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A social-cognitive approach to modeling design thinking styles

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A social-cognitive approach to modeling design thinking styles

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Content Copyright 2020 Hristina Milojevic

A Social-Cognitive Approach to Modeling Design Thinking Styles
by

Hristina Milojevic

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MECHANICAL ENGINEERING)

December 2020
ii
Epigraph

“Invention is the most important product of man’s creative brain. The ultimate purpose is
the complete mastery of mind over the material world, the harnessing of human nature to human
needs.” - Nikola Tesla, My Inventions

iii
Dedication

To my parents, Slobodan and Ljiljana, for giving me the world.
Mojim roditeljima, Slobodanu i Ljiljani, zato što su mi poklonili ceo svet.

iv
Acknowledgments
This work would not be before you had it not been for the guidance of my research
advisor, Dr. Yan Jin. For his dedication, support, intricate discussions, and elaborate thoughts, I
offer my sincere gratitude. I am thankful to professors, scientists and engineers who have
mentored me during my Ph.D. program. Special thank you goes to Dr. Peter Khooshabeh, who
helped plan out and distribute one of my research studies, for celebrating my successes and
empathizing with my difficulties during the final year. Similarly, a big thank you is in order for
Dr. Mihailo Jovanovic who has extended much encouragement and research advice. Thank you
as well to Dr. Jonathan Sauder, whose own Ph.D. research precedes mine, and who has
consistently extended unique and inspiring thoughts about my work. I would also like to express
gratitude to other professors who have served on my dissertation committee and my qualifying
exam committee: Dr. Geoffrey Shifflett, Dr. Mitul Luhar, and Dr. Bingen Yang. Thank you also
to Dr. James Humann for helping me explore the area of human-machine interaction and for
being an all-around great and relatable mentor for the past few years.
I would like to thank all the members of IMPACT lab and the USC department of
aerospace and mechanical engineering, for their support over the years. Special thank you goes
to a number of USC administrators who have also been there for me, especially so Dr. Timothy
Pinkston, Dr. Irice Castro, Kim Klotz, Dr. Jennifer Gerson, and Andy Chen. Finally, I thank
many wonderful students I’ve known, taught, graded, and guided at USC, for being energetic and
inspirational. Some of them are Noah K. Brown, Perri Chastain-Howley, Angelica Girardello,
Vinit Jain, Adam Omary, Ananya Patel, and Nikita Seth. For help with social science statistics,
I’d like to thank Siyabonga Ndwandwe.
v
I would like to thank my research study participants, whose names I will never know and
who are, in any case, too many to name, but without whom my research studies wouldn’t have
been possible. Additional thanks goes to the Autonomous Ship Consortium (which consists of 5
companies: Monohakobi Technology Institute Co., Ltd., Japan Marine United Corporation,
BEMAC Corporation, TOKYO KEIKI Inc., and ClassNK) for sponsoring my latest work on
human factors in autonomous ship operation; to the USC Institute for Creative Technologies for
having me as a research intern in 2019; to De Tao Masters’ Academy in Shanghai for briefly
having me as a visiting research fellow in 2017; to Dr. Zhinan Zhang from Shanghai Jiao Tong
University for assisting with a research study; to USC WiSE (Women in Science and
Engineering) and to 2nd IFAC Conference on Cyber-Physical & Human Systems (CPHS) for
sponsoring some of my conference travel.
For their immense love and support in the very best ways they knew of, I first and
foremost thank my parents, Slobodan Milojević and Ljiljana (Kovačević) Milojević. My dad, a
mechanical engineer in Serbia, guided and mentored me throughout my education in mechanical
engineering. His deep understanding and extensive experience in the field have been a rock
during my own education, in Serbia, Canada, and the U.S. For that I owe him my eternal
gratitude. He is my constant source of inspiration on work ethic and encouraging management.
My mom, a quality engineer in Serbia, guided and mentored me through ensuring I could do
more than solve equations. Her deep dedication to helping me discover and pursue my talents
started me onto the path of this dissertation: a pursuit of creativity, a wonderfully fluid dwelling
of the mind, in the context of mechanical engineering, an exact and performance-oriented
application of natural science. For guiding me into becoming a uniquely curious and well-
rounded engineer, my mom has my eternal gratitude. Mama i tata, volim vas zauvek.
vi
For his love, care, kindness, and for always knowing just what to say or just how to help
me be more productive, I thank my wonderful husband, Jarrod Holliday. His company and his
belief in me helped the last two years of my Ph.D. program fly by. He’s made the bad days better
and the good days amazing, and each day he teaches me, by example, how to be a better person.
For helping me learn about the culture of innovation and creativity, an area essential to
this research endeavor, I thank Humera Fasihuddin and the University Innovation Fellows, Dr.
Harold Fried, Dr. Shane Cotter, Dr. Wendy Sternberg, Frances Maloy, Kate Schurick, Elliot
Roth, and later on Dr. George Tolomiczenko.
For leaving a lasting memory with their teaching and inspiring me to pursue a doctoral
degree, I thank my college professors Dr. Ronald B. Bucinell, Dr. Rebecca Cortez, Stephen
Kalista, Dr. Glenn Sanders, Dr. Harish Gopalan, Dr. Warren Bessler, Dr. Bradford Bruno, Dr.
Erika Nelson-Mukherjee, Dr. Nicole Calandra, and Dr. Bunkong Tuon. If one was to look them
all up, the professors here would show up under departments of engineering, languages, and
literatures. I am grateful to Union College for helping me become an engineer with a creative
and humanistic twist. I would also like to thank my physics teachers, Dragana Milićević and
Milivoje Grandov, for making science so lovable so early on.
My journey to completing this program started with a United World College scholarship,
which led me from Serbia to Canada, to complete high school, and thereon to the U.S. for
college and graduate studies. For being there when I boarded my first plane, said my first
goodbye to my parents, and then moved to Canada; for driving down from Mississauga to
Schenectady to attend family events in college, for co-signing my first lease in Los Angeles, and
for always, always being there for me and taking an interest in me, I’d like to thank Igor and
Jelena Milojević. For helping me move to Los Angeles to begin grad school and for closely
vii
being there for me throughout my first year, I’d like to thank Nathan A. Jones and his parents,
Dr. Cynthia Angel and Dr. Jeffrey Jones.
For being there for me with wonderfully insightful conversations, helpful advice, kind
words that kept me going, or merely for giving me plenty of happy memories to think back to in
tough situations, I would like to thank Michelle Pawlowski, Kathy McCann, Julia Glikina, Vesna
Kovačević, and Reverend Sara Baron.

Hristina Milojevic
October 12, 2020

viii
Table of Contents

Epigraph .............................................................................................................................. ii
Dedication .......................................................................................................................... iii
Acknowledgments.............................................................................................................. iv
Table of Contents ............................................................................................................. viii
List of Tables ..................................................................................................................... xi
List of Figures ................................................................................................................... xii
Abstract ............................................................................................................................ xiv
1 Introduction ................................................................................................................. 1
1.1 Background and Motivation ............................................................................... 1
1.2 Research Issues ................................................................................................... 3
1.3 Thesis Overview ................................................................................................. 4
1.4 Thesis Organization ............................................................................................ 5
2 Related Work .............................................................................................................. 7
2.1 Introduction ......................................................................................................... 7
2.2 Design Theory and Methodology ....................................................................... 7
2.2.1 Design by analogy (DbA) ............................................................................. 10
2.2.2 Design thinking ............................................................................................. 11
2.2.3 TRIZ: Theory of inventive machines ............................................................ 14
2.3 Dual Process Theories....................................................................................... 15
2.3.1 Fast and slow thinking .................................................................................. 15
2.3.2 Cognitive-experiential self-theory ................................................................ 16
2.3.3 Intuition-rationality balance .......................................................................... 17
2.4 Social Cognitive Learning Theories ................................................................. 19
2.4.1 Social learning theory ................................................................................... 19
2.4.2 Accessibility .................................................................................................. 21
2.4.3 Theory of mental self-government................................................................ 22
2.5 Summary ........................................................................................................... 24
3 Thinking Styles in Design ......................................................................................... 25
3.1 Introduction ....................................................................................................... 25
3.2 Conceptual Design and Thinking Styles ........................................................... 26
3.3 Social-Cognitive Factors and Thinking Styles ................................................. 28
3.4 Gaps in the Literature........................................................................................ 30
3.5 Summary ........................................................................................................... 31
ix
4 Modeling Thinking Style in Engineering Design ..................................................... 32
4.1 Introduction ....................................................................................................... 33
4.2 A Dual Process Model of Design Thinking Styles ........................................... 35
4.3 Design Thinking Styles in a Social-Cognitive System ..................................... 40
4.4 The Role of Professional Experience in the Social-Cognitive Design Context 44
4.4.1 Design self-efficacy with respect to triadic reciprocity factors in engineering
professionals 46
4.4.2 Comparison of design self-efficacy between engineering students and
professionals, with respect to thinking styles and personality .............................................. 47
4.5 Summary ........................................................................................................... 48
5 A Dual Process Model of Design Thinking Styles ................................................... 50
5.1 Introduction ....................................................................................................... 50
5.2 Methods............................................................................................................. 51
5.2.1 Subjects and procedure ................................................................................. 51
5.2.2 Survey assessment ........................................................................................ 52
5.3 Personality and Thinking Style ......................................................................... 55
5.3.1 Thinking style profile .................................................................................... 55
5.3.2 Creativity profile ........................................................................................... 57
5.3.3 Design profile................................................................................................ 58
5.4 Findings............................................................................................................. 60
5.5 Summary ........................................................................................................... 61
6 Thinking Styles in a Social-Cognitive System for Design ....................................... 62
6.1 Introduction ....................................................................................................... 62
6.2 Methods............................................................................................................. 64
6.2.1 Subjects and procedure ................................................................................. 64
6.2.2 Survey assessment ........................................................................................ 64
6.3 Results ............................................................................................................... 67
6.3.1 Design self-efficacy relationship with personal and environmental SCT
influencers 69
6.3.2 Design self-efficacy relationship with intuitive thinking.............................. 70
6.3.3 Design self-efficacy relationship with creative behavior .............................. 73
6.3.4 Design self-efficacy relationship with design performance .......................... 75
6.4 Findings............................................................................................................. 77
6.5 Summary ........................................................................................................... 78
7 Professional Experience in a Social-Cognitive System for Design .......................... 79
7.1 Introduction ....................................................................................................... 79
7.2 Methods............................................................................................................. 81
7.2.1 Subjects and procedure ................................................................................. 81
7.2.2 Survey assessment ........................................................................................ 82
7.3 Engineering Experience Levels and Design Self-Efficacy ............................... 84
7.3.1 Self-efficacy of engineering professionals.................................................... 84
7.3.2 Pro-design factors of social-cognitive systems of engineering students and
professionals 87
7.4 Findings............................................................................................................. 90
x
7.5 Summary ........................................................................................................... 91
8 Contributions and Future Work ................................................................................ 92
8.1 Hypotheses Revisited ........................................................................................ 92
8.2 Conclusions ....................................................................................................... 93
8.3 Contributions..................................................................................................... 95
8.4 Future Work ...................................................................................................... 99
Bibliography ................................................................................................................... 102
Appendices ...................................................................................................................... 117
Appendix A: Big Five Personality Inventory (BFI) ................................................... 117
Appendix B: Rational-Experiential Inventory (REI-40)............................................. 120
Appendix C: Rational-Experiential Inventory (REI-10)............................................. 123
Appendix D: Biographical Inventory of Creative Behaviors (BICB) ........................ 124
Appendix E: Creative Behavior Inventory (CBI) ....................................................... 125
Appendix F: Revised Creative Domain Questionnaire (CDQ-R) ............................... 127
Appendix G: Engineering Design Self-Efficacy (DSE) ............................................. 128
Appendix H: Innovation & Invention Index (III) ....................................................... 129
Appendix I: Design Problem (for Chapters 5 and 6) .................................................. 131
Appendix J: Design Problems (for Chapter 7) ............................................................ 131

xi
List of T ables
Table 1: Summary of scores per variable, as well as the relevant scales for said scores ........... 68
Table 2: Correlations of listed scores with respect to design self-efficacy score, per each
category of influencers within the larger sample. Findings which are significant are
marked in bold. ............................................................................................................. 69
Table 3: Multiple linear regression results of Conscientiousness and Openness on .................. 85
Table 4: Multiple linear regression results of Rational and Experiential Thinking on Design
Self-Efficacy ................................................................................................................. 85
Table 5: Multiple linear regression results of experience factors on design self-efficacy ......... 86
Table 6: Descriptive statistics (N-count, mean, standard deviation, standard error of the mean)
of each variable for both students and engineers. ........................................................ 88
Table 7: Independent samples t-test for equality of means between Students and Engineers in
Rational Thinking, Experiential Thinking, Conscientiousness, Openness, and Design
Self-Efficacy. We find Engineers to be significantly higher in all of these dimensions
except for Experiential Thinking, for which there were no statistically significant
differences. ................................................................................................................... 88
Table 8: Multiple linear regression results of Design Self-Efficacy in students. ....................... 89
Table 9: Multiple linear regression results of Design Self-Efficacy in engineers. ..................... 90

xii
List of Figures
Figure 1: Relationships between the social-cognitive model of design thinking styles and
literature it’s founded upon ........................................................................................ 7
Figure 2: Sternberg’s proposed interactions of thinking styles, labeled with rational-experiential
inventory items in adequate areas, to depict possible relationship with theories
completed work builds upon .................................................................................... 23
Figure 3: An early framework for studying design thinking styles ............................................ 38
Figure 4: Bandura’s (1986) triadic reciprocal determinism model ............................................ 42
Figure 5: Social-cognitive design framework, studied partially, with particular focus on (i)
effects of gender and personality on design self-efficacy, and (ii) effects of design
self-efficacy on a set of behavioral factors defined as pro-design behaviors. ........ 44
Figure 6: A framework of self-efficacy in engineering professionals with respect to experience,
personality, and thinking styles ................................................................................ 47
Figure 7: A framework for design self-efficacy comparison between engineering students and
professionals............................................................................................................. 48
Figure 8: An early framework for studying design thinking styles ............................................ 50
Figure 9: Correlations between REI and BFI, where EX, AG, CO, ES, and OP stand for Big
Five personality dimensions: Extraversion, Agreeableness, Conscientiousness,
Emotional Stability, and Openness, respectively. .................................................... 56
Figure 10: Correlations between variables of REI and self-report creativity scales, where BICB,
CBI, and CDQ-R stand for measures of every-day creativity, creative
accomplishment, and creative confidence, respectively. ......................................... 58
Figure 11: Correlations between variables of REI and design, where Novelty assesses the
innovativeness of design, and Usability its likelihood of successful implementation.
.................................................................................................................................. 60
Figure 12: Social-cognitive design framework, studied partially, with particular focus on (i)
effects of gender and personality on design self-efficacy, and (ii) effects of design
self-efficacy on a set of behavioral factors defined as pro-design behaviors. ........ 63
Figure 13: Design Self-Efficacy with respect to personal and environmental influencers; left to
right: gender, binary (female/male), country (China/United States), discipline
(engineering/non-engineering), and personality (extraversion, agreeableness,
conscientiousness, neuroticism, and openness) ....................................................... 70
xiii
Figure 14: Rational mode of thinking and intuitive mode of thinking with respect to Design
Self-Efficacy, contextually studied with respect to the gender, location and
discipline of subjects ................................................................................................ 72
Figure 15: Rational mode of thinking and intuitive mode of thinking with respect to Design
Self-Efficacy, contextually studied with respect to big five personality traits:
extraversion, agreeableness, conscientiousness, neuroticism, and openness. .......... 73
Figure 16: Behavioral creativity scores of BICB, CBI and CDQ-R, studied with respect to
design self-efficacy, in the contexts of gender, location, and discipline. ................ 74
Figure 17: Design novelty and design usability scores, studied with respect to design self-
efficacy, in the contexts of gender, location, and discipline. ................................... 76
Figure 18: Design novelty and design usability scores, studied with respect to design self-
efficacy, in the contexts big five personality traits: extraversion, agreeableness,
contentiousness, neuroticism, and openness. ........................................................... 76
Figure 19: Framework of self-efficacy in engineering professionals with respect to experience,
personality, and thinking styles ................................................................................ 80
Figure 20: A framework for design self-efficacy comparison between engineering students and
professionals............................................................................................................. 81
Figure 21: (a) 95% confidence intervals for mean scores in rational thinking, experiential
thinking, conscientiousness, and openness between students and engineers; (b) 95%
confidence interval for mean design self-efficacy scores between students and
engineers. ................................................................................................................. 87

xiv
Abstract
Engineering design methods, such as systematic design, design thinking, and design by analogy
are supported by cognitive, behavioral, and social characteristics of the engineer performing
design. It is of interest to uncover how a designer’s thinking affects creativity and functionality
of their conceptual designs. Thinking can be considered as a dual process, in terms of the
cognitive-experiential self-theory and decision making: system 1 thinking is
intuitive/experiential (automatic, involuntary, unconscious, fast) and system 2 thinking is
rational/analytic (controlled, voluntary, conscious, slow). In addition to thinking styles,
components of the designer’s identity (such as personality), habits (such as behavioral
creativity) and circumstances (such as level of experience) may contribute to how they address a
design problem (i.e. how they perform on it) and how they perceive their ability to do so (design
self-efficacy). These traits were studied together in order to define a designer’s social-cognitive
system, which defines their contextual existence, consistent of endless reflexive associations
between their design self-efficacy, personal, behavioral, and environmental influencers.
Traditionally, mechanical engineers are educated to solve problems analytically.
However, mechanical design relies on synthesis, an inherently different approach, which may
best be supported by studying some of the relationships from a designer’s social-cognitive
system. Considering the notion of analysis being associated with analytic thinking (system 2) and
synthesis with intuitive thinking (system 1), one of the hypotheses of this research effort is [H1]
that intuitive thinking balances the analytically skewed dual process thinking of an engineer in
order to generate more creative, rather than strictly functional designs. It is also hypothesized
[H2] that there are influencers that can contextualize and aid intuitive or rational thinking in
engineering design, and [H3] that with greater professional experience, engineers enhance the
xv
relationships between their pro-design factors and design metrics. When it comes to individuals
with and without professional experience, the previously studied social-cognitive categorical influencers
(pro-design factors) can be applied to identify effects of experience factors, as well as explore the
differences between experienced and inexperienced groups.
In order to validate these hypotheses, 3 studies were completed. The initial two studies
considered a sample of engineering students and non-engineering students from Shanghai Jiao
Tong University and the University of Southern California. The third study considered a sample
of engineering students from the University of Southern California as well as a sample of
engineering professionals. The approach taken for these studies was based on survey
methodologies and assessments of conceptual design projects. In the first study, a comparison is
done between engineering students and non-engineering students in the form of correlations
between thinking styles (rational and intuitive) and (1) big five personality traits (extroversion,
agreeableness, conscientiousness, emotional stability, and openness to experience), (2)
behavioral creativity (scores of: biographic inventory of creative behaviors, creative behavior
inventory, and revised creativity domain questionnaire), and (3) design performance (design
novelty and design usability). In the second study, the above variables were contextualized with
respect to the social-cognitive theory. Specifically, we analyzed associations driven by design
self-efficacy, for behavioral actions involving rational and intuitive thinking, behavioral
creativity, and design performance, with respect to the influencers grounded in social-cognitive
theory, which are categorized as personal (gender, personality) and environmental (professional
culture, location). The third study involves comparing the pro-design factors, as defined by the
social-cognitive influencers from the second study, between engineering students and
professionals, as well as characterizing design self-efficacy by a range of experience parameters.
xvi
Results of the first two studies demonstrate a relatively strong correlation between
rationality and behavioral creativity, across all subject categories. Additionally, when
comparing engineering students with non-engineering students, it was found that engineers were
more rational, as well as less emotionally stable than the neutral, non-engineering student
sample. It was observed that overall design self-efficacy association was high with rationality,
and low with intuition; its association with conscientiousness and emotional stability was highly
positive. Seeing as the measured sample demonstrated questionable significance of most
findings, those with better significance were considered in the third study, which additionally
factored in experience factors and the distinction between inexperienced and experienced groups
studied. It was discovered that the significant experience parameters involved years in industry
and number of professional awards. Similarly, differences in the relationship of thinking and
personality with design self-efficacy were also found, between the student sample and the
professional sample studied.

