, you decompose the topic of your conversation
into—more or less isolated—parts. Second, you compose your question from
sub-questions, each addressing one important part. The combination of
sub-questions will naturally process the environment’s intermediate responses.
Consequently, the manual, error-prone act of posing multiple follow-up question
will become an automated, robust one. The environment will immediately tell you
when you make a mistake. Eventually, you will feel like an author who can safely

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prepare the script for a play, rather than a person who is trapped in the middle of a
heated discussion.

4.3

The Structure

Considering a common trichotomy for programming language design (Abelson
et al., 1996, pp. 4–31), the environment’s language should have the following
properties:
• Primitive expressions that allow for posing simple questions and a stepwise
unpacking of complex responses.
• Means of combination that allow for composing multiple (sub-)questions to
automatically integrate intermediate responses.
• Means of abstraction that allow for encapsulating recurrent parts of the conversation to further reduce cognitive load and the risk of making mistakes.
You should make use of the language’s properties as follows:
1. Begin your conversation with simple questions using only basic vocabulary.
2. Employ means of combination as soon as you need to refer to multiple (prior)
responses in a single follow-up question.
3. Employ means of abstraction to avoid repetition and verbosity in your questions.
4. As you learn more about the topic of your conversation along the way, decompose important parts into sub-questions and compose follow-up questions from
these.
5. Your ﬁnal question should encompass everything you want to get out of the
current conversation.

4.4

Consequences

If you can manage to reduce the conversation to a single (or few) composite question
(s), your cognitive load will be reduced, too, because the environment can process
even millions of data points without making any mistake. Yet, you have to be sure to

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ask the correct questions or you will be faced with millions of misleading results.
Overall, an “Elaborate Inquiry” is more prone to errors than a couple of simple
(r) questions.
While the environment can be optimized to ensure quick response times for
simple questions using only standard vocabulary, complex questions might take
longer to process. Without taking extra care, your “Elaborate Inquiry” can easily
include operations whose time-to-process grows exponentially. A progress indication can help you assess the impact of your question, but then you need to be able to
pause or cancel the computation to ﬁx your mistake.

4.5

Related Patterns

Especially if you want to “Coach Your Environment,” following “Elaborate Inquiry”
can help you author side effects.

5 Coach Your Environment
Extend the environment’s knowledge by adding information to the current interaction context. Extend its language vocabulary by adding tools for reference in followup questions. Both kinds of additions can either come from your memory (and
experience) or be loaded from external resources.

5.1

A Desire for Exploration

Think about a conversation with another person. There, you can give instructions
like “Imagine that you are visiting a foreign city.” You can keep on describing the
situation like “Now your smartphone stops working.” And ﬁnally, you can ask
questions like “How would you ﬁnd the next good Italian restaurant?” The other
person is able to learn about (imaginary) constraints to then reason within those
constraints while answering questions. Your programming environment can do that,
too. You just have to coach it.
Your questions can be as expressive as the environment’s vocabulary. Thus, if
you install new tools, that vocabulary can grow. For example, there are tools for
static and dynamic analysis that allow for very speciﬁc questions about your
system’s modular structure.
The environment’s responses are only as informative as the data they are based
on. Thus, if you load more information from external sources into the current
interaction context, the same questions can yield more meaningful responses. For

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example, if you ask questions about photographs (e.g., number of recognized faces),
having more photos available will increase the responses’ quality.
You can also coach your environment by writing prototypical programs. In
object-oriented systems, programmers often explore the interfaces of objects. They
instantiate classes, send messages to the instances, and observe the (side) effects. It is
still a conversation with the environment, but with a focus on selected objects in
particular. Side effects play an important role when “setting up the scene” before any
meaningful question can be asked.
Provided that you wanted to learn more about event handling, you prepared the
scene as follows:

First, you instantiated a Morph (i.e., a basic shape) and positioned it on the screen
(here: the world). Then you conﬁgured an event handler to react on mouse clicks.
The handler will either log information on the Transcript (here: event printString) or
change the Morph to a random color (here: Color random). Clicking this prototypical
button is still part of the conversation about understanding event handling. Every
click is a question, the change of color a response. You taught the environment how
to do this.

5.2

The Proﬁle

You are in the middle of a “Conversation in Context.” You repeatedly question the
quality of the environment’s responses. You get the feeling that the environment’s
knowledge about the current topic is too limited.
However, you cannot think of a better way to phrase your questions so that the
environment might provide a satisfactory response.
•
•
•
•

It is difﬁcult to know everything about a topic.
It is easy to learn something new during a conversation.
Your questions must use the vocabulary from the environment’s language.
The quality of responses depends on the environment’s knowledge (i.e., current
interaction context).
• Both language/vocabulary and knowledge/context can be extended during
conversations.

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Therefore, you tell the environment to remember new things so that it can react
differently to your follow-up questions. Technically speaking, your interactions with
the environment should have side effects. You can add information to the current
context by writing down what you know in a primitive form (e.g., text, numbers,
records). You can also tell the environment to load external information from outside
its boundaries (e.g., host ﬁle system, network storage, Internet/Web content).
Considering the environment’s language, you can add new vocabulary like you
would in an “Elaborate Inquiry” using, for example, labels (or names) for new
classes/methods or the information you just loaded into the interaction context.
Note that you can also load external tools into the environment, which usually
bring along additional vocabulary as well. This extra vocabulary can help you
make your follow-up questions more precise.

5.3

The Structure

The environment should provide the following services:
• Extensible Context, which is the common representation of knowledge in the
environment (e.g., ﬁles, objects, tables); thus, it should remain open for ad-hoc
additions during your exploratory journey.
• Extensible Vocabulary, which is your primary source of “building blocks” for
posing (or revising) questions; thus, it should remain open for ad-hoc additions
during the conversations with the environment.
• Interface for Imports, which allows for loading both machine-readable and
directly/manually entered information from outside the environment.
You are responsible for coaching your environment in time:
• Type or draw the facts you already know directly from your memory to then build
upon that knowledge in follow-up questions.
• Investigate the world outside the environment to learn about relevant external
information.
• Be precise: do not load everything you ﬁnd directly into your environment but
consider the relevance of your current “Concept in Shards.”
• Be thoughtful: construct a “Proxy Transport” for data that is hardly reachable or
memory-heavy.

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Consequences

If you are not careful, you can break the environment’s current context. For example,
you can get punished for your coaching if you overwrite existing knowledge with
useless (or wrong) facts. Follow-up questions will then yield misleading results. You
can also tamper with the language vocabulary in the sense that once meaningful
questions cannot be processed anymore. Fortunately, many environments support
“undoing” your latest mistakes to then continue the conversation in a fruitful
manner.
The state of having too limited knowledge can quickly turn into an oversupply of
information. It can then be difﬁcult to narrow down a question to get a response that
you can actually understand. Some external sources might be of poor quality, which
will further increase your cognitive effort because you will constantly have to
scrutinize the value of the environment’s responses. Having no information on a
topic is easy to check, an oversupply, however, time-consuming to explore.
Your questions might head into the wrong direction, which might be why the
environment seems to have no valuable responses for you. Instead of loading new
external information, you might want to backtrack and double-check the point where
your environment became less helpful. Maybe you made a mistake earlier that needs
to be corrected.

5.5

Related Patterns

Both “Coach Your Environment” and “Elaborate Inquiry” are close friends.

6 Concept in Shards
Relevant information might be scattered all over the environment. Do not be satisﬁed
with the ﬁrst useful response. Keep asking questions to reduce guesswork and to
gain more substantial insight. If necessary, import external knowledge from outside
the environment.

6.1

A Desire for Exploration

The symbolic debugger is a conversational tool that manages to connect pieces of
information that can be otherwise scattered across modular boundaries (e.g., packages, classes, methods). Debuggers interrupt the running system and offer a conversation about the system’s dynamic control ﬂow—a snapshot that entails many

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concrete details about abstract code. While details can be overwhelming at times,
programmers have the chance to explore ﬁrst-hand, reliable facts if they manage to
persevere. Here is a typical interface of a debugger in an object-oriented
environment:

However, it can be tempting to prematurely jump to conclusions. Exploring the
control ﬂow step-by-step is not without efforts. Programmers have to collect and
assess the pieces they ﬁnd. Thus, the conversation might become cumbersome,
depending on how the debugging tool helps you manage your ﬁndings. Sometimes,
you can only go forward and not backward, which is problematic when exploring
systems that have many (stateful) side effects. Sometimes, you cannot restart
(or repeat) the conversation if the circumstances (i.e., the system state) are difﬁcult
to recreate. Therefore, you should reﬂect on the value of every debugging situation
that you encounter. Do not give up too soon.

6.2

The Proﬁle

You are in the middle of a “Conversation in Context.” You just got the ﬁrst response
that has information you are looking for.
However, you notice that several important details of the conversation’s topic are
still unclear or missing. You wonder whether you should start to “Coach Your
Environment” about what you know from your own experience. But then you
remember:
• The environment’s knowledge (i.e., current interaction context) encompasses
many (software) artifacts.
• A concept (or topic) might reach across artifact (or module) boundaries.
• It is easy to ask follow-up questions.
• It is possible to combine multiple responses in an “Elaborate Inquiry.”
Therefore, you keep on having this conversation to collect more details. The
concept you are talking about lies “in shards” virtually in front of you. You just have

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to pick up each individual piece to appreciate its value. Do not stop at the ﬁrst piece
but reason over a comprehensive amount of information. While you might not be
able to collect all shards of a concept, the relevant fraction will bring you further.
Note that there might be relevant knowledge located outside your environment.
Therefore, you might have to “Coach Your Environment,” after all, in how to access
the external knowledge.

6.3

The Structure

The environment should provide the following services:
• Means of Collection that allow you to manage “the relevant whole” you have
identiﬁed so far.
• Means of Comparison that allow you to quickly determine the added value for
each new piece you ﬁnd.
• Means of Loading that allow you to load external information from outside the
environment.
You should design your exploratory journey as follows:
1. Stay nosy: keep asking questions and do not be satisﬁed with the ﬁrst promising
response.
2. Finish the conversation when it becomes tricky to ﬁnd anything new and relevant,
yet do not give up too early.
3. Do not worry: you can often bring up the same topic later in a different
conversation.

6.4

Consequences

When collecting pieces of information, you rely on efﬁcient window management
since each piece might come with a dedicated view that cannot be embedded into the
conversation directly. When you then “Pause and Explore” such views, the extra

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conversations could derail your train of thought. You have to be extra careful not to
lose focus.
When comparing pieces of information, it can be challenging to assess the quality
of similar pieces coming from different (external) sources. If you accumulate too
much similar information in your conversation’s interaction context, follow-up
questions can become tricky to pose or their responses cumbersome to handle.
When loading external information into the environment, you can face consequences similar to the ones of “Coach You Environment.” An oversupply of
information can noticeably increase your cognitive effort.

6.5

Related Patterns

While you “Coach Your Environment” to reach a shard outside the environment, a
“Proxy Transport” can help you reduce memory consumption within the environment. An “Elaborate Inquiry” can help you connect all relationships in a
compact form.

7 Proxy Transport
Design a ﬂexible proxy to integrate external information into your interaction
context. Map the external format (e.g., ﬁle contents) onto the internal one (e.g.,
object ﬁelds). Use lazy loading and caching to avoid high memory consumption and
slow access times.

7.1

A Desire for Exploration

The environment’s knowledge is limited. You can surely talk about all the code
artifacts that you have created. After all, code management is among the primary
features of the environment’s tools. However, there is much information only
indirectly related to the software system under construction: tickets in a bug tracker,
emails in your inbox, or documents in a forum. Chances are that your environment
does not manage all these artifacts itself but that other environments take care of it.
There are two main reasons why you should not just copy external knowledge
into your environment. First, its actuality can quickly be compromised if outside
tools keep on updating the information without informing your copy. Second, your
resources and your environment’s resources are limited. It can take a while to copy
data from external environments, which can conﬂict with the time you estimated for
your current task. At some point, your local machine might be out of memory, or the
tools need what seems like an eternity to provide meaningful results. Consequently,

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you have to make external information accessible to be selectively loaded on-the-ﬂy
and cached efﬁciently.
Think about a collection of documents persisted as PDF ﬁles on your disk. During
the last months, you collected several ﬁles and also added much commentary to their
contents. Now you want to explore and process this information from within your
environment. You have to map the ﬁles and its structured contents to objects and
ﬁelds, which your environment can understand. You might also want to edit selected
commentary but keep everything stored in external ﬁles to let external tools work on
those, too. Eventually, you create a Report proxy that is able to read and write PDF
ﬁles:

You can now have conversations in the same way as you would about artifacts
stored inside the environment. Yet, if the external ﬁles get moved without notice, the
ﬂow in your conversation will break. You have to make the trade-off between
availability and efﬁciency.

7.2

The Proﬁle

You are in the middle of a “Conversation in Context.” You want to “Coach Your
Environment” to load external information, maybe because your central “Concept in
Shards” pointed to a shard outside the environment.
However, you realize that the external information sources you selected are
rather demanding in terms of access time and/or memory consumption.
• It is easy to load external information into the current interaction context.
• The environment relies on the context’s common format (e.g., objects, ﬁles,
tables).
• It is easy to map an external format (e.g., ﬁles) onto the context’s format (e.g.,
objects).
• It is difﬁcult to ﬁnd a good mapping, meaning, one that ﬁts your conversation’s
needs (e.g., ﬁle’s contents to object’s ﬁelds).

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Therefore, you “Coach Your Environment” to make that external information
available through proxies as part of the current interaction context. A proxy is an
informational artifact that has special properties:
• It is transparent, which makes it look like normal information stored within the
environment.
• It provides lazy loading, which only loads the pieces required to process a speciﬁc
question to avoid high memory consumption.
• It provides caching, which stores pieces required for one question to be used in
another one to avoid slow access/response times.
• It remains adaptable so that you can revise its mapping rules as you revise your
questions.

7.3

The Structure

The environment should provide the following services:
• Means for “Proxiﬁcation,” which should be a convenient way for you to “Coach
Your Environment” about the useful but costly information outside.
• Interface for Imports, which is constantly accessed “behind the curtains” when
working with the proxy artifact in conversations. Examples include ﬁle handles,
database sessions, and network sockets.
• Means for Debugging, which is needed when a proxy artifact disturbs the ﬂow in
your conversation. The quality of external resources (availability/reliability) is
always hard to control from within the environment. The proxy abstraction might
“leak.”
You should work with external information the same way you do with internal
information:
• “Coach Your Environment” to load external information but consider its costs
(e.g., access time, memory consumption).
• Stay focused: try to ignore the special characteristics of proxies in your conversations, even if you know about it.

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• Be ﬂexible: if a proxy “leaks,” plug it and quickly return to your
conversational ﬂow.
• If you misjudged the costs, turn the proxy into a fully loaded artifact of the current
interaction context.

7.4

Consequences

The biggest issue with proxies is a leaky abstraction in the middle of a conversation.
For example, if you talk about an artifact that you did not know was a proxy, a
sudden increase of response times—because an external resource became
unavailable—can break the entire ﬂow of that conversation. You can then either
choose to become a tool builder to ﬁx it or you can exclude that artifact from your
conversation and risk guesswork.
The second biggest issue is “proxiﬁcation,” because in many environments you
have to construct a proxy transport for external artifacts yourself. It is typically not a
single button click but involves actual programming effort. Depending on the quality
of the external source, you might risk not having a caching strategy to be able to
quickly return to your conversation. However, a leaking proxy might bring you back
to the construction site more quickly than you want. Eventually, programmers often
try to bring external information closer to the environment if it cannot be loaded
entirely into the environment. For example, information from the Web can be cached
in a database on your computer. Your environment can then access that (still
external) database without any noticeable lag of a slow network connection.

7.5

Related Patterns

You will have to “Coach Your Environment” to set up the proxies for external
information, which can itself be part of your conversation’s central “Concept in
Shards.” Note that the Proxy pattern is common in object-oriented software design
(Gamma et al., 1995), which provides implementation guidelines for tool builders.

8 Simple Response
Every question has a simple response. Do not hesitate but ask away; embrace trialand-error. A text will appear, a picture will render, a sound will play—tackle
complexity with a combination of these.

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8.1

135

A Desire for Exploration

Every screen cluttered with a multitude of windows, each one ﬁlled with plenty of
information, is made of simpler, comprehensible elements. Not just the windows
(or views) themselves, but also their contents are made of smaller pieces. Color,
shape, animation, and interaction help users to distinguish a single piece from its
surroundings. We know what buttons look like and that we can click on them. We
know that textual labels in a list view can be selected. We know that such labels
represent abstract handles to more structured information. Think of your address
book as an example with all its details about your friends. Of course, at some point,
we had to learn about these elements, but now we know and expect certain things
from a graphical interface.
During conversations, the environment will give you responses that look rather
simple, even if they entail more complexity. In object-oriented systems, for example,
every object has a print-string, which is a textual representation of its most prominent (and identifying) features. That print-string is used in many tools to provide
you with familiarity during exploration. You will read it and might think: “Ah! I
know this object from before.” Therefore, simple responses will keep you oriented
and focused during conversations.
However, simple responses can be misleading. If you forgot to “Coach Your
Environment” about the identifying features of a new kind of domain object, the
environment can only present generic features. Take the following list of fruit
objects as an example. On the left, there is only a generic representation, which is
difﬁcult to explore. In the middle, the kind of fruit got exposed, which made you
realize that a Tomato might be in the wrong class. On the right, you see a more visual
take on each object’s features:

Consequently, it is your responsibility to “Coach Your Environment” about an
appropriate representation of informational artifacts throughout the conversation.
Only then can you ﬁnd your path through cluttered screens and be successful on your
exploratory journey.

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Remember the Visual Information Seeking Mantra (Shneiderman, 1996): overview ﬁrst, zoom and ﬁlter, details on demand. In the end, you browse and explore
through a multitude of simple responses. Complex structures are only composites.

8.2

The Proﬁle

You are about to start a “Conversation in Context.” A couple of possible ﬁrst
questions begin to form in your mind.
However, you cannot decide on a good ﬁrst question. You fear that the environment’s response might overwhelm you with details you are not yet ready for.
•
•
•
•

It is difﬁcult to get a satisfactory response on the ﬁrst try.
The environment will not punish you for bad questions.
It is easy to revise your wording in follow-up questions.
The environment will provide some response.

Therefore, you do ask away your ﬁrst question and wait for the response. The
environment will present a piece of information from the current interaction context
in a simple form. All artifacts have some kind of textual label, a name if you will.
Such names help you identify and compare different pieces of information. Naturally, texts will be represented as themselves and so will pictures. Sounds are audible
through your speakers. All these simple forms help you assess the quality of your
question.
Note that if your question does not make sense to the environment, it will
complain in a simple manner, too. Usually, you will be instructed to change your
wording or explain the unknown vocabulary. Embrace simplicity; fail early to move
on quickly.

8.3

The Structure

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137

The environment should provide the following services:
• Acknowledge any user input, even if malformed.
• Keep it simple: show text, render pictures, play sound or music.
• Wrap complex data structures into simple representations for a pleasant ﬁrst
encounter with the user.
You should embrace the fact that the environment always listens:
• Embrace trial-and-error during conversations.
• Try asking for alternative representations.
• Ask for more details when struggling with unclear (or shallow) representations.

8.4

Consequences

In object-oriented systems, objects with no real-world counterpart can be difﬁcult to
represent in a meaningful, simple way. For example, people have names and
photographs have colors. Even network sockets have addresses. All these properties
can simply be shown on screen to identify the objects behind them. However, purely
artiﬁcial objects such as observers (Gamma et al., 1995, p. 293) and mediators
(Gamma et al., 1995, p. 273) merely have their class name and maybe a location
in memory to distinguish individual instances. During conversations, the simple/
generic representation of such artiﬁcial objects can be cumbersome to use (e.g.,
anObserver(1234) and anObserver(5678)).
Users might have accessibility issues with speciﬁc on-screen contents due to
diminished vision or hearing. Especially an environment that is crowded with
information—and multiple ongoing conversations—can be challenging for all
kinds of users. The legibility of text might suffer, some colors in pictures might be
hard to see, a speciﬁc sound might not be audible in a noisy setting.
You might want to change the representation of some artifacts during a conversation, but only for that conversation. For example, while the name of a person might
not matter its favorite food does. You therefore “Coach Your Environment” to
represent people differently. However, such changes must be scoped to the task at
hand so as not to interfere with other conversations or your generic perspective on
the domain artifact. We designed the Vivide environment (Taeumel, 2020) for
exactly that purpose.

8.5

Related Patterns

Every “Simple Response” has a non-simple counterpart with a more technical
structure. Thus, each response should be a “Tangible Response” since its simple
representation usually hides many details that you might want to explore.

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9 Tangible Response
Directly engage (e.g., click on) a response as it is represented on screen. The
environment will respond to the “What is this?” question with more details. Return
to the particular conversation later on or spawn new ones.

9.1

A Desire for Exploration

During conversations, you and the environment will ﬁll the screen with (visual)
information. Your questions take space, and so do the environment’s responses.
Therefore, you have to keep track of the meaning of all those visuals. Yet, occasionally, the environment will show you something that you do not quite understand
(or have already forgotten about). Then, the follow-up question is simple: “What
is that?” You can use touch or mouse input to point at something; you can also use
the keyboard to move a cursor to indicate curiosity. In any case, you want to look
“behind the pixels” on screen. A textual label might hide details about an important
artifact. A graphical composite might be connected to data as well.
In the Web, documents offer clickable (hyper-)links. We have gotten used to
hovering over and clicking on anything colorful and suspicious. Yet, your programming environment can offer more tangibility than plain web browsers do. It can help
you deconstruct and explore everything you see. The following examples indicate
tangible interactions during conversations:

On the left, you told the environment to create and show a button that is now
sitting on the screen, waiting to be clicked on. In the middle, you discovered a special
gesture to open a halo (Maloney & Smith, 1995) around a window to take a look at
its composite structure. On the right, you found a link in a text ﬁeld that you can click
on to explore more information. Overall, your environment can help you explore and
understand everything it knows and shows.

A Pattern Language of an Exploratory Programming Workspace

9.2

139

The Proﬁle

You are in the middle of a “Conversation in Context.” You just got an interesting
“Simple Response” and want to learn more about its details.
However, you cannot ﬁgure out how to ask the environment about details without
knowing what lies behind that simple form of the latest response. You are not sure
whether there is any vocabulary to express your intent.
• It is easy to touch or click on something that is visible on screen.
• It is more difﬁcult (but possible) to indirectly reference prior responses in a
follow-up question.
• A response can be a (generic) question on its own: what is this?
Therefore, you directly engage (i.e., click/touch/activate) with the response as it
is represented on screen (Hutchins et al., 1985). The environment will treat this
interaction as a generic “What is this?” question, whose response will be more
detailed and informative, yet keeping it simple in its parts. You then “Pause and
Explore” until you are ready for your next follow-up question. You might already be
satisﬁed and ﬁnish the conversation. You might also have many new different
questions and thus spawn several new conversations. Note that the “Conversation
in Context” maintains a history of prior responses so that you can engage with any of
them later on as well.

9.3

The Structure

The environment should provide the following services:
• Provide visual cues that foster the direct engagement with an on-screen representation (e.g., text color).
• Help the user organize exploration paths for deeply structured artifacts.
• Keep track of any sideline conversations that emerge during exploration.
You should hover over, click on, or touch visual cues:
1. Treat any response as a tangible entity that you can engage with.
2. Effectively work with a multitude of simple forms but do not forget to probe
details so as not to rely on guesswork.

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M. Taeumel et al.

Consequences

Direct manipulation for exploration needs dedicated gestures such as mouse clicks.
Yet, graphical, interactive objects already need many gestures for normal interactions. Thus, for tool builders, the design challenge is to ﬁnd a trade-off between
extra/hidden/overlapping gestures to integrate exploratory and normal use. A special
exploration mode can help to temporarily free reserved gestures from such objects.
Visual clutter affects the size of click targets, which can lead to input errors. It
might be easier to indirectly reference a prior response in a follow-up question than
to interact with it, even if it is visible on screen. Any “Conversation in Context”
should reserve enough screen space for you to compose your question. Even if
graphical objects get occluded, you can rely on programmatic references such as
variable names.

9.5

Related Patterns

The direct inspection of a response leads to “Pause and Explore” and thus another
conversation about the response’s details.

Pause and Explore

Suspend an ongoing conversation if you need to explore and understand details of a
prior response. Start a new conversation to learn more about these details. Later,
resume the original response and make use of your freshly gained knowledge to pose
better questions.

10.1

A Desire for Exploration

A single conversation will probably not satisfy all the information needs that arise
during an exploratory programming task. Chances are that you hit a block in one
conversation that can only be resolved by approaching the topic from a different
perspective in another conversation. The environment has several specialized tools
that each offer their own conversational interactions. Therefore, you keep on
jumping between tools to collect all their insights. The environment’s window
manager helps you organize screen contents and thus all open conversations:

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141

Note that, in the process, you can only focus on a single conversation at a time
(Miyata & Norman, 1986). Also, resuming suspended conversations takes extra time
(Parnin & Rugaber, 2011). On the bright side, you can ask very precise questions
through specialized tools, whose constraints allow them to also provide very speciﬁc
responses. After all, your exploratory journey beneﬁts from a rich tool set and your
ability to select and integrate appropriate tools as you go.

10.2

The Proﬁle

You are in the middle of a “Conversation in Context.” You just exploited a
“Tangible Response” and the environment presented many new details you now
want to dive into because they seem to be a crucial part of your current conversation.
You proceed to start another “Conversation in Context.”
However, you get the feeling that the new information becomes outdated while
you are studying it. You feel in a hurry to get back to your original conversation. But
then you remember:
• The ﬂow in a conversation will stagnate if you cannot understand crucial information. The risk of mistakes through guesswork will rise.
• Every sample in a pool of information is valuable; pieces allow for extrapolating
to the whole concept.
• It is easy to suspend and later resume an ongoing conversation.
• It is easy to spawn new (sub-)conversations to learn more about crucial aspects
of/for other conversations.
Therefore, you take a deep breath and dive into the new information you have at
hand. You start a new “Conversation in Context” to ask different questions. You
suspend the original conversation in the sense that it will be easy for you to resume it
later on. The environment does not bother; it can wait and help you shift focus to
your new topic on interest.
Note that highly dynamic (and live) systems exhibit quickly changing interaction
contexts during conversations. Every response might only capture a single snapshot
in time for you to explore. It might be difﬁcult to generalize from a few snapshots.

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Luckily, it is possible to represent such liveness as steady frames (Hancock, 2003),
which can serve as a more comprehensive topic to talk about with your environment.

10.3

The Structure

The environment should provide the following services:
• Manage to have multiple ongoing conversations at the same time.
• Keep track of topic(s) per conversation.
• Support switching focus between conversations; suspension and resumption
should feel effortless.
You should acquire the ability to work with exploratory conversations at a higher
level:
• Start a new conversation if you want to dive into the details of a prior response in
an ongoing conversation.
• Close (or archive) conversations that you see ﬁnished. New conversations can
always tackle the same topic(s).

10.4

Consequences

Efﬁcient conversation management relies on a good window manager, just like when
collecting your “Concept in Shards.” Too many (overlapping) windows will clutter
the screen and thus affect click targets of a “Tangible Response.” Overall, you might
lose your focus and forget about which conversation was spawned because of which
other conversation.
You might want to merge two conversations if you realize they lead you to
complementary insights. Technically speaking, you might want to merge two
(scoped) interaction contexts to then continue your conversation based on a comprehensive knowledge base.

A Pattern Language of an Exploratory Programming Workspace

10.5

143

Related Patterns

Every time you take a break from one conversation to explore data in the context of
another one, you will ﬁnd you way back to a “Conversation in Context” and with it
all patterns that come along.

Conclusion

We presented the pattern language of an exploratory programming workspace,
which captures the conversational style of workspace-like tools such as Smalltalk
workspaces, Unix shells, and scientiﬁc notebooks for computation. Each pattern in
the language offers guidance for programmers as tool users and tool builders. We
are aware that there might not be a single implementation out there that covers each
and every pattern perfectly. Looking at the consequences we discussed earlier,
there can be many practical trade-offs involved in your favorite programming
environment. For example, direct manipulation of a “Tangible Response” might
be limited in a Unix shell but rather rich in Squeak’s Morphic (Taeumel &
Hirschfeld, 2016). Programmers have to cope with such limitations on a daily
basis.
The impact of a tool builder can be crucial to the success of a project. At best,
there are dedicated programmers that have the resources to continuously evolve the
tools their colleagues are using. Sometimes, however, it can be beneﬁcial to just
realize an idea yourself directly during the programming task without having to talk
about it. There are environments that support evolving the tools directly during use
such as Squeak/Smalltalk (Ingalls et al., 1997) and Lively/JavaScript (Lincke et al.,
2017). Our pattern language can help programmers focus during such construction
efforts.
In the future, we want to connect this chapter’s patterns to more different
real-world projects, tools, and environments. Following traditional patterns of
object-oriented design (Gamma et al., 1995), implementation sketches might
help tool builders to better understand common pitfalls early on. We think that
this pattern language will grow since we already identiﬁed more aspects to
talk about such as “Surprise Response” and “Conversations Re-used.” Change
is omnipresent in the lifetime of a pattern as it gets applied and revised along
the way.
Acknowledgements We gratefully acknowledge the ﬁnancial support of the HPI Research School
on Service-oriented Systems Engineering (www.hpi.de/en/research/research-schools) and the
Hasso Plattner Design Thinking Research Program (www.hpi.de/en/dtrp).

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Practice-to-Research: Translating
Company Phenomena into Empirical
Research
Innovation Behavior in Turbulent Times
Lena Mayer, Katharina Hölzle, Karen von Schmieden, Reem Refaie,
Hanadi Traifeh, and Christoph Meinel

Abstract Doing relevant and rigorous research often leads to a research-to-practice
and practice-to-research gap, which both involved parties need to actively address
and bridge. All parties involved face the challenges presented in the steps of
identifying practice- and science-relevant research questions. This begins with the
translation of these questions into a practice-feasible, but also empirically-based,
research design and is followed by the retranslation of observations into scientiﬁc
language to assess and interpret the observed phenomena. In this chapter, we report
on our research approach to move from innovation practitioners’ narratives to a
quantitative study design. We translate what we have learned from employees about
innovation activities, behaviors, and structures, into a study design to measure
employees’ innovative behaviors overall in a large quantitative study. The resulting
empirical study assesses the link between employees’ Job Insecurity and Innovation
Behavior as well as three assumed moderating effects (Organizational Support,
Participative Decision-Making, Job Autonomy).

L. Mayer (*) · K. Hölzle · K. von Schmieden · R. Refaie · H. Traifeh
HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam,
Germany
e-mail: Lena.Mayer@hpi.de; Katharina.Hoelzle@hpi.de; Karen.Schmieden@hpi.de;
Reem.AbouRefaie@hpi.de; hanadi.traifeh@hpi.de
C. Meinel
Hasso Plattner Institute and Digital Engineering Faculty, University of Potsdam, Potsdam,
Germany
e-mail: Christoph.Meinel@hpi.de
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_8

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1 Introduction
The basis for this chapter is the lead author’s doctoral research project with an
industry partner,1 which we have kept anonymous for reasons of conﬁdentiality. The
company’s headquarters is located in Germany. It employs more than 110,000
people worldwide, with approximately 39,000 employees working at the headquarters itself. We describe how employees shared stories and observations with the
research group and how these narratives led to the empirical design of a survey to
assess employees’ innovation behavior during turbulent times. Shortly after the start
of the project, the corporation was going through a fundamental reorganization
which was set to last for the succeeding 3 years. Next to planned labor shortage,
the new organizational strategy came with newly formed teams. Responsibilities
were additionally shifted from governance units to business units, thereby providing
more autonomy to business units enabling them to act and adapt faster.
One of the complex challenges of conducting research in industry is to match
scientiﬁc research gaps and questions with organizational interests. While industry
partners are looking for direct practical and hands-on implications of the results,
researchers have to follow strict scientiﬁc standards to get valid and reliable results to
add value to an existing body of literature. Another dilemma emerges when
researchers want to study a speciﬁc condition, derived from an academic standpoint
and research gap, which is not or only partially reﬂected in the organizational
context. This dilemma was the impetus for this research project. While the experience was troubling at ﬁrst, it provided an opportunity to seek a more practiceembedded research topic and to observe and listen to organizational relevant topics
with an unbiased perspective.
We describe this approach of identifying company phenomena, matching them
with suitable explanations in the literature, deriving a research gap, deﬁning constructs, and the subsequent study design. In this way we believe industry research
scholars can make a two-fold contribution—both to the scientiﬁc community and by
offering applicable organizational results.

2 Conversations and Narratives: Study Inspiration
In the context of the above described reorganization, we started to talk to a number of
colleagues in the organization’s innovation landscape. Our intention was to gain an
understanding and feeling for what topics were en vogue, what bothered employees,
and what they reported from previous and current innovation projects to identify a
starting point for our research endeavor.

The corresponding analysis reported in our empirical paper was accepted at the 28th Innovation
and Product Management Conference (access via MayerMayer et al., 2021).

Practice-to-Research: Translating Company Phenomena into Empirical Research

149

The ﬁnal inspiration for this study came from a conversation with an employee
who worked in the internal consultancy department of the organization. He told us
about his experiences accompanying two independent innovation project teams.
During the exploration phase2, one of the teams (Team A), which today is an internal
startup, ﬁnished their exploratory activities in around eight months. The members of
the other team (Team B) belonged to a subsidiary company. This team completed the
exploration phase in approximately two months. Surprisingly, Team B was highly
motivated, although these employees were not sure whether or not they would keep
their jobs—the subsidiary company had announced plans to cut staff. This made us
wonder if the pressure and potential of losing jobs urged employees to create results
faster. From a psychological standpoint, we were, however, not satisﬁed with
looking at the phenomena of putting employees under pressure. Hence, we reversed
the question and opted to examine whether organizational factors could foster
employees’ innovation behavior, so they can exhibit their full potential. Walking
away from the conversation, we wondered: In moments of crisis, how do
employees feel? And how does this inﬂuence their innovativeness?
AHA-Moment: The team that had to deal with the fear of job loss was more
motivated and it ﬁnished the innovation exploration phase earlier than the
other team.
We considered this story within the overall context of an extensive restructuring.
Such a reorganization is accompanied by constant change, agitation, upheaval, and
loss, which can lead to feelings of insecurity for individuals (e.g., Keim et al., 2014).
At the same time, innovation research suggests that a safe working environment is
crucial for employees to engage in open conversations, generate innovative ideas,
and allow for new thing to happen unintentionally (e.g., Anderson et al., 2014).
We captured this narrative in 2019 and started to deep dive into the literature to
set up a study design. At the beginning of 2020, we were close to completing our
survey design. This time marked the starting weeks of the Covid-19 spread in
Europe. Hence, in addition to the internal structural alterations in the company,
everyone faced the economic and personal effects of the global Covid-19 pandemic.
This combination of events reinforced the experience of uncertain times and the ﬁt of
our research topic.
During such globally unstable times, the topic of job security is of particular
relevance and it has been discussed in relevant research trend publications, such as
the “Innovation and Covid-19: Impulses for the Future of Innovation”3 report by the
German research institute Fraunhofer (Bauer et al., 2021). The authors state that the
pandemic has led to a heightened insecurity in companies and markets, which could
lead to innovation budget cuts. They conclude that companies should try to prevent
this situation by further increasing their competitive ability in reaction to these
challenges.

Innovation processes within the company are commonly divided into three phases: Exploration/
Discovery/Ideation, Incubation/Prototype, and Acceleration/Grow.
3
Translated from the German report title.

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Furthermore, employee insecurity has also become an emphasized focus point for
important industry bodies: In 2020, the German trade union IG Metall (representing
the metal industry) centered “job security” as their core topic for their collective
bargaining (retrieved from the German newspaper SZ online on November, 4, 2021
www.sueddeutsche.de).
The appearance of our research topic in public and contemporary research
literature assured us that it was not only relevant for our partner company, but that
the phenomena were experienced throughout society and needed to be examined in
more detail.

3 The Translation Process
The internal employee’s story was our ignition point to dive into literature on
creative and innovative behavior, organizational behavior and crisis. Our ﬁrst
impulse was to look for a construct and measure that captures the extent to which
employees feel that their jobs are safe and secure, using the keyword search “job
security.” Quickly, it became obvious that the inverted construct “job insecurity”
(JI)4 was represented more prominently in the existing literature and only a few
papers examined “job security” as a variable (e.g., Probst & Lawler, 2006; Davy
et al., 1997).
Employees are central for organizations. Their innovative behaviors are vital for
the innovation performance of an organization (see, e.g., Strobl et al., 2020).
Organizations should therefore take measures to stimulate the innovation willingness of employees and promote their innovation behavior.
If employees go through a profound internal reorganization while experiencing
the threat of a global pandemic, this can create a feeling of unpredictability and lack
of control (Keim et al., 2014). If on top of that, innovation is expected from
employees, a challenging situation arises for both them and their employer.
Figure 1 depicts how we translated the narrative and the organization’s situation
into scientiﬁc and quantiﬁable terms.
In summary, we derived the following research questions:
In moments of crisis,
– how do employees perceive their job security?
– how does this inﬂuence their innovation behavior?
– which environmental factors could be fostering individual innovation behavior?

Two major conceptualizations for JI exist in the literature: quantitative vs. qualitative JI, and
cognitive vs. affective JI. We decided for the more recent differentiation between quantitative and
qualitative JI (e.g., see review by Shoss, 2017, p. 1913). Quantitative JI captures to what extent
employees fear losing their job, and qualitative JI to what extent they are afraid of losing important
job characteristics (e.g., change of location).

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Fig. 1 The organizational context and factors observed

We chose the organizational levering factors based on observations and stories
from colleagues and based on theory (outlined in subchapter 4, Moderator search).
The most prominent IB scales come from Lukes & Stephan (2017) and De Jong
& Den Hartog (2010). From these, we decided to work with the Innovation Behavior
Inventory (Lukes & Stephan, 2017), because their subscales best represent our
assessment intention and data collection approach in the organization. Also, the
scale is an individual level measurement. In comparison, De Jong & Den Hartog
(2010) designed their Innovative Work Behavior scale to assess employee–supervisor dyads. For our project we set the focus on employees and their personal
perceptions. Hence, IB supervisor ratings did not ﬁt into our planning.
With the two main variables (JI and IB) in place, we screened the literature for
empirical papers on their relationship. The research on the direct link was scarce.
Available research includes papers by Farzaneh & Boyer, 2019; Niesen et al., 2018;
Roll et al., 2015; De Spiegelaere et al., 2014. The hypothesized negative effect for
the direct JI-IB link was not signiﬁcant in some studies (e.g., Farzaneh & Boyer,
2019; Roll et al., 2015). Other scholars found partial signiﬁcant negative effects on
subscale level between JI and IB (e.g., Wang et al., 2019, Niesen et al., 2018; De
Spiegelaere et al., 2014). Next to these inconsistent ﬁndings on the JI-IB link, we
also noticed that none of the researchers had conducted a complete assessment of JI
and IB. For example, Niesen et al. (2018) found a signiﬁcant negative effect between
quantitative JI and idea generation (as one IB subdimension), as well as between
qualitative JI and idea generation. However, Farzaneh and Boyer (2019) could not
conﬁrm a direct negative relationship between JI and IB as hypothesized. For a
literature review on the JI-IB link, we refer to Mayer & Farzaneh (in press).
We also assumed that the inconsistent ﬁndings could be due to boundary conditions. Hence, we decided to add moderators to our study model. We describe the
process of choosing suitable moderators for our company context in the next
paragraph.

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In summary, we identiﬁed three research gaps:
• Inconsistent ﬁndings for the direction of the relationship between Job Insecurity
(JI) and Innovation Behavior (IB)
• Only partial assessments of either JI or IB in current literature
• No moderator studies to this point (only mediator studies)
Next to the independent, dependent, and moderator variables we wanted to
include a personality measure to control for possible personal characteristics that
inﬂuence employees' innovative behaviors. Personality traits are most commonly
assessed with a BIG 5 scale. Therefore, we wanted to add a short-version of the
traditional BIG 5 scale (Muck et al., 2007). However, we encountered another issue.
The company side stated that the items of the personality trait questionnaire touch on
very private characteristics and these bear too great a similarity to the annual
employee HR assessment. Our contact partner summarized that this doubling poll
might be disturbing to employees and raise the question of why personal traits are
assessed to capture innovation topics. Consequently, we dropped the personality
assessment aspect from our survey.

4 Moderator Search
It was important to us to pick moderator variables that were suitable and relevant for
the company context. We ﬁrst collected an extensive list of possible moderators from
the literature5, and then discussed and veriﬁed them with our contact at the company.6 The conversation helped us to identify crucial factors that our contact
experienced as potential levers for employees in the innovation landscape.
As factors hindering innovation aspects, the contact listed “no time, no leadership
by results, little appreciation from leadership and little rewarding, no customer
validation, no training/how-to/coaching, company is not market-based.”
He also named some supportive characteristics, all referring to the employee
themselves: “intrinsic motivation, working closely with the customer, hierarchy
aversive people, those providing ecosystems and networks, experience with internal
projects (based on long history of know-how).” Besides this, he mentioned “many
degrees of freedom (e.g. budget)” as beneﬁcial for employees’ innovative activities.
In other conversations, employees told us about feeling micromanaged, not
having the freedom to get work done but feeling instead like jour-ﬁxes and meetings
prevented them from engaging in their “real tasks.” Another narrative referred to the
board and middle management members. One employee described them “sitting

For example, based on the Shoss’ (2017) integrative review and her “Conceptual Model of
Antecedents and Outcomes of Job Insecurity.”
6
We did this in an open discussion asking which organizational factors he perceives as hindering
and which ones as supportive for employees in the innovation scene.
5

Practice-to-Research: Translating Company Phenomena into Empirical Research

153

Fig. 2 Hypothesized model

behind an internal information ﬁlter,” where they could feel safe. He speciﬁed that
these ﬁlters are to be understood as time-ﬁxed meetings with auditioned content. He
claimed that this prevents dynamics as well as honest conversations or transparency
about current and future states. Nonetheless, “behind that ﬁlter,” board and middle
management members are highly alert and aware that changes are necessary for the
company.
From these narratives and moderator suggestions in the literature (for theoretical
literature reviews, see Shoss, 2017, and De Witte, 2005; organizational relevance for
innovation climate is put forward in Anderson et al., 2014, and Crossan & Apaydin,
2010), we added three moderators to our study design: Organizational Support,
Participative Decision-Making, and Job Autonomy (see Fig. 2; short deﬁnitions are
summarized in 5 Final Study Design & Assessment). We presumed that each
moderator serves as a psychological contract variable that alters the direction of
the JI-IB link (see details in Mayer et al., 2021).

5 Deﬁning Hypotheses
In our quantitative study, we examined the impact of perceived Job Insecurity on
employees' Innovation Behaviors, assuming a negative effect (insecurity leading to
less IB). We also assessed the contextual factors of Organizational Support, Participative Decision-Making, and Job Autonomy for potential mitigating effects.
We formulated the following hypotheses. For more details on how we derived
them from the existing literature, we refer to our empirical paper with all company
results (Mayer et al., 2021).
• H1: Job Insecurity (JI) is negatively related to Innovation Behavior (IB).
• H2: Organizational Support (OS) moderates the negative relationship between
JI and IB such that the relationship becomes weaker as OS increases.
• H3: Participative Decision-Making (PDM) moderates the negative relationship between JI and IB such that the relationship becomes weaker as PDM
increases.