1
1 Introduction
1.1 Background and Motivation
One of the driving elements of US industrial competitiveness has long been the ability to
develop innovative technologies, systems, and products and systems that are desirable and profit-
generating. Design creativity is a key driver for innovation. This research seeks to address some
of the longstanding challenges of research in design creativity, which includes providing
theoretical underpinnings for “think like a child” kind of ad-hoc methods (Brown, 2009),
understanding how intuitive thinking may aid and hinder design creativity, and building a
foundation for training and tool development.
It has been observed that engineering students become less divergent over the course of
their four-year education (Genco et al., 2012). As many models of creativity (Dijksterhuis &
Nordgren, 2006; Epstein, 2003; Freud, 1964; Simonton, 2003; Simonton, 1999) emphasize the
importance of unconscious, stochastic thought in the creative process, it is believed that
engineering profession and curriculum weigh too heavy an emphasis on analytical thinking, thus
possibly hindering creative abilities, and therefore limiting the ability to harness creative aspects
of the designer’s thinking. It has been shown that creative idea generation can be improved to a
certain extent: for example, using Synectics, a simple but intuitive method to stimulate abstract
thinking (Ma, 2006). Such dampened creativity and divergence can be studied from a number of
different angles and perspectives, some of which beg the question: what is the connection of
intuitive (as well as the opposing rational) thinking to engineering design, be it convergent or
divergent, creative or traditional, planned out or hypothetical, conducted late in one’s career or
during their education?
2
From the Engineer of 2020 report by the National Academy of Engineering, “the
humanities, arts, and social sciences are essential to the creative, explorative, open-minded
environment and spirit necessary to educate the engineer of 2020” (National Academy of
Engineering, 2005). This means that nurturing diverse interests in domains of humanities, arts
and/or social sciences after college should also make one a better engineer. Humanities and
social science courses develop an intuitive and inferential component of thinking that functions
independently from analytical reproduction, potentially increasing creative idea generation
potential. “Inductive and deductive reasoning do not suffice to reproduce the phenomenon of
creative behavior,” (Simonton, 2003) as even in technical design, creative ideas are often the
result of stochastic associations between external stimuli and experiences that are often random
and open to a wide range of influences. Most existing conceptual design models, such as
Geneplore (Finke, Ward, & Smith, 1996), design by analogy (Linsey et al., 2009) and Generate-
Stimulate-Produce (GSP) (Jin & Benami, 2010), do not effectively take into account this non-
analytical component of creative idea generation. There is a noticeable lack of understanding as
to why intuitive thinking is so important in engineering.
Ultimately, the main motivation for this research comes from the curiosity for why some
people design better than others. In an attempt to consider the reasons, two possible factors are
deduced: a person’s system of existence (social-cognitive system (Bandura, 1977; Bandura 2001;
Bandura 2005)) and duality of thinking (rational and intuitive (Epstein, 2003; Kahneman,
2011)). When considering the social-cognitive system, one’s creative behavior is likely to be
driven by the person they are (biologically, cognitively, and affectively) and the environment
they belong to (socially, professionally, and physically). When considering intuitive thinking
style, two types of intuition come to mind: one that quickly retrieves memories based on
3
experience, and another that generates “quick and dirty” early ideas for design, which
respectively represent the level of one’s professional experience, and one’s cognitive ability to
quickly generate new design ideas from unrelated sources of inspiration. As such, intuition may
be associated with both past experiences and creativity. These specific views on intuition fuel the
approach that this research considers when addressing the questions of what makes some people
design better than others.
1.2 Research Issues
Our social-cognitive approach to design thinking styles aims to define the relationship
between early-stage conceptual design and a designer’s social-cognitive system. A particular
interest is given to contextualizing the relationship between dual process thinking and design
creativity, through factors affecting the engineer conducting design. As such, three relevant
research issues were identified:
1) Modeling effects of thinking styles on design creativity.
2) Contextualizing design measures with respect to designer’s cognitive traits.
3) Narrowing down a broad system of a designer’s worldview and background, into
quantifiable and sensible parameters of design.
4) Adjusting the preference to study design engineers, for purposes of precision and
authenticity of the research, while populations I had access to involved few of them.
The development of new products and solutions to address complex social and technical
problems requires creative ideas. Creative thinking in design is essential for generating such
ideas. This poses a fundamental research question: What contributes to a designer’s creative
thinking process? To address this question, a social-cognitive approach is adopted, and
4
designing is considered as composed of two thinking processes: intuitive process and analytical
process (Stanovich & West, 2000; Evans 2004), with the full proposed model available in
Chapter 4. The most fundamental research question, with the identified research issues in mind,
expands to include the following break-down of research questions:
{R1}: What type of thinking benefits a designer the most?
{R2}: What factors contextualize the designer’s thinking styles?
{R3}: What is the effect of experience on design thinking styles and other pro-design
factors?
1.3 Thesis Overview
The overall objective of this research is to understand relationships between who an
individual is (biographically, psychologically, or biologically), what types of behaviors they
engage in (how do they prefer to think, how often are they crafty, or how they designed a ping-
pong ball launcher), and where do they do so (geographically, professionally, or socially) and
their design self-efficacy (a belief that they can design and how well). The specific academic
objectives of this research are:
1) To investigate the respective effects of intuition and rationality on design outcomes.
2) To establish a research methodology including measurements and experiment design
for studying design thinking styles within a social-cognitive system.
3) To identify factors which may point to those individuals with the most potential for
becoming successful, creative, and effective designers.

5
1.4 Thesis Organization
This thesis has 6 chapters, including the introduction, the remaining 5 of which are
summarized here.
Related Work – Summarizes the relevant literature background in domains of design
theory, dual process thinking, and social learning.
Thinking Styles in Design - Addresses the state of knowledge on thinking styles in
research areas of interest, allowing for a contextualized transition towards the research models I
built and evaluated.
Modeling Thinking Styles in Engineering Design – Defines 3 models built to evaluate
the relationship between design and thinking styles, starting with base variables and the limited
context, then expanding to consider broad social-cognitive influencing contexts, and lastly
focusing down towards those found the most significant and those left unexplored in the previous
2 models.
A dual process approach to design thinking style modeling - Defines and evaluates the
research model consisting of thinking styles (rational and intuitive), personality, behavioral
creativity, and design challenge performance.
Thinking styles in a social-cognitive system for design - Defines and evaluates a new
contextual model, underpinned by the social-cognitive reciprocal determinism, where the former
4 variables are now distributed among categories of personal and behavioral influencers, which
are driven by design self-efficacy and several processes beyond my scope of this research.
Professional experience in a social-cognitive system for design - Defines and evaluates
a research model of social-cognitive reciprocal determinism, with each of the personal,
6
behavioral, and environmental influencer categories defined by at least 1 variable relevant for
design, and associated with design self-efficacy.
Contributions and Future Work – Summarizes the contributions to the field of design
theory and methodology, with a particular emphasis on design cognition. Lists out
recommendations for future research considerations.

7
2 Related Work
2.1 Introduction
The proposed research relates closely to three areas of research, namely, duality of thinking,
social cognitive learning theories and design theory and methodology research. In the
following, these three areas of research are briefly reviewed and gaps in literature identified prior
to proceeding to discuss this proposed research work on design thinking styles in a social-
cognitive context.

Figure 1: Relationships between the social-cognitive model of design thinking styles and
literature it’s founded upon
2.2 Design Theory and Methodology
The conceptual design process is a vital stage in any product, as close to 75% of the total
cost for a project is determined during this phase (Ullman 2009). Making small changes and
8
creative improvements in the conceptual design process can have large impacts in making a
project successful.
It was found that even experienced designers would fixate on initial concepts and, even
when flaws availed themselves, stick to them through the end of the problem (Ullman et al.,
1988). Cross argues that this could be due to experienced designers being better at choosing
workable concepts and easily adapt them as necessary (Cross, 2004). This coincides with the
singularity principle in Evans’ model of hypothetical thinking, in which people consider a single
hypothetical possibility at any given time, until that possibility is explicitly rejected (Evans,
Over, & Handley, 2003).
Designs can be considered analogous (Qian & Gero, 1996; Visser, 1996, Goel 1997).
Research has been carried out to understand and improve designer’s capability to do analogical
thinking. Design-by-Analogy (DbA) inspires a designer to identify or develop a range of
strategies for solving design problems, basing said strategies in past experience or generally
accessible knowledge. These strategies may be centered around design problem-analogous
examples, cases or scenarios (Goldschmidt, 2001; Linsey et al. in ASME, 2007; Linsey et al. in
ICED, 2007). Research on DbA has been proven to be a powerful tool for identifying many
relevant ideas across many different knowledge domains, one of which is engineering design
(Schunn et al., 2006; Christensen & Schunn, 2007; Tseng et al., 2008).
Shah et al. (2003) developed an approach to align experiments occurring in creative
cognition research with those occurring in design. Specifically, they focused on incubation
(testing the effect of taking a break from a design problem in both the laboratory and design
setting) with the hope of expanding to a more complete design ideation model in the future.
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To evaluate design work, the effectiveness for new ideations can be measured by four
factors (Shah, Smith, & Vargas-Hernandez 2003). These four factors are quantity, quality,
novelty and variety. The scores for novelty and quality can be computed by taking a comparison
of that design ideation and comparing to all the other design ideations. Another popular method
for evaluation of design techniques is self-evaluation, where all the participants vote on which
designs they feel are most novel (Sosa & Marle, 2010). Generally, the voting power (or number
of votes each participant can cast) is limited. Participation in online environment can be analyzed
through text analysis and data mining (Simoff and Maher, 2000).
Function-failure design method has been proposed to find potential modes of failure in
designs. The power of this method comes from creating a matrix of the function components and
the component failures in order to find the function failures (Stone, Tumer & Wie 2003). When
dealing with a large mass of ideas, it has been proposed to evaluate them by their creativity,
usefulness, and feasibility (Kudrowitz & Wallace 2010). Interestingly, the study found that
creativity had a low correlation with usefulness, and quantity of ideas produced had a strong
correlation with creativity. Correspondingly, the number of sketches generated during design
work has also been found to be correlated with the quality of the design work (Yang 2008). A
study by Alexiou et al. (2009) examined the neurological basis of creativity using fMRI brain
imaging, but results are quite limited.
Knowledge itself is not enough to aid design. Framing effects, that is, different ways of
representing information, can allow a deeper breadth of domain knowledge. Tsenn et. al. (2014)
found that biology students were no better at harnessing biological metaphors for design than
mechanical engineering students, which illustrates the importance of both understanding how to
transfer domain-relevant knowledge out of its original context and the importance of design
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education. Some of the prominent design methods which are heavily impacted by designer’s
cognition are presented in sections 2.2.1.-2.2.3., to demonstrate how research transcends into
heavily relied on methodology.
2.2.1 Design by analogy (DbA)
Design by Analogy is an intuitive design method which considers how an existing
embodiment solution relates to a design problem on hand. Often times this implies tying every-
day-use objects or objects present in nature to brainstorming (Ullman, 2009; Qian & Gero, 1996;
Visser, 1996, Goel 1997). For example, a function of “transport” may be connected to vehicles
people drive to work or vessels they board to go on a vacation. If nature-inspired, the ideas may
be tied to observation of birds flapping wings to fly, fish moving fins in order to swim, or
mammals using limbs for walking.
Much contemporary research is dedicated to studying analogies and how they come to
mind (Goldschmidt, 2001; Linsey et al. in ASME, 2007; Linsey et al. in ICED, 2007). A
common challenge may include innovating a pre-existing solution, which seems obvious, and
therefore keys into the fixation of designers conducting early-stage design (Schunn et al., 2006;
Christensen & Schunn, 2007; Tseng et al., 2008). Even if there isn’t a pre-existing solution, one
might quickly fixate on their most obvious analogy, thus failing to reason through multiple
solutions without bias. In fact, in situations where the design stage is rushed or underfunded, one
might be the most tempted to proceed with the analogous design solution, which may or may not
be the best possible solution they could generate to solve the given problem. In a situation like
this, design by analogy hinders rather than facilitates creativity.
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Alternatively, an example of the use of analogies to boost innovation is biomimicry.
While once upon a time humans sought inspiration in nature, this is no longer the necessary
standard, meaning that drawing inspiration for technical designs from components of nature,
such as unique motions of certain animals or leaf structure of certain plants might generate much
more curious and unique solutions to a variety of design problems.
2.2.2 Design thinking
Design thinking can be defined across disciplines, contexts and uses. Its earliest origins
can be traced back to early studies of creativity in cognitive science, some of which began in
1920s with the work of Hargreaves on psychology of imagination (Hargreaves, 1927) and
became more notable in 1950s and 60s through Guilford’s popularization of psychometric
relationship between intelligence and creativity (Guilford, 1967). With some consideration for
pre-existing ideas in human thinking, there came a consideration that to-date design methods
could be classified as rational, while thereon categorization could be expanded to add intuitive
methods as well. Thus began the era of acknowledging thinking behind design processes. While
relevant to the design thinking method itself, thinking and cognition behind the method does not
explicitly define it.
Professional world of product design and business proceeded to consider the user more
and more, as interest in human-centered design kept growing. A notably successful return to
thinking in design occurred in 1990s, when Don Norman’s writing began to bridge the domains
of user thinking and product features (Norman, 1988), thus diving into an omnipresent need to
develop user empathy as one attempts to solve a design problem, by exploring the highlights and
pitfalls of products people encounter the most in their daily lives, such as light switches, phone
systems and rotating doors.
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Today, design thinking is best known as a design and business innovation method
developed by the design firm IDEO and Stanford’s Hasso Plattner Institute of Design better
known as d.school. Both were founded by David M. Kelley, a Stanford professor, engineer, and
entrepreneur. At IDEO, design thinking has been seen through as a helpful methodology to
cultivate the spirit of creativity and innovation. (IDEO U Design Thinking page, 2019)With
heavy emphasis on human factors, IDEO has played a key role in popularization of design
thinking across technical industries and beyond. While design thinking is taught as a design
method, it is also a business method, as well as an innovation and strategy method, which
borrows from the kind of strategic approach designers undertake, and applies it to a variety of
technical and non-technical problems that could be designed for. A few years ago, Kelly & Kelly
(2013) explained that IDEO’s innovation strategy consisted of (1) inspiration, (2) synthesis, (3)
ideation and experimentation, and (4) implementation (Kelly & Kelly, 2013), while Stanford’s
2019 guide on design thinking organizes the steps in a slightly evolved manner.
Design thinking process consists of five modes: (1) empathize, (2) define, (3) ideate, (4)
prototype, and (5) test. As a design method, it allows mode order permutation, mode iteration,
and process iteration. This means that order of modes can be changed, each mode can be
repeated as many times as desired on its own, or the entire process may be cyclically repeated.
Empathize mode involves developing empathy for one’s user and thus making the design
human-centered. Through observations, engagement, one can develop insights into users’ beliefs
and values, which may never get verbalized and may be non-obvious. It is insights like these that
lead to uncovering innovative solutions.
Define mode involves defining “a meaningful and actionable problem statement, also
known as a point-of-view (POV)” (An Introduction to Design Thinking Process Guide, 2019),
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which explicitly states the problem on hand. From here on, this becomes the challenge that gets
refined and improved throughout the design thinking process. An actionable problem statement
addresses who the user or composite user is, what their needs are, and what insights were
gathered, during the empathize mode.
Ideate mode pertains to idea generation while deferring evaluation or judgment, and
focusing on fluency (volume) and flexibility (variety) of ideas. The goal is to bypass fixation on
obvious solutions and encourage imagination. The whole team is included and three voting
criteria are established (examples include “most likely to delight”, “the rational choice”, and “the
most unexpected”) (An Introduction to Design Thinking Process Guide, 2019)
Prototype mode iteratively builds an extremely simple, low-resolution product user
interface (order of cost being cents and order of time being minutes) that allows role-playing
actual user-product interaction. Among other reasons to prototype, a physical representation of
the product is an additional effective way of communication. A low-resolution prototype allows
one to fail fast and fail often without significant allocation of resources, and it allows an almost-
experimental manipulation of any variable prototyped, which leads into the test mode.
Test mode brings any prototypes generated into user testing. Simultaneously, this
conveniently ties the test mode with empathy, driving the process into an iteration. During the
test mode, one should always be asking “why”, requesting feedback, and urging their user to
immerse themselves into the interaction with and experience of the prototype(s).
As one iterates through the design thinking process, one can adapt it and style it as
desired. Ultimately, while design thinking process has been used in product development, it has
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also been used in strategy, management, and business, where professionals of all backgrounds
embrace the “designerly mindset”. (An Introduction to Design Thinking Process Guide, 2019)
2.2.3 TRIZ: Theory of inventive machines
The Theory of Inventive Machines, TRIZ (abbreviated from Russian “teoriya resheniya
izobretatelskikh zadatch” and with direct translation of “theory of solutions to inventive
problems”) is a design method developed by Genrich Altschuller (1926-1998), a USSR-based
maverick engineer, who was contracted by the Soviet government to gather information on
strategic technologies which resulted in patents, worldwide. In his lifetime he had reviewed
400,000 patents, while the latest updates on TRIZ account for 2.5 million reviewed patents.
The goal of TRIZ is to help designers systematically innovate. It is founded on the ideas
of borrowing pre-existing solutions from other disciplines, as inspiration for solving similar
problems in engineering. Additionally, these detected solutions yield patterns, which help drive
new designs. The solutions are organized into a knowledge base with respect to the function they
fulfill. As such, there are many versions of TRIZ, including the two most basic and notable ones:
method of contradictions and 40 inventive principles. (Altshuller, 1984)
Method of contradictions within the TRIZ domain is used for concept generation, as well
as chain project management under the name “evaporating cloud (EC) method”, symbolizing the
step of evaporating the contradictions present within the project. Steps to do so involve taking an
unclear problem, structuring it, evaporating it by use of upgraded and improved solutions (with
respect to the adequate ones), and in turn further refining the problem (Ullman, 2009).
40 inventive principles are grouped into 7 categories: organize, compose, physical,
chemical, interactions, process, and service. Each of the categories contains multiple principles.
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Ullman (2009) offers some practical insights into which principles belong to which category as
well as how to conduct TRIZ design, professionally.
2.3 Dual Process Theories
Engineering design balances the technical requirements of engineering with the holistic
knowledge and artistic skills required to execute a successful design. Traditionally, rarely in
engineering education do students receive much training on how to harness creative and artistic
thinking that depends less on analytical rigor. Logically trained students can be hesitant to think
intuitively, and many current design methodologies reflect this hesitation, which we believe can
limit creative potential, as students and engineers often discard original ideas generated
intuitively before they are fully pursued and evaluated. A balance can, and should, be struck
between the analytical nature of traditional engineering and the intuitive nature of creative
thinking. Therefore, there is a strong need for a better understanding of the intuitive thinking
processes in conceptual design.
Duality of thought processes in the engineering design context is not a new concept.
Rather, plenty of existing works allude to competing modes of thinking and analyze how they
contribute differently to the design process. Perhaps most well known is divergent and
convergent thinking (Guilford, 1967; Runco, 2003).
2.3.1 Fast and slow thinking
Dual-process theory (Stanovich & West, 2000) is an established model from cognitive
psychology that divides cognitive processes into two camps: Type 1 and Type 2. Type 1
processes are fast, intuitive, heuristics-based, and emotional, and answer simple questions like,
"What is 2 x 4?" or when one reads the emotion on a colleague's face. Conversely, Type 2
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processes are slow and analytical, and answer more difficult questions like “What is 24 x 17?”
and also kick in if they detect an error is about to be made (Stanovich, 2011). Engineers are
largely brought up by their practice and education to be analytical, thoughtful individuals,
effectively honing Type 2 processes. However, much of the creative methodology seems to
harness Type 1 processes. It is very likely that Type 1 processes will prove to be stronger
contributors to the creative process than Type 2 processes alone. A correlation has been found
between dependence on intuitive thinking and creative potential, and we look to formalize and
expand on this result (Raidl & Lubart, 2001).
There is plenty of evidence demonstrating both the value and the danger of using Type 1
reasoning. Heuristics-based (Type 1) reasoning is most valuable in a benign environment that
supports the use of heuristics through experience and implicit learning (Kahneman & Klein,
2009). However, in certain instances, Type 1 processes can perform better than Type 2 thinking
(Hartwig & Bond Jr, 2011). Pretz (2008) also found that intuitive methods worked better for
novice problem-solvers, perhaps because they do not know exactly what information is relevant
to a problem and should be analyzed. In his eight stages of creative process model, Saywer
(2012) found that dual-process is constantly on display in these stages. In the past, much more
effort has been spent demonstrating how Type 1 reasoning breaks down in more complicated
situations (Kahneman, 2011; Kahneman, Knetsch, & Thaler, 1991; Tversky & Kahneman, 1974;
Wason, 1960).
2.3.2 Cognitive-experiential self-theory
Cognitive-Experiential Self-Theory of Personality, or CEST, (Epstein, 2003) describes a
dual-systems approach to human cognition that bears much in common with Stanovich and
West’s description of dual-processes (Stanovich & West, 2000; Stanovich, 2011), but differs in
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several key respects. Epstein views the experiential system as a primitive, but necessary system
of cognition and the analytical system as a recent human evolutionary adaptation. The
experiential heuristics processing is a necessary component for survival, rather than full of gaps
in rationality as described by Kahneman and Tversky (1974). Epstein notes that these systems
must be treated independently, for example “high self-esteem at the conscious, rational level may
coexist with low self-esteem at the experiential level” (Epstein, 2003). That is, confidence
acquired through unsubstantiated self-regard can be distinguished from confidence through
mastery and real accomplishment. This relates to Bandura’s (1977) theory of self-efficacy,
which identifies avenues by which people acquire mastery and confidence in their abilities.
Intuition has been explored in the creative problem-solving realm (Eubanks, Murphy &
Mumford, 2010). It was found that while intuitive people naturally produced more creative
solutions, inducing a positive affect and training offered the same advantages. The experiential
system affects behaviors in complex ways, by generalizing, integrating, and directing them, at
times independently and at times with the involvement of the rational system (Epstein 2003).
Another way to define intuitive system in CEST is “faith in intuition” and rational system: “need
for cognition”. Each can be further subcategorized into ability and engagement (rational ability,
rational engagement, experiential ability, experiential engagement).
2.3.3 Intuition-rationality balance
Hogarth (2005) explored the balance of intuitive and analytical thinking in various
realms of problem solving, based on the complexity and potential for bias, such as experience
and characterization of problem environment. He suggests that for problems with low
complexity but high potential for bias, analytical approaches are favored over intuitive
approaches. Conversely, for problems with high complexity, but low potential for bias, intuitive
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approaches are favored over analytical approaches. For problems with high complexity and high
potential for bias (such as a first-year design student approaching their first design problem), it is
unclear whether intuitive or analytical approaches are superior. While the analytical approach
seems to naturally be preferred, it is possible that inexperience may keep an individual from
isolating the critical information required to solve a problem, and as a result fixate on irrelevant
or misleading information.
This coincides with Smith and Linsey’s (2011) definition of fixation. As such, there may
be value in Baylor’s (2001) U-shaped model of intuition, where the level of expertise correlates
with availability of intuition. Novices, not being able to depend on previous experience, can
harness immature intuition (an approach favored by the Synectics process (Gordon, 1961)), and
experts can harness mature intuition, developed through experience and implicit learning. Pretz
(2008) did not find that inexperienced subjects were able to gain much from intuitive thinking,
noting that Baylor’s model should have a third axis of complexity. Gordon (1961) summarizes
well the potential issue with expertise, writing, “Learned conventions can be windowless
fortresses which exclude viewing the world in new ways.”
Cognitive Continuum Theory, or CCT, (Hammond, 1981) uses the same dual-process
building blocks, but describes the outcome of cognition, rather than the process (Sinclair, 2010),
which have been explored by dual-process theory, CEST, and UTT. Using varied combinations
of analytical and intuitive thinking, six modes of inquiry are described on a continuum ranging
from pure analytical thought to quasirationality to intuitive judgments. Quasirationality
(Brunswik, 1956) is a combination of both rationality and intuitive thought. The nature of tasks
determines which mode of thinking will be triggered: well-structured tasks trigger analysis and
ill-structured tasks trigger intuition. In addition, thinking can oscillate between these varied
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modes of cognition (Webster, 1990; Hammond, 1981). It is believed that this oscillation may be
a fundamental aspect of the iterative nature of conceptual design. An initial study to apply CCT
in analysis of design thinking processes was carried out by Moore et. al (2014), and it was
observed that the oscillation between intuitive and rational thinking was visible in the design
protocols studied.

2.4 Social Cognitive Learning Theories
2.4.1 Social learning theory
The larger scope of present research focuses on the designer as an individual, treating
their cognition, behavior, environment, competences, motivation, actions taken towards
completing design-related tasks, and their own design outcomes, as a system of interest. More
specifically, a concept called pro-design behavior is introduced to indicate the largely habitual
thinking and doing behaviors that potentially lead to higher design creativity and better design
performance. Pro-design behavior involves thinking style, creative behaviors, and design
performance, later depicted in Figure 5. A general research question to be addressed is: “what
are important influencers that shape someone’s more pro-design behaviors?”
Limiting the research system of interest steadily to an individual designer, there are fewer
ways to conduct research interventions. While one might be able to displace an engineer into a
new environment, placing them on, for example, a particularly crafted team of designers would
not be an intervention of interest. As such, one of the larger goals of this research is to identify
and propose an intervention that would allow for designer’s most effective use of their dual
process thinking behind creative design processes.
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Early on, the project began with an outlook on proposing a duality to thinking behind
creative engineering design. One way to do so was to rely on Epstein’s cognitive-experiential
self-theory (Epstein, 2003), which proposes human mind as governed by two modes of
processing: (i) rational (need for cognition), and (ii) experiential (faith in intuition). The
preliminary results indicated that in order for one to be creative and demonstrate creativity with
design outcomes, he or she must be approximately equally rational and experiential in their
thinking (Moore et al., 2014). In this case, the research remains within the domain of pure
cognition.
Investigating potentially important influencers requires expanding the scope of study on
both mental and social horizons by including more aspects into consideration. Some social and
mental aspects could be personal, such as gender, height or weight, or personality traits. Others
could be environmental, such as the country or town one lives in, the type of culture they
possess, or the type of space they spend their days in. Lastly, they could be behavioral and
involve habits or actions.
These three social and mental categories are known as influencers in studies of social,
social-cognitive, and social learning theories (Bandura, 1977, 1986 2001, 2005). Within the
influencers that pertain to design creativity, some useful allocations involve:
1. Personal influencers: gender, personality
2. Environmental influencers: country of residence, professional and academic culture
3. Behavioral influencers: thinking styles, behavioral creativity, design performance.
While the three categories of influencers have mutual effects among themselves, the
central variable that affects all three, and being affected by them, is self-efficacy, defined as “the
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belief that one can master a situation and produce positive outcomes” (Bandura, 1999).
Considering self-efficacy is not a field-uniform measure, we study the effects of design self-
efficacy in this particular case (Carberry, 2010). Self-efficacy scales for many different processes
have either been published and opened up for use, or can be self-made (Bandura, 1977). Carberry
et al. (2010) relied on a Massachusetts science and technology/ engineering curriculum
framework, and identified the eight steps of a design process for design self-efficacy estimate
(MA Dept. of Ed., 2001/2006).
2.4.2 Accessibility
Accessibility in social cognition has traditionally been related to the ease of retrieving an
idea or concept that contributes to human judgements and decisions (Higgins, Rholes & Joes,
1977; Srull & Wyer 1979), and as such to answering the question “what happens to come to
mind?” (Bodenhausen & Wyer, 1987; Higgins, 1996; Wyer & Carlson, 1979; Wyer & Srull,
1989). Truisms about accessibility give rise to complexities in more recent research findings.
Such complexities involve:
(i) Judgment is primarily, yet not solely, based on the most easily recalled information
content. It is also based on one’s experience of the recall, and processing motivation (Schwarz &
Clore, 1983; Wyer & Carlston, 1979; Skurnik, Schwarz, & Winkielman, 2000; Grayson &
Schwarz, 1999);
(ii) For judgment to be formed, a target of evaluation and a standard of evaluation are
instantaneously mentally represented, using presently accessible content. How content comes to
mind determines if the evaluation assimilates or contrasts to it (Schwarz & Bless, 1992; Bless,
Igou, Schwarz & Wanke, 2000; Tvertsky and Kahneman, 1973; Clore, 1992);
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(iii) Judgement is additionally affected by processing fluency, perceived ease with which
content comes to mind (Chasteen, Park, & Schwarz, 2001; Gollwitzer, 1999).
One way to think of studying accessibility is in terms of manipulation. In accessibility
research, manipulation encompasses the factor of differentiability. (Schwarz & Bless, 1992;
Martin, Strack, & Stapel, 2001). The critical questions of accessibility emerge to be “what?”,
“how?”, and “how easily?”. Their answers can be manipulated by manipulating differential
valence (combining power of intervention), priming (preparation for action), and fluency (ease of
reaching an assigned requirement). The success rate of manipulation techniques yield rise to new
understandings of features that promote or hinder cognitive accessibility (Wyer & Gruenfeld,
1995; Lombardi et al., 1987; Schwarz & Bless, 1992).
2.4.3 Theory of mental self-government
Theory of mental self-government (Sternberg, 1997) covers 13 different thinking styles,
some more or less aligned with the traditional perceptions of creativity, and some with traditional
perceptions of engineering. The styles are divided per function, form, level, scope, and
learning, which present as intriguing to place in the design domain. In order to do this, I begin
from the basic concepts within the styles. One such concept involves the notion that one’s
thinking styles’ needs are only met when abilities, thinking styles, and environment are all
compatible. If it reads similar to design domains in social learning theory, that is because it is.
Abilities can be considered intrinsic and classified under personal factors, thinking would fall
under behavior, and environment under environmental factors. The central, and most intriguing
idea of thinking styles is that the sweet spot of an individual’s success within lies at the
intersection of these 3 domains.
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Figure 2: Sternberg’s proposed interactions of thinking styles, labeled with rational-
experiential inventory items in adequate areas, to depict possible relationship with
theories completed work builds upon
Sternberg’s goal in defining a term “style” is to propose a bridge between the domains
of cognition and personality, as well as ensure that any single style defined cannot be always
better than its couples (eg. internal style is never strictly better than external style, and vice
versa, as neither is attributed to an ability). He models his overall profile of 13 thinking styles
into an overarching concept labelled as “mental self-government”, which borrows its
organization from that of real governments, and is therefore considered a direct predecessor of
what individuals running a government would make their government be like. His mental self-
government is classified into those 13 thinking styles based on functions, forms, levels, scope
and learnings. Functions generate 3 styles: legislative, executive and judicial. Forms yield 4
styles: monarchic, hierarchic, oligarchic, and anarchic. Levels show 2 styles: global and local.
Scope allows for 2: internal and external styles. Learnings classify another 2 styles: liberal and
conservative. Sternberg argues about pro-s and con-s of each style within different
environments. His primary interest within relates to finding one’s match between thinking
styles, abilities, and environment. (Sternberg, 1997)
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2.5 Summary
Three key areas supporting this research have been explored here: design theory and
methodology, dual process theories, and social cognitive learning theories. While the first field
belongs to engineering, the second is intersectional, and the third belongs to psychology, each
was explored with the current research in mind, and each supports different models and
relationships proposed herein.
Design theory and methodology provides the domain and some of the methods this work
relates to and considers. This is also the space where this work aims to contribute the new
knowledge. The areas of dual process theories and social cognitive theories support this work
from perspectives of understanding present and future design professionals in a variety of
mental, behavioral, and social contexts, which are typically removed from basic considerations
in one’s qualifications, yet greatly influence how a designer may ideate, draw inspiration, or
sustain motivation for any design projects they engage in. The next chapter will review the
contributions dual process theories and social cognitive learning theories have made to the field
of design methodology, and a gap in the literature will be identified.

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3 Thinking Styles in Design
3.1 Introduction
This research aims to enhance and contextualize understanding of thinking in design and
provide support for pro-design thinking in early stage engineering design. In order to do so, dual
process theories were relied on, spanning psychology, neuroscience, and behavioral economics.
Dual process theories all have in common their definition of thinking as two-fold: intuitive and
rational, where most people exhibit one or the other more frequently and more happily. For
example, a person who intuitively assumes that there are wild animals in a forest is more likely
to survive a trip therein than a person assuming there are no animals until sighted. Reason tells
one there is no ground for the latter person to be wrong, but intuition says otherwise. Such
intuition is built over a lifetime of experiences, media images, and books that communicate
presence of wild life in areas uninhabited by humans. Same type of intuition, when taken into the
domain of engineering design, has a great potential to answer the question of why some people
conduct design better than others, much like how some people are better than others at drawing
conclusions about wild life.
Similarly to how art is taught, one gets taught design through a review of methods, tools,
and strategies, before being tasked with realistic design projects, often as part of a team.
Considering that a design project completed through a reproduction of an existing design, a
textbook example, or class example becomes a cliché and cannot define one as a good designer,
then the question that begs an answer becomes what separates individuals from one another in
such a manner that they may generate significantly more novel or more user requirement-
adequate designs than their peers. The approach of particularly great interest involves dual
26
process thinking as the key driving mechanism behind pro-design behaviors and metrics of final
design solutions. It is of interest to know how human intuition might best be harnessed for the
benefit of one’s design work. By examining the process of intuitive thinking in design, key
concepts and processes involved in design ideation are identified and possible intuitive
influencers that can be applied to improve the design ideation performance are investigated.
Engineering design balances the technical requirements of engineering with the holistic
knowledge and artistic skills required to execute a successful design. Traditionally, rarely in
engineering fields do professionals receive much guidance on how to harness creative and
artistic thinking diverging from analytical rigor. Rationally trained engineers can be hesitant to
trust in intuition, and many current design methodologies reflect this hesitation, which is
believed to limit creative potential, as seen in engineering students, who often discard original
ideas generated intuitively before they’ve had a chance to fully pursue and evaluate them
(Chusilp & Jin 2006). A balance can, and should, be struck between the analytical nature of
traditional engineering and the intuitive nature of creative thinking. Therefore, there is a strong
need for a better understanding of the intuitive thinking processes in conceptual design.
Based on the extensive literature of the dual-process approach to human cognition, this
work identifies a set of concepts, processes and metrics to capture the dual thinking processes in
design. This fundamental understanding can open the door to new tools and training schemes for
engineers to become more consistently innovative.
3.2 Conceptual Design and Thinking Styles
The previous work on creative stimulation in conceptual design (Benami 2002, Jin &
Benami 2010) was developed based on Finke, Ward and Smith’s (1996) Geneplore model. The
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Geneplore model consists of the generation of preinventive structures and then the exploration
and interpretation of these structures. The generation is the divergent phase of the creative cycle,
whereas exploration is the convergent phase. Benami (2002) expanded Finke’s model (Finke et
al., 1996) to the engineering conceptual design process. His basic model consisted of design
entities (raw ideas and mature concepts that include the standard descriptions of form, function,
and behavior), which stimulate cognitive processes (memory retrieval, association,
transformation, problem analysis and solution analysis), which produce design operations
(“actions that bring design entities into a design context” such as sketching, questioning, and
suggesting) which generate new design entities. This cycle continues as preinventive ideas
become mature ideas and knowledge until a final design is reached, or can be terminated if the
designer is unable to obtain a satisfactory design
Based on the above creative stimulation work, the iteration in conceptual design was
explored (Chusilp and Jin 2006). The iteration design process model consisted of four key tasks
(analyze problem, generate idea, compose concept, evaluate concept) and three loops (problem
redefinition, idea stimulation, concept reuse). In this work it was found that increased iteration
frequency corresponds with increased quality, variety, and quantity of ideas, but has a mixed
effect on novelty. However, increased problem redefinition frequency may decrease novelty.
This model suggested that the default analytical approach might have suppressed novel ideas
(Chusilp and Jin 2006).
As another extension of creative stimulation work, Sauder (2013) developed a
Collaborative Thinking Stimulation (CTS) model by including collaboration between designers.
Each designer engages in the same individual-processes occurring in the individual stimulation
model, but the external interactions such as sharing or questioning concepts with the other
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designers are also accounted for in the CTS model. The experiment found concepts created by
cognitive processes collaboratively stimulated by seeding and correcting had higher novelty than
those created without stimulation. Seeding and correcting had a strong relation with stimulating
the cognitive process of transformation (Sauder et al., 2013).
A first attempt to distinguish the roles of type 1 and type 2 processes was made in the
design process of students (Moore, Sauder, & Jin, 2014, 2016). In this exploratory pilot study, it
was found that Type 1 thinking was prevalent in the earlier stages, and Type 2 processes more
prevalent in the later stages. This is to be expected, as ideation naturally involves taking
advantage of some quick thinking, like unexpected associations, and later stages involve more
convergent thinking and solution analysis. The most novel ideas in the study were found to be
generated through a balanced combination of Type 1 and Type 2 thinking, a result that warrants
further exploration.
3.3 Social-Cognitive Factors and Thinking Styles
Existing studies on dual process theories of human cognition are less focused on idea
generation and are primarily focused on finding errors in human rationality in making judgments.
While these error findings are valid, there is also a wide agreement in the research community on
positive contribution by intuitive thinking that helps to form expert intuition (Kahneman &
Klein, 2009).
While the current design thinking (Brown 2009) and some educational practices (e.g.
those of Stanford d.School) emphasize the role of intuitive thinking for innovation, there is no
current study of dual-process contributions to the creative design process from a psychology
standpoint. While existing theories allude to a duality of thought processes in the brain, none
29
specifically addresses how the intuitive and analytical processes contribute differently to the
design thinking process. Some offer dual-process insights into simple problem solving tasks
(Eubanks et al., 2010; Hogarth, 2005; Raidl & Lubart, 2001; Pretz, 2008), but none have looked
at generative design tasks.
Design theory and methodology research addresses explicit ways of how people do
design and how people should do design without examining how a designer’s background and
situational circumstances influence the thinking modes (thinking styles) of the designer and how
a balance of those thinking styles affects the designer’s design results.
Dual process theories which reveal duality of thinking in terms of type 1 (fast, intuitive)
and type 2 (deliberate, rational) have been considered in contexts of forming a single, quick,
short-term judgment or bias, but have not been studied in contexts of thinking processes behind
engineering design, which requires connecting problem recognition (eg. analogous problem seen
before), judgment formation (eg. some of the design solution ideas are seen as better than others),
and decision making (eg. choosing one design solution over others). Compartmentalizing and
examining type 1 and type 2 ideas that emerge during thinking in design, offer a great potential
for better understanding how creativity evolves during the design process.
Social cognitive learning theories bring together behavioral and cognitive approaches, to
emphasize the interactive nature of relationships between any of the 3 components of a social-
cognitive system: person, behavior, and environment. Studying these interactions allows
contextualizing, on a large scale, a relationship between a designer and their external, habitual
factors, such as thinking style. A study which offers an insight into such thinking style contexts
for design has not been done and is overdue.
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3.4 Gaps in the Literature
Some gaps in the literature existed on the front of connecting design measures with
thinking measures, towards a set of scientific underpinning that could aid the multifaceted
concept known as “design thinking”. Design thinking is adopted as a design method and an
interdisciplinary ideation tool; however, it specifically motivates one to think like a designer as
they attempt to solve a problem or answer a question. With that in mind, one would expect a
whole lot of scientific research done on what it actually means for someone to “think like a
designer”, yet not a lot has been available to us in the research communities on design
methodology, design theory, design cognition, or design creativity. Very often, unclear or
unproven methods are deployed to evaluate concepts defined inexactly by the researcher, without
proper scientific backing to either the definition proposed or the methods relied on.
The concept of thinking or thought is a challenging one to both define and study. Two
ways to conceptualize thoughts can be borrowed from American philosophers Wilfrid Sellars
and Fred Dretske. Thoughts are considered unique, introspective episodes, separate from
sensations, images, tickles, itches, etc. (Sellars et al., 1997) Thoughts are, furthermore,
associated with intentionality, which implies a power to misrepresent the reality. Thereon, unreal
or untruthful thoughts propagate into imagination. (Dretske, 2002) These are only some of the
definitions for various thinking-oriented concepts. In much of philosophy, psychology, and
neuroscience, thinking can be defined or studied in too many ways to count, ranging from
quantitative, qualitative, merely binary to as many as 13 different styles of thinking or perhaps
even more divisions for concepts adjacent to thinking. This is the reason that this work chooses
to confine thoughts and thinking into the concept of a thinking style, allowing a particular focus
on the performative nature of thinking styles, where a style implies some type of preference on
31
how one thinks, rather than the exact process performed cognitively. Defining thinking from the
perspective of personal preference within allows one to study thinking quantitatively and
confidently rely on answers from self-report scales. Once confined into the term of “thinking
style” this research then chose rational and intuitive thinking styles to study, as they have a clear
path of connection between each: rational thinking style would appear tied with analytic ability
(a key ability for engineers) and the intuitive thinking style would appear tied with the creative
ability, both of which are top concepts of interest in design research communities, but whose
roles in design are not yet clearly understood.
3.5 Summary
This chapter has explored the current state of thinking styles research in both the design
field (design theory and methodology, design education, design cognition) and the social
psychology field, and pointed out the gaps in the literature. While the needs and motivations of
each field are different, this work provides considerations for both, which eventually meet and
intersect in the following chapter. There, three theoretical models are developed and presented,
whereas the latter chapters discuss their evaluation.