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• H4: Job Autonomy (JA) moderates the negative relationship between JI and IB
such that the relationship becomes weaker as JA increases.

6 Final Study Design
For our company partner, the survey story was crucial. It was necessary to communicate transparently with employees and to distinguish our survey from the
company’s own employee survey conducted by HR. The introductory story is
shown in Fig. 3.
Our contact also emphasized that we need to create a win–win situation for
colleagues to participate in the survey. If they take the time to ﬁll in the survey
and provide their data, we need to consider what they can get and expect from us in
return. In response, we added a pdf ﬁle containing creative tips to start online
meetings as a gift—this came at a time when many employees had just shifted to
remote work and were new to facilitating online meetings.

Fig. 3 Screenshot of the introduction to the survey

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6.1

155

Choosing Robust Scales

We chose existing validated scales per variable to get robust results and aimed for
good construct scale reliabilities. For most constructs, we tried to choose prominent,
well-established measures.
All measures in our questionnaire captured self-reported ratings. In order to
counteract common method bias, we applied three recommendations from
Podsakoff et al. (2003). First, we informed participants that taking part in the survey
is completely voluntary, that all data will be aggregated and thereby anonymously
analyzed, and that there are no right or wrong responses. Second, we guaranteed for
proximal separation between the dependent variable (IB) and the independent
variable (JI) in our survey. IB was placed as the ﬁrst itemset, and JI as the last
itemset within the questionnaire. Third, the introduction ensured psychological
separation.

6.2

Final Survey Design

A 5-point Likert response scale accompanied all measures (except for the control
variables). In total, seven persons contributed to the translation (from English into
German and back) of the scales. Four of these contributors work in innovation
research.
Additionally, we wanted to reassure that the item batteries, derived from the
literature, would resonate with the target audience of employees within the organization. Hence, we engaged in a conversation with a senior head of innovation and an
innovation inhouse consultant to capture their feedback and accommodate it where
possible. The ﬁnal measures are listed below.
Job Insecurity (JI). Due to the incomplete assessment of JI in previous JI-IB
studies, we proceeded with measuring Qualitative as well as Quantitative JI.
Quantitative JI measures the level of fear that employees experience at the
prospect of losing their job.
We applied De Witte’s (2000) Job Insecurity Scale (JIS), which has been
validated in several languages (see Vander Elst et al., 2014). Also, the scale yields
scientiﬁc proof for good construct and criterion validity, as well as good reliability.
In particular, Vander Elst et al. (2014) even advise practitioners to use the short scale
for screening purposes. In accordance with our company partner, we included the
following two items: “Chances are, I will soon lose my job,” and “I feel insecure
about the future of my job.”
The qualitative dimension captures to what extent employees fear losing elements
of the job, such as fear of being passed up for a promotion in the near future. We
chose the scale Multidimensional Qualitative Job Insecurity Scale (MQJIS) by
Brondino et al. (2020) as a suitable one. The scale is composed of four dimensions
with eight items in total.

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The four dimensions are Social Relationships, Employment Conditions, Working
Conditions, and Work Content. An example item is “I worry I might get another
supervisor in the future” (from the subdimension Social Relationships).
Innovation Behavior (IB). Employees rated their Innovation Behaviors on ﬁve
subscales (16 items in total) of Lukes and Stephan’s (2017) Innovation Behavior
Inventory. We assessed the subscales Idea Generation, Idea Search, Idea Starting
Activities, Involving Others, and Overcoming Obstacles. Example items are “I am
interested in how things are done elsewhere in order to use acquired ideas in my own
work” (Idea Search subscale), and “I try to involve key decision makers in the
implementation of an idea” (Involving others subscale).
Organizational Support (OS). OS evaluates employees’ perception of their organization paying attention to their well-being and aiding in their professional goals.
We added six items from the well-established Perceived Organizational Support
scale by Eisenberger et al. (2020, p. 104, originally consisting of ten items). An
example item reads, “The organization strongly considers my goals and values.”
Participative Decision-Making (PDM). We applied Bordia et al.’s (2004) four
item scale to rate employees’ Participative Decision-Making. PDM captures the
extent to which employees perceive to be actively involved in decisions made by
the company. For example, one item states “I actively participate in decision-making
regarding things that affect me at work.”
Job Autonomy (JA). Job Autonomy measures how freely and autonomously
employees can act and reﬂect in their jobs. We used eight items from Ramamoorthy
et al.’s (2005) perceived Job Autonomy scale. Example items are “I choose the
methods to carry out my work,” and “I often review my work objectives.”
Control Variables. Our choice of control variables for the survey roots on what
we found in other JI-outcome papers and which were relevant for our company
context (e.g., Brondino et al., 2020; Niesen et al., 2018). We included altogether six
control variables in the questionnaire: Age Group, Gender, Functional Role, Disciplinary Responsibility, Type of Contract, and Organizational Tenure.

6.3

Additional Remark

During our discussion sessions before the company assessment, as well as afterwards when presenting results, we always had to make clear that we are talking about
“subjective, personal” Job Insecurity. Company members and researchers at conference meetings regularly voiced their concerns about measuring how secure
employees feel about their jobs in a corporation. They argued that the company
has solid contractual structures, providing high security and protecting against
gratuitous job loss. The construct we measured, perceived subjective JI. This does
not refer to written contracts that formalize mutual responsibilities and duties.
Following psychological contract theory, subjective JI applies to psychological
contracts in the shape of unwritten mutual expectation between employee and
employer (see Shoss & Probst, 2012).

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7 Conclusion
In this chapter, we have elaborated our embedded research approach to ﬁnd inspiration from narratives within an organization and to translate them into an empirical
study design. Going back and forth between literature and organizational voices
deﬁned and shaped our study. Furthermore, we discussed in close loops with our
company partner. Thereby, we could adapt to company jargon, foresee potential
survey acceptance issues participants might have, and integrate valuable variables
into the company context. With this approach, we believe we could counter the
research-to-practice gap while gaining a unique data access, which otherwise would
not have been possible. Making sure that our observations from practice were
constantly aligned with research, we derive valuable insights and results on innovative behavior in times of crisis also for companies beyond the study's organization
context.
Acknowledgements We thank the HPI-Stanford Design Thinking Program and our company
partner for enabling this study and research collaboration. We want to convey our thanks to
Dr. Robert Rose for his extensive advice and contribution to the survey creation as well as to all
translation helpers. Many thanks to Dr. Sharon Nemeth for copyediting and language support.

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urn-newsml-dpa-com-20090101-200116-99-503753

Timely State Exposure for the Coevolution
of Mental Models and Dynamic Systems
Parastoo Abtahi, Sidney Q. Hough, Jackie Junrui Yang, Sean Follmer,
and James A. Landay

Abstract The computational systems we interact with are increasingly intelligent
and dynamic, as they learn from user interactions and are updated over time.
Principles of good design highlight the importance of understanding user mental
models and providing feedback to expose the internal state of the system. In the
context of intelligent systems situated in the real world, this notion of feedback is
highly temporal and requires a deep investigation of when feedback should be
provided to users. In this chapter, we ﬁrst motivate the need for timely state exposure
in the design of such dynamic systems. We then lay out the design space of timing
strategies for providing feedback, highlighting the frequency of state exposure and
the information necessary for each strategy. We report on a preliminary exploration
of this design space using our news article recommender system. We ﬁnd that one
feasible approach may be to provide feedback only after failures when the system
behavior is incorrect. Finally, we discuss open challenges that need to be studied in
future work, including methods for effective evaluation of user mental models over
time, anticipation of breakdowns, and detection of system failures.

1 Introduction
Every day we interact with computational systems that adapt over time. For example, smartphones learn from usage patterns and optimize their charging rate to
improve the lifespan of the battery.1 When users are typing on a smartphone,

https://support.apple.com/en-us/HT210512.

P. Abtahi (*) · S. Q. Hough · J. J. Yang · J. A. Landay
Computer Science Department, Stanford University, Stanford, CA, USA
e-mail: parastoo@stanford.edu; shough@stanford.edu; jackiey@stanford.edu;
landay@stanford.edu
S. Follmer
Mechanical Engineering Department, Stanford University, Stanford, CA, USA
e-mail: sfollmer@stanford.edu
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_9

161

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P. Abtahi et al.

predictive text algorithms provide suggestions based on prior user activities to make
typing on a small keyboard more efﬁcient.2 Another category of everyday technology that learns and adapts over time is recommender systems (e.g., Spotify music,
YouTube videos). Recommender systems are also an integral part of social media
platforms and news websites and play an important role in the information we have
access to and the media we consume. Recommender systems learn from user activity
patterns and recommend posts and news articles that a particular user is likely to be
interested in, such that they can increase engagement with their platform.
Internet of Things (IoT) devices in smart homes are another set of dynamic
systems. These devices evolve over time as new user data arrives; however, due
to a lack of timely feedback, users’ mental model of the system remains largely
static. For example, the Nest Learning Thermostat learns by observing users’
interactions and identifying patterns. After a week of use, the thermostat learns
users’ temperature preferences and automatically schedules heating and cooling
throughout the day. It treats weekdays and weekends as separate entities and over
time becomes less sensitive to everyday changes. These design decisions as well as
the learned patterns are not effectively communicated to users. The dynamic nature
of the system and the constant change in behavior make understanding and
interacting with the system challenging for users. Particularly, when the system
makes incorrect decisions, by cooling or heating the space to an undesirable temperature, users feel frustrated because they do not understand the learned pattern and
cannot correct it (Pernice, 2015).
The growing gap between a dynamic system’s state and the user mental model
leads to expectation violations and system incorrigibility. It is necessary to expose
the system state to users at different times throughout long-term interactions to
enable user mental models and dynamic systems to co-evolve and to ensure users
are well-positioned to handle system errors in cases of failure. This raises the
question: when and how often should we communicate system changes to
users? Perhaps the simplest strategy is to expose the system state at all times. This
approach is not feasible, because the system may change often, both globally
through software updates and locally as the system learns by observing implicit
and explicit interaction patterns. Moreover, the strategy of constant state exposures
does not scale to a large number of dynamic systems, which is inevitable given the
ubiquity of intelligent systems because they will all interrupt users simultaneously
and compete for their attention.
In this chapter, we explore state exposure timing strategies that enable users to
develop an up-to-date mental model of a system that continuously learns and evolves
over time (see Fig. 1). We begin by providing a brief background on mental models
and discuss how this next generation of computing systems challenges our traditional notions of good design and the user-centered processes we have established
for generating them. We then lay out the design space of timing strategies for state
exposure and report on our preliminary exploration using an article recommender

https://support.apple.com/en-us/HT207525.

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163

Fig. 1 From left to right
showing the coevolution of
a dynamic system (A0–A4)
and the user’s mental model
(MM0–MM1) over time

system. Finally, we highlight the need for new design methodologies to support the
real-time evaluation of user mental models and provide timely feedback.

2 Background
Mental models are “the mechanisms whereby humans generate descriptions of
system purpose and form, explanations of system functioning and observed system
states, and predictions of future system states” (Rouse & Morris, 1986). Understanding mental models is a critical design activity that has been carefully studied in the
human–computer interaction and the human factors literatures (Carroll & Olson,
1988). To improve usability, as designers we evaluate users’ mental models at the
beginning of the design process to create products that are congruent with those
models and we strive to clearly present our own conceptual models to users in our
designs, as shown in Fig. 2 (Norman, 2013). However, emerging intelligent systems,
such as the Nest Learning Thermostat, are probabilistic and dynamic. Due to their
probabilistic nature, traditional design methodologies that assume a deterministic
user ﬂow are no longer applicable. As designers, we are challenged with the task of
predicting how the system will behave over time and we need to design for a
potentially limitless state space, as the system adapts. They are also dynamic,
meaning that the system behavior changes over time both globally through software
updates and locally through learned patterns from user input. Therefore, it is
necessary to develop new methodologies that evaluate users’ mental models in the
long-term and provide continuous feedback to communicate the state of the system
to users. In other words, we need new strategies to bridge the gulf of evaluation and
execution in these systems.
A relevant area of research that has emerged at the intersection of artiﬁcial
intelligence and human–computer interaction is explainable and interpretable
machine learning. While there is an overlap between these topics and the discussions
in this chapter, our exploration is applicable to a broader set of systems that learn and

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Fig. 2 Top: designers communicate their conceptual model of the system to users through the
system image, and users develop a mental model of how the system works through their interactions
with the system (Norman, 2013). Bottom: the open challenges that remain in understanding user
mental models and the role of designers in emerging dynamic systems that change and adapt over
time, highlighting the need for new design methodologies

adapt over time using a range of techniques, from simple heuristics to complex
machine learning. Moreover, most prior research in this space has focused on
studying mental models after explicit user interactions with the system. This
means proposing strategies that an intelligent agent can use to explain a particular
behavior or decision (Gero et al., 2020; Kulesza et al., 2013). Our exploration is
focused on the timing of state exposure in dynamic systems, not only after explicit
user interactions, but also as the system behavior changes over time.
The distinction between implicit and explicit interactions (Ju, 2015) is particularly
important in our discussion of timing strategies. When users are explicitly interacting
with a system (top left quadrant in Fig. 3) they expect feedback from the system and
this instance of interaction may be the appropriate time to provide an explanation.
For example, when the user clicks a button on a 2D graphical user interface, they
expect their input to be utilized in some way and the effects of their input can be
communicated then. However, when the system learns over time from implicit
commands (bottom left quadrant in Fig. 3) it becomes less clear when the changes
in the system can be communicated to users, particularly when the system performs a
silent action (bottom right quadrant in Fig. 3). For an example of such a system we
return to the Nest Learning Thermostat, where the thermostat silently adjusts the
room’s temperature based on the learned patterns. In this chapter, we explore the
design space of when and how frequently dynamic systems can expose their
dynamic state to users to engender more accurate mental models over time as they
learn from both explicit and implicit interactions.
In the remainder of this chapter, we refer to failures as instances where the system
behavior is incorrect and breakdowns as instances where the user mental model is
incongruent with the system, resulting in a system behavior that is correct

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165

Fig. 3 The implicit interaction framework (Ju, 2015) is particularly relevant in the discussion of
timing strategies for state exposure in dynamic systems

(or helpful), but unexpected. We also refer to instances where the system exposes its
state to the user as a form of feedback (shown as pink arrows in Fig. 2), and direct
interactions that the user has with the system as a form of user input (shown as
yellow arrows in Fig. 2).

3 Design Space of Timing Strategies
As we argued in the introduction, never exposing the user to the internal changes in
the system results in inconsistencies between user mental models and system
behavior and ultimately leads to expectation violations. On the other hand, exposing
the internal state at all times is not feasible. Another approach is to provide feedback
during explicit interactions, which does not apply to systems that implicitly interact
with users, as described in Sect. 2. Providing feedback whenever there is a system
update, even if users are not explicitly interacting with the system at that instant,
gives the users a chance to keep up with system changes over time. However with
this strategy, if system behavior changes often, users will be frequently interrupted.
To mitigate this interruption overload, we can draw on prior research tackling similar
problems in mobile phone notiﬁcations. Using concepts from context-aware computing literature (Abowd et al., 1999) we can identify relevant parameters (e.g.,
activity, location) and present notiﬁcations at opportune times (Fischer et al., 2011;
Pejovic & Musolesi, 2014), such as when users are experiencing less cognitive load
and are able to attend to the information (Adamczyk & Bailey, 2004).

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In mobile phone applications, adaptive notiﬁcations have been largely successful
at delivering information to users when is least disruptive (Okoshi et al., 2017).
However, in the broader context of dynamic systems that are becoming more
ubiquitous in our everyday life these strategies may not scale, as many devices
will be competing for opportune moments and our attention. Therefore, there is a
need for exploration of strategies that require less frequent state exposures. One such
strategy is state exposures after mental model breakdowns, which requires the
system to keep track of the user mental model and predict when a foreground action
will be unexpected by the user. Another strategy is after system failures, which
require the system to predict failures from the user’s response, and ideally without
requiring additional explicit input from the user. Finally, we may allow users to
explicitly ask the system for state exposures when needed. However, these instances
may be infrequent, as to prevent user mental models to effectively co-evolve with the
system over time.
The following table summarizes these strategies by describing the extra information that the system needs, the additional user input required, and the frequency of
state exposure on a scale: always, very often, sometimes, rarely, and never.
When
During explicit
interactions
After system updates
Opportune times
Upon breakdowns
Upon system failures
After user queries

System information
needed
None

User input
required
None

Exposure
frequency
Very often

None
The user’s context
The user’s mental model
Failure detection
None

None
None
Ideally none
Ideally none
request

Very often
Sometimes
Sometimes
Rarely
Rarely

4 Article Recommender System
To begin exploring this design space, we built an online machine learning system
that recommended articles from the New York Times (NYT).3 We queried the NYT
API for articles published in 2020, collecting the titles, abstracts, and the news desks
responsible for them. For a given article, the news desk took on one of the following
values: “foreign,” “world,” “business,” “business day,” “US”, “national,”
“arts,” “arts and leisure, “book review,” “books,” “movies,” “television,”
“sports,” “science,” “climate,” “fashion and style,” “style,” “styles,” “technology,” “dining,” “food,” “Washington,” “politics,” and “culture.”
We needed to strike a balance between system explain ability, accuracy, and
learning time. In general, the more complex a model, the less explainable it is and the
3

https://www.nytimes.com/.

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167

longer it takes to train; however, it is more expressive and thus performs better (e.g.,
better predicts user preferences). After some experimentation with smaller systems,
we settled on ﬁne-tuning a large language model (BERT) to classify articles into
topics using their news desks (Devlin et al., 2018), and adding a simple reinforcement learning layer (UCB1) to learn over time which topics and therefore which
articles users preferred to read (Auer et al., 2002). At each time step, the algorithm
selected a topic and then sampled an article uniformly at random from the set of
articles classiﬁed into that topic to present to the user. The user’s indicated preference for this article was then used to update the recommender system.

5 Generating Explanations
To expose the internal system state, we used a standard explainable AI (XAI)
technique known as SHAP (SHapley Additive exPlanations). SHAP allocates credit
to various parameters of the input according to the extent that they contributed to the
output of a function (Lundberg & Lee, 2017). Our function was our end-to-end
learning system: the inputs were the tokens comprising the title and abstract of an
article, and the output was a prediction of whether or not the user would read the
article. A technical note about the reinforcement learning component of our system
is that we did not use the calculated UCB1 values, which determine whether or not to
recommend an article to the user, because these values capture a notion of “exploration” that does not reﬂect the agent’s true expected reward from a given topic (i.e.,
their interest in that topic). Instead, we used internal parameters of the agent that
stored the empirical past reward of recommending articles of a given topic.
After calculating SHAP values for each token, we highlighted the top quartile of
tokens as our explanation, in addition to revealing the topic of the article. This was
done based on the intuition that knowing which words and phrases were the most
relevant to a given decision better enables the user to predict a future decision if the
decision is conditioned on a similar set of words and phrases.

6 User Interface Design
We built a web application with a Flask4 backend and React5 frontend, to display
recommendations to the user and gather user input, as shown in Fig. 4. We showed
the user the title and abstract of a New York Times article and asked the user
(1) whether or not they would be interested in reading the article, and (2) whether
or not they think the system believes they would read the article (see Sect. 7). Then,

4
5

https://www.fullstackpython.com/ﬂask.html.
https://reactjs.org/.

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Fig. 4 New York Times article recommender system. Left showing the user interface for collecting
user input (i.e., their prediction of system behavior). Right showing the user interface for state
exposure, including the system behavior and the explanation (i.e., SHAP values and article topic)

conditionally, we generated and displayed an explanation for the model’s belief by
highlighting words according to their importance, as described in Sect. 5. After every
ten recommendations, we gave the user a more complete “quiz” where they were
tasked with predicting system behavior on a wider variety of example articles (see
Sect. 7). User responses to this “quiz” were for evaluation purposes only and did not
affect the learning of the recommender system.

7 Evaluating User Mental Models
In order to evaluate the accuracy of the user’s mental model, we continuously
sampled for the system’s “forward simulatability.” In machine learning literature,
“forward simulatability” is used to evaluate the interpretability of systems, as the

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user’s ability to correctly predict the model’s behavior signals that the user understands why a model makes certain predictions (Hase & Bansal, 2020).
We asked the user to simulate the system in two settings: after each article
recommendation and in intermittent quizzes. The former allowed us to capture the
user’s representation of a system at every step as the model learned and the system
evolved over time. This approach also allowed us to observe the accuracy of the
user’s mental model with regard to the most relevant content (i.e., the articles that
would be recommended to the user in a real-world setting). The latter gave us a
higher-level overview of the user’s understanding of the recommender system’s
behavior across the entire search space. While the ﬁrst approach is useful, it is a
biased sample in that the articles that are recommended to users are more likely to be
articles that the system believes the user is interested in reading. On the other hand,
the quizzes evaluate the user’s ability to simulate system behavior over all possible
topics.

8 Frequency of State Exposures
For different cohorts, we varied the timing of explanations to understand how state
exposure affects the development of user mental models. For our preliminary
exploration, we chose three different explanation frequencies:
1. Never: In our control group, users never received explanations.
2. Always: In the second group, users always received explanations. While this is an
unachievable standard in practice, we wanted to understand how well-timed
explanations improve the accuracy of user mental models relative to this ideal.
3. Upon failures: Users received explanations when the system mis-predicted
whether or not they would be interested in reading an article. We expected this
frequency to be both economical and scalable in practice. First, explanations for
cases in which the system did make the right decision are less useful, because
while user expectations may not align with system behavior, there is no need for
correction or intervention and as such, it is less critical for the user to have an
accurate mental model. The cases in which the system fails are those for which
the user needs an explanation so that they can adjust their own behavior or
expectations to predict or even prevent failures in the future. Second, system
failures should become much less frequent than system successes over time
(or else the system is fundamentally unusable), so users will not be overwhelmed
with information as they receive explanations with this strategy.

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Fig. 5 User prediction errors based on user input after every article shown for each condition

9 Preliminary Results
We conducted a pilot study with three volunteers (2 male, 1 female, ages 14, 19, and
52), each randomly assigned to one of the three conditions described above. Each
participant used our NYT article recommender system, shown in Fig. 4, for roughly
half an hour. For each article, they predicted system behavior (i.e., whether or not the
system thinks they will be interested in that article) and provided ground truth data
(i.e., whether or not they were interested in the article). After interacting with each
article, for some articles (the frequency of which was determined by the state
exposure strategy in the condition they were assigned to), they were provided with
an explanation in the form of words that were highlighted based on SHAP values.
Finally, after interacting with ten articles, participants completed a more comprehensive, intermittent survey described in Sect. 6.
The results for prediction errors after interacting with each article are summarized
in Fig. 5 and for prediction errors in the intermittent surveys are summarized in
Fig. 6. Due to the limited number of participants, we cannot perform statistical
analyses on the data; however, the stratiﬁcation points toward the following trends:
1. User mental models ossify. Without any state exposure throughout long-term
interaction, user mental models cease to develop along with the changes in the
dynamic system. As a result, error rates may not improve over time unlike other
system exposure strategies.
2. System simulatability may be a function of frequency of explanations. More
frequent system exposures may be inversely correlated with user error in
predicting system behavior.

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171

Fig. 6 User prediction errors in intermittent quizzes are shown for each condition, over a sliding
window of size 10 and balanced over news desks

3. Infrequent, but timely, state exposures may be competitive with constant
state exposures. For example, exposing the state only after failures may sufﬁciently improve the accuracy of the user’s mental model as compared to always
exposing the dynamic system’s state, which is neither economical nor scalable.
While these results are very preliminary, they provide a road map for future work
that can explore the design space of state exposure timing strategies discussed in
Sect. 3. Conducting longitudinal studies with a larger population on crowdsourcing
platforms such as Amazon Mechanical Turk will allow us to study the effects of each
timing strategy in more detail. In the future, we hope to use this study design to
further explore the design space as well as adaptive explanation strategies that may
change over time based on various factors, such as system uncertainty and error rate.

Open Challenges

Many challenges remain in the exploration of the design space of timely state
exposure for dynamic systems, which can be summarized by discussing the following four research questions:

172

10.1

P. Abtahi et al.

How Can We Continuously Evaluate User Mental
Models Over Time?

In our work, we asked users for their prediction of the system’s behavior at each
timestep as a mechanism for evaluating their mental model. This approach has two
main limitations. First, it is not practical for real-world systems, because asking users
to pause and predict system outcomes results in signiﬁcant friction and delay during
their interactions with the system. Moreover, while correctly predicting system
behavior signals that users might have a correct mental model, it does not paint a
complete and accurate picture of their mental model of the system. In other words,
even though a user might correctly predict what the system state will be, they may
not be able to describe why they believe this to be the case.
A range of other techniques have been presented by prior research for evaluating
user mental models, each with its own strengths and limitations. For example, verbal
protocols have been utilized by asking users to “think aloud” as they perform a task
(Rouse & Morris, 1986). Another approach is co-experimentation in which two
people are asked to collaboratively complete a task and the communication of
knowledge between them is used to elicit users’ mental models (Sasse, 1991).
While these techniques provide information about what users are thinking, similar
to forward simulatability techniques, they offer little information about how they are
thinking. Particularly, if mental model representations are pictorial, it would be
difﬁcult for users to accurately verbalize them (Rouse & Morris, 1986). But more
importantly, these techniques add a similar layer of friction that make them impractical for deployment in situ and throughout long-term interactions with a system.
Therefore, effectively and continuously evaluating user mental models of a
dynamic system without introducing signiﬁcant friction during interactions remains
an open challenge. Can we evaluate user mental models without intervention and by
observing and analyzing their implicit and explicit interaction with the system?

10.2

How Can We Predict Mental Model Breakdowns?

As system designers, we have access to the internal state and the inner workings of a
dynamic system, however complex they may be. From this vantage point, once we
have an understanding of (or even an approximation of) the user’s mental model
over time, we might be able to predict when there will be a breakdown (i.e., when the
system might behave in a way that is unexpected to the user). Predicting such
instances offers an opportunity for infrequent state exposure that (1) reassures
users the system behavior is correct and (2) provides a timely explanation to help
user mental models co-evolve with the dynamic system.
While such a strategy may be effective, the mechanics of predicting mental model
breakdowns remains an open challenge. Can we articulate user mental models and
the current state of a dynamic system with similar representations? How can we

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compare these two representations? Through this comparison, can we predict, with
high certainty, when there will be a mismatch between the user’s mental model and
the ground truth (i.e., the current state of a dynamic system)?

10.3

How Can We Detect System Failures?

Another strategy that requires further exploration is state exposures after system
failures. Through our initial explorations, we found that such timely and infrequent
feedback may be comparable to continuous feedback, in terms of facilitating user
mental model updates as the system changes over time. Dynamic systems are
optimized to prevent failures, nonetheless at times they may have incorrect behaviors. In practice, it may be challenging to detect when such system failure occurs.
How might we detect system failures from implicit or explicit user interactions
that follow? For example, if the user ignores a particular news article recommendation instead of reading that article, we may conclude that the system suggested an
incorrect article (i.e., an article that the user was not interested in). How might we
detect failures from user’s emotional responses to the system behavior? For example, by analyzing the user’s response to a voice assistant, including their language or
tone, we may be able to detect negative emotions such as frustration and anger that
may point to a system failure (Cuadra et al., 2021). We may also be able to analyze
user responses indirectly, without access to video or audio during interactions. For
example, prior research has shown that we may be able to detect that users are
stressed indirectly from the motions of their computer mouse (Sun et al., 2014).

10.4

How Should We Present State Exposures?

We used word highlighting based on SHAP values for state exposure. The highlighting provided an explanation of which words were important in classifying an article
into a particular topic category, and the topic label communicated to the user that the
system believes they are interested in articles within that category. While this
technique may be appropriate for a user evaluation of a news article recommender
system, it may not be practical for real-world recommender systems and does not
generalize to other types of dynamic systems, such as those that are not text-based.
This raises the research question: how might we provide feedback about the state
of a dynamic system? Once we identify a suitable state exposure timing strategy and
have the mechanisms by which to do so (i.e., are able to predict mental model
breakdowns or detect system failures), what is the best medium to communicate this
information (e.g., visually, textually, verbally, etc.) and at what level of abstraction?

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Conclusion

In this chapter, we laid out the design space of state exposure timing strategies that
can be utilized to ensure user mental models of dynamic systems evolve over time as
the system learns and adapts. We implemented a news article recommender system
that learns from users’ reading preferences and suggests relevant articles that they are
likely to be interested in. For the state exposure strategy we highlighted words based
on their importance in the system’s decision-making process and utilized the “forward simulatability” technique to evaluate the user’s mental model accuracy of our
dynamic recommender system. We then explored parts of the presented design space
with the system that we implemented. Our preliminary ﬁndings motivated further
exploration of infrequent, but timely state exposures, such as after breakdowns or
upon system failures. Finally, we discussed the open challenges that require further
study for effective utilization of these infrequent state exposure strategies, including
predicting when user mental models might break down and detecting when the
system may have failed. A deeper exploration of these topics will reveal new design
methodologies that do not assume a deterministic user ﬂow, yet facilitate adaptive
feedback enabling user mental models and dynamic systems to co-evolve over time.
Acknowledgements The following icons from the Noun Project have been modiﬁed and used in
this chapter under the Creative Commons License:
•

Computer by N.Style from the Noun Project. Source link:

•

Website by N.Style from the Noun Project. Source link:

•

Conference by N.Style from the Noun Project. Source link:

•

smart camera by N.Style from the Noun Project. Source link:

•

Courier by N.Style from the Noun Project. Source link:

https://thenounproject.com/icon/2946578/
https://thenounproject.com/icon/2614749/
https://thenounproject.com/icon/2855612/
https://thenounproject.com/icon/2986408/
https://thenounproject.com/icon/3340375/

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Designing for Value Creation: Principles,
Methods, and Case Insights from
Embedding Designing-as-Performance
in Digital Health Education and Research
Jonathan Antonio Edelman, Babajide Alamu Owoyele, Joaquin Santuber,
and Stefan Konigorski

Abstract Designing for Value Creation in healthcare entails engaging stakeholders in both academia and industry. Therefore, education and training on value
creation for students and professionals are central to designing better healthcare
systems. In this chapter, we explore the state of digital health design education,
particularly how value creation can be rigorously explored and implemented. A
Designing-as-Performance (MEDGI + PretoVids) approach augments current
healthcare innovation approaches in increasing customer engagement, reducing
risk, and improving proﬁt. Here we review the literature on current Design Thinking
(DT) in healthcare and posit Designing for Value Creation as a way forward based
on our 3+ years of action research in this emerging ﬁeld. Our insights are relevant for
design educators, medical practitioners, and industry actors looking to leverage
Design for Value Creation in healthcare in the digital transformation era.

1 Introduction
Price is what you pay; value is what you get.—Warren Buffet1
Every system is perfectly designed to achieve exactly the results that it gets.—Don
Berwick, MD

Healthcare is transitioning from care within hospital walls into broader digital and
physical spaces outside the direct reach of nurses, doctors, and insurance companies.
Apps, platforms, and digital infrastructure bring telemedicine opportunities and risks
as smartwatches and phone apps serve diverse communities from the old to the very

https://tommccallum.com/2019/04/23/price-is-what-you-pay-value-is-what-you-get/

J. A. Edelman (*)
Stanford, California, USA
B. A. Owoyele · J. Santuber · S. Konigorski
Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
e-mail: Babajide.Owoyele@hpi.de; Joaquin.Santuber@hpi.de; stefan.konigorski@hpi.de
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_10

177

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J. A. Edelman et al.

young. Likewise, the role of healthcare providers, practitioners, and patients is also
in transition with the advent of self-management apps and personalized medicine.
Beyond just innovation for proﬁt, the healthcare industry and individual
healthcare practitioners adopt Design (design modes of doing and thinking) to
explore problems and solutions. Beyond fancy prototypes and esthetically sound
user interfaces for smartphone applications, Design asks the types of questions that
lead to a richer understanding of people (doctors, nurses, patients, family) and thus
has radical implications for creating whole ecosystems of care offerings.
Design also allows for deeper explorations of what people value in healthcare,
their pain and pleasure, and why they value what they value. The design approach is
even more relevant in medicine in recent times, considering COVID-19. This
pandemic has forced many nations and organizations to be innovative with their
products, services, and systems. This increase in an adoption of Design doing and
thinking is evident in a way that it is relevant for both healthcare practice and
academia. Figure 1 shows the annual growth rate of literature related to Design,
design thinking in the medical context.
Much of the literature employing Design is related to understanding health
behavior, and rightly so, many studies focus on the human element in healthcare,
particularly related to behavior change through mobile applications. Many studies
leverage design to evaluate and conﬁgure procedures for randomized controlled trials,
while others leverage qualitative questionnaires to solve problems combined with
surveys and interviews. See Fig. 2 for top keywords in current digital health literature.
Digital Health and Design are thus seen as two sides of a coin that can help reduce
risk and improve treatment outcomes in healthcare, as well as improve proﬁts or
reduce costs, and improve patient outcomes. Digital health offers society a way in
which patients do not have to get sick by leveraging smart devices and machine
learning algorithms to understand genotype and phenotype patterns, i.e., through
personalized medicine and preventive care approaches.
In parallel, design and design thinking education facilitates better outcomes of
research and innovation in healthcare contexts; speciﬁcally, by generating innovative healthcare outcomes. Although Design is being adopted in healthcare, we see a
gap in current design approaches in healthcare contexts.
First, we see a lack of application of shared models across diverse medical teams
in regard to explicitly designing for value creation. Second, we observe that most
approaches adopting Design do not draw on robust and rigorous methods and
principles that have been vetted in a real-life medical context, beyond the anecdotal
approaches to brainstorming and using sticky notes to facilitate “ideation” sessions.
We believe that these current approaches may not deliver credible results in high
performing contexts such as healthcare.
To address this opportunity, we proceed in the following sections of the chapter
as follows:
1. First, we employ bibliometric analysis to map the literature on digital health and
Design in journals, books, and conference proceedings.
2. Then, using visual semantic networks as sensemaking tools, we determine and
show that healthcare is a distinct cluster in current design literature.

Fig. 1 Annual scientiﬁc growth annual growth rate: 49.9% from 1999 to 2021 (SCOPUS Database)

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Fig. 2 Wordcloud digital health-design literature from 1999 to 2021 (SCOPUS Database)

3. Finally, we further zoom in on some key digital health design literature to situate
our contribution in this chapter.
We aim to show the landscape and then showcase our approach to Designing for
Value Creation (D4VC) in healthcare. Using the acronym D4VC from here on, we
show why and how Design can mediate and facilitate the creation of Value for “all”
actors involved, not just patients.
D4VC is about improving user engagement, reducing risk, and reducing
costs. In our approach D4VC, we depart from the dogmatic notions in design
thinking in healthcare approaches that focus too much on the patient while ignoring
other actors/agents/nodes, e.g. doctors, algorithms, families, radiologists. Yes, we
assume that patients deserve better experiences. But we believe this is also the case
with doctors, health professionals, managers, and other non-patient actors.
Patients are central in healthcare. Doctors save lives. There are high stakes for all
stakeholders in the network of healthcare. We need humble and high stakes design
paradigms and methods that are rigorous and robust through and through for multiactor value creation.
Our guiding questions:
• What characterizes the state of value creation research in design methods and
practice in digital healthcare and medicine from a bibliometric perspective?
• How can digital health design education beneﬁt from action research to embedding Designing-as-Performance in its r multi-actor practices?
We have explored these questions using action research, having focused on
educational contexts in which we have been embedded for the past 3 years at the
Digital Health Center at the Hasso Plattner Institute, Potsdam. We have engaged
both students and industry to iterate and ﬁnetune our principles and methods in a

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reﬂexive mode of action research. Our work is far from complete but may offer some
insights into how Design may be embedded in Digital health. Our bibliometric
overview is also not exhaustive as we rely on titles, abstracts, and keywords
available from Scopus to generate these visualizations. Nevertheless, we hope they
inspire others to situate themselves in the emerging digital health design ﬁeld and
perhaps drive research attention toward connecting the unconnected dots shown
throughout this chapter. Finally, we want to state that this work is not a systematic
literature review, although we invite others to explore or search phrases for such
tasks.
The following sections proceed as follows. First, we provide a high-level
bibliometric overview, i.e. a thematic mapping of the digital health and design
thinking ﬁeld, discussing what topics (keywords) have been addressed in healthcare
using Design and design thinking. Next, we zoom in on digital health design
education to present our motivation for entering what we perceive to be uncharted
territory. Here we posit how Designing-as-Performance(D-a-P) principles and
methods can make design approaches more robust, rigorous, and reﬂexive in
healthcare contexts. Finally, to demonstrate the value of our approach, we discuss
3 cases where we iteratively developed and embedded MEDGI and PretoVids, as
two fundamental approaches to designing/exploring products, services, and systems
in digital health education contexts. Again, we emphasize that our goal is to ensure
robust value creation for multi-actor healthcare innovation.
The ﬁnal section summarizes our work, reﬂecting on insights drawn from our
engagement at the Digital Health Center and with patient/user-facing partners.
We end the chapter with selected quotes from our participants over the years.
Finally, we suggest future emerging ﬁelds of Designing possibilities for Digital
Health teams, particularly implications for doing truly transdisciplinary work.

2 Mapping Design, Design Thinking, and Digital Health
Interface
Design in its many forms has been growing its application in the medical ﬁeld.
Design comes in different ﬂavors, such as a human-centered approach
(Bhattacharyya et al., 2019) to place-based innovation (Vechakul et al., 2015).
Many of these experiences have been studied and analyzed in academic contexts,
particularly focusing on user-centered development (Chokshi & Mann, 2018).
Looking at scholarly outputs on the application of Design in medicine and health
serves as a proxy to illustrate the current state of the practice and application of
Design in digital health. This section presents the bibliometric review of the research
on the interface of Design, design thinking, and digital health.
See appendix with visual Mapping of DT and Design in Healthcare

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Bibliometric Review Query

Note on methodology: This bibliometric analysis was conducted in September 2021.
We conducted two (2) searches using the SCOPUS database, searching for the
following queries and results.
Search 1: ("digital health*" AND "design") OR ("digital health*" AND
"innovation")
1555 document results from SCOPUS.
Article (880), Review (305), Conference Paper (241), Book Chapter(41),
Note (33), Conference Review (18), Editorial (18), Letter (7), Erratum (6),
Book (4)
Search 2 : TITLE-ABS-KEY ( ( "digital health*" AND "design thinking*" )
( "digital health*" AND "innovation*" ) ) OR ( "digital health*" AND "usercentered design" ) OR ( "digital health*" AND "human centered design*" )
AND ( LIMIT-TO ( SRCTYPE , "j" ) )
Figure 3 illustrates the keywords (top bigrams) in the title of the literature in the
intersection of design, design thinking, and digital health.

2.2

Zooming in on Digital Health Design-Oriented Education

Design has become increasingly popular in Information Technology, business and,
more recently, healthcare to solve wicked problems and frame them well (Dorst,
2011). Design is also crucial to developing interventions that change health behavior
(smoking, eating habits, alcohol intake) and has been adopted by academia and
industry to provide better alternatives to wellness and self-care (Mummah et al.,
2016). Several authors have highlighted how design and design thinking can
enhance effectiveness and efﬁciency in healthcare education contexts by carefully
considering human patient and user needs (Altman et al., 2018; Boydell et al., 2021;
Chan, 2018). Research suggests that although more attention is now being given to
those equally important other persons involved (doctors, radiologists, nurses), as
reﬂected in work by Muinga et al. (2021); von Thienen et al. (2015), there is still a lot
of uncharted territory in Digital Health design education. Recently, attention has
been paid to medical “people/users” because doctors are people too, and they
deserve beautiful experiences in their work, as do researchers engaged in providing
studies to improve the care of society.
Beyond problem-solving and beautiful user interfaces, the role of design education in healthcare is a very hot topic as it promises inclusion (Almendra, 2013),
understanding implicit bias (Zeidan et al., 2019), supporting multidisciplinarity and
more radical approaches to need analysis (Gardner et al., 2019; Woods et al., 2018).

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Fig. 3 Top bigrams from titles of literature on digital health and design

Design is also valuable for reﬂecting on the opportunities and challenges of digital
technology, e.g. Artiﬁcial intelligence (Ala-Kitula et al., 2018; Belkacem et al.,
2020; Ferreira et al., 2020; Liyao, 2021).
For our research at the Digital Health Center, we see that Design is unequivocally
important in medical education as others have suggested (Badwan et al., 2018; Chao
& Chao, 2018), especially with approaches that leverage digital tools, platforms, and
infrastructure. Da Silva et al. (2020) have developed a cardiopulmonary resuscitation
prototype for health education contexts, while other authors emphasize the Value of
Design Thinking principles in bringing circularity and openness to medical innovation (Albala et al., 2018; Buckley et al., 2021; Deitte & Omary, 2019; Gottlieb et al.,
2017; Hong et al., 2021). In collaborating with educational actors, Industry actors are
also adopting design thinking approaches to explore and exploit digital health
solutions for improving consumer care and usability (Bell, 2003; Janz et al., 2016;
Saidi et al., 2019). Related to digital health design education, DT has been applied in
IoT new product development (Kalyazina et al., 2019) for developing educational,
social media videos on inﬂammatory bowel disease (Khalil et al., 2020).
Furthermore, in terms of mapping digital health design, McLaughlin et al. (2019)
recently did a qualitative review of design thinking frameworks in health profession

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Fig. 4 Digital healthcare conceptual structure based on author keywords search

education. They conclude that although the potential value of design thinking to
healthcare is high, many questions remain because of healthcare systems’ complexity and multi-actor nature. Therefore, teaching medical students design thinking is
valuable but will not sufﬁce to deliver value that is useful and appreciated by users,
doctors, payers, and other practitioners in healthcare.
As shown in Fig. 4, our mapping of the literature on digital health landscape
appears to generate 8 clusters, depicted in the variants colored.

2.3

The Gap/Opportunity in Digital Health Design
Education: Value Creation by Design

In his book, the reﬂective practitioner, Donald Schön emphasizes rigor as a pinnacle
of professionals engaged in practice and action (Schön, 1987). For him, there is
value in professional education to move beyond analytic techniques, which have
been traditional in operations research, and more into the active, synthetic skill of
“designing a desirable future and inventing ways of bringing it about” (i.e.,
methods).
Following a thematic analysis of the Digital Health Education landscape, the
authors suggest that value creation in patient care can be more successful in terms of
reduced risk, learning outcomes, citizen engagement when some degree of design-

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Fig. 5 Top keywords in digital health and design literature

based entrepreneurship is added (Niccum et al., 2017; Sawyer, 2020). To contribute
to current digital health design education from these suggestions, we see immense
opportunity for what we propose, designing for value creation. As shown in the
conceptual structure map below, we see the need to integrate the clusters (i.e.,
bringing design thinking to be a form of design-based entrepreneurship that prioritizes value and its creation for multiple actors). The complexity in healthcare is that
Value is different for every stakeholder in healthcare. Researchers prioritize standards, publishing well-designed scientiﬁc research and systematic procedures; while
practitioners value patient care, providers and payers value healthcare delivery, and
leadership comes to the fore. Patients want to enjoy products and services that lead to
wellness and are open to ways to prevent getting sick in the ﬁrst place. Digital health
design education does not explicitly align all these-sometimes-conﬂicting values in
every innovation artifact, product, service, or system (Fig. 5).
Beyond rigor and robust methods in design thinking, it is crucial that r designing
in digital health be based on empirical insights drawn from watching teams, enjoying
insights from advanced design theory and method. So, following Schön’s strong
position on the use of robust methods, we reﬂexively asked ourselves and the
designers we have worked with some reﬂexive questions that are valuable for
embedding design in healthcare. For example:
What is my design process?
Why am I using these design methods?
How can I get better?