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4 Modeling Thinking Style in Engineering Design
An engineer’s mind is typically assumed to be heavily analytic considering the preference
one must exhibit over other, more humanistic fields. While a preference for analytic thinking is
beneficial in vast majority of engineering work, when it comes to engineering design, a different,
synthesis-driven approach is needed. When it comes to synthesis, divergent, intuitive thinking is
more helpful than convergent, rational thinking, due to the volume of solutions needed over one
exact solution. Thus, here, I propose a multi-step model, built from literature on psychology of
personality, social psychology, and design methodology. These steps were aimed at evaluating
the 3 hypotheses of this dissertation, which are repeated here:
[H1] Duality Hypothesis: There exists an effect of rational and intuitive thinking,
separately, on design creativity (as defined by behavioral creativity and design performance).
Intuitive thinking balances the analytically skewed dual process system of an engineer
towards generating more creative, rather than strictly functional designs.
[H2] Influencing Hypothesis: There are influencers that can contextualize and aid
intuitive or rational thinking in engineering design.
There exist social-cognitive influencers that promote engagement of system 1 (intuitive)
or system 2 (rational), while designing.
[H3] Experience Hypothesis: With greater professional experience, engineers enhance
the relationships between their pro-design factors and design metrics.
When it comes to individuals with and without professional experience, the previously
studied social-cognitive categorical influencers (pro-design factors) can be applied to identify
33
effects of experience factors, as well as explore the differences between experienced and
inexperienced groups.
4.1 Introduction
In the extensive review of literature and the consideration for what best aids the
relationship between thinking styles and engineering design, the context of social cognition was
proven most relevant and helpful in identifying relationships of interest and defining a system an
individual designer’s thinking belongs to and is affected by. The social-cognitive approach aids
considerations for why some design engineers perform better than others, in domains of
functionality, creativity, and quality of their designs. For one, a design engineer is not an isolated
individual. They possess traits known as social-cognitive personal factors, and exist within their
social-cognitive environment (both govern and are governed by the individual’s social-cognitive
set of behaviors, where particular ones helpful or hindering to design can be identified into a set
later defined as pro-design behaviors. The entire social-cognitive system of a designer is
associated with their design self-efficacy.
Moreover, a design engineer is neither purely rational nor purely behavioral, thus they
benefit from thinking intuitively during idea generation, and from thinking rationally during idea
evaluation stage of design. One type of intuition is defined as quick ability to retrieve relevant
memories based on past experiences, while the other type of intuition serves to quickly bring to
mind possible solutions to a problem without necessarily having solved any similar problems in
the past. In the models proposed in Chapters 4.2.-4.4., I make strides towards defining both,
where the latter intuition is explored tangentially, through a set of questions asking about an
engineer’s experience factors. While most problem solving is completed analytically, and
engages the rational-analytic style of thinking, when it comes to having to embody a solution and
34
find a form for it, instead of provide a mere answer, the very earliest stage of design requires
engagement of system 1 (intuitive/experiential mode) of thinking.
An engineering design problem will typically require intuition-driven idea generation,
inspiration for which often emerges from past experiences or memories of objects with
functional similarity, as well as iterations of analytic thinking in order to test or refine possible
solutions. A designer’s thinking style is defined as the manner in which their system 1 and
system 2 are engaged while designing.
The process of examining factors that drive up or hinder an engineer’s design creativity
was initiated by seeking cognitive variables which contextualize one’s thinking, or add various
perspectives to it. If thinking is a manifestation of intelligence (Sternberg, 1997), then certain
types of creativity are manifestations of thinking (Amabile, 1996). As a prelude into
contextualizing the work on design thinking styles into social cognitive domain, first few
variables were proposed after extensive literature search for aspects of cognition that could be
helping or hindering the design process. Such independent variables involved thinking styles
(Epstein, 2003), personality (Goldberg, 1993) and behavioral creativity (Silvia, Widgert, Reiter-
Palmon & Kaufman, 2012). The dependent variables to allow observation of effects of cognition
on design were novelty (Shah, Smith & Vargas-Hernandez, 2003) and usability (Davis et al.,
2002) and grouped together in a design performance category.
After gaining a deeper understanding for factors that aid one’s creativity, this variable set
became too simple, and an idea to evolve it emerged. As this work takes an interest in creativity
from a behavioral standpoint (i.e. past creative or artistic experiences and self-perception of
one’s ability to generate creative works), consideration was given to factors that influence human
behavior, another one of these behaviors being design. Such factors were detected and modeled
35
relying on principles from social psychology. As such, designer’s cultural and professional
backgrounds are considered, grouped into social cognitive category of environment. There are
three social-cognitive domains considered, each pair of which has a reflexive influence among
themselves forming a triad of: person, behavior, and environment. All three are driven by the
independent variable of designer’s engineering design self-efficacy, which most importantly,
plays a role in the inner work of intuitive thinking (Bandura, 1986), but furthermore continues to
affect other aspects of cognition, such as motivation, accessibility, and expectations.
To better understand the roles of professional experience and design self-efficacy in the
social-cognitive model of design thinking styles, a comparison between engineering students and
professionals was performed in domains of two variables of personality (found most significant
from past studies), and two variables of thinking styles, with the goal of better understanding the
relevance of experience on design thinking styles.
4.2 A Dual Process Model of Design Thinking Styles
My efforts in modeling design thinking styles were initially inspired by the work of Cilia
Witteman on assessment of rational and intuitive thinking styles (Witteman et al., 2009) and Paul
J. Silvia on behavioral creativity (Silvia et al., 2012). Originally, my intent was to find a suitable
way to further the work done on design stimulation and iteration in individuals and teams
(Benami, 2002; Chusilp, 2006; Sauder, 2013), the three of which made considerable strides
aiming to understand elements of what one might call design creativity. Hence, when I first read
works of Witteman and Silvia, I thought that asking a large number of design engineers to fill out
what would eventually become a long set of questionnaires, and then studying their answers
alongside conceptual designs they’d only need about half an hour for, could significantly bridge
36
the past findings from psychology (Stanovich & West, 2000; Epstein, 2003) with the efforts of
our research communities in design creativity and design cognition.
Upon having a base conceptual idea for a study, several obstacles became apparent nearly
instantaneously. For one, a large number of design engineers was difficult to find and even more
difficult to convince into giving up their time for a research cause. The most challenging step
would have been identifying who design engineers among a sea of engineers bearing a variety of
titles with potential for relevance but without a necessity for it. Thereon it also became apparent
that even defining who is and isn’t an engineer, without the design focus, would later prove
challenging, seeing as many individuals with engineering degrees pursue careers in management,
science, or finance, while many individuals without engineering degrees secure jobs with
“engineer” in their title. Further information on how this discrepancy was accounted for in my
research can be found later on, in Chapter 4.4. However, in an effort to derive preliminary
findings that may later apply to or contribute to awareness of creativity in design engineers, I
chose to make a population compromise and run the study as proposed, except on two groups of
students taking design classes, one being engineering students and the other being
interdisciplinary. It is therefore fair to say that while this model serves to define and evaluate
design thinking styles, it does so more broadly than it had originally been intended, and it
intersectionally considers engineering design and design education.
This early model of design thinking styles, with variable choices stemming directly from
literature, was evaluated relying on the research framework in Figure 3. In this framework, dual
process thinking, here labeled as “thinking style”, is a central independent set of variables,
which influences all 3 dependent sets of variables studied: personality, behavioral creativity, and
design performance.
37
As an independent set of variables, thinking style contained two: rational (R) thinking
style and experiential (E) thinking style, and I have sometimes referred to them as well as one
binary variable. However, we never sought a statistically sound way to combine them, especially
seeing as literature (Pacini & Epstein, 1999) consistently define them as 2 scores rather than a
single score for thinking that may factor in both systems, and furthermore places a great deal of
value on the two being independent from each other. (Else, what would stop one from arguing
against the duality?). Throughout the chapters of this dissertation, the following names also show
up for rational and experiential thinking styles: rational / analytic / system 2 / type 2 / fast /
heuristics-based and experiential / intuitive / system 1 / type 1 / slow / deliberate. The binary
thinking styles were defined relying on cognitive-experiential self-theory (CEST) which takes up
much of Chapter 2.3.2., due to the ability to evaluate them using the supporting Rational-
Experiential Inventory, REI-40 (Pacini & Epstein, 1999). CEST proposes that people process
information relying on two separate systems: an analytical/rational system and an
intuitive/experiential system, which is in accordance with other dual process thinking theories
reviewed, but is more closely characterized by subjects’ ability to self-assess by answering
inventory questions, as well as further classify rational and experiential metrics into rational
ability (RA), rational engagement (RE), experiential ability (EA) and experiential engagement
(EE). This division into abilities and engagement has been a long-standing obsession of mine,
and while strategic choices led towards never quite grouping design thinking studies per abilities
and per engagement, doing so might, arguably, help contemporary design research in related yet
different ways from those my models had focused on. They are well-matched to concepts
explored in theory of mental self-government in Chapter 2.4.3. (Sternberg, 1997), and may some
day jointly propose a theory of design self-government, where design abilities and design
38
engagement could each align with design thinking styles, proposing a niche framework for
success in the design field as well as one’s satisfaction with choosing a design field of work.

Figure 3: An early framework for studying design thinking styles
Thinking style in this study is defined based on the cognitive and experiential self-theory
(CEST) (Epstein, 2003). By this definition, thinking style of an individual indicates his or her
preference between two cognitive styles, more rational-analytical or more intuitive-experiential.
It is proposed that a designer’s thinking style correlates to his or her personality, behavioral
creativity, and design performance. The correlations may or may not depend on domain, grade-
of-education, cultural background, and gender. This research aims to reveal the correlations and
associated conditions. Thinking style assessment will use Rational-Experiential Inventory (REI)
(Epstein 1999) based survey studies, described in the next section.
Personality, in broad terms, represents an individual’s distinctive character, comprised of
distinct combinations of characteristics or qualities. Big Five personality traits are adopted for
the study (Goldberg, 1992). In Big Five, personality is framed in terms of its five dominant
dimensions: Extroversion, Agreeableness, Conscientiousness, Emotional Stability and
Openness. The personality variable was included in the framework for two reasons. First, it
complemented the definition of thinking style by providing five important personal traits in
addition to two thinking types. It was expected that further integration of the two concepts would
39
lead to better understanding of how design mentality is engaged. Second, there have been studies
that correlate REI scores with Big Five scores, which allowed to contextualize such relationship
for the niche comparison between engineering and non-engineering students.
Behavioral creativity in this study was attributed to an individual to indicate his or her
tendencies of engaging in creative activities, in the past, currently and in the future. Silvia,
Widgert, Reiter-Palmon & Kaufman (2012) treat creativity as an interactive variable,
interconnected with social, cultural, behavioral, and cognitive aspects of life. Eubanks, Murphy
& Mumford (2010) consider creativity as a function of errors, mental models, insight and
intuition, as it pertains to creative problem solving. Furthermore, work of Shah (2012) proposes
that ideas crucial for creativity stem from quantity, quality, novelty and variety. Strong
correlations between thinking styles and behavioral creativity were hypothesized, assuming that
thoughts drive behaviors. A set of three inventory based survey methods were applied to assess
individual designers’ behavioral creativity.
Design performance was assessed by novelty and usability of design results supported
by observed design processes. Correlating thinking styles with design performance based on
design results was a major task for this segment of research. Although REI based studies had
been carried out in the areas of psychology and social psychology, little work existed in the field
of design that addressed the effect of varying thinking styles. From an education perspective, a
better understanding of how students’ thinking style influences their design processes and results
is especially important because it may lead to more effective training of design thinking and
more useful intervention techniques. Design logs were also considered in the process of design
evaluation.
40
These variables were studied from the point of view of correlations, and organized into
the following correlation profiles:
Personality profile depicted correlation of rational and intuitive thinking styles to the 5
dimensions of personality, and considers one’s course of study (engineer, non-engineer). The
goal of this profile is to uncover if there exists a certain personality profile that will contribute to
a student eventually becoming a better designer.
Creativity profile correlated rational and intuitive thinking styles with every-day
creativity (measured and labelled by BICB), creative accomplishment (measured and labelled by
CBI), and creative confidence (measured and labelled by CDQ-R). It provided a general and
broad take on creativity, from the point of frequency of creative behavior or action, and inner
belief about personal creative achievement.
Design profile correlated rational and intuitive thinking styles with a designer’s
performance scores (novelty and usability), providing a quantifiable connection between survey
answers about thinking styles and the hand-written/hand-drawn design logs addressing the
design problem specified in Appendix J.
4.3 Design Thinking Styles in a Social-Cognitive System
Some intuitive pathways for studying design thinking styles in design students were built
and evaluated using the model from Chapter 4.2. (Milojevic et al., 2016). In particular, the
population studied were students from a renowned Chinese university, and much of the model
evaluation focused, beyond the proposed 4 variable categories, on factors such as their field of
study (in engineering or outside of engineering), their gender identity (considering the
overwhelming response rate heading into the binary identities, these ended up defined as male or
41
female only), and the fact that they were completing relevant surveys in China with limited
availability of official translations. At the time, these factors were merely convenient to
categorize the students into and study variable correlations with respect to. Furthermore, as our
primary research interest always remained focused on what factors help people design better,
building a knowledge base about these additional factors seemed natural. However, having now
built a model that considered prospective designers from the points of view that transcended
mental processes, and having explored relevant literature about design self-efficacy, another
prominent design measure with convenience of quantitative evaluation, the simple framework
from Chapter 4.2. was developed many steps further into a social-cognitive design framework,
also referred to as a social-cognitive framework of pro-design behaviors.
Having demonstrated some relationships between thinking style (Pacini & Epstein, 1999)
as a class of independent variables, and three other classes of dependent variables: personality
(Witteman, 2009; Goldberg, 1992), behavioral creativity (Silvia, 2012), and design performance
(Shah, Vargas-Hernandez & Smith, 2003; Kudrowitz, 2010), these variables were reorganized to
fit a social-cognitive framework, reflexively governed and governing design self-efficacy
(Bandura, 1986), as depicted in Figure 4. The former framework demonstrated significant and
consistent correlation between rational thinking and the creativity class of variables. The design
thinking style framework was created in basic terms in order to initiate a study of dual thinking
processes for early stage engineering design and further explore the role of perspective taking in
idea generation in engineering design (Grant et al., 2011; Lamm, 2007). One direction was to
study influencers accessible to a designer. Detecting, studying, and analyzing sets of influencers
(Choi, 2011; Perry-Smith, 2003) accessible to a designer, served an even greater step towards
42
proposing new training methods and supporting tools, aimed to make engineering designers
think in a manner best suited for their available design task (De Dreu, 2008).

Figure 4: Bandura’s (1986) triadic reciprocal determinism model
To further explore ways duality of thinking could be built upon towards an engineering
design duality of processing, in cognitive or practical domains, the relationship between the
designer’s performance, e.g., creative (Choi, 2011), or professional (Schaub, 2005), and the
designer’s social environment was considered. Based on social science theory-driven studies of
creativity, organization (Bechtoldt, 2010), or design (Baird, 2000), the concepts of motivation
and self-efficacy embedded in particular domains (e.g., creative domain, design domain, or
learning domain) quickly emerged as the most considered and least defined. Hence, the research
briefly abandoned its consideration for specific domains, exploring most purely how one learns
the social-cognitive rules and adopts beliefs about oneself.
The process of triadic social-cognitive influencing is closely related to self-regulated
learning, self-management, and self-efficacy. Self-regulation involves self-monitoring, self-
judgement, and self-reaction. While these concepts won’t be integrated in the social-cognitive
43
design framework, they are the drive-concepts that make self-efficacy scoring in the form of a
scale accessible (Zimmerman, 1990).
In order to generate the social-cognitive framework for design, depicted Figure 5, first
step involved proposing the triadic reciprocity with attributes of interest grouped to the three
main factor categories. However, in order to ensure the model is being understood from its
affective standpoint, the approach taken for building the model of social-cognitive career theory
(SCCT) (Lent and Brown, 2008) was adopted to define the secondary driving concepts, such as
learning experiences, outcome expectations, and actions, while self-efficacy remains a primary
driving concept for all social-cognitive domains (Schaub, 2005).
Personal factors are intrinsic to a person within the social-cognitive framework, and
divided into biological (assigned at birth), cognitive, and affective (changes in cognition). In this
case, the personal factors studied will be biological (gender/sex) and cognitive (big five
personality). The environmental factors studied are culture (professional culture, i.e. field of
study/work of subjects) and country (location a subject is in at the time of participation). Finally,
the behavioral factors studied are creative behavior (biographical creativity, behavioral
creativity, and domain-creativity), thinking style (rational and intuitive), and design performance
(novelty and usability), as indicated in Figure 5.

44

Figure 5: Social-cognitive design framework, studied partially, with particular focus on
(i) effects of gender and personality on design self-efficacy, and (ii) effects of design
self-efficacy on a set of behavioral factors defined as pro-design behaviors.
4.4 The Role of Professional Experience in the Social-Cognitive Design
Context
Departing from the original design thinking styles model from Chapter 4.2. towards the
social-cognitive design model from Chapter 4.3. was a significant undertaking. For one, the
original model had been complex enough to evaluate that human subject participation was
repeatedly limited. Several attempts at collecting data in different classes and group settings,
both in China and the U.S. were made, while the collections of surveys were being modified to
accommodate for new models and studies being planned. It could safely be said that I failed fast
and failed often, both in data collection itself, and the attempts to modify my protocols too early,
too late, or both. This last set of models made me the happiest I have been with my research thus
far. Not only was there an optimum achieved in the cognitive demand from study participants so
they would actually come forward and offer their help, but the access to relevant populations was
45
both optimized and there on adequate. I recruited a satisfactory number of engineers and
engineering students, who were willing to complete surveys described in Chapter 7.2.2. and
found a path forward in evaluating the relationships proposed in the models from figures 6 and 7.
Much like the studies before this one, the central goal of this study was to investigate the
interplay of rational and experiential thinking styles in conceptual design and their influence on
other social-cognitive factors, categorized per social-cognitive influencing category (person,
behavior, or environment), which now included personality, design self-efficacy, and certain
factors of experience. The true model proposed here evaluates the framework for design self-
efficacy of experienced professionals, in Figure 6, while the remainder of this chapter focuses on
a repeated and simplified study that combined top-level ideas from the previous two models
(Chapter 4.2.-4.3.).
Literature on social-cognitive theory, dual process theories, and psychology of
personality, demonstrates an ability to formulate a model and metrics to represent how rational
and intuitive thinking, designer's personality, creativity, self-efficacy, and experience come into
play in conceptual and build-oriented design at different stages (no experience compared with a
great deal of experience) in an engineer’s training. I consider pursuit of engineering design
courses to be such designer’s training. While the original intention was to have students and
professionals complete design challenges attached in Appendix J, the global COVID-19
pandemic prevented in-person data collection and, had the study not been modified, no findings
would have been generated at all, for a substantial longer amount of time. As one reviews these
models, it is important to understand that the decision to give up evaluation of conceptual design
performance was not made lightly, and there is a great deal of loss in having missed out on the
46
valuable design research knowledge that could have been generated, had the study been possible
with any measures of design past that of design self-efficacy.
Design self-efficacy (Carberry, 2010) is a self-concept that impacts learning in
engineering students, but, much like any self-efficacy, it also drives one’s entire social-cognitive
system of triadic reciprocity. It is in constant interchange with personal, behavioral, and
environmental influencers within. For this reason, both models depicted in sections 4.4.1. And
4.4.2. were composed to show reflexive relationships among variables studied, with the
exception of “progression of experience” which is a mono-directional factor, only capable of
stagnating or increasing. It is a necessary addition to clear up that in most literature, there is an
expectation of design self-efficacy (or alternative self-efficacy) being an independent variable
and driving all other variables. However, seeing as it does constantly affect and is affected by the
triad, and seeing as the design performance variable, the primary metric of design in this study,
had to be eliminated due to circumstances beyond my control, the following work in this section
and the Chapter 7, where this study is developed and analyzed, reports our findings with design
self-efficacy as the dependent parameter of regression modeling. Alternative approaches may be
explored in future work, or by other researchers taking an interest in this line of work.
4.4.1 Design self-efficacy with respect to triadic reciprocity factors in engineering
professionals
Building off of the social-cognitive design model from Chapter 4.3., further consideration
is given to the categorical concepts that make up the triadic reciprocity in social-cognitive
learning theory (Bandura, 1986). In particular, the interest is placed upon how different
experience factors play into one’s design metrics, and how other factors may aid or hinder this.
The model depicted in Figure 6 categorizes concepts of interest into the triadic reciprocity
47
model, focusing primarily on its own relationship with design self-efficacy (personality and
thinking styles to design self-efficacy), and secondarily on other reasonable relationships one can
consider (personality to experience factors, thinking styles to experience factors). In particular, I
look for preliminary insights into what experience factors contribute to greater design self-
efficacy and how they exist within the social-cognitive system of engineering professionals who
may or may not engage in design in their work.

Figure 6: A framework of self-efficacy in engineering professionals with respect to
experience, personality, and thinking styles
4.4.2 Comparison of design self-efficacy between engineering students and professionals,
with respect to thinking styles and personality
Per the model explored in sections 4.2 and 4.3 where each relationship considered was
categorized per students’ field or class standing, to differentiate expectations one may have from
students in different environmental circumstances, here consideration is given to mere
comparison between design self-efficacy for a sample of engineering students and that for a
sample of engineering professionals employed in industrial, research, or academic sectors. In this
48
case, it is recognized that the distinguishing feature between these engineering professionals and
engineering students is the set of experience factors studied in 4.4.2., with implicit understanding
that these factors will not be factored in here past the note that they exist. Seeing as no focus was
given to measuring that student experience would indeed be close to zero or inapplicable in all
categories professionals provided information about, the “progression of experience” label will
not be quantified or factored into analysis in any way. Rather, pure comparison between scores,
correlations, and multi-variable regressions will be used to describe and define the proposed
comparison.

Figure 7: A framework for design self-efficacy comparison between engineering
students and professionals
4.5 Summary
This chapter describes hypothetical models proposed to describe design thinking styles
within social-cognitive systems or sets of parameters. Section 4.2. presents the original
foundation study for this research, where thinking styles are considered alongside personality
and design performance. Section 4.3. further contextualizes this preliminary work per variables
of social-cognitive theory, social cognitive career theory, and social cognitive learning theory,
and many relationships proposed in 4.3. while theoretically sound, were not explored in the
study, and many others were. In section 4.4. the work was taken into the domain of engineering
49
professionals, seeking to understand the role of experience within a social-cognitive design
thinking system. The models for this are twofold: (1) one model hypothesizes influencing of
experience factors, while (2) the other model hypothesizes differences in design-self efficacy
between engineering students and professionals. In the next chapter, research methods for each
study are described in detail.

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5 A Dual Process Model of Design Thinking Styles
5.1 Introduction
This chapter addresses the Chapter 4.2. Model, which is defined and evaluated relying on
the framework from Figure 3, repeated here as Figure 8. Relying on this framework, the first of
three survey studies was conducted. As such, this study was highly preliminary and focused on
base exploration of relationships between 4 variable categories, the methods for which are
addressed in section 5.2.2.