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What are the ways that busy doctors could get into Design?
What are the fundamentals that should be practiced in care design settings, like in
sports or dance?
What kinds of exercises can be useful in boosting affective, cognitive, and skill-based
outcomes, particularly in the time-constrained and high stakes domain of
health care?
How can we design for a multi-actor healthcare setting where value creation for all
actors is the priority?
In the following section, we present the answers to some of these questions. It is
very much an ongoing work, and our cases reﬂect our initial experiences and
objectives as they have evolved.

3 Action Research Cases: Designing for Value Creation
Our deﬁnition of action research follows that adopted by a disciplined process of
inquiry conducted by and for those taking action. Here our primary reason for
engaging in action research is to assist the “actor,” i.e. the course participant (digital
health students, medical doctors) in improving and reﬁning their Design and skilled
actions. Action research has been well applied to improve action in educational and
learning contexts. (Bilorusky, 2021; Rowell et al., 2017)
Sagor’s quote inspires us: “Perhaps even more important is the fact that action
research helps educators be more effective at what they care about most—their
teaching and the development of their students. Seeing students grow is probably
the greatest joy educators can experience. When teachers have convincing evidence
that their work has made a real difference in their student’s lives, the countless hours
and endless efforts of teaching seem worthwhile” (Sagor, 2000).
We select action research because it is appropriate for making iterative and
developmental interventions in educational settings (Fig. 6).

3.1

Embedding Designing-as-Performance (D-a-P)
Principles and Methods into Health Contexts

To instantiate designing for value creation, we have two foundational elements to the
approach:
The ﬁrst element encompasses the Designing-as-Performance principles.
The second and complementary element encompasses the collection of methods.
In the past years (Edelman et al., 2020; Edelman, Owoyele, et al., 2021; Edelman,
Santuber, et al., 2021), our research group has developed strong stances to address
Design for digital health and radical innovation. These stances encompass principles,

Designing for Value Creation: Principles, Methods, and Case Insights. . .
Fig. 6 Overview of D-a-P 3
principles (blue) and 2
methods (red) for designing
for value creation (D4VC) in
digital health multi-actor
settings

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Increased
Customer/User
Engagement

MEDGI-Shared
team based model
for performing
Design

PRETOVIDS -low
cost video
pretotyping
D4VC

Reduced
Risk/Improved
Safety

Reduced
Cost/Improving
Profit

models, and methods developed and applied under the Designing-as-Performance
approach to Design. Speciﬁcally, we have dedicated book chapters and articles for
an in-depth explanation. In the following section, we brieﬂy introduce Performative
Patterns, MEDGI, and the PretoVids, yet we suggest consultancy the previously
published chapters for more detailed elaboration.
The fundamental premise of the Designing-as-Performance (DaP) approach is
that designing is a performative act. Design sessions are a performance of a corpus
of behaviors with mediating objects and narratives (Edelman et al., 2020). A primary
research aim for our group has been to understand the characteristics and mechanisms of high performing design teams. We have taken a very grounded approach to
understanding performance through a wide lens of other performative disciplines
that engage several performers working together to reach a goal. Observations of
sports, music, dance, and surgical teams have yielded data and analysis of what
constitutes highly effective performance practice. A survey of research concerning
how teams work in situ offers plenty of useful insights into the characteristics and
mechanisms of high performing teams in nearly any domain. Furthermore, examining the teaching methods that cultivate high-performance yields insights into how
teams can be taught to perform well and what constitutes team performance itself
(Edelman, 2011).
Whether in sports or music, training consists of an integrated curriculum of
theory, practice, and cognitive work. No single one of these stands on its own, and
no single one leads to great results. Instead, theory, practice, and psychology
reinforce one another and make each other possible. All the encouragement and
positive mindsets in the world cannot make an Olympic athlete without a thorough

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understanding of body mechanics and correct practice based on mastery of fundamentals (Edelman, Owoyele, et al., 2021).

3.1.1

D-a-P Principles (3): Performative Patterns for Reducing Risks
and Costs, Increasing Proﬁts, and Improving User Engagement

The fundamental premise of the Designing-as-Performance approach is that designing is a performative act, and that design sessions are a performance of a corpus of
behaviors often with mediating objects. We call these behaviors Performative
Patterns.
Performative Patterns constitute team interactions, both micro and macro interactions, that have been distilled from observing high-performance teams at work.
Performative Patterns can be articulated and taught through training routines comprised of relevant theory (frameworks) and repeated practice of well-crafted drills
and exercises. Furthermore, Performative Patterns serve as shared models, or event
schemas (both mental models and interactive models) that enable design teams to
perform well.
It is important to keep in mind that Performative Patterns are models, none of
which are complete in themselves. Each Performative Pattern represents a different
point of view, a different look at the behaviors of high-performance teams. These
behaviors have been deconstructed into understandable, repeatable chunks so that
they can be practiced separately, like passing or shooting in a ball game, or like
scales and chord progressions in music. Neither passing nor shooting alone will win
games; neither scales nor chord progressions alone constitute a great performance,
but the mastery of each is essential for excellence. When Performative Patterns are
practiced separately and reintegrated into a whole, they promise to bring design
teams to new levels of expertise. One value of the D-a-P approach is that design
methods which are empirically proven can be applied to create value in healthcare
contexts in three ways, namely design methods that explicitly focus on reducing risk,
increasing proﬁt, and boosting patient/practitioner/payer engagement.
In this regard, the performative patterns are building blocks that are put to work
toward creating value in the form of the following principles:
1. Increasing proﬁt/reducing cost
2. Reducing risk/increasing safety
3. Increasing user engagement
3.1.2

D-a-P Method A: MEDGI

Following our elaboration of the three principles for design-based value creation,
this section describes what characterizes how design methods are used to
operationalize these principles. This section presents MEDGI as a method for
health-centered value creation.

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M-E-D-G-I is an acronym that stands for Mapping, Educing, Disrupting,
Gestalting, and Integrating and serves as a primary performative pattern based on
work by Edelman and our research group (Edelman, Owoyele, et al., 2021). MEDGI
is both a macro and a micro pattern in that it describes both long-term project
development arcs and moment to moment development team interactions. MEDGI
Re-Design Method originates from 10+ years of research at Stanford, Imperial
College, Politecnico de Milano, and HPI into how small design teams create new
concepts (Edelman, Owoyele, et al., 2021). MEDGI was extracted by watching highperformance teams in action and analyzing their interactions through video analysis
and behavior coding. MEDGI affords that digital health design teams can now move
an existing object-interaction to a state of potentiality and then reform it into a new
object-interaction(Edelman et al., 2020).
On the macro level, Mapping activity is about creating maps of “current objectinteractions in healthcare and their accompanying narratives on a time and space
map” (Edelman et al., 2020). Mapping is a process for creating a shared representation of the current state of digital health affairs. Digital Health Mapping therefore
ensures that the representation of the state of affairs in healthcare contexts is both
externalized and somewhat persistent. A digital health design map further allows
team members to point to and to refer to speciﬁc care-related points in time and space
that they can then address and explore further. Digital health maps can be sketches of
health products, services, or systems. Digital health mapping is driven by the
fundamental question: what is (and is not) here?
An example of Mapping is a journey map of endometriosis care, in Fig. 7.
Educing refers to identifying and highlighting what works and what does not
work in medical contexts, and pain and pleasure points in the experience of actors in
healthcare. On a macro level, Educing often means encoding the map problem and
success areas, literally identifying and highlighting them for the digital health team

Fig. 7 Sample of mapping in digital health education. Source DHDL Course

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Fig. 8 Sample of MEDGI exercise on N-of-1 App redesign. Source DHDL HPI

to see. In digital health, Educing refers to enacting what works and what doesn’t
work, pain and pleasure points, on an experiential level. For example, the notion that
a diabetes management app does not allow a user to log in to see their own data is an
example of an educed insight. The “pain” is that users are not happy or satisﬁed with
being locked out of their own data, even though they now have an app that lets them,
e.g. monitor their sugar intake. Here again, as in each step of MEDGI, the question
drives the digital health enquiry but also acknowledges what works. In digital health
taking stock of what works is as important as taking stock of what does not.
Disrupting in digital health design follows Mapping and Educing with a proposal
to the change to the state of affairs in an object-interaction, and take the form of
questions like, “what happens if...?” (Edelman, Owoyele, et al., 2021). For example,
a digital health design team would disrupt by asking, “What are some of the things
that could happen if we could hear diabetes intake via earbuds?” or “What happens if
we made the diabetes syringe a sensor that calculates the blood insulin state too?” In
digital health Disrupting, our experience is that there is little or no explicit notion of a
solution; the goal is more “towards exposing the potentiality of an object-interaction” (Edelman, Owoyele, et al., 2021) (Fig. 8).
Gestalting follows the previous three steps and implies “roughing-in” or
“sketching” a novel object-interaction. Gestalting is seen as a general picture of
the ﬁeld of health possibilities that could result from the Disruption previously
proposed. Here the digital health design team could sketch the diabetes syringe
that measures insulin with labels of how it could look. Here some level of details are
carefully articulated. In the spirit of rigor, the digital health design team may bring in
a product designer to Gestalt the syringe. If the solution is an app that gives data

Designing for Value Creation: Principles, Methods, and Case Insights. . .
Fig. 9 MEDGI loop
(Edelman, Owoyele,
et al., 2021)

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Mapping

Integrating

Gestalting

Educing

Disrupting

access to diabetes patients, the digital health design team may sketch the interface
using pencil and paper to give form to the concept.
Integrating is what typically follows a well-executed digital health design
process, here we say “the new state of affairs comes together, the crystallized in a
new articulation” (Edelman, Owoyele, et al., 2021). Integrating is about giving
speciﬁcations for manufacturing and distributing the solution so that users (e.g.,
doctors, patients, and family members) can truly enjoy the solution. With Integrating, the value is for radically highlighting a very compelling user experience and
interaction, as well as systems integration for the digital health product service or
system. The focus is on digital health app features like buttons and adjustors,
payment interfaces and giving the solution a novel name. Unlike Gestalting, in
which the question is about how does it look roughly, and how does it work roughly,
Integrating tends toward more questions like “How does it really work excellently?
Is it esthetically pleasing, and will users boast about this product, service, or system
to others?” (Fig. 9).

3.1.3

D-a-P Method B: PretoVids

How can new digital product development teams proto-type new digital products
without writing a line of code or spending a dime on software development to learn
what customers love?
In recent work we presented PretoVids (see Fig. 10), a digital and practical,
research-based method for structured, evidence-based prototyping (Edelman,
Santuber, et al., 2021). PretoVids is a new prototyping technique that is genuinely
relevant for showcasing digital media’s role in design-as-performance in concept
development. We discovered early on that for digital health contexts “the traditional
prototyping methods do not provide an agile heuristic for digital product development teams” (Edelman, Santuber, et al., 2021).

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Fig. 10 PretoVids principles (Edelman, Santuber, et al., 2021).

Furthermore, critically looking at literature on prototyping approaches in
healthcare revealed videos as a robust tool for agile prototyping in designing for
healthcare. In the context of new healthcare product development, PretoVids has
been employed to reduce the many risks of building fully digital projects. It thus
saves precious time for busy doctors when they are engaged in design sessions, and
affords easier communication of value proposition. Moreover, videos PretoVids
allow rapidly sharing and learning, in particular design projects, because these
videos may be handed off to professional video designers in later design phases.
In 5–7 steps, digital health design teams can perform an iterative process to develop
new healthcare product/service/system concepts without spending a penny on software development or writing a single line of code. Please see previous research on
PretoVids for further insights to its workings (Edelman, Santuber, et al., 2021).

4 Cases: Designing for Value Creation in Digital Health
with MEDGI and PretoVids
This section introduces the cases in which we applied the Design for Value Creation
methods (MEDGI and PretoVids) to real projects with partners and medical doctors
as well as master students engaged in designing for digital health. We conducted the
cases provided in the following section in the context of the Digital Health Design
Lab (DHDL), at the HPI Digital Health Center.

4.1

DHDL Course: Value Creation at the Hasso Plattner
Institute Digital Health Center

The Digital Health Design Lab is a hands-on studio course about shaping innovative
products, services, and systems. This course is part of the Hasso Plattner Institute’s
Digital Health Center and offered at a multidisciplinary international master program. Our course website describes the course as follows: “Through richly

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Fig. 11 Participants of the
Digital Health Design Lab at
HPI, using tracing paper to
map the “what’s there” of an
existing medical device
(shape, buttons, screen,
etc.. . .)

illustrated presentations, relevant theory, and hands on exercises, master students
gain insight and experience with the foundations of Design in the context of digital
health, as well as understanding and practicing effective team interaction and
communication” (DHDL, Hasso Plattner Institute, 2020).
Participants are supported in exploring, developing, and communicating a professional vision of their contribution to the ﬁeld of Digital Health. This is “done
through development of individual and group project work relevant to their focus in
Digital Health. Participants are expected either to make signiﬁcant progress on their
Digital Health projects during the semester, or be very well prepared for their digital
health projects in the upcoming semester” (DHDL, Hasso Plattner Institute, 2020)
(Fig. 11).

4.1.1

Learning Goals

These goals are set in order that course participants become familiarized with the
principles and practices of human-centered Design in the context of Digital Health.

4.1.2

Conveyed Competencies

• Students acquire knowledge-related competencies relating to “how Design is an
essential element in creating rich and engaging products, services and user
experiences; how to make radical, relevant and rigorous innovation in the context
of products, services, systems; and how to cultivate a culture of innovation”
(DHDL, Hasso Plattner Institute, 2020).
• Students also deepen their methodological competencies such as case study
creation and analysis; synthesis; prototyping and testing; video prototyping for
ideation and communication; design theory and methodology; workshop creation; presentation techniques. To augment academic work and prepare students

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for real-life engagements, the course provides opportunities for boosting social
competencies: group work, discussions, and structured critiques. The course
offers lectures, group discussion, participant presentations, hands-on exercises,
and project work. This hands-on course requires active participation and engagement from attendees. Participants are expected to contribute verbally and materially to each session of the course, sharing insights, questions and their project
work on a regular basis.

4.1.3

Charite: HPI Digital Health Design Lab

To further iterate the Designing for Value Creation in health care scenarios, we
collaborated with Charité Berlin to engage medical practitioners in exploring how
digital health design education might augment value creation in the Charité’s
education curriculum.

Project Goal: Exploring D4VC in Charité Medical Students Education
The mini hackathon took place entirely online as part of a 3-week module “Digital
Health” at the Charité University. Third-year medical students (sixth semester) and
students from HPI’s Digital Health Design Lab (DHDL) worked together in teams of
three to four people. The teams identiﬁed a challenge (see Table 1), developed ideas
to solve that problem, created a prototype, and presented their solutions in the form
of a short video (PretoVid).
The goal for the medical team was to learn basic methods of designing in
healthcare and to understand the underlying principles.
Table 1 Summary of challenges addressed during the Mini-Hackathon
Challenge
Studying medicine during the pandemic

Increase patient safety on normal wards

Improving Outpatient Care for patients
living with a chronic condition

Description
Online education, no “student life,” and a new, unfamiliar city but no way to get to know it—starting
medical school in 2020/21 is challenging. How can we
improve the ﬁrst-year medical student experience in
the context of the COVID-19 pandemic?
On normal wards, there is often no possibility to connect patients to hemodynamic monitoring. The nursing
staff is not trained to work with such a system and the
infrastructure is not sufﬁcient to install it. As a result,
dangerous situations can be overlooked and therapeutic measures can be initiated too late
How can we improve outpatient care and patient
experience? (e.g., communication with health workers
physician, understanding treatment plan, remotely
monitoring vital signs). Special focus on patients with
chronic conditions

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Fig. 12 Miro layout-hackathon prepared by the teaching team for the participants

One positive externality of doing this workshop online is that participants become
familiar and conﬁdent using online collaboration tools. One remote collaboration
tool we used for the mini-hackathon was Miro Boards (see Fig. 12). These digital
boards provide a shared space for participants to write notes, upload images, sketch
their maps, and display new user interfaces.
• 08.02.2021/10.30-11.15: Team building with preparation for individual small
group work
• 08.02.2021/11.30-12.15: MEDGI for Digital Health—more than just an app
• 16.02.2021/09:30-11:45: Problem Solving
• 22.02.2021/10:45-13:00: Prototyping
• 22.02.2021/14:30-17:00: Flexible working session with mentors
• 23.02.2021/9:30-12:15: Presentation Skills/Prototyping

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• 23.02.2021/13:00-17:00: Flexible working session with mentors
• 25.02.2021/10:30-11:15 Uhr: Evaluation Charité Mini-Hackathon

4.2
4.2.1

Zooming in on Digital Health Design-Oriented Education
StudyU: DHDL

Project Goal: To Explore Pain and Pleasure Points in Current N-of-1 Trial
Research App and then Redesign
This project focused on redesigning StudyU as a research platform. In this regard,
the DHDL team worked with the researchers from the Machine Learning chair at the
HPI Digital Health Center. By using D-a-P Principles and MEDGI, students and
scientist conducted user research to improve the user experience of StudyU. StudyU
is a digital platform for designing and conducting innovative digital N-of-1 trials.
Currently, N-of-1 trials are the gold standard study design to evaluate individual
treatment effects and derive personalized treatment strategies. In engaging with
researchers at the Digital Health Center, we wanted to explore the role of D4VC in
leverage digital tools in initiating a new era of N-of-1 trials in terms of scale and
scope. The challenge that fully-functional platforms are not yet available
(Konigorski et al., 2020). The StudyU platform is open source and includes both
the StudyU Designer and StudyU App.
In the DHDL class we critically applied MEDGI and PretoVids to the redesign of
the StudyU Designer, a platform where “scientists are given a collaborative web
application to digitally specify, publish, and conduct N-of-1 trials” (Konigorski
et al., 2020). The StudyU App is a mobile application with innovative user-centric
elements for participants to partake in the published trials and assess the effects of
different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in
a safe manner.
The vision for StudyU is that it can change the landscape of personalized
treatments both for patients and healthy individuals, democratize and personalize
evidence generation for self-optimization and medicine, and can be integrated in
clinical practice. For personalizing health interventions and evaluating individuallevel treatment effects, N-of-1 trials are the gold standard Design. StudyU is
developed as an open-source, free, and easy-to-use platform called StudyU for
performing digital N-of-1 trials.
The application also contains a study designer that enables researchers to collaboratively design and publish N-of-1 trials, as well as an app that allows participants to
take part in these trials. By leveraging Designing-as-Performance methods students in
the DHDL contributed to its reﬁnement whereby StudyU can contribute to open,
transparent, and reproducible medical research linked to clinical care. Figure 13
depicts the redesign process in Miro.

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Fig. 13 StudyU redesign (Miro)

4.2.2

Sleepfull Project (DHDL Winter Semester 2021 in Collaboration
with StudyU)2

Project Goal: To Design a PretoVids Concept for N-of-1 Study Design
Platform StudyU on the Topic of Insomnia
Studies suggest that 10–30% of all adults suffer from chronic insomnia with a higher
prevalence in older people and pregnant women (Bhaskar et al., 2016). Insomnia and
sleeping problems in general have an impact not only on a person’s mental, but also
physical health.
By addressing sleep conditions with N-of-1 trials, Sleepfull has the potential of
helping 0.5–1.5 billion people in the age group between 15 and 653 (Worldometer,
2021). StudyU allows us to provide this large group of people with a professional
2

The following paragraphs are adapted from the DHDL participants’ reports. Team Members:
Dennis Kipping, Marius Michaelis, Larissa Röhrig, Denise Schmidt, Julius Severin
3
Population Ages 15–64 (% of Total Population), n.d., World Population Clock: 7.8 Billion People
(2021)—Worldometer, n.d.

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N-of-1 trial without needing to leave the house. Sleep is something very personal,
which results in insomnia being a very diverse condition. Thus, N-of-1 trials have an
immense potential in ﬁnding the best treatment for everyone’s personal insomnia.
For a ﬁrst iteration of the Sleepfull N-of-1 trial the following interventions were
chosen:
• Limiting screen time prior to bedtime: The usage of electronic devices before
sleep can have impact on the production of melatonin which results in an
increased alertness and thereby interferes with sleep (Pacheco, 2017)
• Restricting alcohol, caffeine, and tobacco consumption: The intake of certain
substances has a major impact on sleep. Not only on the duration, but mostly on
its regularity, which may result in a long but non-restoring sleep (Spadola et al.,
2019)
• Exercise: There is strong evidence that exercise helps in falling asleep. Nevertheless, timing matters and therefore there should be at least 2 hours between
exercise and bed time (John Hopkins Medicine, 2021)
• Meditation: Studies suggest that mediation, and in particular mindfulness may
improve sleep quality (Pacheco, 2020)
• Consistent sleep schedule: Following a consistent sleep schedule is especially
helpful when it comes to sleep-onset insomnia (Fry, 2018)
• Colored light exposure: Studies suggest that exposure to different light intensities
and wavelengths can affect the circadian rhythm and thereby adapt the sleep–
wake cycle (Tähkämö et al., 2019)
The opportunities the Sleepfull team leveraged (for example, new technologies or
new ways of collecting data or new medical knowledge or new insights into human
behavior) were many. Furthermore, the Sleepfull project leveraged from different
existing and ready-to-use technologies. Those easily accessible technologies were,
on the one hand, used to improve the outcome and adherence of the trial, but, on the
other hand, also to improve future trials by giving researchers the data they need.
More speciﬁcally, Sleepfull combined telemedicine with N-of-1 trials approach.
Insomnia is a disease that does not require instant treatment and interventions.
Therefore, the threshold for people to seek professional help is higher than with
other conditions. When a patient signs up for the Sleepfull N-of-1 trial, it offers the
patient to connect them with a suitable practitioner. Through video consultation, the
patient can then discuss the upcoming trial. Telemedicine also allows the patient to
be monitored throughout the trial and to discuss the outcome of a trial with an expert
and make sure that the right steps are taken.
The use of sensors allows holistic monitoring of the patient. It allows Sleepfull to
personalize interventions based on the current needs of the user. By donating the data
to research, a patient has the chance of not only improving the N-of-1 trial but also of
help researchers to understand insomnia better.
The average person spends around 26 years of their lives sleeping. Sleep plays a
crucial part for our health as it allows the body and the brain to slow down and
recover. At the beginning of the class, the whole Sleepfull team had sleep or a sleeprelated topic as one of the things they love. Many people take sleep as a given and do

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not give more attention to it. But what happens if restful sleep, which is often taken
for granted, becomes a problem?
During the research for the N-of-1, the team members learned about the complex
multitude of factors that can inﬂuence the sleep quality and how different the impact
on the personal sleep of a person can be. In the development of the N-of-1 study
they, therefore, included manifold evidence-based interventions to cover different
perspectives of possible causes for insomnia and other sleep conditions. Sleep is as
diverse as people and therefore, insomnia and its causes are as well. This makes it a
perfect case for N-of-1 trials and gives patients a professional framework to try out
different interventions and thereby learn about themselves and their personal
insomnia.
In looking at the literature and practice we found a signiﬁcant gap regarding an
understanding of the “user,” as understood as a synonym for the patient in healthcare
contexts. While we agree that healthcare systems should serve patients better, there
is a whole spectrum of users that remain underserved. Moreover, not only the quality
of the whole healthcare system improves when focusing on other stakeholders, but
also the value that creates the new product, service, or systems is higher. For
instance, in our projects, we have deﬁned the value created for patients, doctor,
and scientists. This broader scope enables synergies of value to be created, while the
same data entry serves the patient goals, the doctor, and researchers well.
Figures 14 and 15 show examples of sketches of Sleepfull’s patient-facing
interfaces. These have been designed for making patient participation in an N-of-1
study easy, pleasurable, and meaningful. Here, comprehensibility of a time extensive
and somewhat complex process is key for patient compliance and the success of the
study. Patients are able to see where they are in the process, where they have been,
and what the next step is for their N-of-1 study.

Fig. 14 Sleepfull PretoVids frames

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Fig. 15 (a) Sleepfull PretoVids frames from patient view. (b) Sleepfull PretoVids frames from
doctor view

Figure 15 shows a sketch of a doctor-facing interface. Doctors have to track and
manage many patient cases. The Sleepfull Patient Overview page enables doctors to
get a clear view of a cohort of patients and their progress. If a patient needs special
attention, it is clearly indicated on the page. The doctor can easily follow a link to
obtain necessary information about the patient, and contact them if need be (Fig. 16).

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Fig. 16 Sleepfull PretoVids frames from researcher view

5 Reﬂections and Insights for Design Educators
and Practitioners in Digital Health Contexts
Redesigning our current healthcare products, services, and systems into digital
offerings requires multiple perspectives. Together with the technical and medical
capabilities, novel digital health solutions need to cover ﬁnancial, regulatory, policy,
governance, legal, and the like. However, there is more to great design than technical
requirements.
In our daily lives, both analog and digital experience have been designed to
capture our attention and emotions. People—doctors, patients, and researchers—are
watching exciting movies on Netﬂix, meeting interesting people on Tinder, listening
to captivating music on Spotify, going to live theater, eating in well-appointed
restaurants with amazing menus, attending engaging sports competitions, calling
cars with Uber, and getting directions with Google Maps. The attention of doctors,
patients, and researchers is constantly being captured by ingeniously designed
analog and digital experiences that are addictive. In respect to the user experience
of doctors and researchers, digital health design has not shown an equivalent level of
concern for user engagement. Too much of doctors’ time has been allocated for
painful and tedious data entry—precious time that has been stolen from quality time
with their families and from caring time for their patients.
The Designing-as-Performance approach invites designers to think broadly about
value creation in its many forms: technical, ﬁnancial, experiential, and existential. It
enables designers to consider that researchers are people too, and that their experience at work saving lives can be as fabulous as going to dinner and a show.
Designing-as-Performance challenges and empowers digital health designers to
ask questions like:

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“What happens if . . . a digital health patient record was as enjoyable for doctors
and administrators as the home page of Netﬂix?”
“What happens if . . . segmenting and annotating brain MRIs was as engaging for
researchers as a ﬁrst-rate video game?”
“What happens if . . . polygenic risk scores were as understandable and navigable
for doctors as Google Maps?”
A well-structured approach, with a strong theoretical foundation, as found in D-aP, can ease the adoption of Design in education and research. In the three action
research cases we have presented in this chapter, a clear, yet deep, approach to
designing has been key to the success of those projects. With a strong emphasis on
hands-on and learning-by-doing approaches, the theoretical aspect of the methods
used in Design are often lost. In the experiences we have made, a concise and
coherent theoretical explanation of the “why” of what is done helps participants to
make sense of things and be more conﬁdent when executing their tasks.
The Designing-as-Performance approach enables teaching teams and facilitators
to leverage decades of research on design and design practices. Because of this,
practitioners do not need to make up theory because sound design theory is provided
by teaching teams and facilitators. Because D-a-P is fundamentally transdisciplinary,
teachers and practitioners can readily draw from different design traditions and
increase team performance and the impact of their work. Moreover, different
approaches to designing are complementary and are not exclusive. D-a-P draws on
work in human–computer interaction, product design, graphic design, industrial
design, service design, experience design, fashion design, digital/web design, architectural design, mathematics, musical composition, visual and ﬁne arts, opera, pop
culture... the list goes on. This rich diversity of sources helps digital health designers
to ﬁnd multiple references that match and enhance to their experiences, and consequently enriches their sensemaking processes. These rigorous approaches to Design
are immersed, embedded in a continuum of techniques, methods, approaches,
knowledge creation, and ultimately world views. Grounding digital health design
practices in mature ﬁelds of design practice can give our work as designers a muchneeded depth and rigor.
Acknowledgements This research was fully supported by the Hasso Plattner Design Thinking
Research Program and the Digital Health Center. Collaboration opportunity with Charite Berlin is
also acknowledged. Particularly the collaboration with Charite Berlin Researchers Dr Med Lina
Moosch, Ulrike Anders, Dr Med Akira-Sebastian Poncette, and Dr Med Daniel Pach.

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Lopez, B. L. (2019). Implicit bias education and emergency medicine training: Step one?
Awareness. AEM Education and Training, 3(1), 81–85. https://doi.org/10.1002/aet2.10124

Part III

Problematizing Design Thinking
as a Concept

Different Concepts of Human Needs
and Their Relation to Innovation Outcomes
Julia von Thienen, Constantin Hartmann, and Christoph Meinel

Abstract While design thinking has been described in different ways, there is
widespread agreement about two characteristics of the approach: Design thinking
involves a “focus on needs” and works towards “radical innovation”. However,
some authors have argued that these two characteristics actually antagonize each
other. According to their assessment, a focus on needs reduces the innovation
potential of projects, rather than fostering new breakthrough solutions. What is the
logic of these arguments and is design thinking in trouble? The purpose of this
chapter is to shed further light on the concept of needs in design thinking. We review
need theories by three authors—John Arnold, Abraham Maslow and Robert
McKim—who have prominently shaped design thinking theory and practices from
the 1950s onwards. In each case, we summarize the author’s basic statements and
trace relations to present-day methodologies of working with human needs. The
chapter highlights notable agreement among all discussants concerning favourable
approaches to foster radical innovation. We further emphasize the importance of
distinguishing between narrow versus wide accounts of needs, where design projects
with narrow accounts stick closely to user statements that are often highly contextbound, while projects with wider accounts include re-framing and visionary
contextualisation. Design thinking education as offered at the d.school in Stanford
and the D-School at Potsdam involves a wide account of human needs. In this
context, two important skills in order to move from need assessments to worthwhile,
radical innovation are the abilities to uncover need hierarchies from contextdependent desires stated by users to basic human needs, and to identify conﬂicts in
need hierarchies that call for different and better solutions in society.

J. von Thienen (*) · C. Hartmann
Hasso Plattner Institute, Potsdam University, Potsdam, Germany
e-mail: Julia.vonThienen@hpi.de; Constantin.Hartmann@hpi.de
C. Meinel
Hasso Plattner Institute and Digital Engineering Faculty, University of Potsdam, Potsdam,
Germany
e-mail: Christoph.Meinel@hpi.de
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_11

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1 Introduction
Human needs are a key concept in design thinking. This focus is already emphasized
by Meinel and Leifer (2011) in the ﬁrst Springer book on design thinking research.
Design thinkers are said to endorse a “human-centric point of view” (Meinel &
Leifer, 2011, p. xv). They work to solve problems “in ways that satisfy human
needs” (ibid.). The mindset of a design thinker is to “focus on human values” (d.
school, 2010, preface). While “design thinking” is interpreted differently across
institutions and by different authors (Lindberg, 2013; Schmiedgen et al., 2016),
there is widespread agreement about the importance of a focus on needs. “Although
the concept has different interpretations, Design Thinking is usually characterized by
[. . .] a human centered perspective, where innovators build empathy with users”
(Verganti et al., 2019, p. 1).
In all scientiﬁcally grounded endeavours, a thorough explication of key concepts
is part of the expected academic rigour. In this sense, a careful discussion of what a
focus on needs means in more precise terms is certainly desirable. Conceptual
clariﬁcations also help to develop nuanced observations and analyses. For instance,
when design thinkers work with human needs, what potential measures quantify the
sophistication of their approach? How about measures such as the number of
stakeholders the team is in touch with, the frequency of interactions with stakeholders, the diversity of stakeholders that get involved in a project (cf. Royalty &
Roth, 2016), the design thinker’s versatility in need analyses as via Why-HowLaddering (d.school, 2010; von Thienen et al., 2012), or other measures?
Yet, a thorough discussion of human needs is important in design thinking for
reasons far beyond ordinary scientiﬁc diligence. Noteworthy claims have been
made, according to which the focus on needs would only help to improve details
in existing solutions (incremental innovation), while being ill-suited for ﬁnding
novel, worthwhile solution avenues (radical innovation). When design thinking
aspires to both—a focus on needs and radical innovation—such claims are of key
importance and call for careful consideration. Clearly, whether or not a focus on
needs hinders or facilitates radical innovation depends essentially on what a “focus
on needs” means. It is therefore key to explicate this meaning in careful ways.
This chapter begins by reviewing claims in the literature about the design
thinking focus on needs as advancing incremental rather than radical innovation
(Sect. 2). In this context, we also trace the particular understanding of a focus on
needs that underlies this line of argumentation. We then review three inﬂuential need
theories that inform practices at educational facilities for design thinking, such as the
d.school at Stanford and Potsdam’s D-School. These theories have been set forth by
design thinking pioneer John E. Arnold (Sect. 3), Abraham Maslow (Sect. 4), and
Robert H. McKim (Sect. 5). In each section, we also trace the legacy, which the
theory leaves in terms of shaping present-day design thinking practices. How each
need concept impacts the prospects of radical innovation is another important line of
discussion. The chapter closes by highlighting the diversity of human need concepts across authors and communities (Sect. 6). Overall, we look at how conceptual

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decisions concerning the focus on needs impact reasonable outcome expectations in
design thinking innovation projects. The chapter closes with an outlook to a new
development in design thinking, namely a comprehensive framework of human
needs to analyse product risks and beneﬁts in systematic ways.

2 Does a Focus on Needs Foster Incremental
Innovation Only?
Next to the focus on needs, a second design thinking characteristic highlighted in
many introductions is the advancement of worthwhile innovation. Indeed, design
thinking is often introduced as an approach that enables innovation at the high-end
spectrum of creative achievement. The approach is expected to bring about solutions
that are more than just slightly new and valuable. Rather, breakthrough solutions are
said to emerge, or creative quantum-leaps, if you will. In this sense, David Kelley
characterizes design thinking as “a method for how to come up with ideas. These are
not just ideas, but breakthrough ideas that are new to the world” (Kelley &
Camacho, 2016, p. 88, emphasis added). For Tim Brown, design thinking is an
“approach to innovation that is powerful, effective, and broadly accessible [. . .] to
generate breakthrough ideas” (2009, p. 3, emphasis added). Christoph Meinel and
Larry Leifer (2011) also describe an intricate connection between design thinking
and worthwhile, large-scale innovation, positioning that “great innovators and
leaders need to be great design thinkers” (p. xiii, emphasis added).
However, dark clouds loom at the horizon. Does the human-centric perspective of
focusing on needs really harmonize with innovation aspirations as design thinkers
like to believe? Several authors caution us not to assume this carelessly. Indeed,
might there be reasons to believe the opposite? Could it be the case that humancentred design focussed on needs hinders creative quantum-leaps and breakthrough
innovation rather than being helpful?
In this sense, Donald Norman and Roberto Verganti (2014) provide a critical
discussion.
[Human-Centred Design] starts with extensive design research to determine user needs.
However, this process unwittingly restricts the potential solutions to incremental innovations
because, by its very nature, it focuses on things people already know about [. . .]. Not only do
the users of products have difﬁculty envisioning radical new meanings because of their total
immersion in the current context and cultural paradigm, but the more that design researchers
immerse themselves in the existing context, the more they, too, are trapped in the current
paradigms. (Norman & Verganti, 2014, p. 89)

According to the authors, Human-Centred Design (HCD) plays a prominent role
in incremental innovation, but it is not a driver of radical innovation. Instead,
(i) technological change and (ii) a change in the meanings of products or technologies for people are discussed as major sources of radical innovation.
As always in science, conclusions about relationships depend on how you
understand key terms. Norman and Verganti (2014) write about Human-Centred

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Design as a relatively well-deﬁned and deliberately applied framework, which
invokes “an iterative cycle of investigation—usually characterized by observations,
an ideation phase, and rapid prototype and testing” (p. 78). Importantly, the process
involves a “careful analysis of a person’s or even a society’s needs” (p. 79). There is
often a “formal analysis of needs” (p. 79, emphasis added). Moreover, HCD is
understood as exploring “people’s current meanings assigned to products and aims
at detecting existing meanings” (p. 92, emphasis added). According to this understanding, HCD is strongly focused on exploring the status quo. This does not seem to
facilitate radical innovation projects, since these require leaps away from the status
quo. HCD does not appear to provide helpful directions, as to which leaps away from
the status quo could be promising.
Based on a comprehensive review of famous historical and contemporary case
examples of radical innovation, the authors conclude that good directives for breakthrough innovation typically emerge from sources other than (formal) need analyses.
Instead, important impulses can come, for instance, from the “dreams” (p. 95) and
“inner vision” (ibid.) of highly creative individuals.
Another source of inspiration can be “subtle and unspoken” (p. 90) socio-cultural
dynamics that have not previously entered people’s consciousness, and therefore
have not yet been expressed as speciﬁc needs. “The invention of the mini-skirt in the
1960s is an example: It was not simply a different skirt, but a radically new symbol
of women’s freedom that signalled a radical change in society” (ibid.).
When the pursuit of inner visions or subtle socio-cultural dynamics are construed
as different from need-focused approaches in HCD, the concept of “needs” appears
to be understood in a rather narrow sense: something users are aware of, i.e. rather
concrete concerns that users can readily explicate or acknowledge.
In a similar vein, researchers from the MIT Media Lab ﬁnd a focus on needs to be
limiting for innovation projects. More could be achieved with a vision-driven design
approach, rather than by a need-driven procedure.
Looking back through the history of HCI [Human Computer Interaction], we see that
quantum leaps have rarely resulted from studies on users’ needs or market research; they
have come from the passion and dreams of visionaries such as Douglas Engelbart [inventor
of the computer mouse]. We believe that vision-driven design is critical in fostering quantum
leaps, and it complements needs-driven and technology-driven design by looking beyond
current-day limits. (Ishii et al., 2012, p. 49f.)

This discussion is well in line with Norman and Verganti’s description of “inner
visions” as important sources of inspiration for radical innovation. As a subtle
difference, Norman and Verganti highlight the potential of technologies to change
and thus bring about radical innovation. By contrast, Ishii et al. (2012) view
technologies as associated with the status quo. Thus conceived, technologies seem
to provide little directives for the development of radically different solutions for the
future.
Another aspect brought up by Ishii et al. (2012) concerns the endurance of
solutions. In this regard, too, the authors ﬁnd vision-driven design more favourable
than need-driven or technology-driven design.

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The reason why we focus on the vision-driven approach is its life span. We know that
technologies become obsolete in about one year, users’ needs change quickly, and applications become obsolete in about 10 years. However, we believe the strong vision can last
beyond our lifespan. (Ishii et al., 2012, p. 50)

Following such a vision-driven approach, the authors suggest a new way for
people to interact with the digital world: “Radical Atoms is a vision for the future of
human-material interactions, in which all digital information has physical manifestation so that we can interact directly with it” (p. 38). Inspiration for this vision
emerges from careful observations and analyses of how humans interact with
physical objects: “Humans have evolved a heightened ability to sense and manipulate the physical world, yet the digital world takes little advantage of our capacity for
hand-eye coordination” (p. 38).

3 John Arnold’s Need-Based Innovation Theory: Or—
Focussing on Basic Human Needs Instead of Transient
User Needs
Much older and more fundamental than the concept of “user needs” is the concept of
“human needs” in design thinking traditions. This outlook was prominently introduced by John E. Arnold, who laid important groundworks for design thinking at
Stanford Engineering and used the term “design thinking” already (Arnold, 1959/
2016; Clancey, 2016; von Thienen et al., 2017). John Arnold introduced need-based
theories of creativity and innovation. To Arnold, creative work is generally
“concerned with how the basic needs of man can be better satisﬁed” (1959/2016,
p. 63). This focus on needs provides an essential evaluative dimension for creative
performance. By his deﬁnition, every successful creative project yields a novel
solution that “better solves some need of mankind” (p. 66). Analogously, he takes
for granted the goal that “our innovations better satisfy some need of man” (p. 67,
emphasis in original).
According to this theoretical framework, human needs are a key factor in all
innovation projects, whether or not a design thinking approach is explicitly pursued.
However, the explicit endorsement of a focus on needs in present-day design
thinking can likely be of great service, according to Arnold’s theories. It will be
much easier to better satisfy human needs when the innovator is actually aware of
relevant needs and tracks the impact of potential new solutions carefully.
Notably, at Arnold’s time, there is not yet any clear differentiation between
innovator versus user. The creative person can very well work to satisfy their own,
personal needs. However, for Arnold and his successors at Stanford the great art of
innovation is to not make improvements regarding contingent, arbitrary needs, but to
satisfy basic needs. These are shared by many people.
Like Norman and Verganti (2014), Arnold analyses many different creative
projects to better understand the emergence of radical innovation. Amongst other

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case studies, he treats the development of instant colour pictures pioneered by Edwin
Land from Polaroid, the invention of Freon as a refrigerant by Thomas Migley, the
invention of disposable safety razors by King Gillette and many more. In his
analysis, Arnold invokes a major distinction between “organized” versus “inspired”
creativity approaches as avenues towards innovation. Organized approaches typically follow “a logical, orderly, step-by-step type of problem solving technique”
(Arnold, 1959/2016, p. 73). Examples can be: a structured form of marked research,
a systematic trial-and-error approach or comprehensive analyses and
re-combinations. By contrast, projects that pursue an inspired approach do not
follow a well-structured process. A prominent example of an inspired approach is
what Arnold calls the “big dream”.
The big dream approach [. . .] is carried out by asking yourself the biggest question you
possibly can, by dreaming the biggest dream that you possibly can, by sort of soaring off into
space with a grand idea, and then expending every possible effort to answer this big
question, to make this big dream come true, to get some tangible tie between your ﬂight
into space and solid reality. (Arnold, 1959/2016, p. 76)

This approach resembles the pursuit of an “inner vision” described by Norman
and Verganti (2014) or “vision-driven design” set forth by Ishii et al. (2012). In
accordance with these authors, Arnold ﬁnds the big dream approach most promising
for purposes of radical innovation.
Similar to Norman and Verganti (2014), Arnold believes that any well-structured
procedure (like a structured HCD process) is not a promising avenue towards radical
innovation.
Inspired [. . .] approaches [. . .] are those closely associated with the art of creativity rather
than the science. Big leaps in knowledge are apt to occur using these approaches, as
compared with the slow but steady step-by step advancement made using organized techniques. (Arnold, CE, p. 73)

At the same time, for Arnold inspired approaches do not contrast with a focus on
needs, human-centred design or needs-driven design. Quite to the contrary:
According to Arnold, dreaming up new inner visions or meanings can be at least
as human-centred and focused on needs as market research. In fact, inspired
approaches are said to have a greater innovation potential than organized
approaches, and innovation is understood by Arnold as a move towards better
addressing human needs. Thus, dreams and visions can be even better approaches
to satisfying important human needs than organized market research (whether or not
market research is concerned with user needs). All in all, Arnold construes needbased design much more widely than Norman and Verganti (2014) or Ishii
et al. (2012).
Regarding the two approaches Arnold distinguishes—inspired versus organized—he credits both with important beneﬁts. Inspired approaches are considered
most apt for an exploration of radically new, innovative solutions. Organized
approaches are credited with being especially helpful in the development of highly
sophisticated and effective outcomes, with reliable step-by-step progress. Again, this
analysis resembles that of Norman and Verganti (2014), who describe structured

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HCD-processes as “a form of hill-climbing, [..] [which] is only suited for incremental innovation” (p. 79).
In Arnold’s descriptions of organized versus inspired creativity approaches, a
combination of both seems ideal. Present-day design thinking in the StanfordPotsdam tradition attempts exactly such a balance and symbiosis of inspired as
well as organized approaches (von Thienen et al., 2017, 2019; Meinel & von
Thienen, 2021).
Yet, methodologically, how can human-centred design that focuses on needs
overcome the traps of current cultural paradigms? How can it have an impact beyond
the innovators life-time even though current user needs might become obsolete
within just a few years?
From the beginning onwards, in the Stanford-Potsdam design thinking tradition,
human needs have been understood as hierarchically organized: from concrete to
abstract. Concrete needs can be speciﬁc for individuals and/or situations. They are
highly context dependent. Abstract needs are culture-independent, common-human
and assumed to be so enduring as to not have changed much since humanity’s
prehistory.
A re-current example John Arnold discusses is mobility. In modern times, this
topic often surfaces in terms of trafﬁc concerns. People may experience, for instance,
the very concrete needs of “ﬁnding a parking space” or “reaching one’s destination
quickly despite being stuck in a trafﬁc jam”. When these needs are considered on
more abstract levels, human needs come into focus that are important for most
people, such as mobility in general: “Man must be kept mobile—yet not be overly
frustrated” (Arnold, 1959/2016, p. 94). Moving up even further on the ladder of
abstraction, mobility seems to be just one possible solution to satisfy an even more
basic need of mankind, namely our desire to communicate: “Man must be able to
communicate freely with other men and machines” (p. 94).
Up to the present, in this design thinking tradition, needs are analysed hierarchically. “Why-How-Laddering” is a prominent and sophisticated methodology to this
end (d.school, 2010; von Thienen et al., 2012). Presently, the method is often used to
make sense of insights from empathy research with users. Notably, however,
creators can use this method just as well to reﬂect on personal visions developed
without user research, in order to better understand and advance one’s innovation
project.
Why-How-Laddering works by starting with concrete needs (often: conscious
intentions, preferences, desires) and asking why the matter would be important. The
answer will again be formulated as a need statement, typically in verb-form. For
instance, in user research it may be observed that users like to buy baking mixtures
that require the addition of fresh eggs. Of course, egg powder could easily be
including in the baking mixture, entailing an even easier baking procedure for
customers. When asked about their preferences, users may reply that they want to
add something fresh to the product. Why? Some users may say that they want to use
less eggs than the recipe foresees, to reduce cholesterol. Others may reply more
generally that they want to participate in the creation of the product, to control what
ingredients go into it, to ensure ingredients are of good quality and not

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harmful. Why? Continuing with the inquiry, eventually a need will be reached that is
so abstract as to be shared by almost all humans, like “to be healthy”. This can be the
top of the hierarchy, if no further, even more abstract need seems to ﬁgure in the
background.
To clarify branches and details of the need hierarchy, one can also start with a
more abstract need and ask “how” the end would be pursued. For instance: “How do
you try to stay healthy?” “I try to nurture good organisms in my body”, which the
person may try to achieve by eating yoghurt. Concrete needs surface as “solutions”
to satisfy more abstract, basic needs. A visualization of this food example is provided
by the d.school (2010), showcasing the typical branch-structure of a need hierarchy
(Fig. 1).
According to this understanding and methodology, creative developments are by
no means conﬁned to current-day limits when a project focuses on needs. This view
is a fundamental understanding in Stanford-Potsdam design thinking traditions up to
the present. In the same vein, Larry Leifer and Christoph Meinel, directors of the
Stanford-Potsdam Design Thinking Research Program, emphasize in an introduction
to design thinking: “The human needs that we seek to satisfy have been with us for
millennia” (Meinel & Leifer, 2011, p. xv). Here, we may think back to needs
discussed before: “to be mobile”, “to communicate”, “to be healthy”. Such needs
are unlikely to change in the next 100, 200 or even 1000 years.

Fig. 1 In the Stanford-Potsdam design thinking tradition, needs are analysed hierarchically, from
concrete and context-dependent to abstract, common-human and enduring (image reprinted from d.
school, 2010, p. 20, CC BY-NC-SA)

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Here, again, an important difference surfaces between the Stanford-Potsdam
design thinking tradition in comparison to the discussions of Norman and Verganti
(2014) or Ishii et al. (2012). In the Stanford-Potsdam tradition, needs are construed
in a wide sense: from concrete to abstract, from readily explicated desires and
observed behaviours to remote, underlying concerns. Based on such a wide understanding of needs and hierarchical need analyses, radical innovation can easily be
understood to emerge from a focus on needs in innovation projects. By ﬂuently
moving up and down the ladder of abstraction in need hierarchies, radically new
solutions come into sight. In John Arnold’s trafﬁc example, starting with concrete
needs of ﬁnding a parking lot and bypassing a trafﬁc jam, the elaborated need
hierarchy leads to quite radical creative perspectives. In his case discussion, the
abstract need “to communicate” is suggestive not so much of incremental, but rather
of radical innovation projects. Based on his need analysis, Arnold reaches the
questions:
How can we better use public transportation? Can subways be used to carry freight & keep
some trucks out of city?
What new systems of public transportation can I dream up? [. . .]
What means of communication can we substitute for face-to-face meetings? (E.g., closed
circuit TV)
What are some of the wildest approaches? (E.g., disposable cars) (Arnold, 1959/2016,
p. 94)

Thus, differences in how we construe “needs” have major implications for
emerging views about the relationship of need-based design and prospects of
incremental versus radical innovation.

4 Abraham Maslow’s Need Theory: Or—Design Thinking
to Satisfy the Designer’s Basic Needs
Along with John Arnold’s need-based creativity and innovation theory, another
outlook on needs and creativity authored by Abraham Maslow found its way into
Stanford-Potsdam design thinking traditions. John Arnold was a passionate collaborator. He worked and taught together with world-renowned theoreticians, including
people like Abraham Maslow, who contributed a guest lecture in Arnold’s Creative
Engineering Seminar at Stanford (Clancey, 2016) and also wrote a guest essay for
the course manuscript (1959/2016).
According to Maslow, basic human needs exhibit a structure that can be visualized by a pyramid (Fig. 2). “There are at least ﬁve sets of goals, which we may call
basic needs. These are brieﬂy physiological, safety, love, esteem, and self-actualization” (1943, p. 394). Moreover, according to Maslow’s theory, needs on lower
hierarchy levels must be satisﬁed to some degree before needs on upper hierarchy
levels emerge.

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Selfactualization
Esteem

Love

Safety

Physiological

Fig. 2 Maslow’s hierarchy of basic human needs depicted in a pyramid

It is quite true that man lives by bread alone—when there is no bread. But what happens to
man’s desires when there is plenty of bread and when his belly is chronically ﬁlled?
At once other (and “higher”) needs emerge and these, rather than physiological hungers,
dominate the organism. And when these in turn are satisﬁed, again new (and still “higher”)
needs emerge and so on. (Maslow, 1943, p. 375)

At the highest level of the need hierarchy, humans pursue self-actualization. Here,
creativity is said to ﬁgure essentially. Maslow explains self-actualization as follows:
This tendency might be phrased as the desire to become more and more what one is, to
become everything that one is capable of becoming. The speciﬁc form that these needs will
take will of course vary greatly from person to person. In one individual it may take the form
of the desire to be an ideal mother [. . .] and in still another it may be expressed in painting
pictures or in inventions. [. . .] [In] people who have any capacities for creation it will take
this form. (Maslow, 1943, p. 382f.)

Among all three need theories discussed here, in Maslow’s account the focus on
needs remains most remote from “user needs”. Good creators, innovators or
designers are not described as individuals who excel at ﬁnding and satisfying user
needs. Rather, they are described as excelling in the pursuit of their own basic needs,
especially the need of self-actualization: People do something they ﬁnd worthwhile,
creatively.
Maslow’s theory of needs was never fully endorsed in the Stanford-Potsdam
design thinking tradition. In particular, it is not customary to look for ﬁve different
basic needs (physiological, safety, love, esteem and self-actualization) when making
sense of insights from user research. However, Maslow’s theory did have impetus. It
certainly contributed to a lasting ideal of people achieving self-actualization by
means of worthwhile, creative projects. In that sense, design thinking skills appear

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most serviceable to help people make the best out of their lives and tap their full
creative potential.
The concern for self-actualization has been enduring. Design thinking is not only
invoked to advance worthwhile, new products or services. Rather, there is also a
strong interest in how design thinking helps any human in satisfying their own
needs, so as to live a fulﬁlled life. In line with Maslow’s need theory, creativity is
highlighted as essential in this endeavour. In this sense, Bill Burnett, Executive
Director of Stanford’s Design Program, and Stanford lecturer Dave Evans (2017)
have authored Designing your life. How to build a well lived, joyful life. Here, they
write about design thinking independent of product development and user needs.
You’re going to see the beneﬁts of design thinking in your own life. Design doesn’t just
work for creating cool stuff like computers and Ferraris; it works in creating a cool life. You
can use design thinking to create a life that is meaningful, joyful, and fulﬁlling. [. . .] A welldesigned life is a life that is generative—it is constantly creative, productive, changing,
evolving, and there is always the possibility of surprise. (Burnett & Evans, 2017, p. xvi,
emphasis in original)

In related terms, the HPI D-School offers Wayﬁnder education.
Wayﬁnder is a newly developed program by HPI D-School [. . .] in the area of Design
Thinking: for self-leading and designing your own well-lived life and career. [. . .]
The program is based on the "Designing Your Life" Concept and has been extended and
further developed by the HPI D-School.
Wayﬁnder has four major focus areas:
1. Empathy and Self-Awareness: Understanding one's own values and attitudes.
2. Exploring: Shaping career and personal life with purpose and energy.
3. Prototyping: Making good choices and exploring options.
4. Iterate: Learning forward in a strong network. (D-School, 2021, emphasis in original)

Similarly, Bernard Roth (2015), academic director of Stanford’s d.school has
written The achievement habit. Stop wishing, start doing and take command of your
life. Here too, design thinking is not applied to improve product developments by
means of better addressing user needs. Rather, the aim is to help readers better
address their own important needs.
Many who have taken my [design thinking] course over the years credit it with helping them
achieve signiﬁcant personal and professional successes in their lives [. . .]. It’s empowering
to realize you have more control than you ever knew over what you achieve in life. When
you are not happy with an aspect of your life, you can change it! Really, you can. (Roth,
2015, p. 4)

This fundamental concern with the design thinker’s private life satisfaction
shared by many inﬂuential educators would not be explicable if design thinking
were all about satisfying user needs. In the Stanford-Potsdam tradition, design
thinking is considered a very valuable approach for addressing basic human
needs—including everyone’s personal concerns.
However, even when design thinking is applied to deliver product or service
innovation, Maslow’s need theory and topos of “self-actualisation through creativity” help to clarify an important link: There is a connection between the innovator’s
needs and user needs. Humans are social animals, for whom relations to others are

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essential (Tomasello, 1999; WHO, 2004; Plank et al., 2021). In this sense, we can
also appreciate how it is more rewarding for creators themselves to deliver solutions
others care deeply about (because the users’ key needs get satisﬁed) than to deliver
solutions people hardly care about (because barely anything changes regarding the
users’ key needs). From this perspective, there seems to be a serendipitous evolutionary alignment of creators/innovators being better able to pursue their own basic
need of self-actualization when they endorse a design thinking approach, compared
to following other product development strategies that disregard user needs. As
empathic humans, creative designers themselves will be happier when the creative
process leads to design outcomes audiences are deeply grateful for.

5 Robert H. McKim’s Need-Based Design Theory: Or—
Need-Centred Design as Culture Therapy
Robert H. McKim is another design thinking pioneer (McKim, 1959/2016, 1972;
von Thienen et al., 2019, 2021). Next to Maslow, McKim was also a guest lecturer in
John Arnold’s Creative Engineering Seminar at Stanford. McKim contributed an
essay to the course manuscript where he spelled out a design theory based on human
needs.
Like Arnold, who construes all innovation in terms of satisfying human needs,
McKim construes all design activities in terms of satisfying human needs. To
McKim, design is a “response to human physical, intellectual, and emotional
needs—human needs which are partially formed and modiﬁed by the natural and
cultural environment” (1959/2016, p. 200).
McKim differentiates three domains of basic need: the physical, emotional and
intellectual. To McKim, good design is highly respectful of basic needs in all three
domains.
Physical needs include bodily well-being and the ability to achieve action goals.
This covers needs such as staying “alive, fed, and sheltered” (p. 198). There is also a
concern for “physical comfort and sensory well-being” (p. 217). Moreover, people
need to be physically capable of performing the actions and achieving the ends to
which they aspire. Often this has to do with the use of the human senses. It is very
unfavourable to make product users reliant on one sense modality only, such as the
visual sense channel.
Every evening thousands of Americans climb into their automobiles, reach for the headlight
knob, turn instead its identical twin, the windshield wiper knob, or perhaps its triplet, the
cigarette lighter. It is not difﬁcult to ﬁnd examples of “Chinese puzzles” in our everyday
design world. Unfortunately these puzzles are not fun; they are frustrating. (McKim, 1959/
2016, p. 204)

Such an automobile design could be much improved by mindfully addressing
further sensory channels, such as “touch”.

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The headlight—windshield wiper—puzzler [. . .] could easily be minimized in several ways.
(1) Coding the knobs by shape or texture so that their differences would be tactually
clear—day or night. The confusion that arises with these controls usually takes place when it
is dark.
(2) Positioning the knobs according to their respective functions—the windshield wiper
knob near the wipers, the headlight knob near the ignition key for handy use when starting
up at night. (McKim, 1959/2016, p. 204f., emphasis in original)

Notably, McKim’s need-based design approach is human-centric in a straightforward way. This is especially easy to understand in the realm of physical needs.
For instance, humans cannot see well at night in the dark, which then results in
unsatisﬁed orientation needs. However, other species have different perceptual
abilities—like bats who can orient themselves well in the dark. Similarly, humans
have different temperature needs than polar bears or desert foxes. Being mindful of
physical needs implies being mindful of the organism for whom one designs, and
humans typically design for human needs.
Moving on to the second category of basic needs, according to McKim, important
emotional needs of humans include the experience of positive or situationappropriate emotions and living out personal motives. Amongst other lines of
discussion, McKim highlights how designers can help users experience joy by
bearing in mind “the delight which sensory stimuli such as color, shapes, rhythmic
patterns, and textures can bring to the emotions” (p. 212).
Intellectual needs cover objectives such as “understanding something”. Users
need to understand the purpose of tools or the content of messages quickly. Users
need to be able to operate tools with minimal intellectual effort. Sometimes it also
matters to users that designs accord to philosophical ideals. “Understanding nature”
is another example of an intellectual need.
As McKim admits, in real life experienced needs often span several categories—
physical, emotional and/or intellectual. The distinction is proposed, especially for
designers, to be mindful of human needs in a comprehensive way when developing
novel solutions. Each need category should be screened carefully and systematically,
to see if a given design already addresses all relevant basic needs well.
Notably, the design of Radical Atoms proposed by Ishii et al. (2012) would ﬁgure
very favourably in McKim’s framework, precisely because it seems to address basic
human needs so well. In terms of physical needs, Radical Atoms address more than
one human sensory channel. The approach is not only visual, but also tactile. In
terms of emotional needs, Radical Atoms have a much greater potential compared to
Graphical User Interfaces in terms of delighting users via carefully chosen sensory
stimuli, such as shapes, rhythmic patterns and textures. Radical Atoms also have a
huge potential to address people’s intellectual needs, because the material can
rapidly communicate all kinds of information in most ﬂexible ways.
In all three need categories—physical, emotional and intellectual—McKim
expects basic, enduring and less basic, contemporary needs. The designer should
do their best to satisfy basic needs, while the contemporary needs can sometimes
even be morally unjust. In the latter case, designers should abstain from addressing
these not-basic needs. For instance, an automobile customer with the basic need of

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feeling socially recognized may develop the contemporary need to drive an expensive, fast car furiously. A designer who straightforwardly tried to satisfy the customer’s contemporary need by providing a respectively designed automobile would
endanger the driver’s basic needs for physical safety, and also that of others in the
driver’s vicinity.
In light of McKim’s framework, human-centred design or a focus on needs
implies critical re-assessments of the society’s current need hierarchies. In particular,
contemporary needs pursued with a high priority in a culture at the expense of more
basic needs should be corrected through better, often radically different designs. For
instance, some car models in the 1950s were associated with high rates of deadly
accidents. In these and other cases, need hierarchies informing contemporary designs
should be critically re-thought. Both the society’s need-hierarchies and products
emerging from them should be radically re-designed.
In a modern society such as our own, the cultural environment probably has a more decisive
effect upon human needs than does the natural environment. It often causes seemingly
irrational needs for design which appear absurd to the people of other cultures. It causes
fashions and styles in design. It sometimes frustrates the satisfaction of important human
needs. [. . .] A cultural environment which frustrates the healthy satisfaction of human needs
is, in my opinion, a culture which is in for a change. (McKim, 1959/2016, p. 200)

This is where human-centred, radical innovation emerges in McKim’s framework. Good designers should not stop their assessments when faced with concrete
desires expressed by customers, who may actually provide a very coherent image of
what they jointly think they “need”. Instead, a thorough need analysis should take
place. The analysis shall explore need hierarchies systematically. These will often be
characteristic of a particular user group and culture domain. Good design means to
critically re-assess given need hierarchies, to ensure that basic needs get satisﬁed
comprehensively. By contrast, more concrete desires or contemporary needs will
often be less helpful as design objectives. Sometimes they will even be harmful, as
contemporary needs can be morally dubious or detrimental to the pursuit of
basic human needs.
Good human-centred design rethinks culture domains and looks behind seemingly important deliverables. Good design is ready to provide radically different,
more human-centred solutions than those commonly sought and provided in product
or service design.
Clearly we badly need the designer who understands and is capable of responding to the
needs of the whole man. [. . .] He must understand man’s physical needs, needs not only for
power over his environment but needs for physical comfort and sensory well-being. He must
understand man’s intellectual needs, needs for minimizing needless problem solving in
design as well as visual needs for knowledge and order. The designer who designs for the
whole man will also understand man’s emotional needs for designs which satisfy civilized
motivations and which delight the emotions through the senses. This designer must have the
fortitude to exert his inﬂuence on the current cultural environment which is depriving us all
of basic human needs. (McKim, 1959/2016, p. 217)

A key concept in McKim’s design theory is the description of “conﬂicts” in
societal need hierarchies, where culture-speciﬁc contemporary needs clash with the

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satisfaction of basic human needs. According to McKim’s analysis, such dysfunctional tensions in need hierarchies occur often. They are the rule rather than the
exception.
Until today, conﬂicts between people’s needs are considered a great opportunity
for the development of new and radically better solutions in Stanford-Potsdam
design thinking traditions. Trainees learn to be on the lookout for such conﬂicts.
“Identify needs directly out of the user traits you noted, or from contradictions
between two traits—such as a disconnect between what one says and what one
does [. . .]. One way to identify the seeds of insights is to capture ‘tensions’ and
‘contradictions’ as you work” (d.school, 2010, p. 15). Thus, even when a design
team is much concerned with the status quo of needs in a ﬁeld, the identiﬁcation of
need conﬂicts can be a promising source of inspiration for radically new and better
solutions.

6 Reﬂection and Outlook
The question as to whether a design thinking focus on needs facilitates or antagonizes radical innovation looks like an empirical one. We, too, believe that empirical
research is important and can help to elucidate a comprehensive picture. In this
chapter, however, we have started with conceptual clariﬁcations, because the way in
which we conceptualize needs, and their role in design thinking, largely predetermines relations to innovation. For instance, it makes a huge difference whether needs
are understood in a narrow sense, as being strongly linked to user testimonials in a
status quo situation, or whether the term is used in a wide sense, so as to cover
remote, abstract, cross-cultural and enduring human concerns. For conceptual reasons, discussions of design thinking with a narrow account of needs link the
approach to incremental innovation, while discussions with a wide account of
needs link design thinking to radical “breakthrough” innovation. As a key difference, in wide accounts the design thinking focus on needs does not relate uniquely to
user needs, but to basic human needs. Design thinking in the Stanford-Potsdam
tradition invokes a wide account of needs.
The impact of conceptual decisions is also notable with regard to the categorization of projects. For instance, Ishii et al. (2012) categorize the project of Radical
Atoms as vision-driven design, in contrast to needs-driven design. However,
according to Robert McKim’s need-based design theory, Radical Atoms emerge as
an example for good design that addresses human needs comprehensively, and in
that sense is highly human-centric and need-focused.
Beyond impactful differences between narrow versus wide need conceptions in
inter-communal debates, also within the Stanford-Potsdam design thinking tradition
varying ideas can be discerned. For instance, should we differentiate speciﬁc types
of basic human need? John Arnold does not, Abraham Maslow and Robert McKim
do. If we distinguish different types, how many categories of basic needs shall we
invoke, and what is their content? McKim distinguishes three types of basic needs:

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physical, emotional and intellectual. Maslow invokes ﬁve categories: physiological,
safety, love, esteem and self-actualization.
Another point of divergence between the theories concerns the role of designers.
According to Maslow’s theory, people (users) naturally satisfy their more basic
needs ﬁrst. So, more basic needs should be satisﬁed most often in society. By
contrast, McKim believes that cultures often have dysfunctional need hierarchies
where contemporary needs antagonize the satisfaction of basic human needs. Consequently, McKim describes good designers as “culture therapists”. The task of a
good designer is to provide novel (often radically different) solutions that allow
people to satisfy their more basic needs at once, while culture-speciﬁc objectives can
also be satisﬁed when they seem ethically sound. According to McKim’s theory,
good designers rethink the status quo comprehensively, identify dysfunctional
dynamics in given cultures, radically change solution approaches when appropriate,
and thus exert profoundly new and positive inﬂuences on sub-cultures or even
societies.
Overall, one major takeaway of this chapter concerns the plurality of need
concepts in design thinking. Conceptual decisions matter a lot, because they determine the methodology that gets implemented in a design thinking project, giving rise
to very different outcome expectations, alternating between incremental versus
radical innovation.
Another important factor to address is the level of methodological skill and rigour
invested by design thinkers in a project. To what extent a focus on needs facilitates or
antagonizes innovation clearly hinges on the methodological proﬁciency of creators.
How well can they make sense of needs in a ﬁeld? In the need theories informing
Stanford-Potsdam design thinking traditions, two skills have been highlighted as
particulary important. Firstly, design thinkers need to be able to move ﬂuently up
and down the ladder of abstraction in hierarchical need analyses. Secondly, they also
need to be able to identify dysfunctional need dynamics. Problematic need constellations can be diagnosed in a status quo culture when contemporary needs get
satisﬁed at the expense of basic human needs, or when the satisfaction of one
basic need conﬂicts with the satisfaction of another basic need. All such dysfunctional dynamics provide opportunities for breakthrough innovation via novel solutions that radically “change the game” for users, as people now can satisfy basic
needs comprehensively without having to compromise in important need domains.
As an outlook to future developments regarding human need conceptions in
design thinking, we can point to a comprehensive framework of human needs that
is currently being developed (Borchart, 2020; Borchart et al., 2021a, b; von Thienen,
2020). The framework is based on need theories from design thinking traditions,
especially McKim’s conception, and amended by need theories from further sources
(such as Deci & Ryan, 2000; Max-Neef, 2017), concepts of fundamental human
rights in legal contexts, and policy frameworks such as the Sustainable Development
Goals (United Nations, 2021; World Health Organization, 2021). The purpose of the
framework is to support comprehensive reﬂections on product risks and beneﬁts both
during the design process, and afterwards as when legal decisions need to be taken
about the admissibility of novel products. Used in design processes, these systematic

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225

reﬂections can help to create products that are respectful of a broad range of human
needs, so as to close blind-spots and overcome potentially dysfunctional dynamics
where products satisfy some needs at the expense of others. When risks and beneﬁts
have been identiﬁed in available products, this also helps to identify stakeholder
groups—namely people who beneﬁt from the introduction of a new product and
those who may suffer a disadvantage. From the design point of view, a careful
consideration of latter stakeholder groups can also lead to favourable product
re-designs. This novel framework for comprehensive considerations of product
risks and beneﬁts based on human needs will be shared in an upcoming chapter in
this book series.

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Facets of Human-Centered Design: The
Evolution of Designing by, with,
and for People
Jan Auernhammer, Matteo Zallio, Lawrence Domingo, and Larry Leifer

“[. . .] We have developed speed, but we have shut ourselves
in. Machinery that gives us abundance, has left us in want.
Our knowledge has made us cynical. Our cleverness, hard
and unkind. We think too much and feel too little. More than
machinery, we need humanity. More than cleverness, we need
kindness and gentleness. Without these qualities, life will be
violent, and all will be lost. [. . .] You, the people, have the
power—the power to create machines. The power to create
happiness! You, the people, have the power to make this life
free and beautiful, to make this life a wonderful
adventure [. . .].”
—Charlie Chaplin 1940 in The Great Dictator.

Abstract This book chapter outlines different facets of Human-Centered Design,
which evolved over half a century. These facets have different foundational inﬂuences that lead to design by, with, and for people. Designing for people, including
Ergonomics and Human Factors and Interactions Design, originated from early
developments in experimental psychology. Similarly, designing for people with
speciﬁc needs emerged from developments in medicine and rehabilitation, which
resulted in design approaches, such as Universal Design and Inclusive Design.
Designing with people, including Participatory Design, developed from communal
architecture. Designing by people is grounded in the psychology of creativity,
resulting in design approaches, such as Creative Engineering and Design Thinking.
Early developments in social psychology developed over time into Social Design
and Design by Society. These approaches emerged as designers responded to sociomaterial and socio-economic challenges with new Human-Centered Design
approaches. This book chapter aims to raise awareness of the contextual evolution

J. Auernhammer (*) · L. Domingo · L. Leifer
ME Design Group, Center for Design Research, Stanford University, Stanford, CA, USA
e-mail: jan.auernhammer@stanford.edu
M. Zallio
Inclusive Design Group, Engineering Design Center, University of Cambridge, Cambridge, UK
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_12

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of different Human-Centered Design approaches and the need to continuously
respond creatively to these challenges with new design solutions and adequate
design approaches.

1 Introduction
Over the last century, Design has evolved from form-giving to the design and
development of technology, interactions, experiences, and organizations (Archer,
1965; Auernhammer 2020; Buchanan, 2015; Fulton Suri, 2003; Moggridge, 2007).
These developments emerged as new challenges required new design approaches.
For example, Archer (1965, p. 57) expressed: “[. . .] there has been a world-wide
shift in emphasis from the sculptural to the technological. Ways have had to be found
to incorporate knowledge of ergonomics, cybernetics, marketing, and management
science into design thinking. As with most technology, there has been a trend
towards the adoption of a systems approach as distinct from an artifact approach.”
A similar shift is the development of Human-centered Design that emphasizes
people and the living world rather than artifacts and systems. Over the last halfcentury, various contextual challenges and developments have resulted in
approaches, such as Ergonomics and Human Factors, Participatory Design, Inclusive Design, Creative Engineering, and Social Design (e.g., Arnold, 1959; Carlsson
et al., 1978; Chapanis et al., 1949; Clarkson et al., 2013; Rittel, 1987).
Early Experimental Psychologists developed approaches to assessing the psychological ﬁtness for operating airplanes (Benary et al., 1919; Koonce, 1984). These
psychological developments changed from assessing people’s qualiﬁcations to
designing technology for people, resulting in Ergonomics and Human Factors
(Christensen, 1962; Edholm & Murrell, 1973). A similar development for designing
for speciﬁc needs of people resulted from developments in Medicine and Rehabilitation (Rusk & Taylor, 1953). Including non-designers in the design project in
Communal Architecture resulted from the opportunities provided by insights from
various stakeholders, such as urban dwellers and craftspeople (Rudofsky, 1964).
Humanistic Psychology developments of creativity and human values inﬂuenced
humanistic and creative design practices (Auernhammer & Roth, 2021; Christensen,
1976; Maslow, 1954, 1956). Similarly, developments in Social Psychology resulted
in dialectic design approaches to resolve social tensions (Lewin, 1936, 1946, 1947;
Rittel, 1987). Figure 1 outlines the evolution of these diverse Human-centered
Design approaches.

2 Experimental Psychology in Design
One of the ﬁrst professional practices that considered people’s behavioral capabilities and limitations was aviation psychology (Koonce, 1984). In the 1910s, psychologists examined people’s abilities to identify their suitability for operating

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Fig. 1 Several diverse evolutions of Human-centered Design

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airplanes (Benary et al., 1919). These developments shifted from assuring people’s
ﬁtness to use technology to designing technology suitable for people.

2.1

Ergonomics and Human Factors

As early as the 1920s, laboratories focused on studying people’s behavior when
using designed solutions, and one of the ﬁrst Human Factor laboratories in the USA
was the Bell Telephone Laboratories (Christensen, 1976). Researchers and designers
utilized experimental psychology to design for people’s physiological and psychological abilities in defense-related systems (e.g., Fitts, 1946; 1947a, b; Fitts & Jones,
1947; Flanagan, 1947; Loucks, 1944; McFarland, 1946; McGehee, 1943; Weitz,
1944a, b). These experimental psychology approaches were utilized to design
artifacts for everyday life activities (Chapanis, 1951; Chapanis et al., 1949). In
1949, these developments resulted in the establishment of the Ergonomics research
society in the United Kingdom and, in 1957, the Human Factors society in the USA
(Christensen, 1976; Edholm & Murrell, 1973). These societies brought together
interdisciplinary researchers, who started developing new methodologies to investigate psychological and physiological aspects within the interaction between people
and machines and work environments (Chapanis, 1965; Craik, 1947, 1966; McCormick, 1957; Murrell, 1965a, b; Rodger, 1959; Taylor, 1957; Taylor & Garvey, 1959;
Woodson, 1954). The fundamental doctrine of Ergonomics and Human Factors was
to design solutions that allowed people to accomplish a speciﬁc task in the way it
meets the characteristics of those who use it. Le Corbusier (1948, 1955) and Henry
Dreyfuss (1960) developed an anthropometry of the human body to support
designers in designing for people’s physiology. Over the decades, Ergonomics and
Human Factors advanced as a systematic and interdisciplinary approach, designing
for people and society (Bennett et al., 1963; Christensen, 1962; Cumming &
Corkindale, 1969; Grether, 1968; Hanson, 1983; Van Cott & Kinkade, 1972).
Many interdisciplinary scholars developed practices, tools, methods, models, and
theories to design for people’s psychological and physiological attributes (e.g.,
Alexander, 1986; Bailey, 1982; Chapanis, 1996; Proctor & Van Zandt, 1994;
Reason, 1990; Rouse & Boff, 1987; Stanton et al., 2005; Wickens, 1984; Woodson,
1981). These Human-centered Design approaches developed further through new
technological advancements, such as computer systems.

2.2

Human–Computer Interaction

In the 1970s, the design approach and community in Human-Computing Interaction
emerged under various names, such as human or user engineering and cognitive
systems engineering (Bennett, 1976; Brooks, 1977; Card et al., 1980, 1983; Hansen,
1972; Hollnagel & Woods, 1983; Norman, 1982, 1986). For example, Engelbart

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(1962), interested in developing a means to support people in complex problemsolving, developed a design approach to “augmenting human intellect,” resulting in
developments of personal computing. He and his team presented technology, such as
the computer mouse, command input, video conferencing, and word processing, to
augment people’s abilities at the “Mother of all Demos” (Engelbart & English,
1968). The focus changed from analog to interactive digital systems (Hansen,
1972). New psychological approaches were required to design for people’s interactions with digital systems with a focus on storing or retrieving information
(Sackman, 1970). In the 1970s, designers at Stanford Research Institute and Xerox
PARC introduced psychological research to examine and design human interaction
with computers, resulting in the ﬁeld of Human–Computer Interaction (Card et al.,
1983; Cooper et al., 2014). In the late 1970s, the ACM community became more
concerned about human interaction with computer systems to develop peopleorientated systems (Borman, 1996). In 1982, the Computer-Human Interaction
group (ACM SIGCHI) was established, focusing on cognitive and psychological
aspects of people when interacting with digital systems (Card et al., 1983; Clancey,
1997; Foulds & Joyce, 1998; Hollnagel & Woods, 1983; Norman, 1982, 1986;
Suchman, 1983, 1985; Winograd & Flores, 1986; Woods & Roth, 1988).

2.3

Interaction Design

The approaches developed for digital system design found their way back to the
industrial design-side integrating objects, media, and software under the term Interaction Design (Houde & Hill, 1997; Moggridge, 2007, 2010; Norman, 1988;
Winograd, 1996). Cooper et al. (2007) expressed that Bill Moggridge and Bill
Verplank, who worked on the ﬁrst laptop computer, coined the term Interaction
Design in the 1980s. Several decades later, in 2005, the Interaction Design Association was incorporated (Cooper et al., 2007). Designers and scholars focused on
non-utilitarian aspects of design, such as pleasure, emotions, and experiences (e.g.,
Buchenau & Fulton Suri, 2000; Fulton Suri, 2003; Green & Jordan, 2002;
Hassenzahl, 2004, 2018; Houde & Hill, 1997; Jordan, 2000; Norman, 2007). The
focus expanded to designing for embodied cognitive approaches and implicit interactions (Ju, 2008; Kirsh, 2013; Klemmer et al., 2006).

3 Medicine and Rehabilitation in Design
Another Human-Centered Design approach emerged from developments in Medicine and Rehabilitation and the concern of people with diverse needs (Rusk &
Taylor, 1953).

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Design for People with Disabilities

In the 1950s, institutions, such as the Institute of Physical Medicine and Rehabilitation in New York, started developing a coordinated approach for the dissemination
of information concerning self-help devices that might aid disabled persons in the
performance of the daily activities of life and work (Rusk & Taylor, 1953). This
approach advanced and became popular based on the argument that despite scientiﬁc
and medical technological advancements, problems increase for people with disabilities (Nugent, 1961). This movement resulted in the ﬁrst building standard that
addressed disabilities, the ANSI 117.1–1961, and the ﬁrst federal law requiring
accessibility in government buildings, the Architectural Barriers Act (American
National Standards Institute, 1980; Farber, 1975). Designers started evaluating
existing design solutions concerning people’s speciﬁc needs and abilities and utilized these insights to redesign solutions for people with disabilities and toward a
barrier-free environment (Goldsmith, 1963; Morgan, 1976; Sommer, 1972;
Steinfeld, 1979). The human values and concern for people with different needs
resulted in Designing for People with Disabilities. In 1978, Larry Leifer created an
interdisciplinary design program and practice at Stanford’s Rehabilitation Engineering Research and Development Center at the Palo Alto VA (Burgar et al., 2000).
In 1972, scholars at the University of California, Berkeley, established the Center
for Independent Living (Zukas, 1975). This center was a grassroots organization that
created environments designed for independence and empowering people to perform
their daily routines. The emphasis shifted from disability to independent living,
without discrimination of ethnicity, gender, age, and immigrant status (Lifchez,
1987; Lifchez & Winslow, 1979). Scholars argued that such collaborative design
between designers and people with disabilities leads to essential attitudes for designing for people (Finkelstein, 1975).

3.2

Transgenerational Design

The increased attention from institutions and the growth of public interest resulted in
a focus on diverse groups. Design approaches matching the needs of people with
physical and sensory disabilities resulted in Transgenerational Design (Pirkl &
Babic, 1988). Designers considered the broadest spectrum of individuals, such as
young, old, able, and disabled people, without disadvantaging a speciﬁc group
(Pirkl, 1994).

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233

Universal Design

Shortly afterwards the 1990 American with Disabilities Act was established with the
emphasis on considering all people in the design process (Americans with Disabilities Act (ADA) of 1990; Lebovich, 1993). This act resulted in the emergence of
Universal Design (Mace et al., 1991). The Disabilities Act and Universal Design
profoundly contributed to reducing discrimination against people with disabilities in
all areas of public life. Designers are obligated to consider the entire life span of
people, including temporary disability and future use, when designing spaces and
products (Mace et al., 1991; Story et al., 1998). Universal Design became the
precursor of a new wave of approaches in which products must be universally
accommodating and cater conveniently to all people (Goldsmith, 2000). The spectrum of speciﬁc needs became the new prism through which postmodernity examines and deﬁnes itself (Davis, 2002).

3.4

Design for All

In Ireland, the European Institute for Design and Disability was founded to foster the
practice of designing for people of all abilities, which resulted in the Design for All
(Coleman et al., 2003). In 2004, the institute passed “The Stockholm Declaration,”
which emphasized the design for human diversity, social inclusion, and equality
(Bendixen & Benktzon, 2015).

3.5

Inclusive Design

In a similar disposition, the core value proposition of Inclusive Design optimizes the
design and development of solutions for individuals with speciﬁc needs (Clarkson
et al., 2003; Coleman et al., 2007). The design approaches that emphasize design for
all people focus on accommodating and empowering all people. Commercial design
and design for disability can inspire each other through a more playful, creative
approach (Pullin, 2009). Many designers and design scholars have developed
practices and methods to create inclusive solutions, systems, and environments
(Guffey, 2017; Null, 2013; Steinfeld & Maisel, 2012).

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4 Humanistic Psychology in Design
The evolution with the emphasis on the designers’ values, attitudes, abilities, and
activities to design for people emerged in the 1950s. Christensen (1976) suggested
that the emphasis on human values reﬂects the perceived movement up Maslow’s
(1954) scale toward self-fulﬁllment. Noticeably, humanistic psychology has had a
decisive effect on the development of design education and practice (Auernhammer
& Roth, 2021).

4.1

Humanistic and Creative Design

Designers, including John Arnold (1959) and Bob McKim (1959), developed a
humanistic and creative design approach. Both collaborated with psychologists,
such as Abraham Maslow and J.P. Guilford. Insights from gestalt and creativity
psychology, including Wertheimer (1945), Duncker (1945), Maslow (1954, 1962),
Guilford (1950, 1959), and Rogers (1954), informed the creative design practices.
This approach focuses on creatively satisfying people’s physical, intellectual, and
emotional needs (Adams, 1974; Arnold, 1959; Fuller, 1957; McKim, 1959, 1980).
These creative practices aimed to design for impact in the real world (Papanek,
1973). Such design works with conviction and enthusiasm in the intersection of
designers’ and clients’ interests and profound considerations for society (Eames &
Eames, 2015).

4.2

Design Thinking

These design practices based on liberal arts, incorporating science, art, and humanities, developed into Design Thinking (Buchanan, 1992; Cross, 2011; Lawson, 1972,
1980; Rowe, 1987). From a structuralist perspective, psychological theories of
productive thinking resulted in developments in design science and design cognition
(Eastman, 1970; Selz, 1922; Simon, 1969, 1981). Similarly, many design scholars
have built on the work by Gestaltists, including Wertheimer’s (1945) Productive
Thinking to develop insights and approaches in design thinking (Goldschmidt, 1991;
Lawson, 1972, 1980; McKim, 1980; Schön, 1963). In 1991, the research workshop
in Design Thinking focused on design cognition and computational modeling of the
design process, establishing the Design Thinking Research Symposium series
(Cross, 2018). Many design scholars studied and developed design thinking models
and strategies (Dorst, 2015; Dym et al., 2005; Eastman et al., 2001; Eris, 2003; Faste,
1994; Gero, 1996; Goldschmidt, 1991; Jung, 2011; Lawson, 1980; Minneman,
1991; Plattner et al., 2011; Rowe, 1987; Schön, 1983; Tang & Leifer, 1988;
Valkenburg & Dorst, 1998).

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5 Communal Practices in Design
In the 1960s, another movement emerged that focused on designing with people as a
source of inspiration and democratization in design (Rudofsky, 1964).

5.1

Design Participation

Early ideas of involving people in design resulted from the argument that architects
and designers got out of touch with people’s needs, and there is an untapped source
of inspiration from the practical knowledge of the untutored builders and urban
dwellers (Rudofsky, 1964; Turner & Fichter, 1972). Design emerges from different
people in society, and participation in design allows tapping into this source of
emerging perspectives and ideas. A similar idea was developed in the 1960s in the
Netherlands based on Habraken’s (1972) “support and inﬁll” concept, incorporating
different stakeholders (Carp, 1986). In 1971, these ideas were brought together in
The Design Research Society conference with the primary theme Design Participation (Cross, 1972).

5.2

Cooperative Design

In Scandinavia, projects such as NJMF, DEMOS, DUE, and UTOPIA emphasized
the participation of people in design activities (Carlsson et al., 1978; Ehn & Kyng,
1987; DUE Project Group, 1979; Howard, 1989; Nygaard, 1979; Nygaard & Terje
Bergo, 1975; “The DEMOS Project: A Short Presentation,” 1978). These projects, in
collaboration with workers unions, emerged as computer mainframe systems
impacting the work environment. These projects led to Cooperative Design, in
which designers collaborated with non-designers to develop computerized tools
and systems in the workplace (Sundblad, 2011). These developments gave rise to
the term Human-centered Design (Cooley, 1980, pp. 76–77). Cooley (1980, p. 77)
emphasized that people have to decide to ﬁght for the “right to be the architects of the
future, or allow a tiny minority to reduce [them] to bee-like responses.” This design
approach considers the broader socio-economic and socio-technical context and its
impact on people through participation.

5.3

Participatory Design

The collaborative design approaches popularized under the term Participatory
Design. In 1990, the international Participatory Design research community

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gathered at the ﬁrst Participatory Design Conference (Bødker et al., 1995; Robertson
& Simonsen, 2012). Participatory Design democratizes the design practices and
embraces the politics involved in a design project (Björgvinsson et al., 2010;
Kensing & Blomberg, 1998). However, there are several challenges in providing
the conditions for designing with people, such as considering who is participating,
the time frame of continuous participation, power-structures involved in decisionmaking, compensations for participation, and social dynamic where no social community exists and no consensus seems to be possible (Bjögvinsson et al., 2012;
Bødker, 1996; Robertson & Simonsen, 2012). Several designers and scholars
developed techniques and practices for designing with people (e.g., Bjerknes et al.,
1987; Greenbaum & Kyng, 1991; Schuler & Namioka, 1993). Participation in
information system design became common practice (Bannon et al., 2018; Bodker
et al., 2009; Carroll & Rosson, 2007; Smith et al., 2017).

6 Social Psychology in Design
Research in Social Psychology established action research practices to resolve
societal tensions (Lewin, 1936, 1946, 1947). This development provided a social
practice to resolve social tensions that emerged in the interactions of people with the
artiﬁcial, cultural, and natural environment.