Figure 8: An early framework for studying design thinking styles
Survey Study 1 explores the dual process approach to thinking style modeling, relying on
cognitive-experiential self-theory, big five personality, behavioral creativity and cognitive
assessments of design creativity through novel methodology developed by Shah et al. (2003),
which includes analysis of design projects for variables such as quantity, quality, and novelty,
alongside the variable of usability (Davis et al., 2002). The study offers the following:
● Expanded understanding of how intuitive thinking may or may not aid design
creativity
● Insight into the importance of rational thinking
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● Insight into which of the studied cognitive and behavioral traits are associated with
design performance, and as such better designers, the most
It also addresses the following main research question {R1} and hypothesis [H1]:
{R1}: What type of thinking benefits a designer the most?
[H1] Duality Hypothesis: There exists an effect of rational and intuitive thinking,
separately, on design creativity (as defined by behavioral creativity and design performance).
Through review of the literature and personal/professional experience with engineering
design education, the following break-down of [H1] can be made:
[H1a]: Creative behaviors are more common in individuals with more intuitive thinking
style.
[H1b]: Mechanical engineering training tends to confine intuitive thinking and creativity
[H1c]: Intuitive thinking and creativity can be acquired as skills through appropriate
training.
These sub-hypotheses are evaluated in Chapter 5.
5.2 Methods
5.2.1 Subjects and procedure
In this study, 50 undergraduate students of Shanghai Jiao Tong University participated.
As the first sample group, called “design class”, 28 students were enrolled in Innovative
Thinking and Modern Design class for students of upper class standing, and non-restrictive by
field of study (the sample is composed of students studying economics, arts, communications, or
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life sciences). 54% of the sample was female, and 46% male. As the second sample group, called
“engineering class”, 22 students were enrolled in Introduction to Engineering class for freshman
students, restricted to engineering fields of study (68% mechanical, 14% electrical, 14% naval,
and 4% materials engineering). 18% of the sample is female, and 82% male. Both classes are
taught by the same professor, and during the same semester. The students were surveyed using a
Chinese direct translation of each survey. The identifiable survey data was obtained on paper,
during scheduled class hours. After data collection, three correlation profiles were generated.
5.2.2 Survey assessment
Self-report survey methods were utilized to find relationships between the proposed
variables.
Thinking style is assessed using the Rational-Experiential Inventory (REI). REI is a
questionnaire aiming to measure a person’s habitual preference for either rational or intuitive
thinking style (i.e. habitual response to decision situations). It allows responses ranging from 1-5
(completely false to completely true).
Personality is assessed using the Big Five Inventory (BFI). BFI is a scale for
measurement of big five dimensions of personality: Extraversion, Agreeableness,
Conscientiousness, Emotional Stability and Openness. Subjects respond on a scale 1-5 (disagree
strongly to agree strongly), and are then assigned their final score using official scoring
methodology.
Behavioral creativity is assessed using three different self-report scales (Silvia, 2012):
Biographical Inventory of Creative Behaviors (BICB), Creative Behavior Inventory (CBI), and
Creative Domain Questionnaire (CDQ). BICB is a scale of creative behaviors, ranging broadly,
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from common creativity domains such as arts and crafts, to social creativity such as leadership or
coaching. It allows binary (yes/no) responses. BICB is used as an early and simple creativity
assessment. CBI is a scale of creative behavior accomplishments in daily creative activities and
behaviors. It allows four (“never did it” to “more than 5 times”) responses, stating frequency of
creative activities. CBI will allow us to evaluate daily creativity. CDQ is a scale of creative
achievement and personality traits, across different domains (such as acting, computers, writing,
etc.). It allows responses in range 1-4 (“not at all”, to “extremely”). It assesses factors such as
empathy, hands-on creativity, and math/science. CDQ allows us to observe not only general
creativity traits, but rather those also specific to engineers (such as hands-on or science-oriented
creativity).
Design performance is another variable to be evaluated. In order to align design
evaluation with the variables considered throughout the research, it was determined early to
assign one variable dedicated to design performance, which is normally defined as novelty and
usability. The problem assigned to the two subject samples was a modified problem from Atman
et al. (2005) and required a 2-hour completion. For purposes of accuracy, the document was
translated to Chinese. Its English version is shown below.
You’re creating a new game with your fellow engineering students. Your goal is to
launch a ping-pong ball at a bull’s-eye target, which lies horizontally on the ground. As part of
the game, you are to design a ball launcher: a device that can lift up the ball, and deliver it at the
target. The most accurate launch wins. Initially, you are located 5 meters away from the center of
the target. (As you only aim for the center of the target, you do not need to know its diameter,
just location from the center.) Your entire device is not to exceed 1 m x 1 m x 1 m in size
(length, width, and height). You are not allowed to throw the ping-pong ball at the target. You
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are, however, encouraged to pursue novel or unusual solutions, while holding precise delivery
aim imperative.
Furthermore, design project required taking design log and submitting it to instructor.
Such design log was later assessed by an expert panel. The two design measures are described in
greater detail. Usability was accessed via the method of expert panel review, with expert panel
consisting of a professor and 11 graduate students of mechanical engineering working on design-
oriented research projects. Each expert assigned a score of 1-5 to each design log submission, per
their view of usability rating of the project.
Novelty has been standardized as a measure, and used in numerous past studies as a
variable of design (Chulisp & Jin, 2006; Shah, Vargas-Hernandez, & Smith, 2003; Song &
Agogino, 2004). It was calculated using the proposed function. In first design project, main
functions subjects satisfied were identified and assigned weights based on their individual
importance and number of appearances: launch, 𝑓 = 0.5, aim for target, 𝑓 = 0.2, and ball
feeding, 𝑓 = 0.1. In the second project, identified functions and corresponding weights were
identified as: launch, 𝑓 = 0.3, support, 𝑓 = 0.3, and attach spoon, 𝑓 = 0.4.
Upon doing so, different fulfilment of functions are identified, and the repetitions of same
conceptual ideas were counted. Here, novelty sub-score is introduced in equation (1), where j is
an identifier, 𝑇 the total number of ideas generated for 𝑓 , and 𝐶 count of the current solutions
for 𝑓 .
𝑆 = 10 ×
𝑇 − 𝐶 𝑇 (1) 𝑁 = 𝑓 𝑆 (2)
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This sub-score, 𝑆 is included in the expression for novelty score, 𝑁 defined in equation
(2). The higher the novelty score, the more unusual or unexpected the design is.
5.3 Personality and Thinking Style
The three different profiles based on concepts from section 5.2.2. were created for their
correlations of interest. With these correlations some expected trends were noticed, but the
majority of findings were surprising, calling for both new conclusions and reconsiderations
within the study. Such findings are discussed below within their respective profiles, and
supported by the correlation charts.
The thinking style profile correlations expose curious relationships proposing dimensions
of the kind of thinking that subjects had going into the creativity and design assessments. Many
correlations were consistently positive for both rationality and experientiality in both classes.
As rational-experiential inventory (REI) is the central evaluation method of thinking
style, it is relevant to note that the engineering class ranked 10% more rational than the design
class (t = -2.14, p = 0.037).
5.3.1 Thinking style profile
The thinking style profile consists of two charts depicted in Figure 9, which specifically
portrays correlations between the rational and experiential scales of REI, and Extraversion,
Agreeableness, Conscientiousness, Emotional stability, and Openness, the dimensions of
personality accessed by Big Five Inventory (BFI). These correlations structure the proposed
thinking style model, such that proposes the reflexive relationship between two elements therein:
thinking style and personality.
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In most dimensions of Big Five engineering class scores 3-13% higher than the design
class, with the highest difference being extraversion, with 13% higher score among engineers (t
= -2.332, p = 0.012). Design class, on the contrary, outperforms engineers by 12% in the
dimension of emotional stability (t = 1.60, p = 0.058), where the highly rational engineering
sample ranks low.

Figure 9: Correlations between REI and BFI, where EX, AG, CO, ES, and OP stand for
Big Five personality dimensions: Extraversion, Agreeableness, Conscientiousness,
Emotional Stability, and Openness, respectively.
Such findings were correlated in Figure 9, arriving at curious results. Firstly, the
correlation of emotional stability to rationality was found heavily negative in engineers, showing
that the more rational subjects, and especially engineers, were also particularly neurotic. In
accordance, results also demonstrate that intuitive engineers emerge as the only group with not
merely positive, but substantially strong correlation to emotional stability. Hence, the
emotionally stable engineers were those who relied on intuition rather than rationality. Those
who were more intuitive within the design class exhibited a strong relationship with openness,
contrary to intuitive engineers, who maintained a steady negative correlation for the dimension.

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5.3.2 Creativity profile
The creativity profile consists of charts in Figure 10, which specifically portrays
correlations between the rational and experiential scales of REI, and scores of biographical
inventory of creative behaviors (BICB), creative behavior inventory (CBI), and revised creativity
domain questionnaire (CDQ-R), the self-report scales assessing different kinds of creativity.
In cases of two out of three creativity measures, engineering class performed worse than
the design class, scoring 5% less in BICB (insignificant) and 11% less in CBI (t = 1.99, p =
0.026). The engineering sample did, however, score 3% higher in CDQ-R (insignificant). From
the quantitative, it can be deduced that engineers, as compared to the design sample, are
somewhat less likely to be creative on the daily basis, be domain-specific creative, yet possess
slightly more confidence in their creative abilities.
The findings in this study are exceedingly unexpected. From Figure 10, it takes little time
to notice the strong positive correlations between rationality and creativity scores, in both
classes, with design offering slightly stronger correlations. Furthermore, this finding is consistent
with finding of negative correlation between intuitiveness and creativity. The only positive
correlation with the experiential scale holds for the CDQ-R scores of the design class, which
demonstrates positive correlation between intuition and creative confidence in experiential
subjects.
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Figure 10: Correlations between variables of REI and self-report creativity scales, where
BICB, CBI, and CDQ-R stand for measures of every-day creativity, creative
accomplishment, and creative confidence, respectively.
Both findings are surprising, as one would imagine they would be completely opposite.
After all, the belief goes that the intuitive and experiential are those who indulge in the creative.
How could this sample shine such a different light on thinking style-creativity relationship?
Several considerations can be proposed. For one, it is questioned how appropriate standard self-
report creativity scales are for evaluation of creativity in engineers, considering the possibility
that creativity in engineers might be a unique subset requiring another form of evaluation.
Furthermore, no social concept is ever completely binary, and binary approach tends to be a
simplification of reality. As such, categorizing subjects as only rational or only intuitive might be
posing a challenge. For a preliminary study, these findings are highly thought-provoking.
5.3.3 Design profile
The design profile consists of charts in Figure 11, which specifically portray correlations
between the rational and experiential scales of REI, and scores of novelty and usability. These
correlations structure the proposed design model, such that proposes the dependence between
thinking style and design performance. In case of the two scales of design performance, it can be
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stated that the difference between engineering and non-engineering sample was very small and
insignificant.
The findings of this study are possibly the least expected out of the three studies
presented. Figure 11 depicts interesting shifts in correlations between thinking styles and design
performance. For example, it is apparent that usability maintains similar correlations regardless
of thinking style, where merely design class positively correlates both rational and experiential
scales to usability, while the engineering class has a smaller, negative correlation. As such,
observations include stronger correlation of usability to rational scale, than experiential, showing
consistency with previous findings. In case of novelty, a curious behavior is noticed. Novelty of
both classes correlates positively to rationality, and negatively to intuition, which is a finding
opposite from hypothesized. As this is a preliminary finding from studying two small samples of
52 participants in total, two major considerations are given to the result. Upon closer inspection
of the design work handed in, it is observed that students with higher rational scores tend to
provide more thorough, better explained, and more unique designs. Conversely, experiential
students appeared to have given less effort to the task. Hence, the question arises whether the
thinking style of these students corresponds to their college performance. Under an assumption
that educational approach is largely rational, it is a fair assumption that rational subjects would
be better students as well. As such, they would ambitiously deliver better projects. Another
possibility involves cultural differences, as the study was proposed at an American university,
yet conducted in China. Hence, cultural preferences and cultural differences in education might
be impacting the assumption made considering American design students.

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Figure 11: Correlations between variables of REI and design, where Novelty assesses the
innovativeness of design, and Usability its likelihood of successful implementation.
5.4 Findings
The dual process model of design thinking styles has uncovered the following significant
relationships:
1) In non-engineering students, rational thinking is
 moderately correlated with conscientiousness, at 0.57,
 moderately correlated with biographical creativity, at 0.41, and
 moderately correlated with domain creativity, at 0.49.
2) In non-engineering students, experiential thinking is moderately correlated with
openness, at 0.47.
3) In engineering students, rational thinking is
 highly correlated with openness, at 0.76, and
 moderately correlated with domain creativity, at 0.45.
Other, preliminary findings include the following:
1) Rational students (both in and out of engineering) exhibit more favorable
performance in all categories, especially behavioral creativity.
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2) Experiential thinking style correlations are statistically insignificant for most
variables (except for personality – openness) but these preliminary findings allude to
weak negative correlations for engineering students.
3) In design metrics, statistical significance was only approached by usability to thinking
style correlations in non-engineering students, which were positive and high.
5.5 Summary
In this study, early design thinking styles model was defined and evaluated, bringing to
light statistical challenges and the importance of rational thinking, personality factors of
conscientiousness and openness, as well as creative confidence. While a lot of the research effort
here runs into some of the early pitfalls of research, such as a study scope that may be slightly
too broad, number of study participants being too small, or a very early-stage highly conceptual
model, it also brought to light a great deal of potential for quantifiable assessment of parameters
relevant to design research, design thinking, and design creativity.

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6 Thinking Styles in a Social-Cognitive System for Design
6.1 Introduction
This chapter addresses the Chapter 4.3. Model, which is defined and evaluated relying on
the framework from Figure 5, repeated here as Figure 12. Relying on this framework, the second
of three survey studies was conducted. As such, this study was considerably more involved than
the first (Ch. 4.2 and Ch. 5). The interest in understanding what makes some designers “tick”, or,
as previously put, what contributes to a designer’s creative thinking process, remained
imperative. However, having had access to several additional descriptors about the research
participants, such as their location, gender, class standing, and field of study, and having chosen
to study their design abilities through an additional variable (design self-efficacy) which is based
in social-cognitive theory, proposing a more precise framework that addresses which variables
and descriptors were cognitive and which social-cognitive, became a need rather than a
preference.
Survey Study 2 builds upon survey study 1 by systematizing thinking styles and design
into a social-cognitive framework of an individual designer. This system expands to include
social-cognitive learning theory, social-cognitive career theory, and concept of engineering
design self-efficacy (Carberry, 2010). The study offers the following:
● Expanded context for how a designer thinks
● Boiler version of a designer’s worldview and background, in quantitative terms
● A list of potential factors which define designer’s system of existence (work and life)
in social-cognitive terms.
It also addresses the following main research questions {R2} and hypothesis [H2]:
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{R2}: What factors contextualize the designer’s thinking styles
[H2] Influencing Hypothesis: There are influencers that can contextualize and aid
intuitive or rational thinking in engineering design.
Following the framework development, the following break-down of [H2] can be made:
[H2a]: Design self-efficacy will reflect differences within attributes to SCT triadic
model’s influencers studied: gender, location, culture, and personality.
[H2b]: High design self-efficacy scores are associated with high intuitive thinking scores.
[H2c]: High design self-efficacy scores are associated with high behavioral creativity
scores; high design self-efficacy scores are also associated with high design performance scores.
These sub-hypotheses are evaluated in Chapter 6.

Figure 12: Social-cognitive design framework, studied partially, with particular focus on
(i) effects of gender and personality on design self-efficacy, and (ii) effects of design
self-efficacy on a set of behavioral factors defined as pro-design behaviors.

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6.2 Methods
6.2.1 Subjects and procedure
Total of 60 students, pursuing coursework in engineering, design, or both, participated in
the study, from their home universities of the University of Southern California (Los Angeles,
USA) and Shanghai Jiao Tong University (Shanghai, China). The sample gender distribution was
18 female students (30%) to 42 male students (70%). Majority of the sample (75%) was based in
China, consisting of 45 students, while the remaining 25% consisted of 15 students based in the
United States. All were undergraduate students, distributed across class years: 31 students of the
first year (51.7%), 10 students of the second year (16.6%), 3 students of the third year (5%), and
the remaining 16 students of the fourth year (26.7%). Majority of the sample identified as an
engineering student, 46 out of 60 (77%), and 24 (23%) were pursuing a variety of majors, and
referred to as the non-engineering students, in this study. Per location, sample based in China had
33.3% of female and 66.7% of male students, 68.9% of engineering and 31.1% of non-
engineering students. The sample based in the U.S. had 20% of female and 80% of male
students, and was entirely comprised of students in mechanical and aerospace engineering. The
U.S. sample yielded one quarter of the entire sample, while the Chinese sample yielded the
remaining three quarters.
6.2.2 Survey assessment
The framework of social-cognitive framework for design (Figures 5 and 12) is an
expansive triad of personal, environmental, and behavioral influencers, which constantly drive
one-another, drive and are being driven by design self-efficacy, and offer potential for further
propositions of categorical and relational development within. Considering it is an early stage
emergence from bringing social, learning, career, and cognitive theories into the realm of design
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in engineering and interdisciplinary domains, the social cognitive framework for design can be
unveiled into a more intricate theoretical framework driving a more intricate set of outcomes
caused by pro-design behaviors of higher complexity. For purposes of this study, however, the
framework is kept at little to no deviance from the Bandura’s (1986) social-cognitive triad, with
categorical attributes assigned to each influencing category, so as to offer the greatest insight into
the social-cognitive effects on engineering design, in domains of design cognition and design
outcomes, with a potential for application in industrial organization, methodology creation, and
artificial intelligence developments.
The research behind the social-cognitive design framework aims to compare design self-
efficacy based on its characterization by sets of influencers assumed as mutually exclusive, and,
in this case also binary. For example, the concept of Gender is assumed as gender binary, either
female or male, contrary to the adopted view that gender identity and expression may transcend
the binary biological sex (Diamond, 2002). The other two influencers were named Country and
Culture, and are also proposed as binary, in order to define, respectively, the geographic location
of the subjects studied (the United States or China) and the academic culture subjects identify
and professionally growing in (Engineering or Non-Engineering).
All students were asked to complete the following surveys: rational-experiential
inventory (REI), big five personality inventory (BFI), biographical inventory of behavioral
creativity (BICB), creative behavior inventory (CBI), and revised creative domain questionnaire
(CDQ-R), as well as the design-self-efficacy survey, which were then considered in the context
of students’ social-cognitive influencers.
Rational-experiential inventory (REI) is a measure of thinking style preferences, for
rational (need for cognition) or experiential (faith in intuition) mode of processing in thinking
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(Witteman, 2009). Big Five Personality Inventory (BFI) is a measure of personality, commonly
used in psychological and psychiatric diagnosing of personality disorders, alas also beneficial in
merely communicating how a person is, through five specific personality traits being assessed:
extraversion, agreeableness, conscientiousness, neuroticism, and openness (Goldberg, 1993).
Biographical inventory of creative behaviors (BICB) is a measure of behavioral creativity which
considers the number of different habitual, every-day creative activities an individual has
engaged in in the last 12 months, and it defines the proposed variable of biographical creativity
(Silvia, 2012). Creative behavior inventory (CBI) is a measure of behavioral creativity which
considers the number of times an individual has engaged in a tangible, craft or art-driven creative
activity, and it defines the variable of creative accomplishment (Silvia, 2012). Revised
Creativity Domain Questionnaire (CDQ-R) is a measure of behavioral creativity which considers
how one perceives oneself in a variety of areas creativity plays a key role, such as acting,
leadership, computer science, or solving personal problems, and it defines the variable of
creative ability (Silvia, 2012). Design Self-Efficacy survey is a self-efficacy measure, as it
pertains to design tasks and design skills, as well as confidence one exercises in one’s ability to
perform highly in the areas asked about (Carberry, 2010).
The non-questionnaire defined variables are those of design assessment, which feature
design novelty and design usability. Design novelty assesses functional creativity of a design
solution, relative to frequency of said function being proposed within the set of design solutions
being evaluated (Shah, 2012). Design usability is an expert panel-assessed measure of how
effectively design addresses user-needs (Kudrowitz, 2010).
Results of surveys were found using standard scoring methods proposed by each survey’s
author. For surveys that needed to be correlated with one another across many categories, it is
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important to observe that their most concise form is presented in Table 1, contents of which will
be discussed further on.
6.3 Results
The quantified variables described in the methods section, and previously studied in
contexts of correlation to thinking styles assessed through REI (Milojevic et al., 2016), are now
being considered within the expanded, social-cognitive framework proposed in Figure 5. Within
this framework, the triadic social-cognitive influencing model, where each relationship of
influencers (person ↔ behavior, behavior ↔ environment, and person ↔ environment) is
driven by self-efficacy, encompasses elements from the original design thinking styles
framework proposed in Figure 3. As such, the analysis of the results is done with respect to two
personal influencers (gender considered male or female is a biological personal influencer,
and university class standing considered a first-year and upper-class is an affective personal
influencer) and two environmental influencers (location considered China or the U.S. is a
cultural environmental influencer, and field of study considered as engineering or non-
engineering is also a cultural environmental influencer) (Bandura, 2005).
In addition to the proposed influencers considered to extend an association to
relationships studied among the variables discussed in the methods section, under consideration
are also personality-based variables as attributes of the personal influencer category, and
behavioral creativity variables as attributes of the behavioral influencer category (Bandura,
1977).
In this study, four attributes to the social-cognitive influencing categories are considered.
The personal category was attributed gender as a biological cognitive influencer, and personality
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as a cognitive personal influencer. The environmental category is attributed location and
(academic) culture. Following are some of the results.
Table 1: Summary of scores per variable, as well as the relevant scales for said scores
Variable Avg. Score
Scale
min Score max Score
Design Self-efficacy 73.8 0 100
Personality
extraversion 3.12 1 5
agreeableness 3.82 1 5
conscientiousness 3.40 1 5
neuroticism 2.76 1 5
openness 3.44 1 5
Thinking
rational 3.71 1 5
intuitive 3.09 1 5
Behavioral creativity 0.31 0 1
Creative behaviors 1.74 1 4
Domain creativity 2.98 1 5
Design
novelty 8.21 0 10
usability 3.05 1 5

In the analysis of the results, first consideration was given to purely design self-efficacy
scores within the context of influencers available, then consideration was given to three factors
of behavior: thinking styles, creative behavior, and design performance, as influenced by design
self-efficacy, with some context placed upon the previously studied influencers.
Considering the volume of analysis presented here on, it is important to highlight that
correlations were calculated between design self-efficacy and each of: thinking styles, behavioral
creativity, and design performance, with respect to each suitable set of influencers. Such findings
are summarized in Table 2, and reveal many insignificant relationships found. This information
will be used to better analyze data in the upcoming sections.
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Relying on the information listed in the table, it may be stated that the following
correlation values with respect to design self-efficacy are found significant:
● Rationality (REI) correlation with respect to both genders, Chinese location,
engineering field, and personality traits of agreeableness and openness.
● Biographical creativity (BICB) correlation with respect to the engineering field and
extraversion.
● Domain creativity (CDQ-R) correlation with respect to the female gender, Chinese
location, and non-engineering fields.
● Design novelty (N) correlation with respect to the engineering field and
conscientiousness.
Table 2: Correlations of listed scores with respect to design self-efficacy score, per each
category of influencers within the larger sample. Findings which are significant are
marked in bold.

6.3.1 Design self-efficacy relationship with personal and environmental SCT influencers
Design self-efficacy, with listed associated scores, is:
● 5% higher in Men (74.9), than women (71.2);
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● 14% higher in American-based individuals (82.4), than Chinese-based ones (70.9);
● 15% higher in Engineers (76.5), than non-engineers (65.0);
● Negative 42.6% associated with Big Five Neuroticism
● Positive 42.4% associated with Big Five Conscientiousness
● Positive 23% associated with Big Five Openness.
● Positive 13% associated with Big Five Extraversion.
● Positive 4.7% associated with Big Five Agreeableness.
What these findings report is that the most impactful influencers under consideration are
location, discipline, neuroticism (personality), and conscientiousness (personality). Namely,
the more favorable location is the U.S., and the more favorable discipline is engineering.

Figure 13: Design Self-Efficacy with respect to personal and environmental influencers;
left to right: gender, binary (female/male), country (China/United States), discipline
(engineering/non-engineering), and personality (extraversion, agreeableness,
conscientiousness, neuroticism, and openness)
6.3.2 Design self-efficacy relationship with intuitive thinking
Thinking styles were assessed per standard scoring of Rational-Experiential Inventory
(REI), generating two separate scores, for rational and intuitive mode. These scores were then
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analyzed in terms of how design self-efficacy scores associate with them, as well as how this
association is guided by the available influencers from the previous section.
To address the second hypothesis, first the correlation between the overall design self-
efficacy and rational mode is found as 0.49, and the correlation between design self-efficacy and
intuitive mode as 0.02.
These relationships, contextualized by the influencers gender, location and discipline in
Figure 14 and personality in Figure 15, demonstrate the following observations for rational and
intuitive modes.
Rational mode of thinking is associated with design self-efficacy:
● Most positively for subjects located in China
● Least associated for subjects located in the U.S.
● Associated no differently for male or female subjects (association is positive across
board)
● Most positively associated for subjects with highest personality scores being
conscientious, open, neurotic, or agreeable (in that order)
● Not associated for subjects with highest personality score for extraversion.
Intuitive mode of thinking is associated with design self-efficacy:
● Positively for female subjects
● Negatively associated for male subjects
● Positively for subjects located in the U.S.
● Least associated for subjects located in China
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● Associated no differently for engineering or non-engineering disciplines (association
is close to none across board)
● Most positively associated for subjects with highest personality score for extraversion
● Not associated for subjects with highest personality score for agreeableness or
openness
● Most negatively associated for subjects with highest personality scores for
conscientiousness or neuroticism.
The ultimate finding is that the rational mode is better associated with design self-
efficacy than is the intuitive mode, which contradicts our hypothesis. Figures 14 and 15 visualize
in detail these preliminary findings, yet per Table 2. p-values, any findings regarding the
intuitive mode of thinking are insignificant, and rational mode of thinking has a great deal of
significant findings, across domains of both genders, Chinese location, engineering field, and
personality traits of agreeableness and openness.

Figure 14: Rational mode of thinking and intuitive mode of thinking with respect to
Design Self-Efficacy, contextually studied with respect to the gender, location and
discipline of subjects
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Figure 15: Rational mode of thinking and intuitive mode of thinking with respect to
Design Self-Efficacy, contextually studied with respect to big five personality traits:
extraversion, agreeableness, conscientiousness, neuroticism, and openness.
6.3.3 Design self-efficacy relationship with creative behavior
Creative Behavior was scored using the three designated measures of behavioral
creativity:
1) BICB: Biographic Index of Creative Behaviors, to measure biographic creativity
2) CBI: Creative Behavior Inventory, to measure creative behavior
3) CDQ-R: Creative Domains Questionnaire, Revised, to measure domain creativity
To address the third hypothesis, it was found that the correlations between the overall
design self-efficacy and each of these three variables, were 0.23 for biographic creativity, 0.15
for creative behavior, and 0.36 for domain creativity.
In the context of gender, location and discipline – influencers, these variables were
studied with respect to design self-efficacy, as depicted in Figure 16.
Biographical Creativity (from BICB) was associated with design self-efficacy:
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● Most positively associated for location being the U.S., discipline engineering, and
gender male.
● Not associated for subjects based in China.
● Most negatively associated for subjects in non-engineering disciplines.
Creative behavior (from CBI) was associated with design self-efficacy:
● Most positively associated for location being the U.S., discipline being engineering,
and gender being female
● Not associated for subjects in non-engineering disciplines
Domain creativity (from CDQ-R) was associated with design self-efficacy:
● Most positively associated for gender being female
● Not associated with non-engineering disciplines.

Figure 16: Behavioral creativity scores of BICB, CBI and CDQ-R, studied with respect
to design self-efficacy, in the contexts of gender, location, and discipline.
Findings on association of design self-efficacy with behavioral creativity are inviting for
further studies in the domain of our proposed hypothesis of their association being high. Figure
16 visualizes the preliminary findings for creative behavior to design self-efficacy relationship.
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From table 2, it can be stated that none of CBI related findings are significant, while the BICB
findings are significant in domains of the engineering field and extraversion. CDQ-R findings are
significant in domains of the female gender, Chinese location, and non-engineering fields.
6.3.4 Design self-efficacy relationship with design performance
Design performance was assessed relying on two established variables: design novelty
and design usability. These scores had design self-efficacy correlations of 0.11 for design
novelty, and 0.24 for design usability.
These two variables were then studied in the context of influencers of gender, discipline
and personality, as depicted in Figure 17 and Figure 18.
Design novelty was associated with design self-efficacy:
● Most positively associated when discipline is engineering
● Not associated with gender
● Most negatively associated when discipline is non-engineering
● Most positively associated for subjects with highest personality scores in
conscientiousness and openness
● Not associated for subjects with highest scores in agreeableness and neuroticism
● Most negatively associated for subjects with the highest personality score in
extraversion
● Design usability was associated with design self-efficacy:
● Most positively associated with gender being female
● Not associated with discipline
● Not associated with gender being male
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● Most positively associated for subjects with highest personality scores of
agreeableness, conscientiousness, neuroticism, and openness.
● Most negatively associated for subjects with the highest personality scores of
extraversion.
The findings for usability are not significant in Figures 17 and 18, while some of the
findings for novelty are, specifically in domains of engineering field and conscientiousness.

Figure 17: Design novelty and design usability scores, studied with respect to design
self-efficacy, in the contexts of gender, location, and discipline.

Figure 18: Design novelty and design usability scores, studied with respect to design
self-efficacy, in the contexts big five personality traits: extraversion, agreeableness,
contentiousness, neuroticism, and openness.
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6.4 Findings
From the results discussed above, the following findings can be drawn:
1) Highest correlation was between rational thinking and design self-efficacy, at 0.49.
No other studied quantity gets even close to correlating this well with design self-
efficacy.
a. Rational thinking style also lends itself to the highest number of significant
findings among the preliminary ones reported (see Table 2 for exact
significance).
b. One way to describe this would be that those who exhibit high rational scores
also approach their knowledge acquisition of design steps and methods more
rationally, thus being able to claim that they are highly confident about
completing the breakdown of design tasks.
c. Another way to interpret this finding would be that the more rational subjects
would have found themselves in more situations where they would need to
conduct engineering design, thus building greater expertise, confidence, and
motivation for the due process.
2) Intuitive thinking and design self-efficacy are to be studied further, due to low
correlations (the lowest out of all correlations reported), strikingly opposing [H2-b],
however with low statistical significance, generating no definite conclusion. In study
3 (chapter 7), however, intuitive thinking again shows problematic significance and
counter-findings to those hypothesized.
3) For results to yield greater validity, larger sample was necessary, albeit challenging at
that time. (No such sample size issue was present in findings of study 3 / chapter 7.)
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6.5 Summary
In this study, a social-cognitive design model was defined and evaluated, furthering
statistical challenges and providing much-needed categorical context for the importance of
rational thinking, personality factors of conscientiousness and openness, as well as creative
confidence. As defined, the study generated many observations and findings regarding
categorical influencing in the social-cognitive design model. However, many such findings did
not hold statistical significance. Those that did, at least in some dimensions, included gender,
location, personality (with the exception of neuroticism), rational thinking, every-day creativity
(BICB), creative confidence (CDQ-R), and design novelty. While the model presented here
offers a novel take on a triadic framework from social-cognitive theory, expanding to include
adjacent theories from social psychology, several reasons contributed to running the following,
third study, with a narrower focus on social-cognitive influencers and a significant change in
populations surveyed.

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7 Professional Experience in a Social-Cognitive System for Design
7.1 Introduction
This chapter addresses the Chapter 4.4. Models, which are defined and evaluated relying
primarily on the framework from Figure 6 (repeated here as Figure 19) and Figure 7 (repeated
here as Figure 20). Relying on this framework as well as the comparison framework which
considers the same personal and behavioral variables, as they connect to design self-efficacy and
as they compare engineering students with professionals, the last, third out of the three studies in
total, was conducted. As such this study was the most polished. While its models look
considerably simpler than those from study 2, they present a fine balancing act of how much can
realistically be measured in one sitting of a study participant, how much could be evaluated over
the internet (as social distancing rules around COVID-19 pandemic took over at the very start of
the study), and the base theoretical needs of further defining the social-cognitive framework for
design, except this time with substantial contents in the environmental category of influencers, a
theoretically sound and welcome change from the model from Chapter 4.3.
Survey Study 3 aims to incorporate the role of professional experience in a designer’s
social cognitive system, as well as extend consideration to experience factors (Ahmadpoor &
Johnes, 2017), such as years of experience, number of patents, and types of employers, to name a
few. It also compares engineering students to engineering professionals in domains of thinking
styles, personality, and design self-efficacy. The study offers the following:
● A distant foundation for a predictive model of students’ development into engineers
and which factors contribute to it in significant ways.
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● One of the more direct answers to the question on why some people design better
than others, specifically considering potential for success, creativity, and effective
designing.
● Insight into social-cognitive design factors in populations that do not necessarily
require educational considerations, and can instead bring in work place
considerations.
● An insight into the design thinking styles and social-cognitive design factors of
experienced professionals.
It also addresses the following main research question {R3} and hypothesis [H3]:
{R3}: What is the effect of experience on design thinking styles and other pro-design
factors?
[H3] Experience Hypothesis: With greater professional experience, engineers enhance the
relationships between their pro-design factors and design metrics.

Figure 19: Framework of self-efficacy in engineering professionals with respect to
experience, personality, and thinking styles
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Figure 20: A framework for design self-efficacy comparison between engineering
students and professionals
7.2 Methods
7.2.1 Subjects and procedure
Total of 86 engineering students from the University of Southern California participated
in the student portion of this study. The students were of undergraduate or master’s standing, and
recruited through the Viterbi School of Engineering communication channels, relying on
administrators and faculty. A reward of an Amazon gift card for $10 was offered to student
participants.
Total of 108 engineering professionals from the United States participated in the
professional portion of this study. The engineering professionals self-declared as actively
employed at a firm, university, or a government lab. They were recruited through alumni groups
of University of Southern California and Union College (NY), DoD, and email / social media.
The survey was completely anonymous with no identifiers collected whatsoever, and only the
restriction of preventing multiple submissions. Additionally, the definition of an “engineering
professional” stated one must both:
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(a) hold a bachelor’s or higher degree in an engineering-related discipline (for example,
any engineering, computer science, information technology, industrial/UX design, or natural
science degree),
(b) work in an engineering field (in industry, research, consulting, academia, or alike).
7.2.2 Survey assessment
Both engineering students and engineering professionals were asked to complete the
following surveys: Rational-Experiential Inventory (REI-10), Big Five Personality Inventory
(BFI), and the Design Self-Efficacy survey. Engineering professionals were additionally asked to
complete a preliminary innovation and invention index (also referred to as iii). The answers from
all four were then relied on for regression analyses.
Rational-Experiential Inventory (REI) has previously been used in studies from chapters
5 and 6. This study’s new take on it specifically focuses on the format the survey was distributed
in. Rather than distributing the previously utilized REI-40, in this study REI-10 was used, in
hopes of higher completion rates of the survey, among invited participants, seeing as answering
10 questions rather than 40 naturally looks more manageable for prospective participants. REI as
a measure was developed by Epstein and Pacini (Epstein & Pacini, 1999) and inspired this
research through work of Celia Witteman (Witteman, 2009). Self-rate questions such as “I enjoy
intellectual challenges” are used to assess rational thinking, or “Using my gut feelings usually
works well for me in figuring out problems in my life” for experiential/intuitive thinking, relying
on a 5 point Likert scale ranging from completely false to completely true. Complete REI-10 set
of questions can be found in Appendix C.
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Big Five Personality Inventory (BFI) has also been a common metric throughout this
work. It evaluates five dimensions of personality (Goldberg, 1993), however in this study,
questions for 2 of them were selected out, thus generating scores solely for two out of the five
dimensions: conscientiousness and openness. These two dimensions were selected after having
shown the most promise for significant findings, relying on correlations found in Chapter 5
(Milojevic et al., 2016) as well as associations studied in Chapter 6 (Milojevic & Jin, 2018).
Complete set of BFI questions can be found in Appendix A, while the provided scoring
mechanism allows insight into which questions evaluate which dimension. Self-rating for BFI is
completed on a 5 point Likert scale, ranging from disagreeing strongly to agreeing strongly. The
survey explores topics of thoroughness, reliability, creativity, and motivation. The dimension of
conscientiousness contrasts traits such as reliability, thoroughness, and organization with the
opposite traits such as unreliability, negligence, and carelessness. Openness contrasts traits such
as creativity, curiosity, and imagination with the opposite traits imperceptiveness and
shallowness (Goldberg, 1993).
Design Self-Efficacy survey measures an individual’s belief in their ability “to execute
certain behaviors necessary to produce specific performance attainments” (Bandura, 1977),
pertaining specifically to design tasks and skills (Carberry, 2010). This survey is measured with a
scale from 0 to 100 where 0 is an individual’s belief that they cannot do the task at all and 100 is
that they are highly certain that they can do the task, while all self-rate tasks belong to the design
domain. Some examples include “research a design need” and “construct a prototype”. Full
survey is available in Appendix G.
Experience was evaluated relying on a preliminary innovation and invention index. The
Innovation and Invention Index (III) is a preliminary list of questions built in-house and yet
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unevaluated for independence between questions, relying on a study of factors linked to high
volumes of intellectual property (Ahmadpoor & Jones, 2017). Each question on this list was
considered as a separate variable for purposes of this study. The questions topically address
common parameters of experience (e.g. “How many years of engineering experience do you
have?” and “How many professional awards have you received?”). The questions are supported
by the work of Ahmadpoor and Jones (2017) that compares the amount of papers published and
their link to the number of patents an individual holds. Thereon, they created a metric that could
determine the number of connections or degrees of separation between patents and previous
research, suggesting that 80% of cited research articles link forward to a future patent and that
61% of patents link backward to prior research articles (Ahmadpoor & Jones, 2017). The iii
offers a preliminary lens into how foundational research and applications of that research are
seen in a professional environment, further contributing to understanding design self-efficacy
through understanding how experience and research contribute to innovation.
7.3 Engineering Experience Levels and Design Self-Efficacy
7.3.1 Self-efficacy of engineering professionals
7.3.1.1 Design self-efficacy with respect to personality
To estimate the effects of personality (Conscientiousness and Openness) on Design Self-
Efficacy, a multiple linear regression analysis was conducted. The results of this analysis are
summarized in Table 3. This model proved to be a significant predictor of Design Self-Efficacy
(R
2
= .175, F(2,104) = 11.064, p < .001). We found both Conscientiousness (β = 7.271, p < .001)
and Openness (β = 4.845, p = .030) to be significant predictors of Design Self-Efficacy, with
Conscientiousness having the larger effect size. From this, the regression equation (3) was built.
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It describes design self-efficacy of engineers with respect to conscientiousness and openness,
where the intercept and both personality dimensions were found to be significant predictors.
𝐷𝑆𝐸 = 34.6 + 7.3 𝐶𝑜 + 4.8 𝑂𝑝 (3)
Table 3: Multiple linear regression results of Conscientiousness and Openness on
Design Self-Efficacy

7.3.1.2 Design self-efficacy with respect to thinking style
To estimate the effects of thinking style (Rational and Experiential) on Design Self-
Efficacy, a multiple linear regression analysis was conducted. The results of this analysis are
summarized in Table 4. Our model proved statistically significant, but with a modest effect size
(R
2
= .094, F(2,104) = 5.385, p = .006). We found Experiential Thinking to be nonsignificant,
with most of the variance being explained by Rational Thinking (β = 5.877, p = .002). From
this, the regression equation (4) was built. It describes design self-efficacy of engineers with
respect to rational and experiential / intuitive thinking, where the intercept and rational thinking
were found to be significant predictors, but experiential thinking was not.
𝐷𝑆𝐸 = 56.3 + 5.9 𝑅 + 1.5 𝐸 (4)
Table 4: Multiple linear regression results of Rational and Experiential Thinking on
Design Self-Efficacy

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7.3.1.3 Design self-efficacy with respect to experience factors
To estimate the effects of experience factors on Design Self-Efficacy, a multiple linear
regression analysis was conducted. The results of this analysis are summarized in Table 5. This
model proved to be a significant predictor of Design Self-Efficacy (R
2
= .231, F(14,92) = 1.979,
p = .028). We found number of patents filed (β = -.876, p = .020) and employer: government
lab (with respect to employer: firm; β = -9.655, p = .037) to be a significant negative predictors
of Design Self-Efficacy, and number of patents approved to be a significant positive predictor of
Design Self-Efficacy (β = .887, p = .027). Additionally, there exist marginally significant effects
of awards earned (β = .793, p = .062) and employer patent ownership (with respect to no patent
ownership; β = 6.607, p = .060) on Design Self-Efficacy. To capture the predictors of Design
Self-Efficacy, regression equation (5) was built.
𝐷𝑆𝐸 = 80.0 + 𝐵 𝐸 (5)
𝑤ℎ𝑒𝑟𝑒 𝐵 = 𝑎 𝑟𝑒𝑔𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡, 𝐸 = 𝑎𝑛 𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒
Table 5: Multiple linear regression results of experience factors on design self-efficacy

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7.3.2 Pro-design factors of social-cognitive systems of engineering students and
professionals
7.3.2.1 Students and engineers: A comparison of social-cognitive factors and correlations
thereof
Table 6 shows the group statistics of the two thinking styles studied (rational and
experiential/intuitive), two personality dimensions (conscientiousness and openness), and design
self-efficacy in a sample of 86 students and 108 professionals. A total of 5 outliers were removed
from the data set, each with design self-efficacy scores straying more than 2.5 standard
deviations from the mean, leaving a final sample of 83 students and 106 professionals. These
differences alongside their 95% confidence intervals are depicted in Figure 20-a (personality and
thinking styles) and Figure 20-b (design self-efficacy).

(a) (b)
Figure 21: (a) 95% confidence intervals for mean scores in rational thinking, experiential
thinking, conscientiousness, and openness between students and engineers; (b) 95%
confidence interval for mean design self-efficacy scores between students and engineers.
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Table 6: Descriptive statistics (N-count, mean, standard deviation, standard error of the
mean) of each variable for both students and engineers.

These statistics were compared using an independent samples t-test, finding that
professionals score significantly higher than students in mean rational thinking (ΔM = .481, p <
.001), Conscientiousness (ΔM = .250, p = .007), Openness (ΔM = .306, p < .001), and design
self-efficacy (ΔM = 5.415, p = .003). Only experiential thinking had nonsignificant differences.
These results are summarized in Table 7.
Table 7: Independent samples t-test for equality of means between Students and
Engineers in Rational Thinking, Experiential Thinking, Conscientiousness, Openness,
and Design Self-Efficacy. We find Engineers to be significantly higher in all of these
dimensions except for Experiential Thinking, for which there were no statistically
significant differences.