6.1

Social Design

The practices developed in social psychology evolved into action science and
organizational learning, emphasizing the conﬂict between the individual and the
designed organizational system (Argyris, 1957, 1970; Argyris et al., 1985; Argyris
& Schön, 1989, 1992, 1996). Resolving social tensions requires dialogue and action.
In architecture and urban planning, similar ideas emerged, requiring dialectic reasoning to tame wicked problems inherent in a pluralistic society (Rittel, 1987; Rittel
& Webber, 1973). In a similar vein, social informatics focuses on designing information systems and technology to enable social systems, such as organizations
(Kling, 1973, 1977; Kling & Scacchi, 1980, 1982; Kling & Star, 1998). Sociopolitical dynamics inﬂuence the design practices and projects, and the design
outcome impacts society (Frascara, 2002; Margolin, 2002; Margolin & Margolin,
2002). Designing is a socially constructed and political effort that requires many
diverse groups who do not have the economic or political means to generate a formal
design demand (Manzini, 2015; Margolin, 2002; Rittel & Webber, 1973; Whiteley,
1997).

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6.2

237

Design as Social Collaboration

From a bird’s-eye view, design solutions are created collaboratively by, with, and for
many people. This is a design culture in which many people contribute results in
open, free, distributed, and shared innovation (von Hippel, 1988, 2005, 2016). In
management, the term co-design emerged with the emphasis on co-creating value
between customers and organizations. The meaning and creation of value shifted
from a product and a ﬁrm-centric approach to personalized consumer experiences
(Prahalad, 2004; Prahalad & Ramaswamy, 2004). Co-design is a network of
informed, empowered, and active people who co-design valuable solutions with
organizations, resulting in entire distributed communities of people co-designing
solutions. It is also a force for social and political change (Heller & Vienne, 2003).
Such Social Collaboration of designing is an ongoing social activity that requires
people with attitudes, values, attributes, and abilities within a supportive environment, allowing design for various needs and tensions collaboratively (Auernhammer
& Roth, 2021).

7 Conclusion
Different cultural, socio-economical, and other contextual developments in speciﬁc
periods led to the evolution of diverse Human-centered Design approaches. Experimental psychology informed designers in the practices to design for the physiological and psychological needs of people. This approach developed further into
Human–Computer Interactions and Interaction Design. New developments in experimental psychology and new technological advancements, such as the personal
computer, resulting in new Human-centered Design approaches. Today, global
pandemics and climate changes require designers to respond with new design
approaches. For example, collaboration in design is impacted by remote settings
and the use of technology, inﬂuencing the abilities of design teams to respond to
these challenges. Designers need to develop new design approaches to tackle the
many challenges, such as inequality in society and ecological sustainability, creatively. This book chapter illustrated the interrelations between emergent situations
and developments of design approaches and highlights the importance of developing
new design approaches to tackle the many societal, economic, and ecological
challenges of today and tomorrow.

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Decades of Alumni: Designing a Study
on the Long-Term Impact of Design
Education
Sheri D. Sheppard, Helen L. Chen, George Toye, Timo Bunk, Nada Elﬁki,
Felix Kempf, J. L. Lamprecht, and Micah Lande

Abstract Design is an essential part of engineering practice and engineering education. As such, our research aims to examine the longer-term impact of engineering
design education on graduates’ career paths and their practical utilization of design,
post-graduation. We have focused our attention on several decades of alumni from
two speciﬁc graduate course sequences, Project-Based Engineering Design Innovation & Development (ME310) and Smart Product Design (ME218), in order to gain
a deeper understanding of how particular course elements and strategies are directly
linked to what alumni retain and take away from their education. These course
sequences represent two possible Mechanical Engineering depth areas that leverage
a project-based learning approach to allow students to dive deeply into designing and
building functional systems of some engineering complexity.
In this chapter, we describe a multifaceted and mixed methods research effort that
considers decades of graduates from ME310 and ME218 at Stanford University. The
qualitative interviews and quantitative survey studies were designed to establish a
deeper understanding of the longer-term impact of education on career plans and
pathways (particularly as related to engineering innovation and entrepreneurship)
and to also demonstrate the need to take a “bigger view” of graduates’ feedback on
courses and on their formal education more generally.
The analyses of this rich dataset are already bearing fruit by allowing us to
identify speciﬁc curricular “features” that inspire innovative and entrepreneurial
actions. We are also seeing how ME310 and ME218 graduates have built careers
in a variety of professions, are scattered around the globe, and do not follow a
singular career pathway or even dominant industry sector. Some stay highly technical throughout their post-graduate work, whereas others turn to less technical roles
immediately after graduation.

S. D. Sheppard (*) · H. L. Chen · G. Toye · T. Bunk · N. Elﬁki · F. Kempf · J. L. Lamprecht ·
M. Lande
Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA
e-mail: sheppard@stanford.edu; hlchen@stanford.edu; toye@stanford.edu;
felix.kempf@kcl.ac.uk; johannes.lamprecht@tum.de; Micah.Lande@sdsmt.edu
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_13

247

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S. D. Sheppard et al.

1 Introduction
For more than 50 years, design has been a strong component of mechanical engineering education at Stanford University. Within the Department of Mechanical
Engineering, the Design Group offers a robust set of undergraduate and graduate
courses and curricula to support mechanical engineering design learning opportunities. The founding of the Hasso Plattner Institute of Design (d.school) in 2005
marked an evolution of design methodology, popularized as design thinking, beyond
just the disciplinary bounds of traditional engineering. This methodology also served
as an extension to the long tradition of project-based learning in support of engineering design. The creation of engineering design-centered courses started at
Stanford in the early 1960s and showed a steady growth throughout the subsequent
decades. Today there are over 100 design courses offered each year and many
include an emphasis on project-based pedagogies (Arnold, 1953; Adams, 1974;
Auernhammer & Roth, 2021).
In this research we examine the longer-term impact of engineering design courses
on graduates’ career paths and their practical utilization of design, post-graduation.
We have focused our attention on several decades of alumni from two speciﬁc
graduate course sequences, Project-Based Engineering Design Innovation & Development (ME310) and Smart Product Design (ME218), in order to gain a deeper
understanding of how particular course elements and strategies are directly linked to
what alumni retain and take away from their education. These course sequences
represent two possible Mechanical Engineering depth areas that leverage a projectbased learning approach to allow students to dive deeply into designing and building
functional systems of some engineering complexity.
Both ME310 and ME218 share similar pedagogical approaches of a productfocus on learning (Larson et al., 2020) and a type of Maker-based pedagogy (Lande
et al., 2017). Students have some autonomy to explore and direct their design
challenges through the effects of prototyping (Schrage, 1996; Gerber & Carroll,
2012), scaffolded by activities, labs, and milestones (Lande, 2016). Students not
only engage in the application of a mechanical engineering design process but are
mentored in the ingenuity of developing a technology into a product with regular
engagement with a supportive teaching team of instructors and teaching assistants as
design process and technical coaches (Reich et al., 2009; Strong et al., 2019). This
high degree of interaction between the students and teaching team translates into a
healthy network of course alumni who participate on the periphery as coaches or in
quarterly events, presentations, or end-of-year activities.
The speciﬁc content of ME310 and ME218 differs in signiﬁcant ways. ME310 is
focused on team-based design processes for innovative product development with
industry-sponsored projects. Since the mid-2000s, there has also been an element of
distributed collaboration with a number of academic partners around the world
(Carleton & Leifer, 2009). In contrast, ME218 has emphasized the combined
engineering disciplines in mechatronics and employed projects as a means for

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249

students to learn how to integrate these technologies into discrete functioning
systems to achieve operating design goals (Brunhaver et al., 2012; Carryer, 1999).
This chapter summarizes the mixed methods research efforts and analyses
designed to learn more about the impact of ME310 and ME218 on their graduates.
This work addresses the following research questions:
• RQ1: What career paths have graduates from immersive engineering design
courses pursued?
• RQ2: What are the alumni’s attitudes and perspectives on the various components of these types of design courses?
• RQ3: How might course experiences inﬂuence graduates’ career paths, especially as related to being innovative and/or entrepreneurial?
• RQ4: What can we learn from listening to alumni about the effectiveness of
education more generally? What improvements are suggested for engineering
design education?

2 Background
Engineering programs and higher education institutions serve important roles in
preparing graduates to join a rapidly evolving workforce and changing environment
in ﬁelds both within and beyond engineering (Atman et al., 2010; National Academy
of Engineering, 2018; Sheppard et al., 2014; Gilmartin et al., 2017). Decisionmaking about career pathways is iterative and often repeated across one’s professional trajectory, from the ﬁrst position that is taken after graduation to retirement.
New career interests and motivations, emerging technologies, and unanticipated
personal and professional opportunities and challenges may also contribute to
these choices.
In order to respond to the needs of their students and prepare them for these
unpredictable career pathways, educators have made corresponding changes to their
curricula and pedagogies within the ﬁelds of design education and engineering
education through project-based design engineering courses. These courses
operationalize key elements of the design thinking philosophy and practices that
underpin the graduate curriculum offered by the Design Group within the Department of Mechanical Engineering at Stanford (Auernhammer & Roth, 2021). Over
the last 2 years, we have examined how these intensive course-based design experiences contribute to and facilitate intentions related to entrepreneurship and innovation in former students.
This research draws on prior work in three areas:
Academic and Professional Pathways of Engineering Students and Alumni
The individual and environmental factors that contribute to a student’s experience in
engineering education has been a fundamental area of research for engineering
educators. From 2003 to 2010, the Center for the Advancement of Engineering
Education addressed the components of skills, identity, education, and workplace

250

S. D. Sheppard et al.

through studies of students, faculty, and early career engineers (Atman et al., 2010,
Sheppard et al., 2010). About a decade later, the National Academy of Engineering
(2018) highlighted the diversity in the skills that engineers use across occupations
and industries, the need for engineers with diverse backgrounds and life experiences,
and the ability of the engineering community to convey the value of engineering
education to diverse stakeholders.
Employing Qualitative, Quantitative, and Mixed Methods Approaches
The transition from a pipeline to pathways model recognizes that there is no one
singular student experience (Atman et al., 2010) and more expansive research
methods must be used to effectively gain insights into the perspectives of engineering students and alumni (Sheppard et al., 2014). The National Academy of Engineering (2018) found that the limitations of national survey-based datasets
contributed to data gaps that impede the ﬁeld’s understanding of educational and
career pathways. As a result, triangulation among datasets informed by surveys as
well as interviews can strengthen the credibility and generalizability of research
ﬁndings.
Connections to Entrepreneurship and Innovation Outcomes
Gilmartin et al. (2017) documented the ﬁndings from the Engineering Majors
Survey, an instrument designed to investigate undergraduate engineering students’
decisions and plans for the post-graduate transitions into the workplace and to gain a
more nuanced understanding of the academic and professional learning experiences
as well as the individual characteristics that contribute to entrepreneurial and innovation interests and goals. Barth et al. (2020) provide another model of examining
the innovative behaviors and outcomes of early career engineers. The ﬁndings from
Bunk (2021), Elﬁki (2021), and Sheppard et al. (2021a, b) advance the research
literature and the methodologies used to explore how graduate-level curriculum
incorporating both design and engineering components contribute to entrepreneurial
and innovative actions and career interests of alumni.

3 Study Design and Methods
3.1

Overview of the Research Design

The goal of this research is to investigate the longer-term impacts of educational
experiences. As a result, we focused on several decades of alumni who completed
speciﬁc engineering design course sequences: Project-Based Engineering Design
Innovation & Development (ME310) and Smart Product Design (ME218). The
emphasis on course completion contrasts with alumni studies that are at the level
of a program (often for continuous improvement processes such as ABET, formally
known as the Accreditation Board for Engineering and Technology) or administered
by the school or university, where perspectives are gathered as part of alumni
relations, institutional accreditation, and development. Our unique approach allows

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251

us not only to collect enough data for some interesting quantitative data analyses, but
also to explore how careers change over time (in a cross-sectional manner) and
conduct comparative analyses between the two course sequences.
Brief Description of ME310 Project-Based Engineering Design Innovation &
Development, with its emphasis on industry-sponsored engineering design projects,
was ﬁrst taught in the Stanford Mechanical Engineering department in 1967. Programmatically, the ME310 A/B/C course sequence is a graduate-level, academic
year-long, multi-disciplinary, project-based learning, design engineering, student
experience. In the 1990s, ME310 updated its emphasis toward providing an integrative journey of team-based innovative product design development processes (going
from concept explorations to tested prototypes), grounded in real-world contexts
through its industry-sponsored project framework. Team projects with students from
across a growing network of international academic partners reﬂect the increasing
interest in and adoption of globally distributed teamwork in industry. Annually,
ME310’s project portfolio spans a varying multiplicity of product and service
domains, where design challenges often include considerations for human psychology, economics, and business models (Carleton, 2019; Carleton & Leifer, 2009;
Sheppard et al., 2021b).
Brief Description of ME218 This Smart Product Design course sequence originated in the 1970s as a single course for graduate students to design and build
electromechanical systems (also known as “mechatronics”) and has now evolved to
cover continuously advancing technologies and topics in software/ﬁrmware and
electromechanical and electronic hardware design. The ME218 A/B/C/D course
series offered in Mechanical Engineering is a graduate-level, depth sequence option
that spans four academic quarters where students incrementally acquire greater
technical knowledge and master more advanced design, build, and debugging skills
through a series of hands-on laboratory assignments and projects. ME218A emphasizes fundamentals, ME218B applications, and ME218C practice. In ME218D,
student teams dedicate their efforts to industry-sponsored projects.
Crafting a study approach to answer our research questions was not without
challenges, since the instructors and curricular topics for ME310 and ME218 have
changed over the decades—although we believe that their core fundamentals have
remained largely intact. In addition, there have been generational variations given
the changing proﬁle of graduate students admitted to Stanford’s Mechanical Engineering program as represented by the research on Baby Boomers, Gen X, Gen
Y/Millennials, and now Gen Z (Pew Research Center, 2015). The picture is further
complicated by the ﬂuctuating job market, especially as engineering work continues
to evolve and expand.
Figure 1 and Table 1 provide an overview of the various datasets that were
collected and that constitute our overall research design. This mixed methods
study weaves together quantitative data collected through surveys with semistructured interviews that draw out more details about alumni’s course experiences
and subsequent career choices. The research design, funded by the Hasso Plattner

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Fig. 1 Overview of the ME310 and ME218 alumni research design
Table 1 Summary of the ME310 and ME218 instruments, participants, relevant research questions, and researchers
Research
instrument
ME310 Survey:
deployed July
2020
ME310 Interviews: conducted
in November
2020

ME218 Interviews: conducted
December 2020–
January 2021

ME218 Survey:
deployed
August–
September 2021

Participants
ME310 Alumni from 1992
to 2018. Survey sent to
734 ME310 alumni. 41%
response rate.
Alumni identiﬁed from
ME310 Survey Responses,
based on Entrepreneurial
and Innovation SelfEfﬁcacy Measures. 39 interviews conducted out of
75 invitations.
ME218 alumni who are or
who have been successful
entrepreneurs. Interview
candidates were identiﬁed
from recommendations
from E. Carryer, LinkedIn
proﬁles and ME218 public
archive, based on founder
activity. 19 interviews
conducted out of
26 invitations.
ME218 Alumni from 1987
to 2019. Survey sent to
1735 alumni. 33.8%
response rate.

Research questions/goal
RQ1, RQ2, RQ3, RQ4

Lead
researchers
Toye, Chen,
Kempf, Elﬁki,
Sheppard

RQ3, RQ4, with a particular
focus on identifying entrepreneurs and intrapreneurs

Elﬁki

RQ3, RQ4, with a particular
focus on identifying successful founders

Bunk

RQ1, RQ2, RQ3, RQ4, plus
distinguishing how/if the
emersive experiences in
ME310 and ME218 differ
from one another.

Toye, Chen,
Kempf, Lande,
Bunk,
Lamprecht,
Sheppard

Design Thinking Research Program (HPDTRP), evolved over the course of 2 years.
The ﬁrst year facilitated the design and development of the ME310 Alumni Survey,
and the second year expanded the work to include alumni from ME218 which
allowed for comparative analyses.

Decades of Alumni: Designing a Study on the Long-Term Impact of. . .

3.2

253

ME310 and ME218 Alumni Survey Instruments
and Respondents

Both the ME310 and ME218 Alumni Survey instruments share common sections on
Education (degrees earned), Career Pathways (post-course occupations, current/
most recent job, future plans), Attitudes and Skills (self-efﬁcacy and innovative
behaviors) and Demographics. Self-efﬁcacy, the belief in one’s ability to perform a
speciﬁc task or action (Bandura, 1986), and its relationship to Social Cognitive
Career Theory (Lent et al., 1994; Lent & Brown, 2006) serve as the theoretical
foundation informing our alumni research particularly as it relates to engineering,
innovation (Barth et al., 2020; Gilmartin et al., 2017; Schar et al., 2017a, b),
entrepreneurship (De Noble et al., 1999), and design thinking (Schar, 2020).
Table 2 describes the three self-efﬁcacy measures included in the survey instruments
that were particularly relevant to the ME310 and ME218 interviews, along with a
key behavioral measure on innovation. These items concentrate on the attitudes and
skills demonstrated on the job, and build on prior research ﬁndings from the
Engineering Majors Survey (Gilmartin et al., 2017; Schar et al., 2017a, b) and De
Noble et al. (1999), who focused on innovative and entrepreneurial activities and
behaviors. ME310 and ME218 survey participants were also asked how often they
engaged in various activities and behaviors listed in Table 3 as a strategy to build a
more nuanced picture of individuals’ day-to-day work.
Survey Items Tailored to the Course Experience
Each survey also includes survey questions that focused on the attributes of the
course experience, outcomes, and takeaways. Sheppard et al. (2021a, b) provide
more details about the rationale and development of these measures.
Some of the ME310 and ME218 survey questions focused on recalling and
assessing the impact of course-speciﬁc core design strategies and skills, whereas
other questions were designed to prompt reﬂection on their course projects. These
Table 2 Descriptions of key self-efﬁcacy and innovative behavior measures
Self-efﬁcacy measures:
Innovation (ISE.5)
[5 items]
Engineering Task
(ETSE) [4 items]
Entrepreneurial (ESE)
[10 items]
Behavior measure:
Innovative Behavior
(IB) [6 items]

Conﬁdence in one’s ability to: Five-point Likert scale: from “Not
conﬁdent” (0) to “Extremely conﬁdent” (4)
Innovate, i.e. to engage in speciﬁc behaviors that characterize innovative people.
Perform integral technical engineering “tasks” such as “analyzing the
operation or functional performance of a complete system.”
Pursue a new venture opportunity, representing two dimensions
related to developing “new product and market opportunities” and
“coping with unexpected challenges.”
Description: Five-point Likert scale: from “Never” (0) to “Very
often” (4)
Individual behaviors that contribute to a collective innovation process focusing on idea generation, coalition building, idea realization,
and transfer/diffusion.

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Table 3 Work behaviors in current job
General work behaviors
• Presenting your work to senior managers or leaders
• Working on something that generates interest and feedback from others in your organization
• Working in areas or domains that are unfamiliar to you
• Cross-functional collaboration (working with different business units, etc.)
• Working on ambiguous, ill-deﬁned problem
• Leading or directing others
• Documentation
• Ethical questions or considerations
Entrepreneurial, innovative, and engineering work behaviors
• Searching out new technologies, processes, techniques, and/or product ideasa
• Generating creative ideasa
• Promoting and championing ideas to othersa
• Investigating and securing resources needed to implement new ideasa
• Developing adequate plans and schedules for the implementation of new ideasa
• Selling a product or service in the marketplacea
• Designing a new product or project to meet speciﬁed requirementsb
• Analyzing the operation or functional performance of a complete systemb
• Discovering new ways to improve existing productsc
• Seeing new market opportunities for new products and servicesc
• Creating products that fulﬁll customers’ unmet needsc
Note: Respondents were asked to indicate their frequency of engagement in the activities listed
above on a ﬁve-point Likert scale including: Never, Rarely, Sometimes, Often, Very Often
a
These six items are part of the Innovative Behavior construct described in Table 2 (Barth et al.,
2020)
b
These two items relate to engineering task behaviors (Schar, Gilmartin, Rieken, et al., 2017b)
c
These three items relate to entrepreneurial behaviors (De Noble et al., 1999)

strategies and skills varied between the two courses. Furthermore, because of
ME310’s emphasis on the social-dynamics of design, additional questions related
to the team and classmate interactions and performance and the durability of
teammate relationships, both locally and globally, were included. In contrast, a
series of questions on the ME218 survey related to the pervasiveness of continued
activities related to software and electronics design, writing interface ﬁrmware, and
designing mechatronic systems. These differences are summarized in Table 4.
Key Survey Deployment Details
Details of the deployment of the ME310 Alumni Survey are provided in Sheppard
et al. (2021a, b). Many of the same procedures were followed with the deployment of
the ME218 Alumni Survey (e.g., using Qualtrics, working with the Institutional
Research ofﬁce to identify names of students who had completed the course over the
25 year period, and then with the Alumni Relations group within the School of
Engineering to send out email invitations to the identiﬁed alumni). For the ME218
alumni, a commemorative digital poster representing a collage of course photos was
commissioned and sent out to all survey participants. In addition, 19 survey participants were randomly selected in a drawing to receive a gift card to the Stanford
Bookstore and/or an invitation to a virtual meal with the current ME218 instructor

Decades of Alumni: Designing a Study on the Long-Term Impact of. . .

255

Table 4 ME310 and ME218 course experience survey items
ME310 course-speciﬁc questions
• What ME310 course skills do you remember
from ME310? (e.g., design process, public
communications, collaboration, project and
team management)
• Which of these skills have you subsequently
found valuable in your life/career?
• What ME310 design process strategies do you
remember from ME310? (e.g., challenging
assumptions, building quick prototypes, taking
risks with radical design ideas)
• Which of these strategies have you subsequently found valuable in your life/career?
• Did you work on a project with a global
partner team?
• Since ME310, how many social connections
have you maintained (e.g., with your project’s
Stanford teammates, your project’s global
teammates, etc.)?

ME218 course-speciﬁc questions
• What ME218 course skills do you remember
from ME218? (e.g., collaboration, project
planning, development process)
• Which of these skills have you subsequently
found valuable in your life/career?
• Clusters of questions about experiences and
activities related to:
-Designing and building electric circuits
-Developing software applications (coding)
-Writing sensor/actuator interface ﬁrmware
-Designing/building integrated mechatronic
systems
(per chronological contexts: at entry to
ME218, subsequently in ﬁrst and current jobs,
and episodically on personal projects.)

and fellow alumni. It is important to note that the success of both surveys relied
heavily on leveraging the social capital of class alumni to participate.
Survey Respondents
The demographics of the survey respondents among ME310 and ME218 alumni
who had been invited to participate in the survey are shown in Table 5. These
demographics largely mirror those of the larger population in the graduate program
in Mechanical Engineering at Stanford, where there has been a slow and steady rise
in female enrollment over the years. Noteworthy is the underrepresentation of those
who self-identiﬁed as American Indian, Black or African American, or Hispanic.
The ME310 and ME218 survey datasets represent a unique and heterogeneous
network of alumni who graduated from Stanford between 1993 and 2017 for ME310
and 1988 to 2019 for ME218. Despite the extraordinary course histories and multigenerational survey audience, the survey respondents are evenly distributed across
year groups and clusters as visualized in Fig. 2a. An even distribution of survey
participants across year groups allows us to gain robust and valuable insights into
how these courses were perceived over the years. Figure 2b shows the response rate
across the years.
The response rate is deﬁned as people with registered email address divided by
the number of responses. Note: In the ME218 dataset we have 12 missing values
regarding the academic year of enrollment.

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Table 5 ME310 and ME218 course alumni survey respondents
ME310 Alumni (N ¼ 267)a
Respondents
Percent
(a) By Gender
Females
58
Males
208
Missing
1
Total
267
(b) By Race/Ethnicity (mark all that apply)
American Indian or Alaska Native
2
Asian or Asian American
110
Black or African American
10
Hispanic or Latino/a
22
Native Hawaiian or Paciﬁc Islander
1
White
135
Other
4
I prefer not to answer
5
Missing
1

ME218 Alumni (N ¼ 503)a
Respondents
Percent

21.7%
77.9%
0.4%
100.0%

95
402
6
503

18.9%
79.9%
1.2%
100.0%

0.7%
41.2%
3.7%
8.2%
0.4%
50.6%
1.5%
1.9%
0.4%

1
152
16
42
2
290
6
19
3

0.2%
30.2%
3.2%
8.3%
0.4%
57.7%
1.2%
3.8%
0.6%

Note: These two survey samples may include individuals who completed both ME310 and ME218.
At the time of writing, we have not separated that group out
a
The authors decided to consider only complete (deﬁned as 95% of survey progress) survey
submissions leading to an effective sample size of 267 for the ME310 dataset and 503 for the
ME218 dataset. The rationale behind this decision is due to the high number of incomplete survey
responses within the ME218 dataset, i.e., 14.3%. To ensure consistency and comparability the
95% criterion was also applied to the ME310 dataset leading to a loss of 2% of the survey
responses

3.3

Interview Studies

Both interview studies described below employed a multiple case research strategy
for theory building (within the case of the ME310 or ME218 course) so as to focus
on further understanding the dynamics that are present within a single context
(Eisenhardt, 1989). The fundamental source of data stems from semi-structured
interviews, as these are an effective and efﬁcient technique of gathering
information-rich data from numerous and highly knowledgeable informants who
view the focal phenomena from diverse perspectives (Eisenhardt & Graebner, 2007).
Selection and Recruitment for the ME310 Interview Study on Entrepreneurs
and Intrapreneurs, and Development of Interview Protocol
A theoretical sampling approach was taken to identify which ME310 survey participants to interview based on the following criteria:
1. Moderate to high average Entrepreneurial Self-Efﬁcacy (ESE) and Innovation
Self-Efﬁcacy (ISE) scores (see Table 2)
2. Current position: entrepreneur or intrapreneur

Fig. 2 (a) Enrollments and invitations sent to ME310 and ME218 alumni, by year of class enrollments

Decades of Alumni: Designing a Study on the Long-Term Impact of. . .
257

Fig. 2 (continued) (b) ME310 and ME218 alumni survey response rates by year enrolled in course

258
S. D. Sheppard et al.

Decades of Alumni: Designing a Study on the Long-Term Impact of. . .

259

3. Moderate to high average entrepreneurial and innovation work behaviors (see
Table 3)
4. Survey-proportionate gender and generational balance
A four-section structured interview protocol with semi-structured questions was
developed. The ﬁrst section asked for the interviewee’s deﬁnition of and perceived
differences between innovative and entrepreneurial behavior to ensure that the
interviewer and interviewee were aligned on the scope of the topic. The focus of
the second and third sections was on their learning experiences during their professional and academic years (including the ME310 course) in relation to the correlated
Innovation Self-Efﬁcacy (ISE) and Entrepreneurial Self-Efﬁcacy (ESE) items.
Lastly, the interviewees were encouraged to reﬂect back on what they wish they
had learned earlier during their study years. In doing so, they were prompted to
design their own hypothetical course and describe how they would foster innovative
and entrepreneurial engineering leaders.
In total, 39 ME310 alumni interviews were conducted, with men accounting for
67% and women representing 33% of those interviewed. Of this group, 49%
identiﬁed as founders and 46% as intrapreneurs at the time of the interviews. More
details on the study procedures can be found in Elﬁki (2021).
ME218 Interview Study on Successful Founders
A theoretical sampling approach was also used to identify and invite alumni for an
interview. However, because these interviews were conducted before the ME218
alumni survey, a selection strategy was needed that did not rely on survey data. The
task became one of the identifying successful company founders who had taken
ME218 through means such as referrals from the current course instructor, searches
on LinkedIn, and in the publicly available ME218 archive. The following selection
criteria were used to identify 132 ME218 alumni for possible interviewing:
1.
2.
3.
4.
5.

Being a ME218 alumnus while Dr. Edward Carryer was teaching ME218
Holding a co-founder or former co-founder position
Contributing to gender balance
Contributing to generational balance
Contributing to variation in industry

The interview protocol began with a philosophical question prompting the interviewee about their general motivation and interest in entrepreneurial activities.
Interviewees were asked about the inﬂuence of their educational experiences and
ME218 speciﬁcally as the means to explore how courses and other related learning
fostered entrepreneurship, provided valuable skills, and increased Entrepreneurial
Self-Efﬁcacy. Additional questions explored how education could be improved to
prepare students for an entrepreneurial career. These questions also focused on
interviewees’ ideas for improving education and areas they felt were missing in
their education. Interviewees were also prompted to share their deﬁnitions of success
and the pathways they took to achieve success and navigate challenges and failures,
particularly in the startup environment, thereby generating advice for new founders.
Finally, interviewees were asked to describe something they would teach to current

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students in a lecture and how they would demonstrate their acquired skills to
students with entrepreneurial interests.
In total, interviews were conducted with 19 individuals from a variety of industries. Men accounted for 89% and women represented 11% of those interviewed.
More details on the study procedures can be found in Bunk (2021).

4 Results
Our study design allowed us to undertake a variety of analyses to better understand
the longer-term impact of immersive engineering design experiences like ME310 or
ME218 on the career paths and entrepreneurial and innovative interests of alumni.
Opportunities for mixed methods, comparative approaches, and the triangulation of
ﬁndings with the quantitative and qualitative datasets are represented. We have
amassed a signiﬁcant and varied collective dataset on alumni’s course and career
experiences and additional analyses and ﬁndings will be forthcoming, as outlined in
Sect. 5.

4.1

Overall Picture of ME310 and ME218 Alumni

Table 6 shows that some 36.7% of ME310 course alumni were working in mediumto-large ﬁrms (49% when missing responses are excluded), and 15.0% were working
in small ﬁrms (20% without missing responses). This is in contrast to 49.1% of
ME218 alumni who reported working in medium-to-large ﬁrms, and 23.5% working
in small ﬁrms. These results suggest that ME218 course alumni may be more drawn
to smaller organizations than ME310 course alumni.
Another 13.1% (17.6% without missing responses) of ME310 alumni are founders of a for-proﬁt organization, compared with 11.9% of ME218 alumni; both
numbers are signiﬁcantly higher than the 4% number reported in the National
Academy of Engineering (2018) report on academic and career pathways of engineering graduates.
We also see that 7.6% of ME310 alumni (excluding missing data) and 6.4% of
ME218 alumni identify as being a faculty member at a university or college. These
numbers are in the range reported for individuals who choose to pursue a PhD degree
after earning a Master’s degree, then going on to careers in academia (National
Academy of Engineering, 2018).
Both alumni surveys asked respondents to classify their current or most recent job
and their ﬁrst job after completing ME310 or ME218 from a list of 16 job functions.
Respondents were asked to “mark all that apply.” As shown in Fig. 3, the three job
functions that saw notable increases in respondent involvement between the ﬁrst
post-ME310 (or ME218) job and their current job were Marketing/Public Relations,
Sales, and Technical Management (suggesting migration into these functions). The

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261

Table 6 Organizational role best aligned with current or most recent job
Organizational role (current or most recent
job)
Employee for a medium- or large-size
business
Employee for a small business or start-up
company
Founder/co-founder of your own for-proﬁt
organization
Faculty member or educational professional
in a college or university
Employee for a non-proﬁt organization
(excluding a school or college/university)
Employee for the government, military, or
public agency (excluding a school or college/
university)
Founder/co-founder of your own non-proﬁt
organization
Teacher or educational professional in a
K-12 school
Missing
Total

ME310 Alumni
Respondents Percent
98
36.7%

ME218 Alumni
Respondents Percent
247
49.1%

15.0%

118

23.5%

13.1%

11.9%

5.6%

6.4%

1.5%

1.8%

1.5%

4.0%

0.4%

0.2%

69
267

25.8%
100.0%

14
503

2.8%
100.0%

Fig. 3 (a) ME310 alumni respondents reporting various job functions of their current job and ﬁrst
post-ME310 job (N ¼ 267). (b) ME218 alumni respondents reporting various job functions of their
current job and ﬁrst post-ME218 job (N ¼ 503)

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S. D. Sheppard et al.

Fig. 3 (continued)

Table 7 Engineering and non-engineering classiﬁcations of ﬁrst position after course and current
position
Position type:
Engineering
First position after course
Current position

ME310 Alumni
Respondents
220
104

Percent
82.4%
39.0%

ME218 Alumni
Respondents
452
353

Percent
89.9%
70.2%

three job functions that saw notable decreases in respondent involvement were
Design, R&D, and Production/Manufacturing (suggesting migration out of these
functions).
We also asked ME310 and ME218 alumni to classify their ﬁrst and current jobs as
engineering or non-engineering. As shown in Table 7, some 90% of ME218 alumni
described their ﬁrst job as “engineering”; this number decreased to 70% when
classifying their current job. For ME310 alumni, some 82% identiﬁed their ﬁrst
job as “engineering”; this dropped to 39% when describing their current job.

Decades of Alumni: Designing a Study on the Long-Term Impact of. . .

4.2

263

Dissemination of Findings to Date

The ﬁndings from the ME310 and ME218 alumni interviews are documented in
Elﬁki’s (2021) and Bunk’s (2021) master’s theses, respectively. Elﬁki (2021) identiﬁed four main categories (Mastery by Doing, Connection to Real Life, Interdisciplinary Exposure, Supportive Environment) that have the potential to enhance
Entrepreneurial Self-Efﬁcacy and Innovation Self-Efﬁcacy. Bunk (2021) examined
other factors that inﬂuenced ME218 alumni’s decisions to start companies and
further aspects crucial for their entrepreneurial success including Relevant Learnings
for the Entrepreneurial Path (important lessons learned by founders during their
education beyond their ME218 experience), and Success Factors, Entrepreneurial
Motivation Factors, and Conﬁdence for Entrepreneurship, which described elements that have the potential to inspire more students to consider an entrepreneurial
pathway.
Sheppard et al. (2021b) provide details on the design of the ME310 survey,
including logistical considerations for survey deployment and participant incentives.
Cronbach’s Alpha scores, based on the data, for the self-efﬁcacy measures of
Innovation Self-Efﬁcacy (α ¼ 0.72), Engineering Self-Efﬁcacy (α ¼ 0.85), Entrepreneurial Self-Efﬁcacy (α ¼ 0.87), and Design Thinking Self-Efﬁcacy (α ¼ 0.75)
were reported, along with the actual calculated values of these measures based on the
weighted average of associated items.
Sheppard et al. (2021a) was shared results at the annual meeting of the American
Society of Engineering Education in July 2021. In this conference paper and
presentation, variations across the self-efﬁcacy measures among the ME310 alumni
were investigated. Through comparative analyses of the ME310 survey data, we
looked at how leadership roles, employment in various types of organizations, and
job functions might result in differences in self-efﬁcacy and/or innovative behaviors.
We found that greater Entrepreneurial Self-Efﬁcacy and Innovative Behaviors were
exhibited by those who reported more leadership responsibilities and/or who were
currently a founder. Furthermore, those whose current job involved R&D and
design, and whose ﬁrst job had involved one or both of these functions exhibited
greater Engineering Self-Efﬁcacy, but no greater Innovation Self-Efﬁcacy or Entrepreneurial Self-Efﬁcacy than any of the other groups we considered. Those whose
ﬁrst and current jobs did not include any design and/or R&D functions exhibited the
lowest innovative behavior.
As part of our efforts to gain insights into the ME310 course experience for the
teaching team, we experimented with new approaches of data analysis including a
“word salad” question where we presented survey participants with a list of 18 words
and asked them to mark all that were applicable to describing their overall ME310
design journey. Using the R programming language to analyze the data, the word
cloud in Fig. 4 summarizes how frequently an individual word was selected by the
ME310 survey participants. The majority of the words identiﬁed by 301 respondents
(84.1%) were deemed positive (e.g., engaging, enjoyable, inspiring) rather than
negative (15.9%, e.g., painful, frustrating, tiring). These results were included in a

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S. D. Sheppard et al.

Fig. 4 Word cloud
summarizing word
descriptors

report to the ME310 teaching team. This same survey item was also included in the
ME218 alumni survey and we also expect to share the corresponding ﬁndings with
the course instructor.
The spring and fall 2021 Hasso Plattner Design Thinking Research Program
(HPDTRP) community meetings served as important touchpoints for our project
with the workshop format prompting synthesis with the design educator audience in
mind. The multi-disciplinary community of HPDTRP with diverse expertise—
engineering, design, medicine, science, and the humanities—served as a critical
sounding board for how we continue to frame and communicate our ﬁndings to a
broader community of designers and educators. The feedback and suggestions we
received have directly informed the development of the framework in Fig. 5 which
situates the intensive project-based design courses within a broader context of the
pedagogical and curricular components identiﬁed in our research.
We also shared sample ﬁndings from the ME218 survey at the fall 2021 ME218
alumni BBQ, a fall gathering that has been hosted for the last 10 years by the ﬁrm
219 Design. The ME218 research team wanted to thank the alumni for their
participation by providing some preliminary survey insights at the annual ME218
alumni gathering. In order to present the data in a memorable and creative way, the
team used Kahoot!, a game-based learning platform, to create a poll with seven
questions from the actual survey. For example, participants could each pick one out
of four options to guess how many survey respondents are currently founders or
co-founders of their own organizations. After each question, the quiz responses were
compared with the actual survey data and people with the correct answers received

Decades of Alumni: Designing a Study on the Long-Term Impact of. . .

265

Fig. 5 Emerging framework for the design, implementation, and assessment of project-based
design engineering courses. (a) The blue triangle highlights the Course Elements of Design
Thinking, Engineering, and Innovation & Entrepreneurship, with afﬁliated courses (indicated by
the dots) in hypothesized locations; (b) the red triangle identiﬁes the Teaching Strategy dimensions
and their hypothesized alignment with the Course Elements

points. At the end of the poll, the winner with the highest number of points was
announced followed by a short Q&A regarding the actual survey results.

5 Discussion and Next Steps
5.1

Discussion

In this chapter, we have outlined a multifaceted research effort that considers
decades of graduates from two engineering design course sequences at Stanford
University. These studies were designed to establish a deeper understanding of the
longer-term impact of education on career plans and pathways (particularly as
related to engineering innovation and entrepreneurship) and to also demonstrate
the need to take a “bigger view” of graduates’ feedback on courses and on their
formal education more generally.
The analyses of this rich dataset are already bearing fruit by allowing us to
identify speciﬁc curricular “features” that inspire innovative and entrepreneurial
actions. We are also seeing how ME310 and ME218 graduates have built careers
in a variety of professions, are scattered around the globe, and do not follow a
singular career pathway or even dominant industry sector. Some stay highly technical throughout their post-graduate work, whereas others enter less technical roles
immediately after graduation.
This research design has not been without challenges. For example, we worked to
create research instruments that asked questions that would be relevant to someone
who had taken ME310 or ME218 just a few years ago (say 2018) or many years ago

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(say 1992)—we recognize these are generations of engineers are very different. We
were also challenged by wanting the data collection experience for the alumni
(whether through a survey or interview) to be a positive interaction since the user
experience is critical to good design.
At the time of writing, we are in the midst of several lines of inquiry with the
datasets, as outlined below.

5.2

Next Steps in our Research

Here are brief descriptions of several analyses that are underway:
• Lent’s Social Cognitive Career Theory (SCCT) links learning to actions through
self-efﬁcacy development (Lent et al., 1994; Lent & Brown, 2006). Our ME310
interviews allows us to identify features in the ME310 curriculum that have been
impactful in the development of entrepreneurial and innovation self-efﬁcacy. Our
ME310 survey data allows us to statistically model innovative actions and
behaviors in terms of self-efﬁcacy and other aspects of the workplace environment. This is a unique opportunity to utilize the SCCT framework in connecting
higher education experiences to longer-term professional activities.
• In combination, we have 58 interviews of ME310 (N ¼ 39) and ME218 (N ¼ 19)
alumni focused on features of these courses that promote innovative and entrepreneurial ambitions, skills, and interests. The two interview studies have also
identiﬁed additional skills that founders and intrapreneurs have found to be
essential to their career success. We are now examining the combined set of
58 interviews to: (1) develop a more comprehensive list of innovation- and
entrepreneurial-promoting course features and other essential skills; (2) evaluate
and contextualize these course features in the literature on innovator and entrepreneur characteristics; and (3) interpret and disseminate the ﬁndings to diverse
audiences including innovation and entrepreneurship researchers, engineering
educators, and aspiring innovators and founders.
• We added a few additional questions on the ME218 survey asking alumni where
they have been working during the COVID-19 pandemic and how satisﬁed they
were with their arrangements. This set of questions, plus additional items on the
survey, allows us to identify a pool of ME218 alumni to interview in order to
better understand what effective remote teamwork among engineers looks like.
We are particularly interested in developing models of collaboration and leadership approaches.
Lastly, we recently submitted two paper abstracts for consideration for the 2022
American Society of Engineering Education Annual Conference; one teases out
differences and similarities in design education approaches (using ME310 and
ME218 as examples of Project-Based Learning) and the other highlights curriculum
features in the ME218 course that inspire and support founders.

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267

6 Parting Remarks
We hope that this chapter inspires...
• design educators to reach out to their alumni through a variety of means to see
how their education is serving them, and how it might be improved;
• design and educational researchers to create study plans that probe both the shortterm and long-term impacts of design education on careers; and
• today’s engineering students to recognize that learning that requires iteration,
creativity, reﬂection, and hard work is likely to be the learning that “sticks,” and
sometimes the most important part of education are the human relationships that
get built.
Acknowledgments We have an extended team to say “thanks” to: Professors Larry Leifer and
Mark Cutkosky, Dr. Ed Carryer, Crystal Pennywell, Gosia Wojciechowska, Elizabeth Mattson,
Mark Schar, Hung Pham, Elizabeth and Lucy Higgins, Nicole Esther Salazar, Katie Toye, NiclasAlexander Mauss, Lawrence Domingo, and Jan Auernhammer, and colleagues in the Stanford
Designing Education Lab. We could not have completed this study without the help of Alumni
Relations and Student Engagement in the School of Engineering—in particular Drea Sullivan and
Catherine McMillan. We are very grateful for the keen editing-eyes of Tammy Liaw and Sharon
Nemeth, and the organizational talents of Jill Grinager in bringing the Springer volume into
existence each year. We are also grateful to Dr. Claudia Liebethal and Prof. Helmut Schönenberger
from UnternehmerTUM who helped recruit dynamite researchers to join the team. Finally, we are
appreciative to those who participated in the pilot phases of survey design, the many ME310 and
ME218 graduates who completed these surveys, and those who agreed to be interviewed—they
represent the essential ingredients of this research. Finally, we acknowledge the generous support of
this work from the Hasso Plattner Design Thinking Research Program.

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Different Types of Productive Thinking
in Design: From Rational to Social Design
Thinking
Jan Auernhammer and Bernard Roth

Abstract This book chapter outlines the theory of Productive Thinking in Design.
This theory is the psychological foundation of today’s Design Thinking. Productive
Thinking incorporates the psychological processes of ﬁnding a need, problem, or
structural tension and determining a means that satisﬁes the need and harmonizes the
tension. These psychological processes are driven by forces and factors, including
attitudes, attributes, and human values. In this article, we outline ﬁve different types
of Productive Thinking and discuss them in the context of Design. These are
(1) Rational, (2) Situational, (3) Experimental, (4) Dialectic, and (5) Counterproductive. Each type of Productive Thinking is dependent on the situational context for
which a design solution needs to be created. For example, a situation in which a
solution-method can be determined directly requires Rational Productive Thinking,
while unintelligible, ambiguous, and emerging situations require Experimental or
Dialectic Productive Thinking. We emphasize that it is essential to cultivate a
Productive Culture in which individuals can freely, creatively, conﬁdently, competently, and collaboratively design for a harmonious ecological and social whole.

1 Introduction
Our research on the evolution of design at Stanford University identiﬁes foundational theories of today’s Design Thinking (Auernhammer & Roth, 2021). The
psychological theories in Productive Thinking explain how people determine new
solution-methods that resolve grasped problem-situations and structural tensions and
satisfy human needs in everyday life. Various design scholars and practitioners have
developed design practices and strategies to educate and support people in thinking
and collaborating productively (e.g., Arnheim, 2004, 2009; Arnold, 1959; Brown,
2009; McKim, 1972; Schön, 1983). Productive Thinking incorporates thought
processes, driven by psychological factors and forces, in everyday problem ﬁnding

J. Auernhammer (*) · B. Roth
Mechanical Engineering Design Group, Stanford University, Stanford, CA, USA
e-mail: jan.auernhammer@stanford.edu
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_14

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J. Auernhammer and B. Roth

and solving and meaning creation. These thought processes are foundational to
creative accomplishments in science, art, and design (Duncker, 1945; Guilford,
1950; Selz, 1922; Wertheimer, 1945). Productive Thinking occurs in mathematics,
physics, music, painting, design, and sports, to name a few (Arnheim, 2009; Arnold,
1959; Pólya, 1957; Selz, 1922; Wertheimer, 1945). Questions regarding Productive
Thinking are still being investigated in contemporary research in psychology and
neuroscience (Kounios & Beeman, 2009, 2014; Mayer, 1995; Wertheimer, 1996).
In Design, Productive Thinking has been reconceptualized into Design Thinking
over the decades. For example, Eastman (1970) and Simon (1969, 1981)
reconceptualized Selz’s (1922) Productive Thinking through de Groot’s (1965)
work in Thoughts and Choice in Chess as “design” and “design cognition.” Similarly, Dorst (2019) writes that there are many parallels of design models going back
to de Groot (1969). Others, such as Lawson (1972, 1980) and Goldschmidt (1991),
refer to Wertheimer’s (1945) Productive Thinking as theoretical explanations of how
designers think. Schön’s (1963, 1992a, b) work on the displacement of concepts,
design education, and reﬂective practices incorporates Wertheimer’s (1945) Productive Thinking and Dewey’s (1938) Theory of Inquiry. Similarly, Rowe (1987), in
Design Thinking, outlines theoretical positions, including the Würzburg School and
Gestalt Movement. Selz (1922) was part of the Würzburg School (structuralism)
while, e.g., Wertheimer (1945), Koffka (1935), Köhler (1925); Duncker (1945), and
Arnheim (1954, 1969) were Gestaltists (humanism). Arnold’s (1959) and McKim’s
(1972) work incorporate various aspects of Productive Thinking by Wertheimer
(1945), Duncker (1945), and Guilford (1950), as well as Visual Thinking by
Arnheim (1954, 1969). McKim (1972, p. vii) expressed that John Arnold inﬂuenced
him through his “pioneering efforts in educating Productive Thinking.”
Productive Thinking theories are the psychological foundations of Design Thinking. For example, Selz (1922) outlined that directly determining a solution-method
of a task requires grasping a purpose-means relationship (Zweckmittelverbinding).
Simon (1969) reconceptualized Selz’s (1922) work as a means-end analysis, providing the basis for his Science of the Artiﬁcial. It also comes as no surprise that
design researchers identiﬁed that designers determine solutions by building “bridges” between problems (purpose) and solutions (means) in conceptual design tasks
(Dorst & Cross, 2001). The Productive Thinking theories inﬂuenced many ﬁelds,
including artiﬁcial intelligence, creativity, design research, entrepreneurship, innovation management, organization studies, philosophy of science, and human–computer interactions, to name a few (e.g., Card et al., 1983; Guilford, 1950; March &
Simon, 1958; Newell & Simon, 1956; Popper, 2002; Sarasvathy, 2001; Schön,
1992a; Simon, 1957). In that sense, Design Thinking provides a renaissance of
Productive Thinking (i.e., human creativity) in diverse ﬁelds and disciplines. This
book chapter outlines the Psychology of Productive Thinking and discusses different
Productive Thinking types in Design.

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273

2 Psychology of Productive Thinking
Productive Thinking incorporates the psychological processes of how human
beings and other primates grasp problems, needs, and structural tensions within
everyday life and determine a solution-method that satisﬁes the needs and harmonizes situations (Duncker, 1935, 1945; Koffka, 1925; Köhler, 1925; Selz, 1922;
Wertheimer, 1945). Every productive thought process starts with an intent or felt and
experienced structural tension (i.e., a need) in everyday life (Dewey, 1938; Duncker,
1935, 1945; Selz, 1922; Wertheimer, 1945). In a simpliﬁed manner, Duncker (1945,
p. 1) describes this moment of need as follows:
A problem [need] arises when a living creature has a goal [intent or task] but does not know
how this goal is to be reached. Whenever one cannot go from the given situation to the
desired situation simply by action, then there has to be recourse to [productive] thinking.
(By action we here understand the performance of obvious operations.) Such thinking has
the task of devising some action which may mediate between the existing and the desired
situations. Thus the [design] “solution” of a practical problem must fulﬁll two demands: in
the ﬁrst place, its realization must bring about the goal [or harmonious] situation, and in the
second place one must be able to arrive at it from the given situation simply through action.

However, people often do not recognize a moment of tension as our “automatic
system” ﬁnds a seemingly appropriate conclusion or explanation (Kahneman, 2011).
Effortful attention in the here-and-now (i.e., direct experience) is essential in grasping the moment of need, triggering productive thought processes. Additionally, in
real-life (as opposed to laboratory settings), tasks and intents are often unclear,
situations are ambiguous and changing, and diverse people have different intents
and needs that interact, resulting in need-tensions (Lewin, 1936, 1946; Wertheimer,
1945). Therefore, different types of Productive Thinking are required depending on
the situation. Selz (1922) and Duncker (1945) outlined three different types in
relation to distinct situations. Selz (1922) described (1) direct, (2) reproductive,
and (3) coincidental Productive Thinking. Similarly, Duncker (1945) outlined Productive Thinking as (1) totally intelligible, (2) partially intelligible, and
(3) unintelligible depending on the situation. These different types are discussed in
detail in Sect. 3.
In Design, Productive Thinking is the thought process in which an individual
grasps an emerging human need in everyday life and determines and creates a
tangible design (e.g., an artifact, instrument, and/or environment) that produces a
situation that satisﬁes the need. “[D]esign is, after all, the response to a human need”
(McKim, 1959). Such needs are produced by the natural, cultural, and artiﬁcial
environment (Gibson, 2014; Maslow, 1954; McKim, 1959). A human need can
always be satisﬁed in new and interesting ways with no deﬁnitive solution (Arnold,
1959). Great design solutions produce situations in everyday life in which people’s
profound needs are satisﬁed, have emotional value, are meaningful, practical, and
useful, are usable without high cognitive load, and produce as little as possible
consequential structural tensions (e.g., environmental damage) (Krippendorff, 2006;
McKim, 1959; Norman, 1988; Rams, 1995). Identifying profound cultural and

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J. Auernhammer and B. Roth

environmental needs within a living world is an essential part of Productive Thinking in Design. In Wertheimer’s (1945, p. 123) words:
[. . .] the function of thinking is not just solving an actual problem but discovering,
envisioning, going into deeper questions. Often in great discovery, the most important
thing is that a certain question is found. Envisaging, putting the productive question is
often more important, often a greater achievement than a solution of a set question [. . .].

Problem or need-ﬁnding is essential in creative accomplishments in science, art, and
design (Auernhammer & Roth, 2021; Getzels, 1980; Getzels & Csikszentmihalyi,
1976; McKim, 1982; Wertheimer, 1945). Finding profound needs and determining
solution-methods for harmonizing structural tensions are dependent on psychological forces and factors. Wertheimer (1945, p. 199) expressed that various conditions,
forces, and factors may determine the structure of both the problem-situation (i.e.,
grasped tension) and the solution-situation (i.e., felt harmony). Often inertia of
habits, attitudes, and psychological tendencies determine the grasping of situations
and direction of thought process (Wertheimer, 1945). Such psychological tendencies
can lead to premature closure in problem ﬁnding and solving. In the case of
premature closure, individuals fall victim to a seductive simpliﬁcation (Wertheimer,
1945). Roth (2015, pp. 63–75) shows how a problem-reframing technique can
overcome ﬁxation, lead to divergent thinking, and more meaningful solutions.
Over the decades, many studies have identiﬁed how psychological forces, factors,
and principles, such as affect, motivation, needs, attitudes, attributes, behavior, and
environments, inﬂuence the productive or creative processes of perception, imagination, and expression (e.g., Amabile, 1996; Bruner, 1957, 1994; Csikszentmihalyi,
2014; Deci & Ryan, 2000; Guilford, 1950; Hoover & Feldhusen, 1994; Runco,
2004; Ryan & Deci, 2000; Wise, 1987). These ﬁndings are based on different
theoretical conceptions of Productive Thinking.

2.1

Psychological Position in Productive Thinking

Since the early research in Productive Thinking, different psychological positions
have been debated. Two main positions are a structuralist (mechanistic) position,
e.g., Selz (1922) and a Gestalt (humanistic) position, e.g., Wertheimer (1920, 1945),
Koffka (1925), and Duncker (1945). These positions have expanded over the
decades. Selz’s (1922) position was advanced in chess research by De Groot
(1965) and from there into information-processing theory by Simon and colleagues
(Simon, 1981). The Gestaltists position was advanced by scholars, such as Maslow
(1943, 1954, 1956). From the 1960s onward, mainstream psychology favored the
structuralist position (Bruner et al., 1986; Mandler & Mandler, 1964; Simon, 1981).
In design, the distinction between these two positions can be found in the difference
between Simon (1969) and Schön (1992a). McKim (1980) expressed the difference
as “left-hemisphere,” e.g., Newell and Simon (1972), and “right-hemisphere,” e.g.,
his Experiences in Visual Thinking.

Different Types of Productive Thinking in Design: From Rational to Social. . .

275

This distinction has existed since the early developments of the theories in
Productive Thinking. The ﬁrst distinction is the understanding of the task and
situation. Through introspection, Selz (1922) investigated Productive Thinking, in
which the tasks represent the situation. As the situation is equal to the given task, it is
possible to determine a solution-method by grasping a purpose-means relationship,
as there is a very close relationship between a given task and a solution-method. In
contrast, Koffka (1927) expressed that such Productive Thinking is enforced by an
artiﬁcial stimulus and effected by alien and arbitrary factors rather than psychological processes and forces that represent the nature of real-life situations. He argues
that the coincidental constellation of the circumstances brings about the effect, not a
property inherent in the causal process (Koffka, 1927). This position emphasizes that
the task and solution are not “assigned” to one another because the solution is not
triggered by the task-situation but emerges from it. Psychological forces and principles reshape the phenomenal ﬁelds (i.e., the perceived situation) to reduce tensions,
and with each new insight emerges something new, and something new enables new
possibilities (Koffka, 1927).
Secondly, Selz’s (1922) position has been labeled a “machine theory” (Benary,
1923). The stimulus, reﬂexes, and operational results can be determined as an
abstracted schema (i.e., purpose-means or if-then relationships). Selz (1922)
expressed that it is secondary for his theory if the insight occurred from anticipation
as long as a purpose-means (if-then) relationship can be determined retrospectively.
Koffka (1927) expressed that this position does not allow for identifying, e.g., if the
operational result actually emerged from the anticipation of a purpose-means
(if-then) relationship. Individuals are aware of and can anticipate circumstances
without a conscious abstraction of an if-then schema, and this is the most critical
distinction between Productive Thinking from a structuralist and Gestalt position
(Koffka, 1927). From a Gestalt position, Productive Thinking is grasping a “structural tension” through direct experience, which sets forth a motivation toward a
“harmonious structure” (Wertheimer, 1945). Like Wertheimer (1945), Dewey
(1938), from his functionalist position, described the activities of inquiry as resolving an indeterminate (i.e., confusing or conﬂicting) situation and into a determinate
situation, which is an experimental and social activity.
Thirdly, the two positions differ in the understanding of novelty. In Selz’s (1922)
Productive Thinking, novelty is the determination of a new solution-method by
grasping a purpose-means relationship. In contrast, Koffka (1927) expresses that
transformation processes in the perceptual ﬁeld (e.g., Rubin’s Vase: Perceivable as a
vase and two faces) create new environmental conditions. In such cases, the
“abstraction of circumstances” is a truly productive (i.e., creative) process as something novel is created, which was not in psychological existence before. In design,
this distinction is present in Simon’s (1969) interpretation of means-end analysis
(structuralist) and Schön’s (1983) interpretation that the “situations talks back”
(Gestalt and functionalism).
Selz’s (1922) position inﬂuenced the “cognitive revolution” of machine models
of human thinking, replacing behaviorism as the predominant position (Mandler,
2002; Simon, 1981). Many scholars debated aspects inherent in the distinctions of

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J. Auernhammer and B. Roth

the different positions over the decades (e.g., Clancey, 1993; Csikszentmihalyi,
1988; Greeno & Moore, 1993; Schön, 1992a; Simon, 1988; Vera & Simon, 1993).
For example, Neisser (1963) criticized the machine position in human psychology,
and Bruner et al. (1986) expressed that psychology is now in the shackles of
“computational models.” The distinction between the two positions is particularly
evident in research on color vision, as it is an ecologically embedded activity rather
than a form of information-processing (Koffka, 1912; Varela & Thompson, 1990).
The distinction in psychological position matters in the conception of the different
types of Productive Thinking, particularly in situations that are “unintelligible.”

3 Different Types of Productive Thinking in Design
Different situations require and lead to distinct types of Productive Thinking. The
different types of Productive or Design Thinking are (1) Rational, (2) Situational,
(3) Experimental, (4) Dialectic, and (5) Counterproductive. Figure 1 shows the ﬁve
different types of Productive Thinking.

3.1

Rational Productive Thinking

The ﬁrst type is Rational Productive Thinking. In this type, a solution-method can be
directly determined by abstracting the means-purpose or if-then relationship from
the given task, and the situation is totally intelligible (Duncker, 1935, 1945; Selz,
1922). The purpose-means or if-then relationship is inherent in the nature of the
situation, such as mathematical problems (e.g., winning a chess game).
In a given situation, Rational Productive Thinking is essential in which the if-then
relationship is grasped directly through analysis (constitutional co-constrained) and
synthesis (non-constitutional co-constrained). The if-then relationship can be

Fig. 1 Different types of productive thinking

Different Types of Productive Thinking in Design: From Rational to Social. . .

277

directly determined if b is already co-constrained in a and can be analytically
explicated from a and if the situation is paradigmatically constructed for inspection
to grasp a new perspective synthetically (Duncker, 1945). In the direct determination
of a solution-method, a “moments of insight” through synthesis incorporates greater
neural brain activity over the temporal lobes of both cerebral hemispheres (i.e.,
around the ears) and over the mid-frontal cortex, in comparison to when a solutionmethod is determined analytically, which incorporates posterior (visual) cortex
neural activity (Kounios & Beeman, 2009).
In design, Rational Productive Thinking developed further in Simon’s (1969)
means-end analysis (Simon, 1981, 2017). This means-purpose relationship developed further into problem–solution coevolution (Dorst, 2019; Dorst & Cross, 2001;
Maher & Poon, 1996; Yu et al., 2015). Like Duncker’s (1945) analysis and synthesis, various if-then statements (i.e., problem-solution reframing) can be determined
to identify a solution-method (what-how) in relation to an outcome (purpose) (Dorst,
2011, 2015). Such reframing is possible in a stable situation (e.g., conceptual
design). This situation permits determining different means-purpose relationships directly. Such thinking in design often following step-by-step design process
models include (1) grasping the problem (purpose) within the given situation,
(2) determining analytically or synthetically a means, and (3) evaluating the
purpose-means relationship (Archer, 1965; Burton et al., 2015; Pahl et al., 1996;
Pólya, 1957; Simon, 1969; Wallas, 1926). Such Rational Productive Thinking is
possible when a stable situation can be abstracted into a symbolic system, in which
ontologies, such as Function-Structure-Behavior, allow directly determining a
solution-method (Gero, 1990). This type works in symbolic systems, virtual worlds,
or conceptual design in which the situation is stable.

3.2

Situational Productive Thinking

The second type is Situational Productive Thinking, which makes it possible to
determine solution-methods in a more ﬂuid world. Selz (1922) describes this type of
productive thinking as reproductive, in which an observed and abstracted purposemeans relationship is (re-)applied to a new situation. It is partially intelligible
(Duncker, 1945).
A given situation does not allow directly determining a solution-method as the
situation is contextually unique. In such a situation, identifying the common aspects
of a similar situation (e.g., from memory and observing similar situations) and
comparing it to situations that are different reveals a relatively stable aspect of the
situations, making it possible to determine “∎ leads to b.” However, this insight does
not provide why this purpose-means relationship occurs. Identifying why a certain
behavior a leads to a speciﬁc outcome b (“a leads to b”) allows determining a
solution-method (Duncker, 1945). This abstracted purpose-means relationship can
be reproduced in a comparable situation to determine the solution-method in the new
context.

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In design, expert designers recall their repertoire of past purpose-means relationships from memory to generate or determine a solution for a similar situation (Schön,
1983). This type of Productive Thinking applies in situations when designing for
usability and human interactions with everyday things (Norman, 1988). Observing
that people are not able to open a door effortlessly identiﬁes “∎ leads to b.”
Identifying and abstracting why this behavior or interaction occurs allows grasping
the purpose-means relationship. The door signaled through its design (e.g., a handle)
to pull the door, while pushing it would have been the correct behavior. Grasping the
relationship that design a led to behavior b in a relatively stable situation allows
designers to make appropriate changes. Situational Productive Thinking is contextdependent and is based on a stable world on an abstract level, making it possible to
utilize expertise (e.g., recalling means from memory) in new situations. Similarly,
expert intuition is learned by recognizing familiar elements (abstracted “∎ leads to
b”) in a new situation and acting accordingly in an appropriate manner without
exactly knowing why. Such Situational Productive Thinking based on experience is,
however, inadequate in an unintelligible situation, when the situation is novel and
emerging.

3.3

Experimental Productive Thinking

The third type is Experimental Productive Thinking, which is coincidental (Selz,
1922) and unintelligible (Duncker, 1945). This type is required when it is not
possible to determine directly or reproductively (i.e., from memory) an if-then or
purpose-means relationship. A solution-method is determined through anticipation,
resonance, and recentering within an emerging situation. It is what Louis Pasteur
expressed as “chance favors the prepared mind,” and Arnold (1959) expressed as
serendipity in creative design. Three different modes of Productive Thinking in an
ambiguous and emerging situation allow identifying a solution-method. These are
anticipation (Selz, 1922), resonance (Duncker, 1945), and recentering (Wertheimer,
1945).

3.3.1

Anticipation

Selz (1922) describes that an arbitrarily prepared reaction is the anticipation of a
certain behavior (V) to occur in relation to a later occurring opportunity to react (R).
d ) leads to certain behavior, and when the anticiEnvisioning this relationship (RγV
pated or desired behavior (V) actually occurs in response to R without further ado, a
solution-method can be determined. This anticipation is not pure trial-and-error, as
the anticipation provides a direction (i.e., prediction) toward a promising
solution-method. Successful reproduction or reoccurrence of the anticipated
behavior–reaction relationship in ambiguous situations increases the certainty of

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the solution-method (Selz, 1922). Such anticipation of a speciﬁc purpose-means
relationship often reduces the ambiguous situation to speciﬁc aspects and stability.
In design, it is problematic when planned (i.e., anticipated) conceptions are
implemented without experimentation and evaluation. In such a case, the emergence
of the situation is disregarded, as the anticipated solution-reaction may not emerge
from the situation (Koffka, 1927).

3.3.2

Resonance

In ambiguous situations, a solution-method is sought through the resonance effect of
a signal (Duncker, 1945). A solution can be sought out through the correspondence
between the characteristic or property anticipated (i.e., imagined) or signaled (i.e.,
observed) and that which is inherent in what is sought. It is the search for “something
like” (e.g., a metaphor) that could provide a solution-method to an ambiguous
situation. Duncker (1945) described a mode of “search” for “something like” that
solves the problem or satisﬁes the need. He outlined strategies, including
reformulating and reinterpreting the problem or goal, critical analysis of the situation
(what makes it difﬁcult to solve), and learning from unsuccessful attempts, which
provide a resonance that triggers the productive thought process (Duncker, 1945).
The new resonance allows ﬁnding objects and operations (e.g., within visual periphery or from memory), resulting in grasping a new means-to-the-goal (Duncker,
1945). The means-to-the-goal (i.e., a concept) is sought, which is not necessarily
the same as determining an actual working solution-method. Determining a working
solution-method requires reproduction and evaluation (e.g., prototyping and testing).
However, in conceptual design, ambiguous and multifaceted problem-situations are
rephrased (i.e., reframed), leading to reducing the whole (i.e., inherent complexity)
to a speciﬁc aspect for which a solution is identiﬁed and later evaluated in action.
Such one-sided solutions may not resolve the tensions inherent in the situational
whole. Designers reframe or reinterpret the ambiguous situation to a speciﬁc
problem-part (a speciﬁc frame). For example, Schön (1983) outlines, in an educational setting, that reframing (e.g., “Can I solve the problem I have set?” or “Do I like
what I get when I solve the problem?”) allows ﬁnding new solution directions.
However, reducing the situation to satisfy the problem-frame set by the designers
will not necessarily resolve the actual tension that exists in the “whole” situation.

3.3.3

Recentering

Wertheimer (1945) outlines Productive Thinking as grasping and harmonizing
structural tension through direct experience. Wertheimer (1945, p. 197) expressed
this thought process as follows:
S1 is the situation in which the actual thought process starts, and then, after a number of
steps, S2 in which the process ends, the problem is solved. [. . .] S1, compared to S2, is
structurally incomplete, involves a gap or a structural trouble, whereas S2 is in these respects

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structurally better, the gap is ﬁlled adequately, the structural trouble has disappeared: it is
sensibly complete as against S1.

In ambiguous situations, it is not possible to determine a purpose-means (i.e., if-then
or problem-solution) relationship. It is rather a process of harmonizing structural
tensions. For example, a piano player plays a set of tones while assessing the
harmony. The structural tension in the played set of tones triggers a direction toward
improvement that continues until the player plays and grasps a harmonious melody.
This harmony is a Gestalt quality that individuals directly experience (von Ehrenfels,
2014). Creating a harmonious esthetic and emotional Gestalt is the process of
creating meaning (Krippendorff, 2006; Wertheimer, 1996). Determining a harmonious whole follows different psychological and productive thought processes. The
grasped structural trouble of a problem-situation triggers certain strains, stresses,
tensions in the thinker and yields vectors, psychological forces (i.e., motivation) in
the direction of improving the situation and changing it accordingly (Wertheimer,
1945). It requires recentering to discover, envision, go into more profound questions, and understand the whole situation (Arnheim, 2009; McKim, 1980;
Wertheimer, 1945). Recentering is the change from, e.g., understanding a situation
centered on the “ego” (i.e., one-sided) to grasp the whole situation from diverse
centers (i.e., different needs, experiences, and points of view) (McKim, 1980;
Wertheimer, 1945). Recentering allows perceiving the situation as a completely
new situation. It is not reframing the purpose-means relationship within the situation
as the new understanding of the task emerging from the situation, changing the
situation entirely (Koffka, 1927; Wertheimer, 1945). Visual thinking supports perceiving centers, recentering, envisioning, and enacting (i.e., mock-up building) a
new whole (Arnheim, 1969; McKim, 1980). Through these embodied psychological
activities, the whole situation dynamically evolves through recentering as grasping a
new whole provides new inherent tensions, resulting in envisioning a new possibility
for a harmonious situation. The inherent Gestalt quality can be directly experienced
through the enactment of this new situation. Individuals’ attitudes and values play an
essential role in productive recentering (Arnheim, 2009; McKim, 1959; Wertheimer,
1945). Generally speaking, there are two different attitudes deeply rooted in human
nature that determine the “centers,” which are (1) self-centered (centricity) and
(2) self-actualized (eccentricity) (Arnheim, 2009).
Self-centered Productive Thinking is driven by ego (e.g., own needs or perspective). Psychologically, in a self-centered or centric tendency, things are understood
as being directed toward or away from the self, and actions are controlled by one’s
own needs, wishes, pleasures, and fears (Arnheim, 2009). Productive Thinking is
essentially about harmonizing structural tensions for oneself or one’s own social
group. Such self-centric Productive Thinking can occur when designers are “gifting”
people (Dell’Era et al., 2020; Verganti, 2009). Papanek (1973), a student of John
E. Arnold at MIT, expressed the self-centered tendency in design as a movement
coming from the arts. Designers express themselves egocentrically at the expense of
spectators and/or consumers, and many design statements have become highly
individualistic and autotherapeutic (Papanek, 1973). Papanek (1973) compares this

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self-centric Productive Thinking in Design to a community of intellectual elites in
Hermann Hesse’s Magister Ludi, who reduced all knowledge to a uniﬁed framework, lost all contact with the outside world (to others), and just engaged in playing a
glass bead game. The self-centered design fulﬁlls designers’ needs, e.g., impressing
peers intellectually, with little real-world contribution (Papanek, 1973). Selfcentered companies (e.g., Facebook, as revealed by a recent whistleblower) produce
designs that generate societal tension favoring their own interests.
Self-actualized A different mode of Productive Thinking is based on selfactualization. Self-actualized people accept themselves and others as they are
(Maslow, 1954). A self-actualized human being is reaching out to others beyond
the ego. Human meaning in life is in the relationship and care for others (Frankl,
1992). Arnheim (2009, p. ix) expressed this relationship of self-awareness and
relevance to others as “composition,” as follows:
The task in life of trying to ﬁnd the proper ratio between the demands of the self and the
power and needs of outer entities was also the task of composition [or invention]. This
psychological relevance justiﬁed the concern with the formalities of composition. Far from
being limited from playing with pleasant shapes, artistic form turned out to be as indispensable to human self-awareness as the subject matter of art and indeed as the intellectual
investigations of philosophy and science.

Similarly, Eames and Eames (2015) expressed that great design happens in the
intersection of designers’ interest (i.e., motivation), clients’ wishes (i.e., socioeconomic complexity), and social concerns (i.e., profound concerns of the living
world). Wertheimer (1945) provides an example of two boys playing badminton for
self-actualized Productive Thinking in social settings. The older boy outperforms,
the younger, making it an unpleasant game for the younger boy. An insight of “let’s
see how long we can keep the ball going between us” changed the rules of the game,
making it a more enjoyable experience and harmonious Gestalt and social situation
(Wertheimer, 1945). In such Productive Thinking, individuals’ attitudes are becoming imperative as changes of centering from self to others are characteristics of
profoundly important accomplishments in human beings and society (Wertheimer,
1945, p. 170). Attitudes that are ego-based harmonize for one’s own satisfaction
(e.g., winning), while self-actualized attitudes result in psychological processes of
Productive Thinking that harmonize for togetherness. Maslow (1954) expressed that
self-actualization requires overcoming primary and ego needs. This perspective
provides a humanistic perspective in Productive Thinking in which the human
experience matters (e.g., in playing chess), in contrast to the ﬁxed means-end
conception of design.
This attitude inherent in self-actualization is essential when designing for people
(McKim, 1959). Designers are not neutral in situations, as beliefs and values
inﬂuence every step of the design (Rittel, 1987). Translating structural tensions in
people’s lives into a harmonious and valuable experience requires qualities, such as
attitudes, human values, and need sensibility (Auernhammer & Roth, 2021). For
example, need-ﬁnding is a deliberate effort of recentering to others. It aims to
understand structural tensions from diverse points of view through awareness and

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direct experience within the situation. Grasping and envisioning a new and valuable
Gestalt of the situation allows creating and prototyping concrete artifacts and actions
that change the situation as a whole. This new situation can be directly experienced
and assessed for its structural tension or harmony. Designing a harmonious situation
includes considering needs, meaning, usefulness, and usability. In self-actualized
Productive Thinking, designers translate the structural tension in people’s lives (i.e.,
needs) into a new tangible and valuable design that changes the whole situation
(McKim, 1959). Such humanistic Productive Thinking is essential in social situations. However, the complexity increases when designing for a pluralistic society,
making the situation essentially a “wicked problem” (Rittel & Webber, 1973).

3.4

Dialectic Productive Thinking

The fourth type is Dialectic Productive Thinking. This type of Productive Thinking
is required as it is not possible to grasp and experience all felt tensions of diverse
groups inherent in pluralistic society. Such social situations cannot be grasped and
resolved as a purpose-means relationship as there is no stable state (Schön, 1973).
In social situations inherent in a pluralistic society, many needs of diverse groups
produce emerging and ever-changing situations of need-tensions (Lewin, 1936,
1946). These situations are “wicked problems” inherent in a pluralistic society
(Rittel & Webber, 1973). In such situations, social tension cannot be comprehended
before solving it, as each situation is viewed from many diverse perspectives, and
there is no correct perspective (Rittel, 1982; Rittel & Webber, 1973). Rittel (1982)
emphasizes that tensions in social situations have no ﬁnal solution as they can
always be resolved in new and different ways. Every social tension is a symptom
of another “solution” (e.g., one’s group harmonic situation is another’s group social
tension) with no time limits to the consequences, making testing a solution impossible as each trial changes the situation (Rittel, 1982). Each social intervention
results in unique societal consequences, making the problem solvers (i.e., designers)
responsible for what they are doing (Rittel, 1982). Such social situations as a whole
can only be grasped indirectly by identifying the inherent value-tension within the
social interactions of diverse groups. A social Gestalt (e.g., need-tension or harmonious situation) can be directly experienced in the many everyday social interactions
of diverse groups and within a culture (Lewin, 1943, 1946). Resolving these social
tensions requires a productive dialogue (Lewin, 1936, 1943, 1946, 1947; Rittel,
1987).
Lewin (1936) stated that Dialectic Productive Thinking requires the conditions of
a high level of ﬂuidity within a social situation, which is an essential dynamic
property of a situation in which social change is possible. Such Dialectic Productive
Thinking is required to destruct the forces maintaining the “old equilibrium” (e.g.,
social tensions) and establishing or liberating forces toward a “new equilibrium”
(e.g., social harmony) (Lewin, 1943). Such a ﬂuid social situation is a space for

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collaborative experimentation and Dialectic Productive Thinking. It is a psychologically safe and free space (Freiraum) for new collaborative creativity and experimental social action (Auernhammer, 2012, 2020; Auernhammer & Hall, 2014;
Edmondson & Lei, 2014; Rogers, 1954). It is imperative to create the ﬂuidity
necessary for change and take steps to bring about the permanence of the new
situation through self-regulation (Lewin, 1943). In this productive discourse, it is
vital to indicate political and general moral and ethical attitudes or value-tension
inherent in the productive dialogue. Otherwise, one cannot comprehend the inhered
social complexity because of all the implicit deontic assumptions involved (Rittel,
1987). In other words, Dialectic Productive Thinking is a social process of freeing
diverse groups of people from social tensions and empowering them to have agency
for translating their value-tensions into a productive dialogue, resulting ideally in a
Gestalt that is harmonious for all diverse groups.
In design, Dialectic Productive Thinking is often facilitated through participatory
design practice, engaging speciﬁc groups that are debating through socio-material
interactions new design objects, resolving social tension in relation to a speciﬁc
design task (Bjögvinsson et al., 2012; Cooley, 1980; Ehn & Kyng, 1987). In urban
planning, diverse groups, such as urban dwellers, crafters, and architects, come
together in the early stages of the design project (Rudofsky, 1964). This Dialectic
Productive Thinking is a dialogue between diverse groups in which the resulting
design is later tried out in real-life settings, with often a focus on use and usability.
Dialectic Productive Thinking in Design is a political discourse (Margolin, 2002). In
a living and healthy society, this productive dialogue among diverse groups is an
ongoing activity of grasping social tensions and resolving them into more harmonious social situations.
A different productive discourse is the ongoing translation of need-tensions into
harmonious social situations. It builds on self-actualized Productive Thinking. Such
Productive Thinking requires grasping how one’s own and others’ needs generate a
need-tensions ﬁeld between diverse groups and translating the social ﬁeld into a
harmonious situation (Lewin, 1946). For example, Michele Wells shows in Theater
for Humanity how theater provides a psychologically safe space, allowing enacting
experiences and interactions of social tensions between different communities
(Wells, n.d.). Social need-tensions are experienced directly, and the whole is grasped
by enacting the tension from different centers. These enacted experiences allow a
continuous recentering of the whole (Wertheimer, 1945). Such enacted productive
dialogue makes the value-tensions explicit and experiential, triggering psychological
forces to collaboratively harmonize the Gestalt of the social situation (Lewin, 1943,
1946). Social “design” is guiding the embodied productive dialogue of grasping the
inherent value-tensions in social interactions by directly experiencing them, exploring new situations collaboratively, and translating the need-tensions into a concrete
harmonious social situation. Recreating the harmonious Gestalt in an everyday
social situation may require various means, artifacts, and activities as well as
human attitudes and values to resolve need-tensions inherent in a pluralistic society
and produce situations valuable for the diverse groups in society. Such Dialectic

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Productive Thinking is essential for social innovation (Manzini, 2015; Margolin,
2002).

3.5

Counterproductive Thinking

The last type of Productive Thinking is Counterproductive Thinking. Much of
everyday thinking is reproductive in which individuals unconsciously and habitually
accomplish their everyday tasks and satisfy their needs. However, our thinking and
behavior can also be counterproductive.
Several situations result in counterproductive thinking. Depending on the situation, different Productive Thinking types can be counterproductive. Continuously
emerging and highly context-dependent situations, the so-called wicked problems,
cannot be approached through rational if-then relationship determination (Rittel &
Webber, 1973). In situations where there is a loss of a stable state, people react with
“resistance” and “defense mechanisms” (Schön, 1973). Such reactions can lead to
counterproductive thinking and behavior in ambiguous situations. Tolerating ambiguity and openness to experiences are essential attributes in Productive Thinking
(Rogers, 1954). Similarly, approaching situations that are totally or partially intelligible (i.e., simple problems) do not require Dialectic Productive Thinking. For
example, designing the interactions of people with a door allows determining
if-then relationships (Norman, 1988). Such situations do not require a productive
dialogue. The situation determines the required type of Productive Thinking.
The overemphasis of one type of Productive Thinking over others can become
counterproductive. For example, the Design Methods Movement overemphasized
Rational Productive Thinking, and shortly after, one of its leading members, Christopher Jones (1977), expressed how his view has changed on the movement and that
he dislikes the machine language, the behaviorism, and the continual attempt to ﬁx
the whole of life into a logical framework. Maslow (1954) expressed this counterproductive thinking as mean-centering (e.g., methods) rather than problem-centering
(e.g., situational circumstances). Purely Rational Productive Thinking can be counterproductive when designing for human experiences. For example, treating people
like an if-then problem (e.g., as simulated in agent-based models) reduces human
relationships and experiences, resulting in structural tensions as people are
stereotyped.
Productive Thinking can be used to produce structural tensions in people’s lives,
which are highly dependent on human attitudes and values (McKim, 1959;
Wertheimer, 1945). Providing knowingly undetermined, false, or not working
“solutions” (e.g., stereotypes and fake news) for ego needs (e.g., political gain,
user engagement, and ﬁnancial success) creates or increases structural tensions
(e.g., fear or depression) in people’s lives. Such Productive Thinking in Design
can be highly counterproductive for living beings. Not all design is good design and
glorifying “design” is counterproductive. Therefore, it is imperative to develop a

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Productive Culture, including human values, attitudes, attributes, beyond simple
“methodologies” and cookbook patterns (McKim 1980; Auernhammer & Roth,
2021).

4 Developing a Productive Culture
The different types of Productive Thinking provide different psychological processes, factors, and strategies to determine means and resolve tensions. There is a
need for many individuals to contribute creatively and productively to resolve the
many emergent social and environmental tensions. Such collective Productive
Thinking requires a Productive Culture. A Productive Culture is not merely one
where many individuals create and determine valuable solutions. It is about a critical
mass of people collaborating, accepting, supporting, and helping each other productively. Productive thought processes are determined and inﬂuenced by psychological
forces and factors that emerge in everyday interactions with people and the environment (e.g., how we treat each other). A Productive Culture is when a critical mass
of people inherently hold attributes, attitudes, values, and abilities, such as care for
others, conﬁdence in creative ability, and collaborative mindsets (Auernhammer &
Roth, 2021).
Since the beginning of the Design Division (today Design Group) and Joint
Product Design program at Stanford University in the 1950s and 60s, the main
aim has been to educate a humanistic and creative philosophy of design (Adams,
1974a, b; Arnold, 1959, 1962a, b; Auernhammer et al., 2022; Auernhammer & Roth,
2021; McKim, 1959, 1972). Therefore, a particular focus has been placed on
experiencing and educating self-actualized Productive Thinking by centering and
recentering on the many people involved in the problem-situation. Exercises and
practices, such as need-ﬁnding and personal design statements, focus on understanding one’s own needs and others’ tensions, with the aim of developing attributes,
attitudes, values, and abilities (Auernhammer & Roth, 2021). Experiencing human
values and feeling someone else’s tension aims to develop need sensitivities. With a
self-centric attitude, the same design practices are enacted to design for one’s own
needs (e.g., power-hunger), potentially causing structural tensions in people’s lives
(McKim, 1959). As Wertheimer (1945) and McKim (1959) emphasized, attitudes
and human values are imperative in Productive Thinking. Developing a Productive
Culture requires ﬂuency and ﬂexibility in Productive Thinking. Technical problems
require more Rational Productive Thinking, while human needs require Experimental Productive Thinking. Educating different types of Productive Thinking develops
ﬂuency and ﬂexibility in thinking. In self-actualized Productive Thinking, it is
essential that design decisions are directed and informed by care for and engagement
with other living beings, who are impacted by the design solution in their everyday
life. Too often, design choices are directed by seniority. Developing a Productive
Culture requires exploring and understanding the whole situation collaboratively. It
is a non-hierarchical culture. Such a culture can be tremendously freeing when
people support and help one another, and thereby producing psychological safety

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and freedom for creativity (Arnold, 1959; Rogers, 1954). In such a culture, designers
can create, build, break, and learn from mistakes in a safe environment (e.g., Design
Loft and Product Realization Lab culture). Individuals build creative conﬁdence
through countless projects (Kelley & Kelley, 2013). To enable such an educational
environment, John E. Arnold redeveloped the student shop (today Product Realization Lab) program in the late 1950s to put creativity and design skills into practice
(Beach, 1974). Such a productive environment allows people to explore and develop
solutions that contribute to society. A Productive Culture is established through a
critical mass of self-actualized, conﬁdent, capable, and considerate individuals. This
culture enables Productive Thinking in Design, and it holds the potential to resolve
the continuous emergent tensions in the real world collaboratively and productively.
Acknowledgment The ﬁrst author would like to thank Larry Leifer, Bernie Roth, and the wider
design community at Stanford for cultivating this self-actualized behavior. It is tremendously
freeing when people support and help one another.

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The Cultural Construction of Creative
Problem-Solving: A Critical Reﬂection
on Creative Design Thinking, Teaching,
and Learning
Xiao Ge, Chunchen Xu, Nanami Furue, Daigo Misaki, Cinoo Lee,
and Hazel Rose Markus

Abstract While people around the world constantly come up with ingenious ideas
to solve problems, the expressions of their ingenuity and their underlying motivations and experiences may vary greatly across cultures. Currently, the role of culture
is often overlooked in research and practice aimed at understanding and promoting
creativity. The lack of understanding of cultural variations in creative processes
hinders cross-cultural collaboration in problem-solving and innovation. We challenge the unexamined American perspectives of creativity through a systematic
analysis of how ideas, policies, norms, practices, and individual tendencies around
creative problem-solving are shaped in American and East Asian cultural contexts,
using the culture cycle framework. We share initial ﬁndings from several pilot
studies that challenge the popular view that only agentic change-makers are seen
as creative problem solvers. In the context of design, designers are culturally shaped
shapers who are motivated to solve problems in creative ways that resonate with
their cultural values. Our research seeks to empower designers from non-Western
societies. We urge design educators and practitioners to explicitly incorporate
culturally varied ideas about creative problem-solving into their design processes.
Our ultimate goal is to ground the theories and practices of design thinking in
cultural contexts around the world.

X. Ge (*)
Center for Design Research, Stanford University, Stanford, CA, USA
e-mail: xiaog@stanford.edu
C. Xu · C. Lee · H. R. Markus
Stanford SPARQ, Stanford University, Stanford, CA, USA
N. Furue
School of Management, Tokyo University of Science, Tokyo, Japan
D. Misaki
Faculty of Engineering, Kogakuin University, Tokyo, Japan
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_15

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1 Introduction
As soon as winter begins, locals in Harbin, a city in the northeast part of China,
cannot wait to stock up piles of napa cabbage on their porches and balconies. As the
temperature quickly falls below zero Celsius in the dry winter, the perfect conditions
are created to keep the cabbage fresh and tasty for several months. Stews with
cabbage, glass noodles, pork, and tofu make this perfect dish throughout the dark
winter. For people in northeast China, the tradition of eating this dish in winter dates
back to thousands of years ago during the Tang Dynasty. To Harbiners, storing
stacks of cabbage is not just a casual tradition, it is a cultural practice that bridges
millions of families and connects the young with the old and the present with the
past. In Harbin, such connection to the past and to people is a prevalent element in
everyday life, manifesting itself in both material and symbolic cultures.
In San Francisco, by contrast, the experience of food is an ever-changing fashion.
People seek diverse dining experiences—from unique menus to unconventional
dining occasions. Chez Panisse, a popular high-end restaurant, for instance, is
famous for its distinct culture and cuisine, whose menu “changes everyday”
(Chesbrough et al., 2014). High-tech companies in Silicon Valley are venturing
into new territories to reimagine food production and consumption (e.g., Dance,
2017). In San Francisco, people discover novel sensations and constantly seek to
break with tradition.
These food practices in San Francisco would be considered creative in the USA,
because creativity in the USA has been represented and exercised as “defying the
crowd” (Sternberg & Lubart, 1995) and “breaking tradition” (Lubart, 1999). As a
consequence of reinforcing such cultural ideas in practice, creative idea generation is
widely perceived as serving to instigate change: coming up with new ideas to change
the status quo, stand apart from the past, assert control over their surrounding
environment, as well as establish their uniqueness from other people.
If we were to adopt such a theory of creativity, Harbin chefs who do not seek
changes in their food tradition would be seen as less creative. The role of culture,
however, is not explicitly discussed either in deriving the theory or in promoting
certain creative practices. For example, the ampliﬁcation of radical change and
transformation as a goal and attitude is widely observed in various settings of design
education and innovation practice regardless of the participants’ cultural backgrounds. Creative idea generation is widely understood as “a structured way of
breaking out of structure,” as Tim Brown, chair of IDEO, famously quoted in his
book Change by Design (Brown, 2009). “Enable change in Japan through design
and creativity,” is the mission of IDEO Tokyo (IDEO, 2021). Culture is too often
overlooked, such that the interpretation of East Asian behaviors and practices
becomes rather a projection of American ideas. This in turn leads to misunderstanding and misjudging East Asian creativity. Online discussions of “Can Asians be
creative?” and popular book titles such as Can Asians Think? (Mahbubani, 2002,
cited in Morris & Leung, 2010) provide a glimpse of such stigma.

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Does creative behavior have to be associated with changing, breaking, and
seeking freedom? Instead of advocating change and disruption, many places in
East Asia promote connection and preservation. The contrast between food practices
in San Francisco and in Harbin is an example of the broad cultural difference. While
American society tends to be centered on instigating agentic change-making in the
environment, East Asians place importance on preserving continuity with good
practices in the socio-physical environment, and adjusting the self to changes that
occur in the environment. Therefore, the predominant cultural value in East Asia
may activate a different creative process that is associated with returning to, preserving, sustaining, and connecting. People who exemplify such a different creative
process are less talked about or known in the USA.
In design thinking, teaching, and learning, when our design educators and
managers celebrate some American cultural values and restrict others, either consciously or not, this puts people with different values and tendencies at a disadvantage. With the salient power dynamics between educators and students, managers
and junior employees, this means alienation, misjudgment, and disconnection. If
these cultural differences are ignored, valuable mindsets and practices that originated
in the USA, such as design thinking, cannot expand to East Asia.
To address this issue, we examine how crucial differences between American and
East Asian cultural values affect why and how people come up with ideas for
problem-solving. In Sect. 2, we review prior cultural and cross-cultural perspectives
on creative problem-solving in design. In Sect. 3, we apply the culture cycle
framework (Markus & Hamedani, 2019) to systematically reveal differences in
historically derived ideas, policies, norms, cultural practices and products, and
beliefs around creative problem-solving. In Sect. 4, we focus on examining some
cultural differences using pilot studies. In Sect. 5, we reﬂect on why there is a lack of
attention to East Asian creative processes and the consequences of this, address the
relevance of our work for design education and practice, discuss some of the
limitations in our studies, and describe future directions. Our paper concludes with
Sect. 6.
Our work furthers the current understanding about the different, but equally valid
and meaningful motivations and behaviors that underpin creativity across different
cultures. In doing so, we hope to inspire educators and practitioners to adopt a more
culturally resonant approach to design thinking, teaching, and learning.

2 Critical Reﬂection on Design Thinking Based
on Designers’ Cultural Needs
We would ﬁrst like to revisit design thinking development and research by putting
on a pair of cultural perspective goggles. The underpinning role of culture was made
visible in design thinking from the very beginning. John Arnold (1913–1963), the
founding father of design thinking at Stanford, conceived designers as other-directed

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Fig. 1 Life Magazine captures John Arnold’s unconventional teaching (Hunt, 1955)

rather than inner-directed, and recognized that culture has a direct inﬂuence on how
designers view the world (Clancey, 2016). Yet Arnold’s view on culture is also a
product of his own cultural experience—he criticizes other-directedness and calls for
a recognition of one’s unique individual mind—the so-called Uncommon Man
(Clancey, 2016). This is consistent with the mainstream belief in the USA that
motivation and action spring primarily from desires, beliefs, and attributes of the
independent self (Markus & Kitayama, 1991), which can be activated by freeing
individuals from constraints of the environment and of tradition.
Arnold’s iconic, unconventional course at MIT in the 1960s also set an example
for this (Fig. 1). He presented students with design problems for clumsy birdlike
inhabitants on an imaginary planet called Arcturus IV, which was based on the belief
that creative problem-solving could be trained by temporarily freeing students from
their accustomed environment and placing them in a new imaginary one (Clancey,
2016). In his study of Arnold’s philosophy, William Clancey concludes that, in
Arnold’s view, “the creative individual is a positive non-conformist.” Such a view of
creativity is reﬂected in the recent development of design methods and tools, such as
the rules of brainstorming (Sutton & Hargadon, 1996) and methods for breaking free
from “blocks” in problem-solving (Adams, 2019). In the early years, visitors of
Stanford Mechanical Engineering Design Group were sometimes taken to drag
racing competitions to understand the “American design” and engineering (masculine) creativity. These tools and practices were designed to allow designers to
systematically free themselves from institutional and cultural constraints. However,
they do not address designers’ cultural and emotional needs and motives, especially
outside of the context of America. Could it be that creativity is fostered by selfsacriﬁce rather than individual freedom in certain cultural contexts? Rolf Faste’s
work offers some insights on this question.
In the development of design thinking, Rolf Faste played an important role in
bridging Zen and Japanese esthetics with Western thinking of design (Kelley, 2003;
Irani, 2019). The design thinking adage of bearing a beginner mindset is partly
inﬂuenced by Zen (Irani, 2019). In his unconventionally visual essay (Faste, 1995),

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295

where “poached egg” is used as a metaphor for understanding innovation and
culture, Faste inquires into the nature of Japanese creativity. He reﬂects that “Professor Koxvai, widely regarded as Japan’s ﬁrst Jungian psychologist, suggested that
I look at Japanese myths and fairy tales if I wished to understand attitudes about
creativity, whether in Japan or elsewhere.” What is perceived as good, creative,
desirable, and meaningful in Japan? Faste observes that Japanese stories communicate a very different set of cultural values than Western stories do—
Western myths, be they older tales like Andersen’s Ugly Duckling or newer ones like
Segal’s [sic] Jonathan Livingston Seagull, all involve heroic and macho images of individual
separation and triumphant return. In comparison, Japanese stories are striking for their
images of feminine and nurturing self-sacriﬁce.

Faste’s analysis suggests that Western creativity is strongly associated with
masculine individuality and expressing self-direction, whereas Japanese creativity
seems to suggest the exact opposite. For the last 10 years of his life, Faste worked on
an unﬁnished book titled Zengineering, which incorporates Japanese ideas of
“engaging life in real-time” and “non-judgmental mindfulness” into American
engineering design practices (Rolf A. Faste Foundation for Design Creativity, n.d.).
Consistent with Faste’s hunch, some design creativity scholars in Japan propose
that the desire for creating design concepts is essentially led by an inner sense, a
sense of resonance in the mind with the product one is working on (Taura & Nagai,
2013; Nagai & Taura, 2017). They argue that design artifacts, although different
from what is found in the natural word, “nevertheless, ‘naturally’ resonate with the
human mind” (Taura & Nagai, 2013). Notably, what is often regarded as an
important criteria of design in the USA—novelty, is conceived to be “implemented
as a by-product of concept generation, but not as a causal factor of creativity”
(emphasis added). Moreover, the scholars argue that “if a new concept is pursued
merely on account of its uniqueness, we say that this pursuit never approaches an
ideal” (Taura & Nagai, 2013).
As an example from other cultural contexts, Panagiotis Louridas has addressed
the cultural needs of designers, using “bricolage” as a metaphor to illustrate how
traditions and norms which are accumulated over thousands of years deﬁne, forge,
and guide the French ways of design and tinkering (Louridas, 1999). A systematic
approach for researching design culture is brought in through the ethnographic work
of Pamela Hinds and colleagues (Hinds & Lyon, 2011; Kim et al., 2012; Liu &
Hinds, 2012). Notably, the analysis of the cultural construction of design behavior
acknowledges forces from different cultural layers (Hinds & Lyon, 2011). Their
exploratory work suggests that Asian designers tend to blend in, whereas European
and North American designers prefer to stand out, and that design qualities, such as
creativity, are conceived differently across cultures. However, how modern conceptions, or implicit theories, of creativity are different across cultures has not been
adequately tested empirically. In the ﬁeld of engineering, creativity is an increasingly
popular topic in engineering education research and is considered a core component
of globally engineering competencies (Lucena et al., 2008), yet little cross-cultural
research has been done (Ge et al., 2021). Overall, research evidence is still too scarce

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to draw any conclusive remarks about the cultural needs of designers in creative
problem-solving.

3 Creative Problem-Solving: A Culture Cycle Analysis
The desire to create ideas seems to be universal, yet beliefs and experiences about the
what (new and different, or similar and connected), how (independent with passion,
or interdependent with hard work), who (male or female), and why (instigating
transformation or preserving connection) may differ across cultural contexts. We
take the perspective that culture shapes ideas, practices, interactions, and beliefs
around creative problem-solving. In delineating how creative problem-solving is
culturally constructed, we use a culturally responsive analytical framework, called
“culture cycle” (Markus & Hamedani, 2019; Plaut et al., 2012), to frame and analyze
prior research on creativity and problem-solving. Where the word “culture” is used,
we intend to align with Adams and Markus (2004) in understanding culture as
consisting of:
explicit and implicit patterns of historically-derived and selected ideas and their embodiment
in institutions, practices, and artifacts; cultural patterns may, on one hand, be considered as
products of action, and on the other as conditioning elements of further actions (Adams &
Markus, 2004, p. 341).

In this conceptualization, culture can be found both in the psychological tendencies of people and in the material and symbolic representations that people create
(Plaut et al., 2012). Culture cycle is a framework that delineates and simpliﬁes the
many vectors of culture as “dynamically interacting and interdependent layers...
made up of ideas, institutions, and interactions that guide and reﬂect individuals’
thoughts, feelings, and actions” (Markus & Hamedani, 2019).
In this paper, we organize the culture cycles into three interacting layers: historically derived ideas and philosophies, institutional policies, norms, practices, and
interactions, as well as psychological tendencies—all of which are important in
understanding the cultures of creative problem-solving. Figure 2 gives a visual
overview of our culture cycle analysis. It delineates two different possible realities
of creativity; one is American linearity and the other is East Asian circularity (Biao,
2001). The line in the USA independent model of creativity not only represents the
divide between human and nature, subject and object, and mind and matter, but also
stands for the linearity of creating—it is about progressing forward. The circle in the
East Asian interdependent model of creativity represents oneness and an integration
of these elements, as well as the circularity of creativity—it is about returning to the
origin. While our goal here is to characterize differences, we acknowledge that these
differences are relative and that cultures are dynamic, complex, interacting, and
changing.

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Fig. 2 Line and circle, as a metaphor (Biao, 2001) for two different possible cultures of creativity
and a summary of culture cycle analysis. This model is built upon an earlier version of the ﬁgure in
Misaki and Ge (2019)

3.1
3.1.1

Historically Derived Ideas of Creative Problem-Solving
Historically Derived Ideas in the USA

The modern American concept of creativity has a philosophical tradition of inquiry
into “the origin of new entities and new ideas” (Weiner, 2000). In the West, people
have the long tradition of valuing precise, conceptual knowledge and systematic
sciences, which can be traced back to Descartes. Western epistemology tends to
value—place truth in—abstract ideas and theories, rather than concrete personal
experiences or the embodiment of knowledge. This tradition of inquiry reﬂects the
Western epistemology that humans are separate from others and objects, and that
humans as rational thinkers obtain knowledge deductively by reasoning.
Historical events and movements such as the Enlightenment have inﬂuenced the
modern American conception of creativity—a process through which people can

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direct their own destiny. Freedom of choice is therefore often regarded as a prerequisite to enable solving problems creatively. According to Wight, the Western idea
of “individual creativity”—whereby new ideas originate in the human mind and in
the ability of the individual (Wight, 1998)—became widely acknowledged during
the Enlightenment. At this time, people started to emphasize the importance of
individual rights and elevate individual rights in order to understand the universe
and to direct their own destiny (Szczepański & Petrowicz, 1978; Albert & Runco,
1999, cited in Niu & Sternberg, 2006). As a result, Westerners tend to consider
creativity as an ability that one unleashes from within and expresses outwards.
Because the modern concept diverges greatly from the ancient divine beliefs of
creativity in the West, the latter is not reviewed here (for more, see the philosophical
roots reviewed in Niu & Sternberg, 2006).

3.1.2

Historically Derived Ideas in East Asia

In contrast, creativity in East Asia emphasizes a reliance on situated experiences
(unseparation of mind and body) and a deep connection with other people and things
(unseparation of self and others; unseparation of humans and things). East Asians
have a tradition to believe that knowledge can be attained inductively from sensory
experience (Nonaka & Takeuchi, 1995). The clear distinctions between humans and
objects and between embodied experiences and conceptual knowledge can be found
rather exotic by East Asians. Instead, what is historically valued is their indistinction,
or the so-called oneness—oneness of humanity and nature (“Tian Ren He Yi”), of
body and mind (Biao, 2001), and of self and other (Markus & Kitayama, 1991).
Oneness is a central idea in Taoist philosophy. Fundamentally, each thing contains
within it the entire universe; each thing contains the universe by “feeling with”
(having sympathy with) the universe (Chang, 1970). This philosophical view was
particularly reinforced during the neo-Confucianism movement. From the
neo-Confucianism perspective, things and people in the world are indistinct from
one another in that they share the same nature or substance. The shared substance
supplies a deep connection among people, creatures, and things, which has been
documented to partly explain East Asians’ interdependent construal of the self
(Markus & Kitayama, 1991).
These ideas are inevitably evidenced in East Asians’ understanding of creativity.
Cultural research of creativity broadly accepts that Taoism has a great impact on
Chinese creativity (Niu & Sternberg, 2006; Kuo, 1996). Yan (2015) argues that
while Western creativity is associated with conquering nature, East Asian creativity
is about seeking harmony with nature (“Tian Ren He Yi”). The Chinese ancient
military treatise, The Art of War (Tzu, 1971), provides ample examples to substantiate the view that a deep understanding of particular situations is the foremost
important capability of creative military strategists or problem solvers (Yan,
2015). Ancient creative problem solvers in China are often depicted as situationattending “observers” (Langer, 2009; Rudowicz & Yue, 2000) who can draw
connections from the past (Niu & Sternberg, 2002) rather than being “bolt from

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the blue” unconventional thinkers. According to the Taoist classics, the creative
process is a process of “the inner apprehension of dao, when all the distinctions
between subject and object vanish” (Niu & Sternberg, 2006). Chu argues that,
“[Chinese] creativity is related to meditation, because it helps one to see the true
nature of the self, an object, or an event” (Chu, 1970, p. 340). In Japan, the creative
process is broadly recognized as entering into a free state of “pure experience”
(Yuasa, 1987; Nishida, 1960) that transcends body-mind and subject-object distinction. This has been used to explain, for instance, the critical social process of
crystallizing new products (Nonaka, 1994) and the superior stage performance of
master actors (Yuasa, 1987).
Oneness is also exempliﬁed in what has become known as “dialecticism,” a form
of folk wisdom in Chinese and other East Asian countries’ cultures, which is seeing
oneness of—and seeking a balance between—contradictory propositions in
problem-solving. Peng and Nisbett (1999) argue that Chinese ways of dealing with
seeming contradictions often result in “retaining basic elements of opposing perspectives by seeking a ‘middle way’.” Partly because of seeing a shared nature with
others and the environment, people consciously experience facilitating and
restraining forces (Lewin, 1999), to borrow Lewin’s words, from the external, active
environment, which act upon them and induce constant changes. A “middle way” is
perceived to best handle constant changes. The Chinese Proverb—“Sai Weng Shi
Ma,” for instance, tells a story of an old man who ﬁnds good in the bad, yet also
foresees misfortune in an apparent fortune. Interestingly, although Chinese people
admire the versatility that is embedded in ambivalent attitude or a lack of clear
position-taking (e.g., “Bian Yi,” in Yan, 2015), such an attitude and behavior can be
considered quite undesirable in the USA.

3.1.3

Cultural Ideas Between the USA and East Asia

According to linguist Liu (1995), “创造力(chuang zao li)” or “chuang zao xin” (both
words mean creativity in Chinese) comes from a modern Japanese word, “sozosei,”
which was translated from the modern English word, “creativity” (Note: Niu and
Sternberg cited the Japanese word as “kozosei,” which might be a typo, e.g., in Niu
& Sternberg, 2006). In Chinese history, the terms “chuang zao li” and “chuang zao
xing” are rarely used (Yan, 2015). Nowadays, although “creativity” is no longer a
rare word in China, it is relatively new and carries the meanings and cultural ideas of
Western creativity. In Hui and Lau’s investigation of educational policies on creativity education in four Asian societies, they ﬁnd that mainland China is the only
place where creativity is not clearly deﬁned (Hui & Lau, 2010). As Yuanqiang Zhou
at Tsinghua University contends, “‘creativity’ is a product of the West, of course it’s
a Western thing” (via personal communication).
Many efforts have been made to reconcile the cultural differences. For instance,
in analyzing the philosophical roots, Niu and Sternberg (2006) argue that Chinese
natural creativity and Western divine creativity share many similarities. And
although Western conceptions of creativity may go against the notion of oneness,

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they match well with Taoism in terms of the pursuit of mental freedom. Many argue
that while Confucianism presides over Chinese social life, Taoism presides over
their own mental life (e.g., in Lu Xun’s 1918 Letter to Xu Shou-tang; Zhang & Chen,
1991; both cited in Peng et al., 2006). “Obey publicly and defy privately,” as Hwang
(2000) puts it.
This may explain why many great minds in history are free from conventions and
pragmatic concerns despite their Confucian practice. For example, Wei and Jin
Dynasties (CE 220–420) are known as a mental freedom era. Poet Li Bai
(CE 701–762), arguably the most famous poet of Chinese history, is also known
for his high-level pursuit of mental freedom. The Japanese Physicist Nobel laureate
Hideki Yukawa (1907–1981) greatly attributes his creativity to his systematic study
of Taoism in his book Creativity and Intuition (Yukawa, 1973). He remarks that he
is personally docile but mentally rebellious—“I can never work on a problem that
I’ve been told to solve by someone else. My subconscious always rebels against
being ordered to do something. Personally, I look on myself as a docile kind of
man.”
In the USA, the co-existence of social conformity and mental freedom may posit
tension and contradiction and induce eventual separation spatially in content and
temporally in process. This reﬂects an Aristotle’s “either/or” frame (Li, 2014). Yet
from the Chinese philosophical perspective, contradictions are meant to co-exist in
harmony. To some extent, “mental freedom” in Taoist tradition also suggests a
meditative practice of losing oneself (therefore, the self is set free mentally) to
connect and fuse with every other thing. Csikzentmihalyi has also mentioned that
people can experience this “ﬂow” during the utilization of Eastern styles of meditation (Csikzentmihalyi, 1997). Yet the experience of “ﬂow” is not unique in the East
and can be found across many cultures.

3.2
3.2.1

Policies, Norms, and Practices Around Creative
Problem-Solving
Policies, Norms, and Practices in the USA

In the USA, creative ability is considered essential in revitalizing the economy
(Bilton, 2010) and breaking up established systems (Lubart, 1999). As such,
creativity-conducive policies and regulations emphasize the provision of autonomy
and freedom. For example, Simonton argues that creativity favors a civilization that
is composed of a large number of peacefully coexisting independent states rather
than dominated by empire states (Simonton, 2000). Policies and practices about
creative ability value the creation of the new, whereas connection with the old is less
relevant. Amongst others, Venturelli (2005) argues that, “the challenge for every
nation is not how to prescribe an environment of protection for a received body of art
and tradition, but how to construct one of creative explosion and innovation in all
areas of the arts and sciences.” To the opposite, in many ﬁelds, distinction from the

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past or tradition is viewed as a sign of creative behavior. In entrepreneurship
practice, for instance, entrepreneurs are often regarded as a force of “creative
destruction” to destablize the established business and disrupt the control of the
mainstream industry to enable the formation of new ones (Webster, 1977; Levitt,
2002).
We are in the “creative economy,” as Richard Florida puts. According to Florida,
about 30% of the workforce are in the creative sector today, in comparison to only
10% in 1990 (Florida, 2007). “The creative sector accounts for nearly half of all
wage and salary income in the United States. That’s nearly $2 trillion, almost as
much as manufacturing and services combined” (Florida, 2002, 2007). In recognition of that, modern work environments have started to encourage autonomy,
freedom, and management empowerment. For example, Google’s 80/20 rule grants
its employees free time at work. “This empowers them to be more creative and
innovative,” write Larry Page and Sergey Brin in their Founders’ IPO letter in 2004.
Today at Google and Moonshot Factory, creativity is represented as “10X thinking.”
Frederik G. Pferdt, Chief Innovation Evangelist at Google, encourages Google
employees to think big, and to go for monumental change, not incremental improvement (Lafargue, 2016).
No question about it—creativity is increasingly recognized as an essential goal of
K-12 and college education in the USA. As Obama remarked at the ESSA signing
ceremony, “we’re going to have to have our young people master not just the basics
but also become critical thinkers and creative problem solvers.” Educational practices that push for tests and standardization, especially through repeated training,
face growing criticism of killing creativity. Sir Ken Robinson’s talk Do Schools Kill
Creativity? (Robinson, 2006) has remained the top viewed TED talk since 2006.
Since the No Child Left Behind Act was initiated in early 2000, it has received
numerous criticisms about how it kills creativity (Goldstein, 2017). In response to
these problems, the Every Student Succeeds Act was proposed, which emphasizes
more creativity-conducive measures, such as creating more access and choice for
students (Goldstein, 2017). As an example of college education, Stanford
University’s Viewbook (2009) begins with the statement—“The wind of freedom
blows.” It continues that the university gives students “the freedom to be themselves:
innovative, creative, and unconstrained by any predetermined look or affect” (p. 22,
cited in Plaut et al., 2012). In school and at work, “creativity” has become a desirable
way of interacting and a new norm of being—it is conﬁdence, self-expression,
scientiﬁc pursuit, and leadership. Popular books—The Adjusted American, a classic
from the 60s and more recently Orbiting the Giant Hairball, to name a few—
promote nonconformism and address how to seek one’s freedom to attain a sense
of self, to remain creative in bureaucratic work environments and ossiﬁed society.
Who are creative geniuses in the USA? In the technology ﬁelds, Steve Jobs is
often recognized as the most creative person of all time. At age 20, the
non-conformist cofounded Apple and at just 29, he introduced Apple Macintosh,
which soon radically transformed the personal computer industry. In science, often
referred to as the most creative genius of the last century, Einstein is one of the few
who perfectly bridged, what is perceived in the USA as the intuitive side of

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creativity—art and the rational side of creativity—science. Creative individuals such
as Einstein are believed to be creative not because of their hard work (Lin-Siegler
et al., 2016). Images of creative people in the USA (e.g., young Steve Jobs) highlight
their vision, ability, and passion—“obsessive interest,” as Richard MacCormac puts
it (Lawson & Dorst, 2013), not efforts or experience per se.
The American pursuit of reason, such as in creative science, gives rise to theories
and structured processes of creative idea generation, based on the belief that “creative potential” can be systematically acquired. At Stanford d.school and IDEO, the
educational practice of sticking to a structured process (Kelley, 2012) and some
brainstorming rules reﬂects such a notion in the USA. By following these general
creativity-nurturing principles, creative ideas can be unleashed from the independent
and conﬁdent human minds. Creative problem-solving is understood as conﬁdent
self-expression and quantity. Self-expression is widely believed to signal independent thinking (Kim & Markus, 2002), whereas quantity suggests quality. The Father
of Brainstorming—Osborn made popular the idea that “quantity helps breed quality”
(Osborn, 1953). Quantity of ideas (ﬂuency) has been a standard measure of creativity
in empirical studies in the USA (see more in Sect. 3.3). “Go for quantity,” as one of
the brainstorming rules says. According to David Kelley, founder of d.school and
IDEO, the emphasis on quantity of ideas is to “generate more ideas so that they can
choose” (Kelley, 2012).

3.2.2

Policies, Norms, and Practices in East Asia

As discussed above, the East Asian words of creativity come from the West and
inevitably carries with it the Western cultural ideas. Despite its relative short history
in Chinese language, the word has been quickly incorporated into formal documents
and daily use, especially among the younger generations. China’s government has
played a big role in the promotion of creativity. However, little research is done and
therefore little is known about how much of its meaning and cultural practices get
carried over to the East and internalized by the East in its translingual practices (Liu,
1995). Government statements and news reports seem to suggest an acculturation.
Speciﬁcally, driven by advancing technological and economic development, scientiﬁc and technological creativity is the major concern in mainland China’s policy
(Hui & Lau, 2010). Creativity as an individual’s ability is often described as
“innovative spirit.” As the Higher Education Law (Ministry of Education, China,
1998, cited in Hui & Lau, 2010) states, “cultivating an innovative spirit in the
personality development of young talents is an important strategy.”
In the last 15 years, China’s government has been greatly advocating the cultivation of creativity and innovation, as reﬂected in its ﬁve-year plans. According to
Wang (2015), the state documents of ﬁve-year plans from 1949 to 1996 primarily
described creativity as the potential of individuals or a way in problem-solving
(we should “creatively” solve the problem), and innovation-related creativity only
appeared in science and technology sections, serving as the synonym of scientiﬁc
research. The term “self-independent innovation” ﬁrst appeared in the ninth ﬁve-year

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plan (2001–2005), which, according to Wang (2015), was an ofﬁcial signal of China
joining the “creativity warfare” in competition with the West. Yet, the contextual
meaning of “self-independence” in “self-independent innovation” emphasizes a
collective effort to be independent from the West, rather than suggesting individualism. This notion of being less reliant internationally is reinforced in China’s 14th
ﬁve-year plan as well (Mallapaty, 2021).
Since 2009, numerous innovation and entrepreneurship demonstration zones
have sprung up across cities and provinces, under the direction of China’s State
Council. These strategies are driven by the needs of economic development and
global competitiveness. Although these ofﬁcial policy documents borrow concepts
of creativity from the West, the emphasis of creativity and innovation almost always
comes together with preserving tradition, as well as sustaining and strengthening the
classics and the cultural roots. For instance, in the recent series of ﬁve-year plans
(e.g., Xinhua, 2021), innovation has been regarded as a savior to revive the bankrupt
traditional industries and as a promising way to sustain Chinese traditional cultural
products and practices, such as historical villages, Chinese medicine, and traditional
handcrafts, highlighting the collectivist goal of social contribution and utilitarianism.
The Chinese political system and social structure of today have a direct impact on
what messages are promoted in social media. “Innovation” and “tradition” are often
paired in news reporting, such as:
继承传统、创新经典 (Sustain the tradition; Innovate the classics)
正确传承比盲目创新更重要 (Correctly passing ideas to next generations is more
important than blindly innovating)
传承是基础、创新是生命(Inheriting ideas from the past makes the foundation, based
on which innovation offers [new] life)
传承不泥古、创新不离宗(Inheriting tradition ﬂexibly; Innovating without going far
from the root)

In East Asia, modern designs often emphasize preserving the past, connecting
with traditional cultural values, and ﬁnding consistent meanings in modern practices
of traditional ideas. New architectural designs would be endowed with traditional
values—Kengo Kuma’s design of the JP Tower is such an example (Kengo Kuma
and Associates, 2012). JP Tower was a project to preserve and renovate the historic
Tokyo Central Post Ofﬁce Building by adding a new skyscraper structure. The
architect behind JP Tower is Kuma, a renowned Japanese architect who most
recently designed the Japan National Stadium for the Tokyo 2020 Olympic
Games. A New York Times interview with Kuma (Saval, 2018) describes Kengo
Kuma’s design vision as “a story of returning to the values of traditional Japanese
architecture.” In Kuma’s mind, architectural design should “through acquaintance
with local materials and methods, relate itself harmoniously to its surroundings.”
Who is the Steve Jobs in Japan? The name that comes into many Japanese
people’s minds is Gunpei Yokoi, known as the “father of handheld games.” Yokoi
founded the product philosophy of “lateral thinking of withered technology (枯れた
技術の水平思考)” at Nintendo (Yokoi, 2021). The idea behind this philosophy is to
refrain from cutting-edge technologies, and instead focus on past technologies and
develop ideas by viewing these ancient technologies through the lens of lateral

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thinking. This philosophy has not only shaped Nintendo’s product development
policy, but also inﬂuenced generations of designers and technologists in Japan.
The Chinese TV show National Treasure featuring stories of past creations is yet
another example that emphasizes the importance of bridging the past and the present.
Started in 2017, now in its third season, it has remained one of the highest rated
shows in mainland China. It received the best TV show award in the 24th White
Yulan Prize of Shanghai TV Festival in 2018. The show allows the audience to
admire the hardworking creators’ superb skills and high-level experiential state that
the creators were able to achieve. The depiction of superb craftsmanship often
highlights the creators’ ability to merge themselves with their creation and become
one with it.
Indeed, although the word “creativity” is relatively new in Chinese history, there
are many Chinese characters, terms, and phrases that entail the idea of solving
problems in creative ways, such as “Xin Ying” (新颖), “Jiang Xin Du Yun” (匠心
独运), “Qi Si Miao Xiang” (奇思妙想), and so on. Yet, the contextual meanings of
these words, terms, and phrases diverge from American conceptions of creativity. A
thorough review of anthropological, philosophical, and psychological literature
suggests that while American conceptions of creativity focus on novel solution/
product outcomes and individual autonomy and uniqueness, East Asian conceptions
of creativity, as consistent with the message of National Treasure, emphasize the
creator’s inner processes and fulﬁllment (Lubart, 1999; Paletz et al., 2011; Shao
et al., 2019).
East Asians tend to value the embodiment of direct, personal experiences during
the process of creation, where ambiguity is preserved yet logic is unquestioned. The
emphasis of “on-the-spot” personal experience, rather than reliance on abstract
theories in Japanese management, is a manifestation of such an epistemological
tendency (Nonaka & Takeuchi, 1995). The Chinese character wu (悟) depicts such a
creative mental process that uses metaphorical, intuitive imagination to jump from
the known to the unknown (Li, 2012). According to Peter Ping Li, “almost all
Chinese leaders prefer wu in their thinking process to rational analysis. In particular,
Yun Ma, the CEO of Alibaba, is an excellent example of a wu leader. He practices
Zen as well” (cited in Sundararajan & Raina, 2015).
In China, every child grows up learning legends about Zhuge Liang, and classic
stories about Effendi, Cao Chong, and Sima Guang, to name a few. For instance, the
old tales about Sima Guang, who saves a drowning child by quick-wittedly breaking
the water tank, and Cao Chong, who creatively solves the problem of weighing an
elephant with a boat and rocks, are part of the required reading in the ﬁrst and second
grade of elementary school education. These people are depicted as capable of
creatively and calmly solving impossible problems in urgent situations. The Chinese
saying “急中生智 (Ji Zhong Sheng Zhi)” depicts a calm thinker who comes up with
ingenious solutions amid crisis. Ingenious problem-solving acts are associated with
calm and keen observations with few words, as opposed to passionate, eloquent
expression of outside-the-box ideas. The East Asian cultural value of silence rather
than speaking is more thoroughly examined in Kim and Markus (2002).

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In addition, creative people in East Asia, from business magnate Yun Ma, to
famous songwriter and singer Jay Chou, to the great chefs Tetsuya Saotome and Jiro
Ono, consistently place far greater emphasis on their effort and experience as
opposed to ability or passion as the cause of achievement. For example, great effort
is needed in the disciplined, embodied creative practice of Japanese ink painting,
called sumi-e (or Suiboku-ga), which has a philosophical origin in the Taoist notion
of “uncarved block.” Artists in Japan would spend years applying sumi-e ink brush
painting to attain higher states of creative experience—the so-called creativity of no
mind (Mushin) (Steinbock, 2013). As Yan (2015) argues, “‘aha’ moments in East
Asians’ creativity come from hard work, great effort, and long-term accumulation of
knowledge and experiences.”

3.3

Psychological Tendencies of Creative Problem-Solving

Culture can also be found in people’s psychological tendencies. Partly because of
holding different ideas, norms, practices, and interpersonal interactions across cultural contexts, those who are believed to be creative people, and what are perceived
as creative activities and traits, also vary from place to place as do what motivates
people to create and solve problems.
Unfortunately, most cross-cultural empirical research employs methods and
theories of the American models that are derived from independence-embracing
ideas, norms, and practices (Lubart, 1990). Consistent with such perspectives,
creative people are considered those who have independence of judgment (Barron
& Harrington, 1981), freedom and choice (Robinson, 2006), risk-taking boldness,
and conspicuous behaviors (Simonton, 2000), choose to be in the creative mode—
divergent thinking (Guilford, 1967; McCrae, 1987) and exhibit out-of-the-box
thinking (Weisberg & Markman, 2009; Sternberg & Lubart, 1995). These people
are considered nonconformists (Sternberg & Lubart, 1995; Grant, 2017), good at
expressing self-direction and agency (Amabile et al., 1996; Hennessey & Amabile,
2010; Glăveanu, 2014)—“belief in yourself” (Kusserow, 2012), represented as
heroic and masculine, especially in business domains (Bilton, 2010). They are
perceived to actively seek loose environments that endow freedom and autonomy
(Amabile et al., 1996) and hold positive and activating emotions (Baas et al., 2008).
Figure 3 provides an overview of the empirical research paradigm of creativity in
America. Researchers often operationalize creativity using standardized measures of
ﬂuency, originality, and ﬂexibility (Guilford, 1967; Torrance, 1966), such as the
Torrance Test of Creative Thinking. Modern creativity studies in the USA also
typically deﬁne creativity as the generation of ideas that are both novel and appropriate or useful (Amabile et al., 1996; Oldham & Cummings, 1996). Here, creativity
is measured through Consensus Assessment Technique (CAT) or external judges’
evaluation of idea outcomes. The famous Duncker’s candle problem (Duncker,
1945) is based on the idea that to be creative, people must think ﬂexibly (i.e.,

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Fig. 3 Empirical research paradigm of creativity in the USA

cognitive ﬂexibility) and be able to break free from their setting (De Dreu et al.,
2008).
In the independent self-construal, the environment is largely perceived as inert, or
as the background against which the self stands out (Nisbett et al., 2001). From this
perspective, the role of the self is to inﬂuence the environment and to enable change
(Markus, 2016; Nisbett et al., 2001; Markus & Hamedani, 2019). The motivation to
promote change—radical transformation—is widely held to underpin the generation
of new ideas and value creation. As the motto of Stanford Graduate School of
Business goes, “change lives, change organizations, change the world.”
Implicit theories about other creative processes, such as focusing on inner
processes, oneness, or connection with others, are less examined. Where dialectic
one-ness (Peng & Nisbett, 1999), holistic thinking (Nisbett et al., 2001), or other
associated tendencies are examined, research on the relation between these tendencies and creativity is extremely limited. Conﬂicting ﬁndings and opinions, for
instance, in the research about the relation between dialectical thinking and creativity, build up more roadblocks for pushing forward new theories of creativity (Paletz
et al., 2018). Implicit theories that may be more relevant in East Asia still, unfortunately, mostly stay at the theoretical and philosophical level.
For instance, creativity research often takes for granted the supposedly positive
relation between self-directed autonomy and creativity and, as a result, attributes

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other-directedness as a negative signal of creativity. Consider the following quote: “I
believe creativity is born by pushing people against the wall and pressuring them
almost to the extreme.” This remark, which can be frowned upon in the USA, is from
an executive at Honda, famously quoted by Hirotaka Takeuchi and Ikujiro Nonaka
(Takeuchi & Nonaka, 1986). Underlying this message is a belief that making people
work harder would produce more creative outcomes (Forrester, 2000). The
admonishment-based approach (Kitayama et al., 1997) is embraced in Japan where
other people and group relation is a big source of agency (Markus & Kitayama,
1991) and motivation and action are grounded in a sense of self as interdependent
with others and with the environments (Markus, 2016; Nisbett et al., 2001; Markus
& Hamedani, 2019). However, people in the USA would think such a practice would
kill the enjoyment, interest, and satisfaction that are considered necessary for
unleashing creativity from within individuals.
Similarly, people in the USA that champion “change” to solve social problems
may be puzzled at East Asians’ resistance to change. East Asians take the demanding
job of preserving the past and sustaining the connections seriously, where “change”
can be seen as an unconstrained, irresponsible mission that requires less effort. For
East Asians, the context is more likely to be perceived to constantly produce
changes. As a result, the role of the self is to contextualize, observe, connect to,
and adapt to changes that come from others and the environment.
Empirical studies that solely employ an American perspective can lead to mixed
ﬁndings about East Asian creativity with highly questionable validity (see a review
in Morris & Leung, 2010). In their research to identify people’s concepts of
creativity among Mainland, Hong Kong and Taiwanese Chinese, Rudowicz and
Yue (2000) surveyed associative adjectives of the word “creativity,” which admittedly carries American ideas and practices, rather than addressing it from an East
Asian perspective. Niu et al. (2007) assessed both Hong Kong and U.S. participants’
creativity in terms of appropriateness, humor, and originality based on the Consensus Assessment Technique (CAT) (Amabile et al., 1996). Interestingly, while
“humor” and “originality” are possibly valued dimensions of creativity in some
American populations, they may be largely irrelevant based on Rudowicz and Yue’s
(2000) observation of mainland Chinese participants and Taura and Nagai’s prediction (2013). In addition, some studies ﬁnd that Chinese personality is incompatible
with creativity (Hui & Rudowicz, 1997; Rudowicz & Yue, 2002), yet in some other
studies, Chinese students performed better (Saad et al., 2015; Wang et al., 2018) or
similarly (Riquelme, 2002) as their American counterparts in carrying out creative
idea generation tasks. Other problems include cross-cultural survey in the English
language (Zha et al., 2006) and biased sampling (Sundararajan & Raina, 2015).
Problems have also emerged in cross-cultural studies that involve other countries
and contexts. For instance, quantity was found to be an irrelevant factor of creativity
in a study of Moroccan students (Peng et al., 2021).
The overreliance on these standardized measures of creativity is not only inappropriate in cross-national studies, but also problematic in studies within the multicultural nation of America (e.g., Brannon et al., 2015; Jackson et al., 2019). For
example, in Brannon and colleagues’ research (2015) of the double consciousness of

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African Americans (i.e., independent and interdependent self-schemas), they examine whether engagement in African American culture improves students’ academic
ﬁt and performance for African Americans. However, the evaluation and measurement of academic performance based on which the studies were run solely reﬂect an
independent schema (e.g., ﬂexible thinking, excitement).
Creativity researchers have started to criticize the application of Western tests to
different populations because rather than uncovering culturally valued and culturally
varied traits and characteristics, such traits may be overlooked (Mistry & Rogoff,
1985; Runco & Bahleda, 1986; Runco & Johnson, 2002; Glăveanu, 2010). The lack
of culturally responsive conceptions and measures of creativity is partly responsible
for our confusion and continuous misconceptions about East Asian attitude, behavior, cognition, and emotion around creative problem-solving.

4 Overview of Exploratory Studies
Instead of adopting standard creativity measures developed in WEIRD contexts
(Henrich et al., 2010), we have deviated from the standard but biased measures.
We want to examine paradigms of creativity from culturally relevant perspectives. In
this section, we share initial ﬁndings from a few pilot studies, which are part of our
ongoing efforts to explore the cultural differences of creative problem-solving.
Speciﬁcally, we use survey studies to examine across cultures the desirability and
applicability of the American views, which encourage being agentic change-makers
in creative problem-solving.
In the ﬁrst study (Sect. 4.1), we posit that instead of seeing the self as the source of
agency, people tend to gain agency from context (such as the past, other people, and
the physical environment) in East Asia. Our initial ﬁndings suggest that there is a
cultural difference in people’s perceptions about the context’s agency in producing
good ideas, creating changes, and performing human-like tendencies. In addition
(Sect. 4.2), people’s evaluation of ideas is affected when their perceptions about the
role of the context are manipulated. In the next section (Sect. 4.3), we hypothesize
that instead of “change,” “preservation” (e.g., connection with the context) is likely
to fuel idea generation in East Asia. The ﬁnding suggests that indeed, culturally
resonant narratives (e.g., change versus preservation) affect people’s perceptions and
motivations in creative problem-solving. Most recently (Sect. 4.4), we are building a
new composite measure to examine in different countries people’s perceptions
around which make an idea better, speciﬁcally—“breaking” or “connecting.”
We are still in the iterative design process of the cultural creativity surveys,
including balancing contextualization and generalizability, reﬁning the scales, and
improving reliability and validity for both American and East Asian participants.
The ﬁndings shared here are meant to stir conversation and reﬂection, rather than
making assertions about cultural differences.

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4.1

309

Cultural Variations in Perceptions of the Agency
of Context

In the USA, creative problem-solving champions individual self-direction, agency,
and autonomy. In comparison, East Asian creativity highlights the agentic roles of
the context (e.g., other people, past ideas, and the physical environment). Therefore,
we hypothesize that East Asians tend to perceive the context (i.e., factors external to
individuals) has more agency than individuals and that the opposite is true for
Americans.
In a series of exploratory surveys, we incorporate measures to examine beliefs
about the source of change, the source of good ideas, or the source of a broad range
of tendencies (e.g., kindness, authority, wisdom) on Likert scales from
1 ¼ completely from individuals to 7 ¼ completely from context. The source of
change and the source of good ideas are both examined through a one-item measure,
and the source of animated tendencies is based on a composite measure that has high
internal consistency. Through iterative survey design, in the surveys about the
source of good ideas and the source of animated tendencies, we have provided
examples, such as cultural practices, history, and natural environment, to clarify
what “context” or factors external to individuals consists of.
Since 2019, we have distributed these surveys among USA (adult samples
recruited online from Proliﬁc, Mturk, and college student samples), Chinese (adult
samples from online survey platform Wenjuanxing), and Japanese participants (adult
samples recruited from the online survey platform Lancers and college student
samples). Across the board, American participants are less likely to see context as
a source of change, good ideas, or various animated tendencies than their Chinese
and Japanese counterparts. These differences are all statistically signiﬁcant. For
instance, in one study (Fig. 4), we use an adult sample recruited from Proliﬁc in
the USA (N ¼ 187, mean age ¼ 32.3, 86 women and 6 others) and Wenjuanxing in
China (N ¼ 176, mean age ¼ 32.2, 74 women and 2 others). We ﬁnd that compared
with U.S. participants, Chinese participants indicate a stronger belief that “change”
comes from context, t(361) ¼ 2.974, p < 0.01. In another study (Fig. 5), we recruited
participants from Mturk in the USA (N ¼ 150), Wenjuanxing in China (N ¼ 87),
Lancers in Japan (N ¼ 165), as well as Japanese engineering college students
(N ¼ 124). Similarly, online participants in China (t(236) ¼ 2.522, p ¼ 0.012) and
in Japan (t(314) ¼ 2.448, p ¼ 0.015), as well as Japanese engineering students
attending a university in Japan (t(273) ¼ 3.736, p < 0.01) are more likely to believe
that “good ideas for solving problems” come from context than their American
counterparts. Another study on the source of change can be found in our previous
conference presentation in Ge et al. (2021).
To sum it up, compared to Americans, East Asians are more likely to see a
connection with the context and impart agency to factors external to the self. This
difference carries important implications for creative problem-solving. We suggest
that in East Asian societies, practices to elicit a more generative mindset would be
more effective if they placed a greater emphasis on the role of the context and

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Fig. 4 Compared with U.S. participants, Chinese participants indicate a stronger belief that
“change” comes from context, t(361) ¼ 2.974, p < 0.01

Fig. 5 Compared with U.S. participants, online participants in China (t(236) ¼ 2.522, p ¼ 0.012)
and in Japan (t(314) ¼ 2.448, p ¼ 0.015), as well as Japanese engineering students (t(273) ¼ 3.736,
p < 0.01), indicate a stronger belief that “good ideas for solving problems” come from context

actively involved factors external to the self (e.g., tradition, other people, situations)
in motivating people to come up with sound ideas for solving problems.

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311

Relation Between Perceived Agency of Context and Other
Factors and Its Manipulation

In addition to engaging in a comparison between American and East Asian samples,
we have also conducted analyses within the U.S. population. Using an online sample
of American adult participants, we ﬁnd that locating the source of change within the
context correlated negatively with identifying as being an American, r(277) ¼
0.20, p < 0.001, as well as trust in American institutions, r(277) ¼ 0.17,
p < 0.01. Perceived sources of change, however, do not correlate with participants’
demographic or political orientation, thus suggesting that this is a more general
psychological phenomenon.
In addition to measuring people’s perceptions of the context, we also
experimented with providing materials to directly affect people’s perceptions,
which allows us to study the downstream consequences of shifting perception and
to understand the direction of causality involved. We recruited 325 U.S. participants
from Proliﬁc (mean age ¼ 35.69, SD ¼ 13.38, 204 women, 115 men, and 6 other,
6 did not pass attention checks). Participants were randomly assigned to one of the
two conditions. In one condition, they were informed that change comes from
individuals. In the other condition, participants read that change comes from context
or factors external to individuals. The source of change manipulation was successful,
t(318) ¼ 9.26, p < 0.001. After the manipulation, participants were asked to evaluate
a series of products or ideas (see examples in Fig. 6) and indicate their perceptions of
these ideas.
Our empirical ﬁndings suggest that people’s perceptions about the relationship
between the self and the context (e.g., others and the environment), such as the
context’s agency, can partly explain how they understand product ideas. Speciﬁcally, participants who were informed that the individual (rather than context) produces change perceived ideas to be more unique, t(319) ¼ 2.21, p ¼ 0.03, and were
also likely to believe that these products serve the purpose of change rather than
preservation, t(319) ¼ 1,72, p ¼ 0.087.

Fig. 6 Examples of ideas and products used in the manipulation survey to examine how people’s
perceptions of an idea’s purpose and uniqueness are affected by their beliefs about the agency of the
context. (a) Hippo Water Roller (2022). (b) Multi-purpose Tent in Desert (Seikaly, 2015)

312

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X. Ge et al.

Engineering Students’ Motivation for Problem-Solving

Technological companies in Silicon Valley promote the notion to change, transform,
and disrupt the status quo for the better. We examine whether this notion is culturally
speciﬁc to the context of the USA. Based on our theorization, what drives engineers
to create and solve problems could vary across cultures. We hypothesize that
engineering students in Japan are more motivated to generate ideas when a task is
framed to preserve rather than change a certain situation. In contrast, American
engineering students are more motivated by a task frame characterized by change as
opposed to preservation. To test this hypothesis, we recruited two different engineering student samples—one from Proliﬁc in the USA (N ¼ 209, mean age ¼ 24.3,
80 women, 128 men, and 1 other) and the other from an engineering university in
Tokyo, Japan (N ¼ 158, mean age ¼ 19.96, 7 women, 149 men). Participants were
randomly assigned to come up with a new idea to solve a problem, randomly
assigned from a corpus of problems, the goal of which was either to change or to
preserve a certain target. After writing down their ideas, participants reported their
levels of motivation during the idea generation.
We found that Japanese engineering students are more motivated to generate
ideas when the goal is framed in terms of preservation (e.g., come up with ideas to
preserve local transportation) rather than change (e.g., come up with ideas to change
local transportation), t(151) ¼ 1.88, p ¼ 0.062. However, no statistically signiﬁcant
difference between conditions is found for American engineering students,
t(202) ¼ 0.6, p ¼ 0.5. However, there is a trending moderation effect. This
means that the effect of problem framing on participants’ motivation level is
moderated by their perceptions about where change comes from, t(357) ¼ 1.63,
p ¼ 0.1. Within the American sample, some participants also hold similar views as
Japanese participants and perceive context as a source of change. These subsets of
American participants also tend to be more motivated by preservation than by
change.
These initial ﬁndings thus suggest that people’s perception of the context affects
their motivation to solve problems. This psychological link holds whether we
compare people from two different cultural groups or analyze the diversity of
people’s perceptions and motivations within the American group. More details
about the methods, analysis, and results of this study can be found in our recent
conference paper in Ge et al. (2021).

4.4

Which Makes an Idea Better: “Breaking”
or “Connecting?”

We are currently focusing on the temporal dimension of connecting with or breaking
from context. What characteristics make an idea better? What relationships between
current ideas and past ideas do people in different cultures desire? We hypothesize

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313

that Americans tend to evaluate ideas more positively when these ideas are perceived
as breaking away from past ideas, whereas East Asians may prefer ideas that are
connected with practices and ideas people had in the past. Based on our culture cycle
analysis, the process of creation that emphasizes continuity with the past and the
environment in East Asia is idealized in a dramatically different way than in the USA
where such connection is often missing.
Through iterative design and pilot testing based on U.S. participants, we have
designed a 12-item composite measure that looks at the characteristics of ideas for
problem-solving: six items characterize “continuity” and describe current ideas as
connecting with, grounded in, and revitalizing past ideas; another six items capture
“discontinuity” and picture current ideas as departing from ideas in the past. We
believe that the current empirical approach is promising and can pave the way for
discovering systematic cultural differences. We will continue to investigate the
perceived desirability of ideas that stand away from or build upon past ideas in
different cultural contexts.

5 Discussion
Based on our comprehensive analyses of how creative problem-solving is shaped in
American and East Asian cultural contexts, we discuss the problems and societal
consequences of solely relying on a singular view of creativity. We also offer some
implications of our work for creative (design) thinking, teaching, and learning.
Finally, we reﬂect on ongoing empirical efforts and summarize several of the other
directions that we are pursuing.

5.1

Embracing Diversity Inherent in the Human Processes
of Creative Problem-Solving

The dominant ways of creativity assessment (e.g., in Fig. 3) not only dominate
scientiﬁc research, but also decide who excels in school. This essentially rejects
diversities that are inherent in the human processes of creativity and creative
problem-solving. Why does research on implicit theories that may be more relevant
in East Asia still mostly stay at the theoretical and philosophical levels? Is it because
we lack exposure to other perspectives and ways of being? In the increasingly
globalized society, culture clashes are supposedly abundant. Yet it is difﬁcult to
overcome the confusion and rejection when one is confronted by a different reality of
creativity. “It is the conventional way of deﬁning creativity that prevents us from
measuring it beyond the rigid frame we use in research,” argues Lutz Eckensberger
(cited in Sundararajan & Raina, 2015). As Gustav Ichheiser (1970) eloquently
writes:

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[W]e fail to understand that people whose personalities are shaped by another culture are
psychologically different—that they see the (social) world in a different way and react to it as
they see it. Instead, we tend to resolve our perplexity arising out of the experience that other
people see the world differently than we see it ourselves by declaring that those others, in
consequences of some basic intellectual and moral defect, are unable to see things “as they
really are” and to react to them “in a normal way.”

Without resolving the confusion and rejection of other ways of creative being,
however, empirical studies that employ an American perspective will continue to
reinforce misleading conclusions, such as the lack of creativity in Chinese and
Japanese people (Riquelme, 2002; Rudowicz & Hui, 1997), and that individualistic
culture outperforms collectivistic culture in cultivating creative talents (e.g., Goncalo
& Staw, 2006), and so on.
On the positive side, there is an emerging effort to resolve the tension between the
East and the West. Amongst others, Averill et al. (2001) propose the notion of
emotional creativity to incorporate the East Asian perspective of situated experience
into the Western model of creativity. There is an emerging consensus in management
research that researchers should adopt an interdisciplinary and multiperspective
approach in general (see Suddaby et al., 2011 for a review). We urge creativity
research across cultures to employ a beginner mindset on what creativity really
means and entails for different cultural contexts.
On the practical end, the booming “creative economy” (Florida, 2002) continues
to evolve without critical reﬂection on the current evaluation of people’s creative
processes and performance. Creativity should be one of the inclusive educational
and managerial targets. Schools such as Harvey Mudd College (Cheryan & Markus,
2020) have started launching programs to increase the belongingness and cultural ﬁt
for underrepresented individuals and groups. This is not enough, as long as our
evaluation of students’ or employees’ creative performance is still narrowly deﬁned
by the WEIRD (Henrich et al., 2010). We should not leave underrepresented
members with the default option to struggle and adapt to the dominant cultural
values (Choi, 2010) or drop out (Felder & Brent, 2005). Embracing diverse cultural
ideas and practices is a grand challenge. The independence-based educational and
organizational settings, as well as the ideas, policies, norms, practices, and products
within such settings, all need to be re-imagined, in such a way that people of all
backgrounds are truly equally welcomed.

5.2

Critical Next Steps in Design Thinking, Teaching,
and Learning

The current paper’s title is named as an allusion to Dym and colleagues’ iconic
design education paper (Dym et al., 2005), and the discussion here indeed is to
extend their efforts to broaden design thinking (from rational to inclusive), design
language (from math to multimodal), design behavior (from individual genius to
group effort), and design participation (from male dominance to diversity and

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inclusion, e.g., Agogino’s work, cited in Dym et al., 2005). Importantly, Dym et al.
(2005) criticize the teaching practice of equating divergent thinking to creativity and
urge a critical reﬂection on “what deﬁnes creativity.” We build upon their work by
providing a critical cultural perspective. Speciﬁcally, we argue that what deﬁnes
creativity is culture.
Culture and global context are part of the fundamentals of design. This is reﬂected
in the multicultural student composition of globalized design classrooms (Fruchter &
Townsend, 2003; Daniels et al., 2010; Carleton & Leifer, 2009). We also see an
increasing exchange of best practices, especially one-directionally from the USA to
the rest of the world. For instance, creative learning process and methods stemming
from the best practices of the Stanford d.school and Design Group, IDEO, SAP, and
MIT D-lab, to name a few, have inﬂuenced educational practices and organizational
management in many places around the world (e.g., Misaki et al., 2020; Ge &
Maisch, 2016; Drain et al., 2017). For decades, MIT Creative Capacity Building
program at D-Lab has provided creativity training for rural communities around the
world (Drain et al., 2017). However, for educators and creativity training ambassadors, the consequences of holding false assumptions that certain types of people lack
creativity based on certain selected beliefs in the USA, are dire. International
students may get culturally biased grades and undergo psychologically difﬁcult
times. For instance, in the popular Stanford class ME310—Global Design Innovation, Japanese and Chinese students coming for a co-ﬁnal presentation with their
U.S. student partners may be poorly evaluated for insufﬁciently explaining how their
ideas break the status quo, which is considered desirable in the USA but not so in
East Asia. The situation is problematic given that student evaluation is based less on
traditional exams of fundamental science knowledge, and increasingly more subject
to culturally shaped subjective opinions. A critical next step is to ﬁght against the
long-term stigma about the creative ability of certain student groups.
A good design teacher today should have a nuanced understanding of the various
cultural values and norms that shape designers’ creative behaviors. The current paper
has offered a comprehensive analysis with promising study outcomes to potentially
expand the understanding of creativity among design educators. An open mind to
understanding creative diversity is critical to addressing the remaining question of
how to truly fulﬁll the cultural needs of students of all backgrounds in creative
problem-solving.
By conceptualizing designers as culturally shaped shapers, we call for
design educators and practitioners to explicitly incorporate cultural values into
their design processes. We hope to stimulate reﬂections on principles and practices
of design thinking that are widely applicable, as well as to uncover assumptions
about design that are culturally speciﬁc.

316

5.3
5.3.1

X. Ge et al.

Reﬂection on Studies and Future Work
Study Limitations

We have shared initial ﬁndings from several directions of our ongoing empirical
efforts. Potential issues of reliability and validity may exist in the current four
exploratory directions. For instance, in the study of culturally varying perceptions
of the context’s agency, although clear deﬁnitions and instructions are given, we
mostly use a one-item, 7-point Likert-scale question—where do you think change
(or good ideas) come from? In a more recent version, we start examining human-like
tendencies with multiple-item questions, which have reached good internal
consistency.
In the study of motivations underpinning creative problem-solving, we have
framed the problem-solving question as “Come up with a new idea for. . .”. In
reﬂection, we recognize the use of the word “new” resonates with and possibly
elicits independent ideas of creativity for both American and East Asian participants.
We have adopted “good” instead of “new” or “novel” in later versions of our survey
exploration. Additionally, we have learned to adopt neutral-to-positive words. For
instance, “sustain” and “connect” may be better words than “preserve,” the latter of
which could have a negative connotation among some U.S. participants.

5.3.2

Future Work

We are currently extending this line of work to develop a more comprehensive
framework to examine people’s perception of the location of a host of different
psychological states and tendencies. Further investigation of cultural differences in
this aspect would shed light on the psychological mechanisms by which people are
motivated to either pursue continuity or discontinuity when generating ideas. We are
hopeful that this body of knowledge would inform the design of interventions or
educational materials to best tap into people’s motivation for solving important
personal or social problems. For instance, while educational systems in different
societies are similarly confronted with the challenges posed by the COVID-19
pandemic, people may respond quite differently to different deﬁnitions of the social
problem. “Come up with ideas to transform the educational system” and “ﬁnd ways
to sustain learning” are two distinctive calls for solutions to the educational challenges posed by COVID-19. The former resonates with cultural values of instigating
agentic change-making, while the latter appeals to people valuing continuity. We
maintain that such differences in the framing of problems matter for crafting
culturally resonant materials to motivate people to come up with ideas to tackle
pressing societal problems.
Our theorization will continue to guide future designs of new surveys, and the
ﬁndings will be examined against belief systems—historically derived ideas, norms,
practices, and cultural products across different cultural contexts.

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5.3.3

317

Other Potential Empirical Approaches

In parallel to the survey study, we have explored other empirical approaches. For
example, how to take advantage of naturally-occurring materials designed to motivate people to solve problems? In this regard, we are considering leveraging the
IDEO Open Innovation—a platform that encourages people worldwide to collaborate and build on each other’s capabilities and ideas.
Our plan is to extract texts of various problem descriptions from the online
archives of IDEO Open Innovation and a comparable web platform in East Asia to
discover the extent to which descriptions of problems reﬂect culturally relevant
values of creativity. For example, one current problem in IDEO Open Innovation
is framed as “Agents of change: Atopic Dermatitis challenge.” The purpose of the
task is described in the following way: “Let’s work together to increase the understanding of atopic dermatitis (AD), help break social stigma, and put a stop to the
bullying faced by those with AD.” We see this example as representing a problem
that is framed mainly in terms of changing and breaking the status quo. We suppose
that if the same task were to be framed to reﬂect an East Asian value, it would read:
“Preserving the dignity: Atopic Dermatitis challenge.” Accordingly, the purpose of
the task would be described as “Let’s work together to help people with atopic
dermatitis (AD) to continue leading their normal lives, support public understanding
of AD, and keep the sympathy towards those with AD.”
The difﬁculty with this method is to ﬁnd a counterpart of IDEO Open Innovation
Platform in East Asian Contexts, such as in Japan or China. We have collected some
design prompt course materials in Japan. However, the materials are limited and not
ideal for text analysis. In parallel, we have been looking for appropriate social media
data, website archives, and newspaper articles that are comparable to one another in
East Asian and American societies.
Another source of data are the archives of popular TV shows in which entrepreneurs pitch new ideas to a panel of potential investors. Based on our initial observation about the winners of funding from the two shows, it would be fruitful to
compare the popular Japanese TV show “Dragon’s Den” with the American TV
show “Shark Tank.” We are still seeking proper analytical tools to examine this
archival data.
We also plan to examine cultural variations in theories of creativity through ﬁeld
experiments. Online competitions, for instance, would be a good avenue to
crowdsource ideas for solving real-world problems. The outbreak of the global
COVID-19 pandemic has made health and well-being a central issue around the
world. In this case, both participants in the USA and in East Asia would be recruited
to take part in our competition to solve real-world problems related to health and
well-being.
Multimodal data of body movement, speech, and text could be collected and
utilized to distill different cultural signals in participant responses.

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6 Conclusion
Does creativity have to be associated with changing, breaking, and seeking freedom?
We argue that it does not. We take the perspective that creativity is constructed by
culture. In other words, culture underpins creativity, and therefore, the processes of
creative problem-solving can vary across cultures. We have conducted a systematic
analysis of how historically derived ideas, norms, practices, and psychological
tendencies around creative problem-solving are shaped in American and East
Asian cultural contexts, using the culture cycle framework. We also share preliminary ﬁndings from a few pilot studies. For design thinking to beneﬁt people across
the globe, it is crucial to consider the role of culture. We construe designers as
culturally shaped shapers who are motivated by ideas that are resonant with their
cultural values. Ultimately, our goal is to ground theories and practices of creativity
and design thinking in cultural contexts around the world, encourage an empathetic
understanding of designers from all backgrounds, and discover and promote culturally resonant creative practices for solving problems.

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Design Thinking as a Catalyst and Support
for Sustainability Solutions
Nicole M. Ardoin, Alison W. Bowers, Veronica Lin, and Indira Phukan

Abstract Despite often-heroic efforts from dedicated scientists, researchers, educators, policymakers, activists, and everyday citizens, sustainability issues impact
communities across the globe, with achievable solutions remaining daunting. Solving these wicked challenges requires creative ways of thinking, including an oftenradical reimagining of the problem space itself. There is a growing recognition that
individual action is insufﬁcient as sustainability issues involve a complex web of
actors, scales, and systems. Supported by an exploratory literature review, we argue
that design thinking is a useful, appropriate, and necessary approach to support
collective action in addressing sustainability issues. We outline ﬁve design thinking
characteristics that support this argument: design thinking inspires creativity; is
participatory and people-focused; encourages and inspires diversity in thought and
action; adopts a holistic, systems-thinking mindset; and offers a streamlined, actionoriented approach. We conclude with reﬂections on this union between design
thinking and sustainability action and suggest future directions for aligned research
and practice.

N. M. Ardoin (*)
Graduate School of Education, Stanford University, Stanford, CA, USA
Stanford Woods Institute for the Environment, Stanford University, Stanford, CA, USA
Emmett Interdisciplinary Program in Environment and Resources, Stanford University,
Stanford, CA, USA
e-mail: nmardoin@stanford.edu
A. W. Bowers · V. Lin · I. Phukan
Graduate School of Education, Stanford University, Stanford, CA, USA
e-mail: awbowers@stanford.edu; vronlin@stanford.edu; iphukan@stanford.edu
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation,
https://doi.org/10.1007/978-3-031-09297-8_16

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1 Introduction
The complexity, as well as temporal and spatial scales, of today’s sustainability
challenges—such as climate change, biodiversity conservation, zoonotic disease,
and water management, among others—makes these problems some of the most
wicked the world has faced. To address such enormous challenges, various nonproﬁt
organizations and multilateral agencies strive to collaborate in innovative ways,
offering a vision and path forward that motivates individual and collective action.
The United Nations Sustainable Development Goals (SDGs), for example, offer a
blueprint for the future stretching to 2030 and beyond. Developed collaboratively
and implemented by UN member states, this ambitious set of 17 goals describes a
shared vision for working toward global peace and prosperity for people and the
planet (United Nations, 2015).
The UN SDGs and similar approaches recognize that sustainability problems are,
at their core, people problems (Clayton & Brook, 2005; Schultz, 2011). Thus,
addressing and solving today’s intertwined sustainability challenges requires
shifting human patterns of behavior toward those that are more sustainable
(Balmford et al., 2021). Each of the SDGs, and the interplay among them, highlights
the deeply intersectional nature of such issues. Poverty, health, racial and social
justice, economic disparity, and environment are intertwined, and deliberate collective action across society, coupled with a systems-thinking perspective, is necessary
for transformative change (Lubell, 2002; Matson et al., 2016; Reid et al., 2021;
United Nations Environment Programme, 2021). Finding the “recipe” to move
individuals and communities to action in a manner that beneﬁts the collective, but
relies on the goodwill and agency of the individual, remains a challenge.
Over the past two decades, researchers in our Social Ecology Lab at Stanford
have collaborated with a range of partners, from nonproﬁt conservation organizations to government agencies, from local-to-national scales. We conduct descriptive
and interventional mixed-methods studies using a range of interdisciplinary theories
and approaches derived from social ecology, social psychology, rural sociology,
cultural anthropology, learning sciences, and design. Applied to a range of
collective-action issues—including, but not limited to, climate change, ocean health,
land conservation, and invasive species control—we have frequently employed
structures and mindsets of design thinking during the intervention-development
process. We consistently ﬁnd design thinking useful in generating, iterating,
reﬂecting, and adapting at various stages and, in particular, ﬁnd that such perspectives and tools assist when working across a diverse group of individuals. For
example, we have conducted design thinking workshops with the goal of developing
interpretive programs and materials to encourage pro-environmental behaviors in
visitors to natural areas. Using a design thinking approach allowed us to involve a
range of stakeholders, including tourism professional, artists, scientists, researchers,
and park staff. Design thinking methods and mindsets employed in the workshops
resulted in the generation of richer prototypes and ideas that were grounded in both
research and practitioner needs.

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327

This use of and interest in design thinking—and our belief, in particular, in its
efﬁcacy for sustainability challenges—spurred our desire to explore the ways in
which the research literature describes the union of design thinking and sustainability. In this chapter, we present a synthesis of and reﬂection on that exploratory
review of research. We examine the processes and insight-driven aspects of design
thinking, considering how such an approach enhances the translational impact of
sustainability work.

2 Background
Since the mid-twentieth century, researchers and practitioners have championed
design as playing a role in achieving sustainable outcomes (Ceschin & Gaziulusoy,
2016). Some researchers have suggested that, due to the historic focus on consumption and proﬁt, designers shoulder one of the greatest imperatives to change the
cultural narrative by designing in a way that engenders a more sustainable society
(Ceschin & Gaziulusoy, 2016; Dewberry & Sherwin, 2002; Fry, 2020; Manzini,
2007). Many designers have expressed an interest in using their skills and knowledge to improve the world in which we live, including addressing sustainability
challenges (Blizzard et al., 2015). Subsequently, designers have developed several
sustainability-focused approaches, such as eco-design, green design, and design for
sustainable behavior, to ensure that the design process considers sustainability
(Ceschin & Gaziulusoy, 2016; Dusch et al., 2011; Young, 2010). Such approaches
emerge from and reside most comfortably in the business world, promising tools,
methods, and mindsets to help designers develop more sustainable products.
Sustainable product development indeed is likely to play a central role in creating
pathways toward a more sustainable future. Andrews (2015), for example, outlines
the critical role designers play in a theorized circular economy, a sustainabilityfocused economical model of production and consumption. As the scale and complexity of sustainability issues continues to emerge, however, it is clear solutions will
not be achieved by creating sustainable products alone—an idea that is, at times,
critiqued as a framing of environmental issues within the technocratic, neoliberal
perspective that such issues can be “ﬁxed” by increased production and consumption
rather than moving toward minimizing our lifestyles and footprints (Prothero et al.,
2010; Dilnot, 2017). This tension encourages a consideration of factors beyond the
environmental and economic factors associated with product development (Ceschin
& Gaziulusoy, 2016; Spangenberg et al., 2010), pushing into sociocultural, political,
and ethical decision-making, to name just a few areas of emphasis. Bjögvinsson et al.
(2012, p. 102) describe this necessary shift as a “move from designing ‘things’
(objects) to designing Things (socio-material assemblies).” These propositions align
with our description, above, of sustainability problems as people problems, requiring
a reframing of sustainability issues as broader societal considerations.
As Rittel and Webber (1973) describe, intricate and tangled problems cannot be
deﬁnitively bound by the same rules and guidelines that have been developed to

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solve “tame” scientiﬁc problems. Indeed, by the very social and collective nature of
sustainability issues, we enter into a discussion of a “public good,” thus departing
from deﬁnitive solution sets governed by clear-cut guidelines. Instead, socioenvironmental issues, such as sustainability, call for solutions that must be qualiﬁed,
are often partial, might only work for certain scenarios and populations, and are
subjective by nature. In short, sustainability problems are the ultimate deﬁnition of
the now-common descriptor “wicked” (Balint et al., 2011), or those that are “social
system problems which are ill-formulated, where the information is confusing,
where there are many clients and decision makers with conﬂicting values, and
where the ramiﬁcations in the whole system are thoroughly confusing” (Churchman,
1967, p. B-141). Wahl (2006, p. 294) postulates that “sustainability is the wicked
problem of design in the twenty-ﬁrst century.”
To address the wicked nature of sustainability problems, design researchers and
practitioners have acknowledged the need for alternative problem-solving
approaches (Gould et al., 2019; Maher et al., 2018; Shapira et al., 2017). Dewberry
and Sherwin (2002, p. 135) write about a new way of thinking in design to “capture
the imagination, hearts and minds of people” and suggest a shift “away from issues
of sustainable production and technology that characterize the supply side alone
towards a ‘softer’ more human-focused approach that addresses notions of desire
and consumption.” Earle and Leyva-de la Hiz (2021, p. 583) call for “holistic and
creative problem-solving that integrates a variety of stakeholders, traverses traditional disciplinary boundaries, considers extended time horizons, and adopts a
system-level perspective (Bansal & DesJardine, 2014; Romme, 2003; Shapira
et al., 2017; Shrivastava, 2010).” Design thinking has thus emerged as a promising
approach to imagining and implementing sustainability solutions (Bermejo-Martín
& Rodríguez-Monroy, 2020; Brown & Wyatt, 2010; Erzurumlu & Erzurumlu, 2015;
Greenberg & Karak, 2020), with researchers making an explicit link between design
thinking and the United Nations Sustainable Development Goals (Clark et al., 2020;
Maher et al., 2018). Many researchers and practitioners alike call out the natural ﬁt
between design thinking and addressing wicked problems (Buchanan, 1992; Buhl
et al., 2019; Clune & Lockrey, 2014; Earle & Leyva-de la Hiz, 2021; Jobst &
Meinel, 2014; Kagan et al., 2020; von Thienen et al., 2014). In the subsequent
section, we explore the design thinking characteristics that have emerged from our
literature review, and which underlie this premise.

3 Design Thinking Characteristics That Support
Sustainability Solutions
Our exploratory literature review conﬁrms alignment between the sustainability
solution space and design thinking approaches. We acknowledge the diversity in
design thinking approaches and methods (Lindberg et al., 2010; Schmiedgen et al.,
2016) and view this versatility and ﬂexibility as a strength when addressing

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sustainability challenges. In our review of the research, rather than adopting one
deﬁnition of design thinking, we view design thinking broadly as a creative approach
to solving real-world problems (Royalty et al., 2021). In its many instantiations,
design thinking centers on several core principles, including that it inspires creativity; is participatory and people-focused; encourages and inspires diversity in thought
and action; adopts a holistic, systems-thinking mindset; and offers a streamlined,
action-oriented process (Brown & Wyatt, 2010; Buhl et al., 2019; Clark et al., 2020;
Earle & Leyva-de la Hiz, 2021; Fischer, 2015). These primary characteristics
support a coming together of individual knowledge, dispositions, and skills that
combine to create an ecosystem of shared experiences built on a foundation of
collaboration, trust, and synergistic action. We discuss each of the characteristics
below and then focus on how they support collective action.

3.1

Inspires Creativity

Sustainability issues require innovative solutions that are most likely to emerge from
new ways of thinking about and looking at data, context, and problems (Clark et al.,
2020; Greenberg & Karak, 2020; Sandri, 2013; Stables, 2009; Westley et al., 2011).
Additionally, when working with sustainability issues, people need to be able to
envision alternative futures (Kagan et al., 2020; Miller et al., 2014), a task complicated by feelings of hopelessness that can accompany the realization of the seriousness and urgency of sustainability challenges (Ojala, 2012; Wals, 2020; Hes & du
Plessis, 2014). To achieve workable sustainability solutions therefore requires creativity, alongside rational hope that derives from imagining and taking action toward
a better—more just, equitable, peaceful, and biodiverse—world.
Design thinking concurrently inspires and requires creativity, strengthening creative conﬁdence while engaging in problem-solving by drawing on both convergent
and divergent thinking (Clark et al., 2020; Geissdoerfer et al., 2016; Jobst et al.,
2012; Kagan et al., 2020; Shapira et al., 2017). Core design thinking processes, such
as visioning, brainstorming, ideating, experimenting, testing, and learning from
initial failures, contribute to generating novel solutions by facilitating a mindset
focused on creativity, innovation, and growth. These processes encourage imagination around a range of possibilities and support a hopeful and positive attitude about
the future, all of which are critical to sustainability problem-solving (Buhl et al.,
2019; Wahl & Baxter, 2008). Design thinking’s creative conﬁdence mindset and
iterative nature encourage participants to engage in solution options without fear of
failure or judgment. When discussing design thinking and wicked problems,
Buchanan suggests that “what many people call ‘impossible’ may actually only be
a limitation of imagination that can be overcome by better design thinking” (1992,
p. 21). He goes on to say that design thinking does more than result in quick ﬁxes;
rather, it leads to “new integrations of signs, things, actions, and environment that
address the concrete needs and values of human beings in diverse circumstances”
(Buchanan, 1992, p. 21).

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Participatory and People-Focused

Although the phrase “Save the Earth” may be a popular T-shirt and bumper-sticker
slogan, scholars and activists alike increasingly question its wisdom. Many emphasize that the planet itself does not need saving; rather, human society is in trouble
and, along with harming ourselves, we are causing irreparable damage to the lifesupport systems of other living creatures and their ecosystems. Within this frame, we
can discuss people as both the cause of and solution to sustainability issues—and,
moreover, as those who must take action (Clayton & Brook, 2005; Schultz, 2011).
Addressing sustainability challenges requires participation from all, including but
not limited to policymakers, corporate entities, nonproﬁts, academics, and everyday
citizens (Fischer, 2000; Schmidt et al., 2020; Lang et al., 2012; Ardoin & Heimlich,
2021). Sustainability science researchers have called for knowledge co-production
that includes collaboration among scientists and the range of impacted stakeholders
(Blackstock et al., 2007; Miller et al., 2014; Moallemi et al., 2021). Relatedly, from
the outset, design thinking’s participatory approach involves end users in the
solution generation process and encourages active participation by all those
involved, regardless of levels of prior formal knowledge or academic/policy expertise, recognizing that ﬁrsthand experience is key to solution generation and success
(Kagan et al., 2020).
Another key aspect of design thinking is the low barrier to entry: all abilities and
knowledge sets are valued (Brown & Wyatt, 2010; Fischer, 2015). A range of
stakeholders are sought out and invited to contribute, with the process’s ethos
emphasizing the message: “we are all in this together” (Clune & Lockrey, 2014;
Shapira et al., 2017). Frequently described as user-centric, design thinking honors its
business roots in product development. When employed to address sustainability
issues—where the desired end-product is often a solution to a sustainability challenge—the “users” may include a broad swath of stakeholders (individuals, as well
as organizations) given the inescapable impact of sustainability-related concerns.
For example, as everyone breathes air, drinks water, experiences the climate, and
requires nutritious food, a healthy and well-functioning, sustainable ecosystem
beneﬁts everyone (Buhl et al., 2019). Designers describe design thinking as
human-centered, in both process and product, as it draws on human emotions,
ingenuity, intuition, and imagination (Bermejo-Martín & Rodríguez-Monroy,
2020; Brown & Wyatt, 2010; Meinel & Leifer, 2015). Thus, design thinking
processes have been demonstrated to be effective in engaging communities in
sustainability issues (Erzurumlu & Erzurumlu, 2015) and generating high levels of
user involvement (Alexandrakis, 2021).

Design Thinking as a Catalyst and Support for Sustainability Solutions

3.3

331

Encourages and Inspires Diversity in Thought and Action

In sustainability research there is a frequent call for transdisciplinary work involving
collaboration among different ﬁelds of study as well as between academic and
non-academic stakeholders (Brandt et al., 2013; Lang et al., 2012). Additionally,
taking action to address sustainability issues is improved when those involved come
from diverse backgrounds and experiences (Ardoin et al., 2022). While invoking a
participatory approach to sustainability problem-solving can lead to greater levels of
participation, intentional efforts must be taken to ensure that these increased numbers represent diverse groups. Design thinking offers an approach to do just that
given its inherent focus on diversity (Buhl et al., 2019; Zheng, 2018). Design
thinking goes beyond encouraging diversity and inclusion, it requires diverse
teams (Fischer, 2015).
A beneﬁt of the people-focused aspect of design thinking described above is that
design thinking welcomes all people. Reaching broad audiences results in the
inclusion of groups that may have been previously neglected (Geissdoerfer et al.,
2016). Research documents that design thinking tools and structures are most likely
to produce innovative, effective outcomes when participants bring different perspectives, skills, and knowledge (Buhl et al., 2019; Fischer, 2015; Maher et al., 2018).
Including people from a range of backgrounds and with different experiences,
guided through a structured design process, can result in improved solution generation as it facilitates open-mindedness and creates a larger pool of varied resources
(Bermejo-Martín & Rodríguez-Monroy, 2020; Buhl et al., 2019; Fischer, 2015;
Shapira et al., 2017). Rather than merely bringing a diverse group of people together,
design thinking processes also provide the tools and mindsets to anticipate and
facilitate potential conﬂicts that can occur when worldviews collide; thus, such
structures can facilitate development and support a wider acceptance of proposed
solutions (Geissdoerfer et al., 2016; Maher et al., 2018).

3.4

Adopts a Holistic, Systems-Thinking Mindset

In acknowledgement of the complex web of multi-scalar economic, social, and
environmental factors and interacting systems that underlie sustainability,
researchers and practitioners often advocate for applying a systems-thinking lens
to developing sustainability solutions (Higgins, 2014; Nguyen et al., 2012; Williams
et al., 2017). Systems thinking requires consideration of the whole picture that is
essential to ﬁrst understanding sustainability issues and then move into solving
complex challenges (Buhl et al., 2019; Palmberg et al., 2017; Saviano et al.,
2019). In design, concurrently, this broader systems perspective emerges when
designers recognize that product design and development need to consider social
and structural factors—such as lifestyle choices and consumption patterns—in

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addition to environmental and economic factors (Ceschin & Gaziulusoy, 2016;
Spangenberg et al., 2010).
Design thinking proponents praise its holistic, integrative nature (Earle & Leyvade la Hiz, 2021; Wahl & Baxter, 2008), emphasizing that it encourages holistic
problem-solving (Brown & Wyatt, 2010; Buhl et al., 2019; Dewberry & Sherwin,
2002) and allows participants to see relationships among the “natural” or nature-rich
world, the human-built environment, and the sociocultural dimensions that occupy
these spaces (Maher et al., 2018). This systems-thinking approach is supported by
design thinking’s transdisciplinary nature, moving beyond interweaving ﬁelds and
disciplines to applying the novel insights derived from those combinations to new
areas of practice (Brandt et al., 2013; Lang et al., 2012), as discussed above in
relation to diversity. Intentionally bringing together experts from varied ﬁelds, as
well as non-expert stakeholders, aids participants in seeing the bigger picture and
moving beyond each individual’s limited perspective and area of expertise (Fischer,
2015; Wahl & Baxter, 2008).

3.5

Offers a Streamlined, Action-Oriented Process

Addressing sustainability issues often presents a tension: their critical and pressing
nature, combined with their vast temporal and spatial scales, means time is of the
essence. Thus, any problem-solving approach that results in rapid, effective, largescale action is advantageous (Abou Chakra et al., 2018; IPCC, 2021; Lang & Wiek,
2021). At the same time, the scientiﬁcally grounded, culturally responsive, and
particularistic nature of sustainability issues means that they require care, deep
understanding, and intensive study to address. Design thinking can help address
this tension by tapping into people’s creativity and supporting systems thinking as
part of exploring alternative futures (Dewberry & Sherwin, 2002; Kagan et al., 2020;
Pruneau et al., 2014).
With its inherent solutions focus, inclusive nature, and ability to rapidly onboard
stakeholders with a range of backgrounds, design thinking can spur people into
action, quickly and effectively (Greenberg, 2021). The streamlined nature of design
thinking processes, combined with the inherent implementability of the design
approach, makes it particularly effective in connecting research with actionable
solutions. Moreover, design outcomes are built on a foundation of breadth as well
as a depth of expertise, providing avenues for people from different disciplinary and
professional backgrounds to work together with empathy, understanding, and curiosity. In this way, much of design thinking’s efﬁciency comes from its accessibility.
One does not have to be a designer or a content expert to contribute valuable insights
(Brown & Wyatt, 2010; Erzurumlu & Erzurumlu, 2015; Shapira et al., 2017),
although given the complexity of sustainability issues, experts with specialized
knowledge need to be part of the process (Fischer, 2015; Kagan et al., 2020).
Supported by ideas of co-design and participatory design (Bjögvinsson et al.,
2012; Steen, 2013; Sutoris, 2021), dedication to including those affected by the

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333

design work facilitates gathering a group of people not only to envision solutions but
also to take action on them. In such a scenario, everyday citizens are just as
legitimately part of the visioning and implementation process, as are scholars,
nonproﬁt professionals, and government ofﬁcials (Fischer, 2015; Ardoin &
Heimlich, 2021).
Additionally, design thinking is structured in a way that facilitates and values
efﬁcient design, streamlining the process of innovation through to real-world impact.
As such, participants are encouraged to fail early and often through processes such as
rapid prototyping and multiple iterations pilot-tested in on-the-ground settings
(Thakur et al., 2020; Wölbling et al., 2012). Such aspects of design thinking can
empower people to take action and improve their self-efﬁcacy and sense of agency
(Greenberg & Karak, 2020; Kramsky, 2017; Maher et al., 2018; Young, 2010).
Finally, researchers note design thinking’s power to shift cultural practices and ways
of thinking through inclusive engagement and iterative action (Buchanan, 1992;
Earle & Leyva-de la Hiz, 2021). Such societal transformations are essential if we are
to achieve sustainability goals in a timely manner.

3.6

Connection Between Design Thinking and Collective
Action

Across the literature, evidence surfaces that design thinking characteristics that lend
themselves to developing sustainability solutions also center on principles of collective action (Ardoin et al., 2022). A topic of scholarly interest for decades,
collective action has grown in interest more recently as socio-environmental
issues—such as climate change, invasive species, and COVID-19, among others—
have emphasized the need for societal movements to attend to challenges in ways
that surpass individual efforts (Harring et al., 2021; Jagers et al., 2020. Perhaps most
famously described by Hardin in his (1968) Tragedy of the Commons and in
Ostrom’s (2000) rebuttal, describing the potential for sustainability among
common-pool resources, many scholars in the environment, natural resource, and
sustainability arenas have worked to reframe collective action as more than the
compilation of individual actions (Agrawal, 2003; Ferraro & Agrawal, 2021).
They emphasize that collective action—a distinct phenomenon arising out of, and
sustained by, social processes and structures—is a phenomenon that may be cultivated and nurtured, and that numerous potential pathways exist for motivating
individual and collective behaviors to address complex sustainability problems
(Agrawal, 2003; Cleaver, 2007; Lukacs & Ardoin, 2014; Niemiec et al., 2016).
Table 1 provides a summary of the ﬁve design thinking characteristics addressed
in this review and their connection to sustainability solutions. Design thinking has
been deﬁned, explained, and theorized in multiple ways and the ﬁve characteristics
detailed here are those that emerge as most salient from our literature review of
research and discussion on the relationship between design thinking and

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Table 1 Design thinking characteristics and their connection to sustainability solutions
Design thinking characteristic
Inspires creativity

Participatory and people-focused

Encourages and inspires diversity
in thought and action

Adopts a holistic, systemsthinking mindset

Offers a streamlined, actionoriented approach

Connection to sustainability solutions
Design thinking emphasizes process over product, meaning
that the journey of working toward a solution can be just as
important as whether one actually gets to a single “right”
answer in the end. The creative conﬁdence mindset and
iterative nature of design thinking encourage participants to
try taking action and work toward a solution or many
possible solutions. Without fear of failure or judgment,
individual and collective creativity can lead to accelerated
innovation and new ways of thinking, which are critically
needed in the sustainability space.
Rather than telling people what to do in a top-down manner,
a design thinking approach supports curiosity, collaboration, and active, participatory learning. Design thinking
lowers, and at times removes, the barrier to entry during the
initial solution generation process as a broader group of
participants are encouraged to contribute in unencumbered
ways. Focusing on people’s needs and experiences builds
ownership and enthusiasm, concurrently encouraging participants to construct their own knowledge about sustainability, grounded in personal and socio-culturally relevant
experience, including the causes, impacts, and potential
solutions.
The design thinking process signals that we are all in this
together, similar to the collective mindset recognizing that
sustainability challenges require perspectives from all
experiences, walks of life, and points of view. Design
thinking engages, and indeed requires, diverse teams; this
diversity enhances the richness of content, innovation, and
action that arises from the group process. Diverse groups
provide a greater number and variety of resources upon
which the sustainability solution process can draw and help
ensure that the solution set produced is equitable, inclusive,
and appropriate.
Combining design thinking and sustainability means
emphasizing the multiple spatial and temporal scales of
sustainability throughout the design process. By encouraging and employing a systems lens, design thinking helps
participants envision the interconnectedness of complex
sustainability challenges and, in the process, imagine more
creative, collaborative, context-appropriate solutions.
Design thinking’s supportive environment can empower
people or teams to take action, which can be especially
important in situations where they might otherwise be
daunted by a large-scale problem or one without an immediate solution. Design thinking provides structures and
support to organize the problem-solving process in an efﬁcient, streamlined way, encouraging communication and
collaboration among a diverse range of stakeholders,
engaged across complex sustainability challenges. The
action orientation focuses everyone on a solution space in a
collective, community-oriented way.

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sustainability solutions. In future work, we plan to further explore these connections
and examine other perspectives on design thinking that have not yet been explored in
the context of sustainability.

4 Reﬂection and Outlook
Through our exploratory literature review and the resulting synthesis, we emphasize
the ways in which the design thinking process supports and encourages sustainability solutions to move from ideation to action. Making these literature-based connections helps ground what we see in design thinking practice to theoretical
underpinnings and also illuminates areas in the academic literature where we can
expand our thinking around design thinking and sustainability. Theory building and
testing and generating ideas within the speciﬁc content area of sustainability and
climate design, for example, offer an opportunity to further push this work ahead in
terms of scholarship and practice. In this short chapter, we have barely scratched the
surface of the robust bodies of inter- and transdisciplinary literatures that might
inform the work going forward in this and related domains.

5 Conclusion
Although there is some initial discussion on the suitability of design thinking for
addressing sustainability challenges (e.g., Clark et al., 2020; Young, 2010), we push
further to suggest that design thinking approaches are vital and necessary to include
in the toolbox for addressing today’s urgent sustainability issues. We have undertaken this exploratory review to identify research in support of this idea. Our hope is
that by illuminating research-based connections between design thinking and sustainability, more researchers and practitioners will become aware of the powerful
intersections between these two ﬁelds. Thanks to the ﬂexibility, innovation, and
inclusive nature inherent in design thinking approaches, they prove particularly wellsuited to address the wicked nature of sustainability problems as well as the need for
collective action to produce, iterate on, and apply innovative solutions. We thus
invite colleagues to join us in this ongoing conversational space as we reﬁne our
work in design/sustainability theory and practice and continue to pursue sustainability solutions with the requisite care, urgency, and creativity that such issues
necessitate.

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