7.3.2.2 Students: Design self-efficacy with respect to personality and thinking styles
To estimate the effects of our independent variables on Design Self-Efficacy in students,
a multiple linear regression analysis was conducted. The results of this analysis are summarized
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in Table 8. This model proved to be a significant predictor of design self-efficacy (R
2
= .293,
F(4,78) = 8.073, p < .001), with rational thinking having the most significant effect (β = 7.525,
p < .001), and experiential thinking having a significant negative effect (β = -5.000, p = .017).
No significant effect was observed from Conscientiousness, likely due to its high correlation
with rational thinking (r = .511, p <.001). Openness produced a nonsignificant but observable
effect (β = 3.878, p = .088). Relying on the regression model from table 8, a regression equation
(6) was built, wherein included personality factors aren’t predictors of design self-efficacy.
𝐷𝑆𝐸 = 50.2 + 7.5 𝑅 − 5.0 𝐸 + 1.7 𝐶𝑜 + 3.9 𝑂𝑝 (6)
Table 8: Multiple linear regression results of Design Self-Efficacy in students.

7.3.2.3 Engineers: Design self-efficacy with respect to personality and thinking styles
To estimate the effects of our independent variables on design self-efficacy in Engineers,
a multiple linear regression analysis was conducted. The results of this analysis are summarized
in Table 9. Our model proved to be a significant predictor of design self-efficacy (R
2
= .189,
F(4,102) = 5.952, p < .001). We found only Conscientiousness to have a statistically significant
effect on Design Self-Efficacy (β = 6.716, p = .001). Relying on the regression model from table
9, a regression equation (7) was built, wherein only Co significantly predicts design self-efficacy.
𝐷𝑆𝐸 = 33.5 + 3.0 𝑅 + 0.4 𝐸 + 6.7 𝐶𝑜 + 2.6 𝑂𝑝 (7)
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Table 9: Multiple linear regression results of Design Self-Efficacy in engineers.

7.4 Findings
In the past two studies, as explained in sections 5.4. and 6.4, a key challenge was
insufficient participation (i.e. number of subjects). This study met and exceeded the threshold for
validity and significance, which was set at roughly 70 subjects per group. This study,
unfortunately, encountered an obstacle to in-person subject participation, which led to
eliminating what was to be a primary set of dependent variables: design performance, which was,
in sections 4.3 and 6.1 categorized into actions driven by behavioral influencers.
That said, the research that was done in this study (Ch. 7) found the following to be true
of engineering professionals:
1) Rational thinking was related to design self-efficacy the most (contributing towards a
regression model more than any other variable).
2) Two factors of big-five personality (Conscientiousness and Openness) were verified
as significant positive predictors of design self-efficacy
 they were previously found significant in past studies as well, in similar
models.
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3) In the environment and experience category, the following were found to affect
design self-efficacy:
 negatively: government lab employment and the number of filed patents
 positively: number of approved patents
 (with nearly sufficient significance) positively: number of awards
 (with nearly sufficient significance) negatively: patent ownership by employer
(as opposed to self-owned or no patent ownership)
This research also found the following to be true of comparison between engineering
students and professionals:
1) Engineers scored significantly higher than students on every measured variable
except for intuitive thinking, where they scored approximately equally.
2) Both rational and intuitive thinking were found to be significant predictors of design
self-efficacy for students, but personality dimensions were not predictors.
3) Conscientiousness was found to be a significant predictor of design self-efficacy in
engineers but not in students, while openness may have contributed to a small extent,
in students only.
7.5 Summary
In this study, a comparison of design self-efficacy with respect to thinking styles and two
dimensions of personality, was conducted. Alongside the comparison, as separate social-
cognitive model for design among engineers, which factors in professional experience, was built
and evaluated. The methods and findings for both were presented in this chapter. Chapter 7 is
followed by Chapter 8, on contributions and future work.
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8 Contributions and Future Work
This final chapter summarizes the research conclusions, contributions, and
recommendations for future work. This dissertation primarily addresses the 3 hypotheses
introduced in Chapter 4, as well as expands understanding for points of inquiry from Chapter 1
(more specifically in sections 1.1.-1.3.), such as questions on why some people design better than
others or what contributes to a designer’s creative thinking process. This research addresses the
three research questions and hypotheses through first proposing theoretically sound models (Ch.
4.2-4.4.), and then evaluating them (Ch. 5-7). Specific details on the state of hypotheses validity
are presented in section 8.1.
8.1 Hypotheses Revisited
Here, the top-level hypotheses described at the beginning of Chapter 4 are discussed and
addressed.
[H1] Duality Hypothesis: There exists an effect of rational and intuitive thinking,
separately, on design creativity (as defined by behavioral creativity and design performance).
[Validated] Rational thinking had at least one significant relationship with biographical
creativity, domain-creativity, and design self-efficacy. Low correlations found between intuitive
thinking and design creativity did not demonstrate adequate significance, and the finding was
dismissed. Tangentially, intuitive thinking was a significant predictor of design self-efficacy in at
least one study, leading to a small piece of evidence of its contribution to a factor related to
design creativity.
[H2] Influencing Hypothesis: There are influencers that can contextualize and aid
intuitive or rational thinking in engineering design.
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[Validated] This is addressed most clearly through significance values of thinking
considered with respect to influencers, being acceptable. This occurred for the following
influencers: gender, location, field (engineering), personality (agreeableness and openness).
Some significance was found for every-day creativity and creative confidence as well.
[H3] Experience Hypothesis: With greater professional experience, engineers enhance
the relationships between their pro-design factors and design metrics.
[Inconclusive] The last research study demonstrated that the difference in experience
levels being none or “some” (greater than 1 year in an engineering field) yielded an increase in
all scores of variables studied (rational thinking included, alongside 2 dimensions of personality,
and design self-efficacy) but one (intuitive thinking). The regression models also showed
stronger contributions of significant variables towards design self-efficacy in professionals than
in students. Seeing as the regression equations did not have the same significant predictors, it is
impossible to validate or invalidate the experience hypothesis.
8.2 Conclusions
Major take-aways from each of the three research studies conducted are presented and
discussed here.
For survey study 1 (Ch. 4.2. & 5), I addressed the theme that thinking style influenced
design creativity and proposed a framework of study to identify relevant concepts (or variables)
and investigate correlations between them. My preliminary study yielded relevant yet
troublesome results. Major considerations were given to the finding of dominantly rational
subjects performing better in domains of creativity and design, than their dominantly
experiential peers. As such, the question was posed whether the tradition and discipline present
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in engineering education really call for much change. Furthermore, the proposed binary thinking
styles were challenged in terms of their ability to truly evaluate hypotheses of interest and aid in
answering major research questions. That said, years after this origin study was completed and
theory aplenty later, it is my professional belief that this was and still is one of the simplest, most
elegant ways to study thinking, regardless of challenges it may had brought. A final
consideration was given to the behaviors which might have been specific to Shanghai Jiao Tong
University, one of the best universities in China, gathering the finest engineering students from
around the country, and the origin of my first group of participants. While no confident proof
was found on this, it may be that the highly selective universities select out the highly intuitive
students who may not also score highly in rational thinking, due to entrance exam designs.
For survey study 2 (Ch. 4.3. & 6), I observed one disproven sub-hypothesis and two
hypotheses in need of further consideration from the social and behavioral contexts for design.
(from Ch. 6.1.).
I have, in the end, found that the highest correlation with design self-efficacy exists for
the rational mode of thinking, at 0.49. No other studied quantity gets even close to correlating
this well with design self-efficacy. Rationality also lends itself to the highest number of
significant findings among the preliminary ones reported. One way to describe this would be that
those who exhibit high rational scores also approach their knowledge acquisition of design steps
and methods more rationally, thus being more able to claim that they are highly confident about
completing the breakdown of design tasks. Another way to interpret this finding would be that
the more rational subjects would have found themselves in more situations where they would
need to conduct engineering design, thus building greater expertise and thus greater confidence
and motivation for completing the process repeatedly.
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The associations found for intuitive thinking mode to design self-efficacy was by far the
lowest, and did not carry any statistical significance. A potential next step was proposed as
finding a better method of complete this analysis more wholesomely. Additionally, of the five
traits, neuroticism never yielded any significance across different correlations studied.
For survey study 3 (Ch. 4.4. & 7), the results were twofold. When it came to comparing
engineers with students, engineers scored significantly higher in every measured category, aside
from intuitive (experiential) thinking style, where the scores were approximately equal.
Significant predictors of design self-efficacy were found to be (a) conscientiousness, for
engineers but not students, and (b) rational thinking style, intuitive thinking style, and openness,
for students only. When it came to understanding engineers better relying on a social-cognitive
model thereof, design self-efficacy had a number of significant contributors, including (i)
rational thinking, (ii) conscientiousness and openness, (iii) type of employer being a government
lab and the employer owning any upcoming IP were negatively related to design self-efficacy.
Much of the relationships evaluated by the models were statistically insignificant, yet again,
regardless of how many people were surveyed in total.
8.3 Contributions
By building models based on the extant theoretical frameworks, conducting extensive
survey-based studies, and deriving findings based on the analysis of the study results, this work
has made the following contributions to the engineering design research community and design
practice and training.
(1) A dual process framework of design thinking styles (Milojevic, Girardello, Zhang & Jin,
2016; Milojevic & Jin, 2016). While an initial and observational pilot study was carried out
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based on the analysis of the protocol data (Moore et al., 2016), this research developed the
first model of engineering design thinking from a dual thinking process perspective by
relating the dual design thinking process (i.e. design thinking style) with the designers’
personality, behavioral creativity and design performance. The survey studies based on the
model have demonstrated the significance of the relationships identified by the model and
revealed the directions of expansion for the future research work.
(2) Model of pro-design behaviors in a social-cognitive context (Milojevic & Jin, 2018). One
direction for expansion upon the first contribution was to further contextualize the variables
of interest and expand the focus towards design self-efficacy, which was of considerable
interest to the design research communities at the time. Seeing as self-efficacy as a concept
belongs to social-cognitive theories, their concepts were studied and relationships were built
into Figure 5, where specific variables of interest were categorized in a manner typical to the
social-cognitive theories. This allowed development of a reliable approach to study
designer’s internal and external characteristics, and soundly relate seemingly unrelated
concepts through quantitative survey measures. Additionally, evaluating the model generated
findings on how design self-efficacy reflexively affects each influencer studied.
(3) Model of professional experience in a social-cognitive system for design. Upon defining the
second model, with its broad scope of variable categorizing, the scope was narrowed down
towards the 3 categories of influencers that comprise the reported relationships. Additionally,
one focus of this research as a whole was always to assess factors at play in design and
intuitive thinking. Studying engineering professionals was significant from the perspective of
defining a preliminary set of factors that may contribute to professional intuition (e.g. where
a designer seamlessly comes up with an elegant solution quickly and intuitively). Hence, in
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an attempt to generate a preliminary insight into the experience factors that may or may not
contribute to design self-efficacy, the relationships defined in chapter 7 were proposed,
developed, and evaluated, thus contributing a report of significant experience factors that
affect design self-efficacy. Similarly, prior factors were also studied again, to compare to the
previous, baseline findings. This model is the first model where sample size does not hinder
statistical significance and the findings therein can be considered reliable. Hence, statistical
significance or lack thereof is reported with much higher confidence.
(4) The implications of research findings. The implications are two-fold. For one, significant
relationships uncovered in this work specifically highlight rational thinking and design self-
efficacy relating with the highest number of other factors studied. Most specifically:
 Rational thinking is found to relate to conscientiousness, openness, biographical
creativity, domain creativity, and design self-efficacy
 Intuitive thinking is found to relate to openness
 Design self-efficacy is found to relate to rational thinking, intuitive thinking,
conscientiousness, number of patents filed, number of patents granted, and employer
type (government lab).
These relationships then build the following implications:
 Rational thinking is an inevitable need for engineering students, when approaching
design tasks and projects
 Both rational and intuitive thinking play this type of role for non-engineering students
 Faith in design skills is driven up by rational thinking and dedication for engineering
professionals.
98
(5) Clearer understanding of influencers to better designers. Contributions of this work transcend
the conclusions derived from the specific findings based on the study results. By studying
many concepts that define the human mind, and comparing, correlating, and otherwise
exploring their relationships with more applied concepts (eg. design performance, design
self-efficacy) or concepts of environment (external to the mind), each of the contributing
models has brought the design research one small step closer to understanding the aid or lack
thereof to the inner workings of a designer’s mind.
(6) The methods used to build the 3 theoretical models, and the approach towards evaluating
them are a contribution in itself. This is a pioneering piece of work in the design research
community, where ad-hoc methods are often employed, comparable to those of Norman
(1988) and at times closer in nature to the practice of design than to supporting science,
social science, or academic works. This dissertation is a step forward in the direction of
finding published, previously vetted and approved methods from other fields, to support
hunches design researchers have and often explore without sufficient support of literature.
One way, then, becomes to evaluate measures of the self through survey methods, broadly
used in psychology, psychiatry and management consulting among many, and circle the
findings back to those in design (through other standard evaluation techniques for conceptual
or more applied designs, the first one of which that comes to mind is by Shah et al. from
2003).
(7) An innovative interdisciplinary approach towards developing design research. Over the
years, my advisor observed the challenges of our research community, and put forward the
idea to connect them with psychology of personality and social psychology. This idea
inspired the three models presented here, which offer significant theoretical developments in
99
studying engineering designers, engineering students, and other design students. Truly, a new
door got opened with this work, and there may be many more directions in which to build
upon the base models provided in this dissertation.
8.4 Future Work
Some recommendations can be made regarding the future work, so this research track can
continue on. They are grouped into 3 major categories, which are then broken down further.
1) Improvement of current approach:
 The first two models can be re-evaluated on a sample size of over 70 participants
(where 70 is the threshold number for significance in populations such as engineering
students, mechanical engineers, engineering professionals, etc.) as done when the
third model was evaluated in Ch. 7.
 The first model from Ch. 5 should be evaluated without the categorical analysis (i.e.
eliminate comparison between engineering students and those outside of
engineering). This could be done by solely surveying engineering students or
professional engineers and focusing on finding statistically significant correlations
only, rather than building a preliminary case for how correlations compare between
student populations.
 Creativity should be evaluated beyond behaviors or habits. This implies identifying a
different type of creativity, which would allow quantitative evaluation. There exist
surveys on creative personalities, among others, which could be deployed. Primarily,
however, one’s creative habits and actions should not be the main pillars of how
creative they are. A less practical, and more cognitive, take on creativity should be
100
explored, relying on surveys that form statements in the format of “I am….” / “I
feel…” as opposed to “In the last 12 months, I have performed a creative action” or “I
have once or multiple times in my life written a short story”.
 Consider longitudinal studies to cover mechanical engineering students and non-
engineering students (e.g., architecture or art design students) over a four-year period
to see how different teaching methods are applied and more effective to nurture
intuitive thinking and what effect it may have on growth of students’ creativity.
2) Expansion of current models
 Rational and intuitive thinking styles should be studied further through abilities and
engagement; they could also be studied qualitatively, in order to address multiple
streams of feedback regarding the difficulty of putting a number on someone’s mode
of thinking.
 Design performance should be studied beyond Shah’s measures.
 Design problems proposed in Appendix J should be used for design variable
evaluation, instead of the former design problem about the ping pong ball launcher,
which was too solution-specific.
 The current models could also be computationally modeled, relying on a cognitive
modeling tool such as ACT-R or GOMS.
 Connect this work with work done on organizational innovation and attempt to
collaborate with a business consultant studying an engineering firm. An abundance of
data could be obtained (a challenging task in the past) and beyond the scope of this
study, environmental factors could be studied in many different ways relying on the
interests from organizational innovation.
101
3) Interventions:
 Idea generation or conceptual design should be studied at different times of day:
control group in the afternoon, negative intervention in the early morning, and
positive intervention late at night.
 Priming by highly creative materials (positive intervention) and highly analytic
materials (negative intervention) should be considered with respect to no priming,
prior to working on a design problem.
 Environmental intervention could also be conducted, requiring that a conceptual
design be completed in 3 different locations, by different participants. A baseline
(control group) environment could be an office or a classroom, a positive
environmental intervention could be working from a maker-space or an incubator,
and a negative environmental intervention could be working from home. Sufficient
relevant literature could solidify the best set of assumptions on which environment
should enhance or hinder conceptual design creativity.

102
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Appendices
Appendix A: Big Five Personality Inventory (BFI)
Here is a number of characteristics that may or may not apply to you. For example, do
you agree that you are someone who likes to spend time with others? Please write a number next
to each statement to indicate the extent to which you agree or disagree with that statement.
Disagree
strongly
Disagree a
little
Neither agree
nor disagree
Agree a little
Agree
strongly
1 2 3 4 5

I AM SOMEONE WHO….
1.

Is talkative.
2.

Tends to find fault with others.
3.

Does a thorough job.
4.

Is depressed, blue.
5.

Is original, comes up with new ideas.
6.

Is reserved.
7.

Is helpful and unselfish with others.
8.

Can be somewhat careless.
9.

Is relaxed, handles stress well.
10.

Is curious about many different things.
11.

Is full of energy.
12.

Starts quarrels with others.
13.

Is a reliable worker.
14.

Can be tense.
15.

Is ingenious, a deep thinker.
16.

Generates a lot of enthusiasm.

118
Disagree
strongly
Disagree a
little
Neither agree
nor disagree
Agree a little
Agree
strongly
1 2 3 4 5

17.

Has a forgiving nature.
18.

Tends to be disorganized.
19.

Worries a lot.
20.

Has an active imagination.
21.

Tends to be quiet.
22.

Is generally trusting.
23.

Tends to be lazy.
24.

Is emotionally stable, not easily upset.
25.

Is inventive.
26.

Has an assertive personality.
27.

Can be cold and aloof.
28.

Perseveres until the task is finished.
29.

Can be moody.
30.

Values artistic, aesthetic experiences.
31.

Is sometimes shy, inhibited.
32.

Is considerate and kind to almost everyone.
33.

Does things efficiently.
34.

Remains calm in tense situations.
35.

Prefers work that is routine.
36.

Is outgoing, sociable.
37.

Is sometimes rude to others.
38.

Makes plans and follows through with them.
39.

Gets nervous easily.
40.

Likes to reflect, play with ideas.

119
Disagree
strongly
Disagree a
little
Neither agree
nor disagree
Agree a little
Agree
strongly
1 2 3 4 5

41.

Has few artistic interests.
42.

Likes to cooperate with others.
43.

Is easily distracted.
44.

Is sophisticated in art, music, or literature.

Scoring mechanisms for Ex (extraversion), Ag (agreeableness), Co (conscientiousness),
Ne (neuroticism), and Op (openness) are included here. Please note that instead of neuroticism,
emotional stability (Es) was at times reported, where emotional stability is the reverse score for
neuroticism (Es = 6-Ne).
Ex = Average (Q1, 6-Q6, Q11, Q16, 6-Q21, Q26, 6-Q31, Q36)
Ag = Average (6-Q2, Q7, 6-Q12, Q17, Q22, 6-Q27, Q32, 6-Q37, Q42)
Co = Average (Q3, 6-Q8, Q13, 6-Q18, 6-Q23, Q28, Q33, Q38, 6-Q43)
Ne = Average (Q4, 6-Q9, Q14, Q19, 6-Q24, Q29, 6-Q34, Q39)
Op = Average (Q5, Q10, Q15, Q20, Q25, Q30, 6-Q35, Q40, 6-Q41, Q44)

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Appendix B: Rational-Experiential Inventory (REI-40)
Please select the response which best applies to you, on the 1-5 response scale:
Definitely
not true of
myself
Slightly true
of myself
About
halfway true
of myself
Mostly true
of myself
Definitely
true of
myself
1 2 3 4 5

1. I have a logical mind. 1 2 3 4 5
2. I prefer complex problems to simple problems. 1 2 3 4 5
3. I believe in trusting my hunches*. 1 2 3 4 5
4. I am not a very analytical thinker. 1 2 3 4 5
5. I trust my initial feelings about people. 1 2 3 4 5
6. I try to avoid situations that require thinking in depth about something. 1 2 3 4 5
7. I like to rely on my intuitive impressions. 1 2 3 4 5
8. I don’t reason well under pressure. 1 2 3 4 5
9. I don’t like situations in which I have to rely on intuition. 1 2 3 4 5
10.
Thinking hard and for a long time about something gives me little
satisfaction.
1 2 3 4 5
11. Intuition can be a very useful way to solve problems. 1 2 3 4 5
12.
I would not want to depend on anyone who described himself or herself as
intuitive.
1 2 3 4 5
13. I am much better at figuring things out logically than most people. 1 2 3 4 5
14. I usually have clear, explainable reasons for my decisions. 1 2 3 4 5
15.
I don’t think it is a good idea to rely on one’s intuition for important
decisions.
1 2 3 4 5
16. Thinking is not my idea of an enjoyable activity. 1 2 3 4 5
17. I have no problem thinking things through carefully. 1 2 3 4 5

121
Definitely
not true of
myself
Slightly true
of myself
About
halfway true
of myself
Mostly true
of myself
Definitely
true of
myself
1 2 3 4 5

18. When it comes to trusting people, I can usually rely on my gut feelings. 1 2 3 4 5
19.
I can usually feel when a person is right or wrong, even if I can’t explain how
I know
1 2 3 4 5
20. Learning new ways to think would be very appealing to me. 1 2 3 4 5
21.
I hardly ever go wrong when I listen to my deepest gut feelings to find an
answer.
1 2 3 4 5
22. I think it is foolish to make important decisions based on feelings. 1 2 3 4 5
23. I tend to use my heart as a guide for my actions. 1 2 3 4 5
24. I often go by my instincts when deciding on a course of action. 1 2 3 4 5
25. I’m not that good at figuring out complicated problems. 1 2 3 4 5
26. I enjoy intellectual challenges. 1 2 3 4 5
27. Reasoning things out carefully is not one of my strong points. 1 2 3 4 5
28. I enjoy thinking in abstract terms. 1 2 3 4 5
29. I generally don’t depend on my feelings to help me make decisions. 1 2 3 4 5
30. Using logic usually works well for me in figuring out problems in my life. 1 2 3 4 5
31. I think there are times when one should rely on one’s intuition. 1 2 3 4 5
32. I don’t like to have to do a lot of thinking. 1 2 3 4 5
33.
Knowing the answer without having to understand the reasoning behind it is
good enough for me.
1 2 3 4 5
34.
Using my gut feelings usually works well for me in figuring out problems in
my life.
1 2 3 4 5
35. I don’t have a very good sense of intuition. 1 2 3 4 5
36. If I were to rely on my gut feelings, I would often make mistakes. 1 2 3 4 5
37. I suspect my hunches* are inaccurate as often as they are accurate. 1 2 3 4 5

122
Definitely
not true of
myself
Slightly true
of myself
About
halfway true
of myself
Mostly true
of myself
Definitely
true of
myself
1 2 3 4 5

38. My snap judgments are probably not as good as most people’s. 1 2 3 4 5
39. I am not very good at solving problems that require careful logical analysis. 1 2 3 4 5
40. I enjoy solving problems that require hard thinking. 1 2 3 4 5
*hunch = feeling, guess
Scoring mechanisms for R (rational thinking style) and E (experiential / intuitive
thinking style) are included here.
R = Average (Q1, 6-Q4, 6-Q8, Q13, Q14, Q17, 6-Q25, 6-Q27, Q30, 6-Q39, Q2, 6-Q6, 6-
Q10, 6-Q16, Q20, Q26, Q28, 6-Q32, 6-Q33, Q40)
E = Average (Q3, Q5, Q18, Q19, Q21, Q34, 6-Q35, 6-Q36, 6-Q37, 6-Q38, Q7, 6-Q9,
Q11, 6-Q12, 6-Q15, 6-Q22, Q23, Q24, 6-Q29, Q31)
For reference, these thinking styles can further be broken down into rational ability (RA),
rational engagement (RE), experiential ability (EA), and experiential engagement (EE), the
scoring mechanisms for which are provided as well:
RA = Average (Q1, 6-Q4, 6-Q8, Q13, Q14, Q17, 6-Q25, 6-Q27, Q30, 6-Q39)
RE = Average (Q2, 6-Q6, 6-Q10, 6-Q16, Q20, Q26, Q28, 6-Q32, 6-Q33, Q40)
EA = Average (Q3, Q5, Q18, Q19, Q21, Q34, 6-Q35, 6-Q36, 6-Q37, 6-Q38)
EE = Average (Q7, 6-Q9, Q11, 6-Q12, 6-Q15, 6-Q22, Q23, Q24, 6-Q29, Q31)
123
Appendix C: Rational-Experiential Inventory (REI-10)
Please rate yourself in response to these questions, where:

1. I try to avoid situations that require thinking in depth about something. 1 2 3 4 5
2. I like to rely on my intuitive impressions. 1 2 3 4 5
3. I’m not that good at figuring out complicated problems. 1 2 3 4 5
4. I don’t have a very good sense of intuition. 1 2 3 4 5
5. I enjoy intellectual challenges. 1 2 3 4 5
6.
Using my gut-feelings usually works well for me in figuring out problems in
my life.
1 2 3 4 5
7. I am not very good in solving problems that require careful logical analysis. 1 2 3 4 5
8. I believe in trusting my hunches*. (*hunch: an intuitive feeling or guess) 1 2 3 4 5
9. I don’t like to have to do a lot of thinking. 1 2 3 4 5
10. Intuition can be a very useful way to solve problems. 1 2 3 4 5

Scoring mechanisms for R (rational thinking style) and E (experiential / intuitive thinking
style) are included here.
R = Average (6-Q1, 6-Q3, Q5, 6-Q7, 6-Q9)
E = Average (Q2, 6-Q4, Q6, Q8, Q10)

Completely
false
Slightly
false
Neutral
Slightly
true
Completely
true
1 2 3 4 5
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Appendix D: Biographical Inventory of Creative Behaviors (BICB)
Please answer as truthfully as you can. Next to activities you have been actively involved
in, check the box, circle the number, or write in “yes”.
IN THE PAST 12 MONTHS HAVE YOU…
1.

Written a short story.
2.

Written a novel.
3.

Organized an event, show, performance, or activity.
4.

Produced a TV / Play script.
5.

Designed and produced a textile product.
6.

Redesigned and redecorated a bedroom, kitchen, personal space, etc.
7.

Invented and made a product that can be used.
8.

Drawn a cartoon.
9.

Started a club, association or group.
10.

Produced a picture.
11.

Had an article published.
12.

Formed a sculpture using any suitable materials.
13.

Criticized a scientific theory.
14.

Produced your own food recipes.
15.

Produced a short film.
16.

Produced your own website.
17.

Produced a theory to explain a phenomenon.
18.

Invented a game or other form of entertainment.
19.

Chosen to lead / manage others.
20.

Made someone a present.
21.

Composed a poem.
22.

Adapted an item and used it in a way that it was not designed to be.
23.

Published research.
24.

Choreographed a dance.
25.

Designed and planted a garden.
26.

Produced a portfolio of photographs.
27.

Acted in a dramatic production.
28.

Delivered a speech.
125
29.

Mentored/coached someone else to improve their performance.
30.

Devised an experiment to help understand something.
31.

Made up a joke.
32.

Been made a leader / captain of a team/group.
33.

Composed a piece of music.
34.

Made a collage.

Scoring mechanism for biographical creativity (BICB) is:
BICB = Average (Q1:Q34) where each Q score is 1 for “yes” and 0 for “no”.
Appendix E: Creative Behavior Inventory (CBI)
The Creative Behavior Inventory is a self-reported checklist of creative behaviors and/or
activities that the respondent has previously engaged or participated in. For each item, circle the
answer that best describes the frequency of the behavior in your adolescent and adult life.

HAVE YOU EVER…
1. Painted an original picture. A B C D
2. Designed and made your own greeting cards. A B C D
3. Made a craft out of metal. A B C D
4. Put on a puppet show. A B C D
5. Made your own holiday decorations. A B C D
6. Built a hanging mobile (a suspended decorative structure). A B C D
7. Made a sculpture. A B C D
8.
Had a piece of literature published in a school or university
publication.
A B C D
Never did
this
Did this
once or
twice
3-5 times
More than 5
times
A B C D
126

9. Wrote poems. A B C D
10. Wrote a play. A B C D
11. Received an award for an artistic accomplishment. A B C D
12. Received an award for making a craft. A B C D
13. Made a craft out of plastic or a related material. A B C D
14. Made cartoons. A B C D
15. Made a leather craft. A B C D
16. Made a ceramic craft. A B C D
17. Designed and made a piece of clothing. A B C D
18. Prepared an original floral arrangement. A B C D
19. Drew a picture for aesthetic reasons. A B C D
20. Wrote the lyrics to a song. A B C D
21. Wrote a short story. A B C D
22. Planned and presented an original speech. A B C D
23. Made jewelry. A B C D
24. Had art work or craft work publicly exhibited. A B C D
25. Assisted in the design of a set for a musical or dramatic production. A B C D
26. Kept a sketch book. A B C D
27. Designed and constructed a craft out of wood. A B C D
28. Designed and made a costume. A B C D

Scoring mechanism for creative behavior (CBI) is:
CBI = Average (Q1:Q28) where each Q score corresponds to A=1, B=2, C=3, and D=4.

Never did
this
Did this
once or
twice
3-5 times
More than 5
times
A B C D
127
Appendix F: Revised Creative Domain Questionnaire (CDQ-R)
Please rate how creative you believe yourself to be in the following domains, by circling
on a 1-5 scale:
Not at all Below average Average
Above
average
Extremely
1 2 3 4 5

HOW CREATIVE WOULD YOU RATE YOURSELF IN…
1. Acting. 1 2 3 4 5
2. Algebra / geometry. 1 2 3 4 5
3. Chemistry. 1 2 3 4 5
4. Computers / computer science. 1 2 3 4 5
5. Crafts. 1 2 3 4 5
6. Dancing. 1 2 3 4 5
7. English literature / criticism. 1 2 3 4 5
8. Interior design / decorating. 1 2 3 4 5
9. Keeping a journal / blog. 1 2 3 4 5
10. Leadership. 1 2 3 4 5
11. Life sciences / biology. 1 2 3 4 5
12. Logic / puzzles. 1 2 3 4 5
13. Mechanical abilities. 1 2 3 4 5
14. Money management. 1 2 3 4 5
15. Painting / drawing. 1 2 3 4 5
16. Playing with children. 1 2 3 4 5
17. Selling people things. 1 2 3 4 5
18. Solving personal problems. 1 2 3 4 5
19. Teaching / education. 1 2 3 4 5
20. Vocal performance / singing. 1 2 3 4 5
21. Writing poetry / prose. 1 2 3 4 5

128
Scoring mechanism for domain creativity (CDQ-R) is:
CDQ-R = Average (Q1:Q28) where each Q score corresponds to a 1-5 self-rate.
Appendix G: Engineering Design Self-Efficacy (DSE)
This section contains 2 parts:
• Preliminary practice task, and
• The actual assessment task.
Please rate your degree of confidence by recording a number from 0 to 100, using the 10-
increment scale below:
Cannot do at
all

Moderately can
do

Highly certain can
do
0 10 20 30 40 50 60 70 80 90 100

Part 1: Practice Rating
To familiarize yourself with the survey rating form, please complete this practice item
first. If you were asked to lift objects of different weights right now, how certain are you that
you could lift each of the weights described below? (Please circle.)
Lift a 10 lb (4.5 kg) object 0 10 20 30 40 50 60 70 80 90 100
Lift a 20 lb (9.1 kg) object 0 10 20 30 40 50 60 70 80 90 100
Lift a 50 lb (22.7 kg) object 0 10 20 30 40 50 60 70 80 90 100
Lift a 80 lb (36.3 kg) object 0 10 20 30 40 50 60 70 80 90 100
Lift a 100 lb (45.4 kg) object 0 10 20 30 40 50 60 70 80 90 100
Lift a 150 lb (68 kg) object 0 10 20 30 40 50 60 70 80 90 100
Lift a 200 lb (90.7 kg) object 0 10 20 30 40 50 60 70 80 90 100
Lift a 300 lb (136 kg) object 0 10 20 30 40 50 60 70 80 90 100
129

Part 2: Design Rating
If you were asked to carry out engineering design process to design a part/product right
now, how certain are you that you can perform each of the tasks described below? (Please
circle.)
Conduct engineering design 0 10 20 30 40 50 60 70 80 90 100
Identify a customer/design need 0 10 20 30 40 50 60 70 80 90 100
Research design need 0 10 20 30 40 50 60 70 80 90 100
Develop design solutions 0 10 20 30 40 50 60 70 80 90 100
Select the best possible design solution 0 10 20 30 40 50 60 70 80 90 100
Construct a prototype 0 10 20 30 40 50 60 70 80 90 100
Test and evaluate a design 0 10 20 30 40 50 60 70 80 90 100
Re-design 0 10 20 30 40 50 60 70 80 90 100

Scoring mechanism for design self-efficacy (DSE) is:
DSE = Average (Q1:Q8) where Q1:Q8 covers ratings for each of the 8 tasks.
Appendix H: Innovation & Invention Index (III)
The following questions ask about your professional experience
For the fill-in-the-answer questions (1-10) please provide only exact numbers in the
answer boxes, since ranges, verbal descriptions, or blank answers are not accepted. If any
question doesn’t seem applicable, write in “0”. If you cannot recall the exact numerical answers,
please estimate them to the best of your ability.
130
For multiple choice questions (11-12) please select only one answer. If you feel like no
option is suitable, please approximate to the best of your ability.
No. Questions Ans:
1. How many years of engineering experience do you have?
2.
Think of your primary area of experience.
How many other areas do you have experience in?

3. How many US/worldwide/equivalent patents have you filed?
4. How many US/worldwide/equivalent patents have you had approved?
5.
How many papers (conference AND journal papers, combined) have you
published?

6. How many invited talks/lectures have you held?
7. How many projects have you worked on?
8. How many projects have you led?
9. How many professional awards have you received?
10. How many times have you been promoted/changed jobs for the better?
11.
Select the type of institutional setting that best applies to your current primary employer:
(a) university (b) government lab (c) firm
1

12.
Who is the assignee (owner) of the majority of your patents?
(a) I am. (b) My employer is / was.
(c) I have no patents
pending or granted.

1
A firm can mean any privately owned business, corporation or service. This option
includes self-employment. If you do not currently work, neither for a public / private entity nor
yourself, you are not eligible to take this survey.
131
Appendix I: Design Problem (for Chapters 5 and 6)
“You’re creating a new game with your fellow engineering students. Your goal is to
launch a ping-pong ball at a bull’s-eye target, which lies horizontally on the ground.
As part of the game, you are to design a ball launcher: a device that can lift up the
ball, and deliver it at the target. The most accurate launch wins. Initially, you are
located 5 meters away from the center of the target. (As you only aim for the center
of the target, you do not need to know its diameter, just location from the center.)
Your entire device is not to exceed 1 m x 1 m x 1 m in size (length, width, and
height). You are not allowed to throw the ping-pong ball at the target. You are,
however, encouraged to pursue novel or unusual solutions, while holding precise
delivery aim imperative.”
Appendix J: Design Problems (for Chapter 7)
Design problem-1: Ping-pong ball launcher
“You’re creating a new game with your fellow engineering students. Your goal is to
launch a ping-pong ball at a bull’s-eye target, which lies horizontally on the ground. As
part of the game, you are to design a ball launcher: a device that can lift up the ball, and
deliver it at the target. The most accurate launch wins. Initially, you are located 5 m (16.4
ft) away from the center of the target. (As you only aim for the center of the target, you
do not need to know its diameter, just location from the center.) Your entire device is not
to exceed 1 m x 1 m x 1 m (3.3 ft x 3.3 ft x 3.3 ft) in size (length, width, and height).
You are not allowed to throw the ping-pong ball at the target. You are, however,
encouraged to pursue novel or unusual solutions, while holding precise delivery aim
imperative.
Please create a design log for this problem. In your design log, describe the design
process(es) used, and use labeled sketches to describe both your design process and the
final product.”
Design problem-2: Duplo brick transporter
“You need to transport a 2x2 Duplo brick, from point A to B, which are apart 38”. To
meet this need, you are to design a Duplo brick transporter: a device that can lift up the
Duplo and transport it as instructed. You can only store and use energy in one or multiple
rubber bands and potential energy from gravity; you must release your device in a way
that imparts no additional energy. 2x2 Duplo brick dimensions are 1.26 in x 1.26 in x
0.76 in plus a nibble height of 0.18 in. At the start, transport device must fit in an
11”x11”x11” cube, with brick centered at point A.
Please create a design log for this problem. In your design log, describe the design
process(es) used, and use labeled sketches to describe both your design process and the
final product.”

132
Design problem-3: Boat propulsion (Jin & Benami, 2010)
“Oars often propel boats that operate manually (human powered). However, oars can be
difficult to maneuver. Inexperienced operators tire quickly, and if the oars are not used
correctly, they rock the boat, and splash water on the deck where people are sitting. Your
task is to develop designs for alternative means (besides oars) to manually propel boats.
Please create a design log for this problem. In your design log, describe the design
process(es) used, and use labeled sketches to describe both your design process and the
final product.”
Design problem-4: Aircraft hose design
“Large commercial aircraft are refueled between flights from mobile tankers containing
aviation kerosene. The ends of the hoses from tankers are fitted with special connectors
that are attached by ground crew. A hose end connector and socket are to be designed to
allow the attachment and removal of hoses to be carried out quickly and safely.
Please create a design log for this problem. In your design log, describe the design
process(es) used, and use labeled sketches to describe both your design process and the
final product.”
Design problem-5: Burrito folding device (Richardson et al., 2011)
“A large chain restaurant receives a lot of customer feedback on their burritos being
folded poorly or inconveniently by the food preparation staff. For example, their burritos
are overly loaded with ingredients and cannot be entirely folded or they are folded in a
way that doesn’t mix ingredients enough through the rolling process. It is difficult for
restaurant management to frequently re-train their staff or oversee every single burrito
being made. Thus, they’ve contracted you to propose a conceptual design for a burrito
folding device.
Please create a design log for this problem. In your design log, describe the design
process(es) used, and use labeled sketches to describe both your design process and the
final product.”

Abstract (if available)

Abstract Engineering design methods, such as systematic design, design thinking, and design by analogy are supported by cognitive, behavioral, and social characteristics of the engineer performing design. It is of interest to uncover how a designer’s thinking affects creativity and functionality of their conceptual designs. Thinking can be considered as a dual process, in terms of the cognitive-experiential self-theory and decision making: system 1 thinking is intuitive/experiential (automatic, involuntary, unconscious, fast) and system 2 thinking is rational/analytic (controlled, voluntary, conscious, slow). In addition to thinking styles, components of the designer’s identity (such as personality), habits (such as behavioral creativity) and circumstances (such as level of experience) may contribute to how they address a design problem (i.e. how they perform on it) and how they perceive their ability to do so (design self-efficacy). These traits were studied together in order to define a designer’s social-cognitive system, which defines their contextual existence, consistent of endless reflexive associations between their design self-efficacy, personal, behavioral, and environmental influencers. ❧ Traditionally, mechanical engineers are educated to solve problems analytically. However, mechanical design relies on synthesis, an inherently different approach, which may best be supported by studying some of the relationships from a designer’s social-cognitive system. Considering the notion of analysis being associated with analytic thinking (system 2) and synthesis with intuitive thinking (system 1), one of the hypotheses of this research effort is [H1] that intuitive thinking balances the analytically skewed dual process thinking of an engineer in order to generate more creative, rather than strictly functional designs. It is also hypothesized [H2] that there are influencers that can contextualize and aid intuitive or rational thinking in engineering design, and [H3] that with greater professional experience, engineers enhance the relationships between their pro-design factors and design metrics. When it comes to individuals with and without professional experience, the previously studied social-cognitive categorical influencers (pro-design factors) can be applied to identify effects of experience factors, as well as explore the differences between experienced and inexperienced groups. ❧ In order to validate these hypotheses, 3 studies were completed. The initial two studies considered a sample of engineering students and non-engineering students from Shanghai Jiao Tong University and the University of Southern California. The third study considered a sample of engineering students from the University of Southern California as well as a sample of engineering professionals. The approach taken for these studies was based on survey methodologies and assessments of conceptual design projects. In the first study, a comparison is done between engineering students and non-engineering students in the form of correlations between thinking styles (rational and intuitive) and (1) big five personality traits (extroversion, agreeableness, conscientiousness, emotional stability, and openness to experience), (2) behavioral creativity (scores of: biographic inventory of creative behaviors, creative behavior inventory, and revised creativity domain questionnaire), and (3) design performance (design novelty and design usability). In the second study, the above variables were contextualized with respect to the social-cognitive theory. Specifically, we analyzed associations driven by design self-efficacy, for behavioral actions involving rational and intuitive thinking, behavioral creativity, and design performance, with respect to the influencers grounded in social-cognitive theory, which are categorized as personal (gender, personality) and environmental (professional culture, location). The third study involves comparing the pro-design factors, as defined by the social-cognitive influencers from the second study, between engineering students and professionals, as well as characterizing design self-efficacy by a range of experience parameters. ❧ Results of the first two studies demonstrate a relatively strong correlation between rationality and behavioral creativity, across all subject categories. Additionally, when comparing engineering students with non-engineering students, it was found that engineers were more rational, as well as less emotionally stable than the neutral, non-engineering student sample. It was observed that overall design self-efficacy association was high with rationality, and low with intuition

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Asset Metadata

Creator Milojevic, Hristina (author)

Core Title A social-cognitive approach to modeling design thinking styles

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Mechanical Engineering

Publication Date 11/13/2020

Defense Date 10/22/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag behavioral creativity,BFI,BICB,CBI,CDQ-R,design cognition,design creativity,design methodology,design novelty,design self-efficacy,design theory,design usability,engineering professionals,engineering students,Engineers,experience,fast and slow thinking,innovation,invention,OAI-PMH Harvest,Personality,pro-design behaviors,rational and intuitive thinking,REI,social-cognitive theory,thinking style

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Jin, Yan (committee chair), Jovanovic, Mihailo R. (committee member), Khooshabeh, Peter (committee member), Sauder, Jonathan (committee member), Shiflett, Geoffrey R. (committee member)

Creator Email dr.hristina.milojevic@gmail.com,milojevi@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-391648

Unique identifier UC11666324

Identifier etd-MilojevicH-9119.pdf (filename),usctheses-c89-391648 (legacy record id)

Legacy Identifier etd-MilojevicH-9119.pdf

Dmrecord 391648

Document Type Dissertation

Rights Milojevic, Hristina

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA