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Design Thinking: A New Foundational Science for
Engineering*
ADE MABOGUNJE, NEERAJ SONALKAR and LARRY LEIFER
Center for Design Research, 424 Panama Mall, Stanford University, Stanford, CA 94305, USA. E-mail: ade@stanford.edu,
sonalkar@stanford.edu, leifer@cdr.stanford.edu
Design thinking has seen rapid growth since mid-2000s far beyond the engineering and arts disciplines traditionally
concerned with design. In universities, it is increasingly being used to create programs where graduates from multiple
disciplines can learn to develop a design orientation to problem solving. This rising popularity of design thinking as a
generalist training methodology positioned alongside the MBA program while helpful, obscures the central role it plays in
the engineering disciplines. In this paper, we argue for design thinking to be recognized as a foundational science for
engineering alongside Physics, Chemistry and Biology.
Keywords: design thinking measurement; design teams; product based learning; technology innovation; innovation eco-systems;
economic growth
1. Introduction—What are the criteria for
becoming a foundational science for
engineering?
In his State of the School address in May 20, 2003,
the dean of engineering at Stanford University, Jim
Plummer, announced the launch of a new initiative,
the creation of a bioengineering department. 24
Incremental faculty billets had been approved by
the provost to be shared between the school of
Engineering and the school of medicine. The dean
argued that our ability to measure biological sys-
tems at the molecular level and to understand the
molecular basis of living systems had turned biology
from being a qualitative science to being a quanti-
tative science like physics, chemistry, and mathe-
matics. He concluded saying Biology is ready to
become a new foundational science for engineering.
Could the same be said of design thinking? In this
paper we will make the case that design thinking, is
not only foundational to engineering, it lies at the
core of what engineers know and do. We will further
argue that while in the past, Engineers have looked
to other fields to validate their work, Engineering as
a discipline has now come of age, and it is time to
acknowledge the critical role design plays in engi-
neering [1]. Finally, we will show that design think-
ing can indeed be measured, and by using the same
criteria as was used in Biology, it will be possible to
conclude that design thinking is ready to become a
new foundational science of engineering.
2. Context—Is context critical in design
thinking?
Unlike the natural sciences, which strive for context
independence [2, 3], design thinking and indeed
engineering is highly context dependent. Therefore
in this paper, we will describe our experiences at a
very local and specific level. This will include refer-
ences to Stanford University, IDEO a product
consulting firm in Palo Alto, the Silicon Valley,
the United States of America, and reference to
specific people working in these environments.
This is not in any way to play down the contribu-
tions of people working in other regions of the US,
and there are several people that come to mind,
however the contextual nature of the phenomenon
we are studying and practicing, requires us to make
this choice. Indeed we make the following func-
tional comparison:
‘‘The Silicon Valley is to the Information Revolution
what the Steam Engine was to the Industrial Revolu-
tion.’’
Thus just like all the components of the Steam
Engine like the Boiler and Pumps have to be co-
located and connected for energy to be generated,
the design components of an engineering and eco-
nomic engine like the Silicon Valley have to be co-
located and connected. To do otherwise is mean-
ingless from an engineering point of view. Once
built and reasonably understood, the Steam
Engine can be rebuilt and improved upon in other
contexts, because then, the initial and boundary
conditions can be well accounted for. Therefore
we extend our sincere apologies to those who may
feel their work and contributions have not been
highlighted in this paper. We hope in future, we
will have a way of adding multiple and redundant
citations, if anything to highlight similar and related
work going on in other geographies. With that
caveat in place, we will now describe the context
* Accepted 30 September 2015.1540
International Journal of Engineering Education Vol. 32, No. 3(B), pp. 1540–1556, 2016 0949-149X/91 $3.00+0.00
Printed in Great Britain #2016 TEMPUS Publications.
of our work, and our primary motive is to make
scientific work such as corroboration and replica-
tion possible.
Engineering has been often been described as the
application of science and mathematics to the needs
of society, however, up until now, we knew and
taught our students very little about finding out and
understanding the needs of society. Thus we indir-
ectly turned out half-baked engineers, engineers
who could only serve the needs of the military,
and engineers who were easily exploited by others.
With the advent of the design-thinking paradigm,
this situation has changed. Engineers are now
taught how to engage with society through the use
of empathy, multiple points of view, teamwork,
prototyping, and iterative testing and feedback. In
effect, a critical link has been made between engi-
neering analysis, the motion, space and materials
science facing part of the discipline and engineering
design, the human facing part of the discipline.
As engineering faculty, most of us did not teach
engineering design either because we did not under-
stand it and/or it included humans in the loop,
which not unlike the phenomenon of friction and
inertia, made the formulation of problems more
difficult not to mention the derivation of solutions.
This attitude, due in part to a lack of understanding
and in part to fear, often made some of us have some
contempt towards the broader discipline of design.
In reality we have often done a limited amount of
design especially for problems where we can con-
veniently ignore the unpredictable human element.
Based on the more recent recognition of critical role
played by design in the profitability of companies,
the cumulative work of the engineering design
research community and recent breakthrough in
our ability to measure design behavior, we can
now change this situation for the better.
This breakthrough at Stanford cannot be
divorced from the location of Stanford within the
Silicon Valley and the role the University has played
in impacting society. Wherein the dean of engineer-
ing, Frederick Terman, himself an engineer, re-
conceptualized the role of the university as follows
and we quote:
‘‘Universities are rapidly developing into more than
mere places of learning. They are becoming major
economic influences in the nation’s industrial life,
affecting the location of industry, population growth,
and the character of communities. Universities are in
brief a natural resource just as are raw materials,
transportation, climate, etc.’’ [4].
Stanford has thus led the way in the accelerated
creation of new ventures, new markets, and
increased rate of capital formation. [5]. The fore-
going really begs the question—just what is design
thinking? And what if any is its relationship to
engineering?
3. Definition—What is design thinking?
In arriving at an understanding of design thinking
or design more generally, we have found it useful to
sketch out the five Venn diagrams shown in Fig. 1,
and ask people to identify which most closely
matches their understanding of design. Reading
from left to right we hope to evoke the following
questions—Is design synonymous with engineer-
ing?; Is design at the core of engineering?; Is
engineering one of the sub-fields of design?; Is
design a partially separate and distinct field from
engineering?; and finally—Is design a completely
separate field from engineering? So we encourage
our readers to take a moment to make their selection
at this point before reading further.
The term design thinking was used as early as
1991. It was the title of the Proceedings of a Work-
shop meeting titled ‘‘Research in design thinking,’’
held at the Faculty of Industrial Design Engineer-
ing, Delft University of Technology, from May 29–
31. However the current popularity of the term can
be attributed to the product development consul-
tancy IDEO and its founder David Kelley. It is
useful here to get a few snapshots of how Kelley
sees and defines design.
‘‘What led you to find IDEO?’’ ‘‘Well, I had just fallen
in love with design at Stanford. I was a regular engineer
before I got to Stanford where I got into a program that
taught the human side of engineering. I really fell in
love with the notion of doing something for humans
rather than just doing something technologically. Sili-
con Valley was just starting to do really well and then it
became clear that a lot of companies had the need to
have somebody who was sensitive to the design, the
aesthetics, the humans and the engineering stuff. That
Design Thinking: A New Foundational Science for Engineering 1541
Fig. 1. Defining design relative to engineering.
combination seemed really obviously needed in Silicon
Valley. That made it easier to start IDEO’’ [6].
‘‘What I mean by design is doing things with intention,
trying to decide what’s important to somebody, build-
ing a bunch of prototypes and showing them around,
developing a point of view and getting it out so that it
has impact in the world. So design is really a process of
making impact on the world by doing this kind of
creation of something new to the world and then
getting it out there’’ [6].
‘‘You’re sitting here today because we moved from
thinking of ourselves as designers to thinking of
ourselves as design thinkers,’’ . . . ‘‘What we, as
design thinkers, have, is this creative confidence that,
when given a difficult problem, we have a methodology
that enables us to come up with a solution that nobody
has before’’ [7].
‘‘Design thinking is a human-centered approach to
innovation that draws from the designer’s toolkit to
integrate the needs of people, the possibilities of
technology, and the requirements for business success.
Thus, the method focuses on the three main elements of
a product or solution: people, technology, and busi-
ness, all of which evolve around the customer’’ [8].
We have used several quotations from Kelley,
because it highlights a phenomenon we have
found to be prevalent among design thinkers—
namely a tolerance for ambiguity [9]. Design thin-
kers on the other hand tell us, they observe things
from multiple perspectives. Perhaps the three most
important features in the foregoing quotations are
that (1) there is no one single definition of design
thinking and an important part of the process is
‘‘developing a point of view;’’ (2) the process of
design is emergent, and non deterministic; (3) as
Kelley points out there is a coherent, integrated, and
concerted effort towards creating something new to
the world, for the customer, and getting it out there.
This view of design, in a way, attempts to over-
come the problem of coherence that has dogged the
social sciences. In 1998, E. Wilson made a thought
provoking observation in comparing the rate of
progress in the medical vs. the social sciences. He
remarked:
‘‘There is also progress in the social sciences, but it is
much slower, and not at all animated by the same
information flow and optimistic spirit . . . The crucial
difference between the two domains is consilience: The
medical sciences have it and the social sciences do not.
Medical scientists build upon a coherent foundation of
molecular and cell biology. They pursue elements of
health and illness all the way down to the level of
Biophysical Chemistry . . . Social scientists by and
large spurn the idea of the hierarchical ordering of
knowledge that unites and drives the natural sciences.
Split into independent cadres, they stress precision in
words within their specialty but seldom speak the same
technical language from one specialty to the next’’ [10].
We have been aware of this problem in the social
sciences for a while, and it can be a stumbling block
to progress in design thinking. To guard against
such a situation while advancing the field, we have
found the definition used by Herbert Simon in his
book—The Sciences of the Artificial—to be the
most parsimonious and comprehensive. The parsi-
mony principle is basic to all science and tells us to
choose the simplest scientific explanation that fits
the evidence. In the next paragraph, we present
Simon’s definition of design:
‘‘Historically and traditionally, it has been the task of
the science disciplines to teach about natural things:
how they are and how they work. It has been the task of
engineering schools to teach about artificial things:
how to make artifacts that have desired properties
and how to design. Engineers are not the only profes-
sional designers. Everyone designs who devises courses
of action aimed at changing existing situations into
preferred ones. The intellectual activity that produces
material artifacts is no different fundamentally from
the one that prescribes remedies for a sick patient or
one that devises a new sales plan for a company or a
social welfare policy for a state. Design, so construed, is
the core of all professional training; it is the principal
mark that distinguishes the professions from the
sciences’’ [11].
In this way, the first three Venn diagrams will satisfy
this definition of design, which is the one we have
adopted for our research. We also hope the reader
will see the similarity between Kelley, the practitio-
ner’s view and Simon, the researcher’s view. Having
defined design, the natural question that follows is
whether it can be improved, and this will be covered
in the next section.
4. Can the process of design be improved
upon in a systematic way?—previous
approaches
There have been several lines of work that have been
aimed at improving design in a systematic way. We
will mention four approaches here to give a broader
view of the research enterprise whose collective and
cumulative effort are only now coming to a head.
These remain to this day, critical and important
milestones.
4.1 Approach #1: augmenting human intellect [12]
The project on augmenting human intellect was
championed by Doug Engelbart and his team at
the then Stanford Research Institute, SRI. This was
the precursor to the Mouse, Windows, Hyperlinks,
and the World Wide Web. Doug, an electrical
engineer, felt that the rate at which problems in
the world were growing was much faster than the
rate at which solutions were being developed.
Therefore, they set about looking for ways to use
other parts of the human body for problem solving.
The idea of the mouse for example uses our hands.
Ade Mabogunje, Neeraj Sonalkar and Larry Leifer1542
The links we have on the web pages mimics the way
we recall things using our associative memory. We
can jump from one idea to another idea while
reimagining and recreating the context in the pro-
cess. There were several other inventions made by
this group in the 1960’s several of which have not
been inserted into the public space.
4.2 Approach #2: automating human intelligence
[13]
Allan Turing is often called the father of artificial
intelligence, and his exploits during the Second
World War are legendary. Then there was the
work by Herbert Simon and Allen Newell and
several others at Carnegie Mellon University
focused specifically on Problem Solving [14] and
Design. This line of work can be considered the
precursor to several firsts in artificial intelligence
such as the IBM chess player named deep blue and
later Jeopardy quiz show game winner the IBM
Watson computer.
4.3 Approach #3: situating human cognition,
learning and action
We argued earlier that Design is context dependent
and over the years several social scientists and
computer scientists have been able to describe the
different ways in which the context shapes outcome
of the activity. We recall here the work of the
following researchers. Note that unlike hardware
like the computer mouse, and interactive algorithms
like deep blue and Watson, social scientists write
books, it is their design artifact. So in Table 1 we
have provided a cursory list of works emphasizing
the important role played by context.
Thus we can see, that a lot has been written, and a
lot has already been built; yet we do not seem to have
a systematic way to improve design. For our pur-
poses, we would like to improve design in a way
specific to an Engineering viewpoint. And here we
encountered in the above approaches, three main
limitations, which will be discussed in the next
section.
5. Limitations—What are the current
constraints to improving design in a
systematic manner?
The three limitations are (1) Engineering-Design is a
very broad and dynamic field and coverage is
difficult; (2) Nobody knew what performance
improvement really looked like; (3) There were no
standard ways of measuring performance. We will
briefly discuss each in turn.
5.1 Limitation #1 engineering design is a very
broad field
Engineering Design is quite a broad discipline and
practiced in a variety of industrial contexts. For
example, the defense sector, the health sector, the
automobile sector, the housing sector, the transpor-
tation sector, the communication sector, the energy
sector, and we could go on and on. Medicine also
has several sectors—Orthopedic, Ophthalmology,
Psychiatry, Obstetrics and Gynecology, Urology,
Neurology, Internal Medicine, and so on. However
there is one difference. In medicine it all revolves
around the tangible human body. In engineering,
the nature of the client’s problem is different, the
media is different, the laws and regulations are
different, the scale of the artifacts are different, to
name a few. There isn’t a unifying whole.
5.2 Limitation #2. Nobody really knew what
performance looked like
We are reminded here of a story told by Doug
Engelbart to explain to people the function of the
mouse. In the 1960s when it was invented, people
had a hard time figuring out why it was necessary.
Engelbart, we assume out of some frustration,
fastened a heavy mud brick to a pencil and had
people write with this weighted pencil, and then
removed the brick and had them write again, so they
could experience the difference.
Well the same is true about engineering design.
Economics as a discipline measures factory
output—inventories and production rates. They
Design Thinking: A New Foundational Science for Engineering 1543
Table 1. A Sample of the Research describing the relationship between context and human performance
1 Vygotsky, L. (1934/1962). Thought and Language [15].
2 Dewey, J. (1938). Experience and Education [16].
3 Bandura, A. (1977). Social Learning Theory [17].
4 Suchman, L. (1987). Plans and Situated Actions: The Problem of Human/Machine Communication [18].
5 Brown, J. S., Collins, A., Duguid, P. (1989). Situated cognition and the culture of learning [19].
6 Lave, J., Wenger, E. (1990). Situated Learning: Legitimate Peripheral Participation [20].
7 Donald, M., (1991) Origins of the modern mind: Three stages in the evolution of culture and cognition.
Cambridge, MA: Harvard University Press [21].
8 Clancey, W. J. (1997). Situated Cognition: On Human Knowledge and Computer Representations [22].
measure nothing related to the intellectual activity
we call design.
5.3 Limitation #3. Lack of Standard Measurement
Units
In traditional engineering and the natural sciences,
we typically measure variations in terms of seven
basic units, the SI units. These are—Mass, Length,
Time, Temperature, Current, Light Intensity, and
Quantity of Substance. All other constructs can be
built from these primitives. So for example the
construct of ‘‘force’’ is composed of—mass,
length, and time. Thus in the absence of such
standards, the coordination of progress in design
research and practice will be very arduous.
6. Overcoming Limitations—How can the
constraints to improving design be
redesigned and minimized?
Luckily we were able to find resources that allowed
us to overcome the first two limitations.
6.1 Overcoming limitation #1 engineering design is
a very broad field
At Stanford, we had been running an Industry
sponsored design course, ME210 (presently called
ME310) since 1970. In this course a wide variety of
companies pay the cost of materials and services for
teams of graduate student to work on real industry
problems [23]. Today the list of companies includes
those in the automobile, health care, software,
defense, and several others. Thus, when the course
is in session, we could observe in one place, teams of
engineers working on problems across a wide spec-
trum of industries, and we could interact with them.
The logistics of such a wide coverage could be
reduced if we used the class as our laboratory,
which is what we did.
6.2 Overcoming limitation #2. Nobody really knew
what performance looked like.
Here we were very fortunate. It was the 80 and 90s,
and Toyota had become the bestselling car in
America. This feat was perceived as a national
security threat and led to several important changes
in industry, academia, and government in the US
[24–26]. In the national science foundation, the
design theory and methodology field was created
and charged as follows:
‘‘. . . the results of research in this field can improve the
creativity of engineering design and the effectiveness of
product design and development, leading to improved
national productivity and international competitive-
ness’’ [27].
There were a lot of researchers studying the internal
processes of Japanese companies, and we will
describe here the results of two such studies.
Study 1: In a study of the number of patents filed
by US, European, and Japanese companies from
1968 to 1986, US companies filed 2500 in 1968 and
this gradually fell to 1500 by 1986. The European
companies filed approximately 800 patents in 1968
and this rose to about 1400 in 1986. The Japanese
companies filed below 100 in 1968 and this rose to
about 2200 in 1986, a jump of over 2000 in 15 years,
surpassing those of the US and Europe by almost
1000 [28].
In another study, Kim Clark and Takahiro Fuji-
moto spent 5 years studying automobile companies
in Japan, Europe, and the US. They were able to
illustrate that the Japanese were able to produce
better cars in shorter lead times because their design
and manufacturing teams shared information ear-
lier in the process and more frequently [29].
They called this technique, overlapping problem
solving. This technique showed the interelationship
between three variables—knowledge level, commu-
nication frequency, and lead-time. The technique
later became known as concurrent engineering. This
was the first large-scale effort to directly link knowl-
edge work to economic impact. Now we had a vague
idea what performance could look like and how
important multi-disciplinary teamwork was to engi-
neering. This left the third problem of finding
standard ways of measuring performance.
6.3 Overcoming Limitation #3. Lack of standard
measurement units
We were not able to find any way around this
limitation. We therefore created a laboratory to
study the class. In our lab we focused on developing
metrics of the design process, ways to measure the
process [30]. We looked at several variables. Includ-
ing variables that described the external environ-
ment of the class, which happened to be the Silicon
Valley. In the next section we will describe some of
the variables.
7. Towards a scientific understanding of
design—how do you measure the design
process?
Given that the work we have been doing spans close
to 30 years, we will only provide a few highlights
here to give a flavor of our research methodology
and some of the results. In order to measure the
design process as reliable as possible, we have opted
for the use of video recordings for primary data. The
video not only captures the behaviors of the
designers, it also captures an aspect of the context
of their work and interaction. The video can then be
later analyzed to produce secondary data. In addi-
Ade Mabogunje, Neeraj Sonalkar and Larry Leifer1544
tion the subjects can be interviewed to provide their
own interpretation in addition to the interpretation
of the research team. Finally, as we developed new
insights about the design process, we could review
older tapes to re-analyze them from a different
point of view. This method of analysis borrowed
greatly from the field of Anthropology [31]. Coin-
cidentally, Anthropology has been described as the
most humanistic of the sciences and the most
scientific of the humanities. This has served us
well. In addition to Anthropology we have drawn
from several other fields most notably engineering,
computer science, and psychology. From engineer-
ing, we have borrowed metaphors for creating
change in system. From computer science, we
have designed interventions in form of tools to
perturb the system. And from all four including
anthropology, we have searched out frameworks to
explain the behaviors we observed, which will now
be presented.
7.1 Framework #1: Systematic reduction in size of
the problem
Figure 2, illustrates the method we described earlier
which allowed us to overcome the limitations of
scope of past approaches, while preserving the
context. Thus the laboratory was set up to interact
with the classroom which in turn was set up to
interact with Industry.
7.2 Framework #2 Engineering feedback control
It can be said that one of the greatest contributions
of engineering to the modern word is the feedback
instrumentation system and methodology illu-
strated in Fig. 3. In it we measure and compare the
actual performance of a system (the Plant) with the
intended performance and make continuous adjust-
ments to reduce the variance.
Using this analogy, we extended the feedback
concept to include the process of design and the
context of design. The resulting feedback system is
shown in Fig. 4.
It is important to observe that this is not a study of
the product, neither is it a study of the consumer.
Instead it is a study of the producer. We were
studying ourselves as engineering-designers. We
were using our methods and techniques to improve
ourselves. Next we will describe a useful framework
for organizing the various measurements we
worked with, followed by a sample list of the types
Design Thinking: A New Foundational Science for Engineering 1545
Fig. 2. Using an industry sponsored product-based learning class and a laboratory
to overcome several limitations to developing a scientific understanding of design.
Fig. 3. A conceptual model of the basic feedback control system.
of measurements we made from the video observa-
tions of engineering designers at work.
7.3 Framework #3 Model of triadic reciprocal
causation
To give some coherence to the indicators and
constructs we developed, we have found it useful
to group them according to the model of triadic
reciprocal causation [17].
According to this model, behavior, personal
factors such as cognition, and environmental influ-
ences all operate as interlocking determinants that
affect each other bi-directionally, See Fig. 5. In the
following examples, primary data from our lab were
investigated in the first two categories—Person and
Behavior, which we describe as disposition and
communication variables. The environmental vari-
ables, which we described as institution variables,
have come from secondary data sources. Table 2 is a
summary of all the variables we have collected and
tested.
1. Brief description of generative design questions
In 2003, Ozgur Eris, found that the performance
of design teams in creative problem solving was
based on their ability to ask both of two types of
questions [36]. The first, he called deep reasoning
question. These are the types of question often
taught in critical thinking classes. The second, he
called generative design questions. These are ques-
tions that do not have to be true or false. He found
that these sort of questions account for over 20 % of
the questions creative teams pose during problem
solving.
2. Brief description of noun phrases
In 1997, one of the authors found that the
appearance of new noun phrases in design docu-
mentation could be used as a proxy for work
progress during design [35]. Thus a rough count of
unique noun phrases will give an early indicator of
the novelty of the design.
3. Brief description of the emotion disposition
In early 2000, John Gottman published the result
of work he had done in studying married couples.
Gottman brought them into his lab, which was
similar in several respects to our own lab. Based
on 15 minutes of video based observation and
analysis of the video, Gottman was able to predict
which couples would divorce with an accuracy of
94% [47]. Malte Jung, then a graduate student at
Stanford, applied Gottman’s method to the study of
design teams. Here the team was composed of soft-
ware engineers engaged in an activity called pair
programming. Jung was able to replicate Gott-
Ade Mabogunje, Neeraj Sonalkar and Larry Leifer1546
Fig. 4. A conceptual model of the basic feedback control system modified to study the design process in context.
Fig. 5. The model of triadic reciprocal causation between a
person’s internal environment, their behavior, and their external
environment.
mans’s study in two ways. First, he showed that
teams with regulated affect i.e. the cumulative
positive minus negative affect over time had a
positive gradient, performed better than teams
with unregulated affect i.e. the cumulative positive
minus negative affect over time had a negative
gradient [38].
4. Brief description of moment-to-moment concept
generation
Improvisation is one of the most creative forms of
interaction during design. Neeraj Sonalkar was
convinced it held the key to understanding the
sources of creative behavior amongst designers.
However, he felt that the only way to demonstrate
this was to track the interaction between designers
on a moment-to-moment basis. Several attempts to
find an appropriate representational schema failed.
Finally using the rules of brainstorming and impro-
visational theatre as guides, he adapted a notational
system called Force Dynamics Notation to repre-
sent the interaction sequences during design. From
this he was able to develop twelve symbols for
notating design interactions. See Fig. 6. From this
he was able to demonstrate for the first time the
conditions under which the exceptions to brain-
storming rules and improvisation rules occur [37].
In addition, the visualization was able to shed light
on how transitions between different types of infor-
mation for example, proven facts (certainties) and
plausible conjectures (possibilities), actually occur
in design conversations.
5. Frustrations and paradigm shift
Despite the long list of variables and explanations
we developed, we found most of the foregoing
experiences most unsatisfactory. Like many other
design researchers we entered the field of design
research with backgrounds in analytical engineering
and science—the ‘‘sciences of the natural’’ as
Herbert Simon put it. But we were also designers
at heart, wanting to change existing conditions for
the better. Our dissatisfaction stemmed from the
fact that on one hand we could do scientific research
as observers detached from the situation; on the
other hand we could do social science research with
a contextual understanding but without changing
the situation itself for the better; the growing list of
variables and explanations made us despair. As we
struggled with this problem we came upon an
unusual source of inspiration in the person of
Mozart. Mozart as recorded in the Amadeus Bio-
graphy, made the claim that: ‘‘It is only though
music, through opera that over 20 people can talk at
Design Thinking: A New Foundational Science for Engineering 1547
Table 2. A Sample of the Variables Measured in our lab grouped according to the model
of triadic reciprocal causation
Communication Variables
1 Gestures (Tang 1989) [32]
2 Ambiguity Preservation (Minneman 1991) [9]
3 Temporal Transitions (Baya, 1996) [33]
4 Categorical Transitions (Brereton 1997) [34]
5 Noun Phrase Generation (Mabogunje, 1997) [35]
6 Generative Design Questions (Eris, 2003) [36]
7 Describing Moment-to-Moment Concept Generation (Sonalkar 2012) [37]
Disposition Variables
1 Team Emotional Balance (Jung, 2010) [38]
2 Team Composition (Schar, 2012) [39]
3 Team Diversity (Kress, 2012) [40]
Institution Variables
1 Innovation and Economic Growth (Solow,1956 & 1962) [41, 42]
2 Vision (Founding Narrative, ReVeL Handbook, 2005) [43]
3 Rules (Regulation of Capital Formation, Hwang & Horowitt, 2011) [44]
4 Network Density (Zoller, 2010) [45]
5 Age differences (Park et al, 2002) [46]
Fig. 6. The 12 symbols of the Interaction Dynamic Notation
System.
the same time and it would not be noise, but a perfect
harmony’’ [48].
We began to see the Interaction Dynamic Notion
differently because like musical notation, it inte-
grated all our foregoing measurements in a single
view and gave a sense of coherence to the phenom-
enon we were studying. Furthermore when we
presented the notation to practitioners, they could
immediately relate to it and found it meaningful.
The notation allowed us to describe design at the
moment-to-moment level [49]. With the notation as
the foundational unit, we can consider all other
metrics as layers built upon this basic unit. Fig. 7
shows this basic notation with a layer consisting of
the ideas on top of it.
6. A partial measurement system for design
Arriving at this point is significant because for the
first time we can observe design interaction at the
moment-to-moment level. While the notational
system had been developed as a means to observe
the phenomenon of concept generation, we realized
that in and of itself it was a foundational measure-
ment system of human-human interaction—indivi-
sible without loss of meaning, integrative, and
partially-comprehensive. The fact that it was
easily understandable to researchers and practi-
tioners alike meant that we could use the abstraction
to formulate new behaviors and test them out in
practice. While the notation is more comparable in
form to genetic sequences than to mathematical
equations its parsimony and appropriateness for
the scientific description of design at a fundamental
level cannot be overlooked. Figure 8 shows four
different kinds of representations that have been
used in Engineering-design work including the
notation system just described which is called—
Interaction Dynamics Notation.
8. Discussion part I—what are the
economic implications?
Till now, most measurement of innovation and
economic growth was done only partially. Eco-
nomic measurement systems tend to be about the
institutional arrangements within which events
unfold and about the events that occur post-produc-
tion. In this later instance, economists typically
track variables such as factory inventories and
store sales figures. Our work, describes what engi-
neering designers do during the conceptual design
phase and pre-production. Combining the two
systems will give us the first comprehensive mea-
surement system for design. For the descriptions of
the institutional arrangements and post-production
measurement system we have relied on the work of
others. Specifically we have been fortunate that the
Silicon Valley has garnered the interest of several
researchers some of whose work we have listed in
Table 2. We have also included two other studies
[45, 46] that are not specific to the Silicon Valley but
offer useful additional insights in accounting for its
rapid growth.
8.1 Brief description of the relationship between
innovation and economic growth [41, 42]
Early in the field of economics, they were able to
identify three major factors of production. They are
land, labor, and capital. In a break through study in
1956, Solow found that increases in these three
factors were not sufficient to explain all of the
growth he observed in the data [41]. There was a
residual. This residual, he attributed to innovation
in the factors. For example, without purchase of
additional land, an innovation say in new fertilizers
could increase crop yield, and thus economic
growth. The Silicon Valley has been at the epicenter
Ade Mabogunje, Neeraj Sonalkar and Larry Leifer1548
Fig. 7. The dynamic notation of a segment of design conversation showing how the ideas generated can be
integrated with the underlying exchange pattern. Idea responses are shown in dotted lines.
of innovation in semiconductor and software, and
this will account for a good part of the economic
growth.
8.2 Brief description of the significance of human
vision in economic growth [4]
Earlier we quoted Frederick Terman’s vision of the
role a university could play in the life of a commu-
nity. To better understand the significance of Ter-
man’s view it is useful to consider the basic circular
flow of income in economics (see Fig. 9).
In this figure, households supply the factors of
production to a factor market. Business firms
acquire their resources from this factor market,
and sell their output for a profit in the product
market, where households do their shopping. The
income generated from the sales flows back to pay
the rent for the land, wages for labor, and profit for
capital. One interpretation of Terman’s vision is
that universities themselves could be considered
factor markets, where not only is knowledge
traded but labor can be transformed fundamentally.
Design Thinking: A New Foundational Science for Engineering 1549
Fig. 8. Different ways of representing functions and behavior in Engineering including for the first time the behavior of the humans
in the creative loop.
Fig. 9. The basic economic flow diagram.
In the older economic system, this would be equiva-
lent to saying that universities can transform labor
into landowners. However, we slipped into the
knowledge economy and this is precisely what
seemed to have happened in the Silicon Valley
with the additional assistance of a new type of
entrepreneurs called venture capitalists. The
impact of venture capitalists on the Silicon Valley
economy is profound and in the remaining of this
section I will highlight three effects.
8.3 Brief Description of the significance of
prevalence of dealmaker entrepreneurs and
investors, with concurrent ties to multiple firms on
rate of economic growth [45]
In 2010, Ted Zoller made an attempt to describe the
relationship between dealmakers in 16 different
geographical zones in the US—including Boston,
Austin and the Silicon Valley. By deal makers,
Zoller included angel investors, venture capitalists
and others involved in the buying and selling of
ventures, such as corporate investors responsible for
mergers and acquisitions. While Boston appeared to
have a density comparable to the Silicon Valley, a
deeper analysis showed that the activity of deal-
makers in the Silicon Valley differed significantly
from those in other regions. Specifically when he
looked at the number of dealmakers who sat on the
board of 10 or more companies, the Silicon Valley
had about 90 while Boston and other regions of the
country had a number in the neighborhood of 30
and below. When one thinks of venture capitalists as
cross pollination agents facilitating recombination
of ideas and ventures, a difference between 30 and 90
makes a big difference [45].
8.4 Brief description of the significance of age
differences [46]
While network effects are social in nature, there is
another difference, which is more biological in
nature. One cannot help but notice the young ages
of most founders in the Silicon Valley, and the older
ages of most investors. It is also well known that
most good investors bring more than money to the
table. They bring their knowledge, experience, and
network. In one curve the processing capacity
decreases with time, and in the other curve the
world knowledge increases with time. In a study
on aging by Park et al., they were able to show a
general decrease in processing capacity with age,
and a general increase in world knowledge with age
[46]. In most regions of the world, the extra proces-
sing speed of your people is often discounted. In the
Silicon Valley, it appears this has been taken advan-
tage of. Thus a team of young founders and older
investors has when compared to a computer, a large
memory and a fast processor. This appears to be an
optimal combination.
8.5 Brief description of the significance of change in
behavior on economic growth [44]
Victor Hwang and Greg Horowitt are two venture
capitalists that are connected with Innovation eco-
system. In 2012 they wrote a book titled—The
Rainforest: Secrets to Building the Next Silicon
Valley. What was important about their book was
their distillation of the behavior of people in the
Silicon Valley to a set of rules, which contrasted
sharply with rules followed by entrepreneurs, inves-
tors, and other supporters elsewhere in the world.
While most governments had followed the conven-
tional wisdom of building industrial parks close to
universities as a way to spark innovation in a
geographical region—Hwang and Horowitt drew
attention to the cognitive and affective changes
required for the new knowledge economy. Notice
the mentality of a land-based economy in the idea of
industrial parks. We believe the proximity of indus-
try to universities is important, but it is not the
dominant factor. In Table 3 we list the differences
Hwang and Horowitt drew between the rules of a
production economy, and the rules of an innova-
tion.
8.6 A comprehensive measurement system for
design
When we took the foregoing into consideration, it
seemed feasible that we were in fact describing two
separate and distinct economic systems: One, type–
1, powered by the traditional university and the
other, type-A, powered by a non-traditional uni-
versity like Stanford. This is represented in Fig. 10.
Ade Mabogunje, Neeraj Sonalkar and Larry Leifer1550
Table 3. The Rules of the Plantation (Industrial Production System), contrasted with the Rules of the Rainforest (Innovation Ecosystem)
[44]
Rules of the Plantation Rules of the Rainforest
Excel at your job
Be loyal to your team
Work with those you can depend on
Seek a competitive edge
Do the job right the first time
Strive for perfection
Return favors
Break rules and dream
Open doors and listen
Trust and be trusted
Seek fairness, not advantage
Experiment and iterate together
Err, fail, and persist
Pay it forward
Given that start-ups are not always successful, it
can be inferred that the outer loop is less permanent,
and that it kind of pulsates with time. Having an
economy such as the outer circle that pulsates in this
way could have the effect of enabling the inner
economy (the larger economy) to benefit and
grow. In this way, the metaphor we made earlier,
that the Silicon Valley was to the information
revolution, what the steam engine was to the indus-
trial revolution, could be realized.
9. Discussion part II—What are the
academic implications?
By academic we refer to the practices of research
and teaching of engineering design.
9.1 Consolidating the gains and advancing the
research
On the research side, we now understand the cog-
nitive and communicative basis of artifact creation
and diffusion. We can measure artifact creation at
the human interaction level, and can measure its
diffusion at the economic level. We can also model
the behaviors associated with artifact creation, and
design new ones. We can create environments that
impact a team’s ability to develop innovative solu-
tions to difficult problems. Design has been a
difficult process to research and understand. We
have been able to take advantage of several oppor-
tunities in the last 30 years to fit many pieces of
design theories, methods, and activities together.
There is still more work to be done, and we feel this
phase of development will benefit greatly from a
consolidation of the fundamentals of design as we
have outlined in this paper.
9.2 Professionalizing design and advancing the
teaching
We hope to have shown that context matters in
design, and that by understanding and taking the
context into account one can begin to derive a
comprehensive measurement of design. It should
therefore come as no surprise to readers that we see a
strong need for design education to be run in a
manner similar to a teaching hospital. The hands-on
nature of design projects and the importance of
context make this inevitable. Today’s teaching
hospitals consist of physicians who could be general
practitioners or specialists, and then it has physi-
cians who also have their doctoral degree. Finally it
has a new class of researchers engaged in ‘‘transla-
tional research.’’ These researchers are charged with
translating breakthroughs in science to the bedside.
At the beginning of this paper we mentioned that 24
incremental faculty billets had been approved by the
Stanford provost for the bioengineering depart-
ment. While we anticipate a similar expenditure in
design, we expect the faculty roles to be more
diverse—mathematics application, mechanics, and
materials science research focused; design process
research focused; teaching focused; and practice-
focused. The first group is the current state of things
in many schools with a few having the occasional
professor of practice.
9.3 Potential impact
There are several areas of impact.
9.3.1 For the first time we are creating a feedback
system for an innovation eco-system with
Engineering Design Teams in the loop (See Fig. 4)
The expected impact is a program of continuous
Design Thinking: A New Foundational Science for Engineering 1551
Fig. 10. It is hypothesized that the economy of the US may be modeled as two fairly
independent circuits, whereby the outer dynamic circuit pulsates and helps to drive the
larger inner circuit through innovation.
improvement in Engineering education, produc-
tion, innovation, and comprehension. Unlike ants
and spiders who build ant hills and spider webs but
cannot study themselves as they do it, we as engi-
neers can now scientifically study ourselves as we
engineer our products, services, ventures, ecosys-
tems, and environment.
9.3.2 The second impact builds on the growing
economic importance of design
There has been a shift in the relative economic value
of commodities, products, services, and experiences
over the last several years. We see the shift in pricing
and in increasing order from commodity, to pro-
duct, through service to the design of experiences
[50, 51]. For example, according to Pine and Gil-
more, it takes less than a half a dollar to produce a
cup of coffee, and more than four dollars if it is
bought or consumed in a high-end cafe
´such as
Starbucks.
Similarly, it is now common knowledge that
technology companies like Xerox make more
money selling papers than actual machines. The
design thinking movement while spearheading this
trend will not only lead to early detection of un-met
user needs and market opportunities, it will also
provide graduates of the professional school the
sources of much coveted employment in the innova-
tion industries.
9.3.3 The third impact is a much broader societal
one
For example, there has been a steady decline in the
number of fortune 500 companies created in Europe
over the last 200 years. Furthermore, over the last 50
years, Europe has created only 3 new fortune 500
companies, while the US has created 57 [52]. It goes
without saying that the Silicon Valley has been very
influential in this area, and some credit goes to the
vision of Stanford’s School of Engineering, and
their behavior in the context of those times.
The proposed professional school will be in a
position to take the vision even further. We foresee
a situation similar to Medical Education where the
Flexner report of 1910 [53] ushered in the formal
professionalization of medical education within the
academy—using the John Hopkins University as
the first model. Stanford will be ushering in through
this professional school a strong triadic coupling
between Mechanics, Mathematics Application, and
Ade Mabogunje, Neeraj Sonalkar and Larry Leifer1552
Fig. 11. The network of dealmakers (i.e. entrepreneurs and investors who combine characteristics of seriality, brokerage, and mediation) in
12 prominent technology metropolitan centers in the US. While the research described in this paper has been context specific, focusing on
Stanford University, IDEO, the Silicon Valley Region, and specific people working in these environments, the control volume concept
allows us to generalize the results to other geographies.
Materials Science research; Coaching, Teaching
and Practice of Engineering-design; and Design
Process Research focused on understanding the
process of building the innovator and the innovative
team in context.
In other words, while the research described has
been context specific, the education enterprise pro-
vides a mechanism for corroboration and replica-
tion. We have focused on Stanford University,
IDEO, the Silicon Valley Region, and specific
people working in these environments as key ele-
ments within our control volume. The same control
volume can be drawn in other locations to examine
the pattern of interactions and the emergent con-
sequences. Indeed such an attempt has been made in
recent years. Ted Zoller, referenced earlier, in his
research on technology based metropolitan areas in
the United States, found that ‘‘the prevalence of
dealmaker entrepreneurs and investors, with con-
current ties to multiple firms, is a better predictor of
firm births than the prevalence and density of
entrepreneurs and investors alone.’’ [45] Fig. 11,
shows the entrepreneurial network analysis devel-
oped in his work.
10. Discussion part III—can design
thinking really be a foundational science
for engineering like physics, chemistry,
biology, and mathematics?
In the course of doing this work and sharing the
results, one of the interesting questions posed to us
regards our claim that design thinking is a founda-
tional science for Engineering. We believe it is a
valid question, and one for which our response is an
unequivocal yes. It is a valid question because it
challenges both the questioner and us to re-examine
our underlying beliefs about the nature and defini-
tion of engineering. Three such beliefs come to the
fore here.
10.1 Within the engineering community, it is
possible to distinguish two kinds of people
Those who came to the discipline because they were
good at mathematics and those who love to build
things. For the former, engineering is mathematics,
and for the later, mathematics is a tool. This
simplified grouping can be found in the other
sciences where it can be mapped to the divide
between the theorists and the experimentalists.
Thus for a subset of the engineering community,
the absence of a mathematical representation of
design thinking could be troubling. For this
group, we would like to point to the work of
Armand Hatchuel and Benoit Weil at the Ecole de
Mines in Paris, where they have developed a math-
ematical representation to explain the creative pro-
cess in design engineering [54]. For the other subset
of the community, those who like to build things, the
description of design thinking using an abstract
representation does not feel concrete enough. For
this group, the correspondence between the audio-
visual records of the activity, the visual notation,
and their felt sense of the experience has provided
sufficient assurance of the objectivity of the nota-
tional scheme. As with most measurement systems,
work needs to be done to continue to increase the
accuracy, precision, reliability, and range of applic-
ability.
10.2 The second underlying belief concerns a
practice of modeling that is unique to engineering
analysis though not ubiquitous
It is the control volume analysis. Given that it is not
ubiquitous, and thus perhaps given little attention
outside of the field of thermodynamics and fluid
mechanics, we will provide an extended definition
which has been taken from Walter Vincenti’s well
researched article on the history of the control
volume analysis methodology [55].
‘‘As used in thermodynamics and fluid mechanics, the
control volume is an arbitrarily chosen volume, fixed in
space and with fluid flowing through it. . . . An
imaginary control surface, shown . . . by the closed
dashed line surface. . . . fluid (either liquid or gas) must
by definition flow through the control surface at some
location, . . . This requirement stems from the fact that
the control volume is designed specifically to analyze
problems involving the flow of fluids, as occurs in
engines, rockets, boilers, turbo-machinery, and a
multitude of other devices with which engineers must
deal. The importance of the control volume is that it
provides, for flow devices, a convenient framework in
which to apply the physical laws governing mass,
momentum, energy, and (for an occasional problem)
entropy. Such application leads to general integral
equations involving changes inside the volume, trans-
port of various quantities through the control surface,
and forces acting on the material within the volume.
These equations provide a prescription for doing the
bookkeeping, so to speak, on the physical quantities
going in and out of the control volume. The engineer
chooses the volume in a given case both to facilitate
application of the equations and with an eye toward the
quantities he knows and those he needs to find. In the
advice of one text, the boundaries should be put ‘‘either
where you know something or where you want to know
something.’’
From the foregoing, it will be easy to see that the
research presented here has involved a shift in the
control volume. This in turn has led to a more
comprehensive understanding of engineering. It
should be self-evident that such a comprehensive
view of engineering, which treats the engineer as a
system variable, will lead to the creation of better
artifacts. Fig. 12 shows the change in control
volume from the traditional limited view of engi-
Design Thinking: A New Foundational Science for Engineering 1553
neering to a modern and more comprehensive one
captured by the notation.
Vincenti concludes his description of the control
volume as follows:
‘‘Control-volume analysis thus comprises two ele-
ments: (1) the control volume (or control surface),
and (2) the control-volume equations. With these
elements the engineer has a systematic, explicit
method for thinking about a large and important
class of engineering devices.’’
Correspondingly, we have considered (1) the Silicon
Valley as the control volume, and (2) the interaction
dynamic sequences as the control-volume equa-
tions.
As engineers, and engineering educators we
quickly realize that in this new science, we can
play two different roles with respect to our students.
First, that of an independent scientific observer who
transmits knowledge to a student, according to
traditional means. Second, that of an engaged
scientific experimenter, formulating new hypothesis
based on the notation and the control volume, and
testing these out in practice. We believe this second
approach is more dynamic, relevant, innovative,
and adaptive to a changing context of engineering
education, industry, and the natural environment.
We have gone at length to quote Vincenti because
as most will realize, Engineering History is not a
required subject for most engineers today, hence the
origin of some of our most useful methodologies is
not well known.
10.3 The third underlying belief is perhaps the most
daunting, and concerns the evolutionary nature of
engineering and our relationship with mathematics
Mathematics in a way gives us a sense of control,
and an instrument to make accurate predictions. To
have a foundational science not based on mathe-
matics is new and threatening. Yet as humans and as
scientists, we have an obligation to share and
explain our results to the best of our knowledge
and abilities. The notation is a culmination of over
25 years of research. In the introduction to an earlier
work, we stated the following comments of a
grammarian:
‘‘In the newer technologies—notably in engineering—
the {nomenclature} conventions are not systematic or
clear; the {engineers} themselves are either unaware of
the lack of clarity and system, or do not choose to make
the effort to repair it. Therefore anyone who under-
takes to read technical documents must make his way
through agglomerations like these:
the highest previously available intrinsic coercive force
single side band transmission
high frequency stability
high-energy particle accelerator
internal transducer excitation supply
the segmented multiple ablative chamber concept
combustion chamber crossover manifold coolant pas-
sages
. . . This situation will stay with us until the {engineers}
establish some firm conventions and hold to them as
chemists and mathematicians hold to theirs.’’
A Grammar of Standard English (Conner, 1968) [56]
It should be clear by now, that if engineers estab-
lished some firm conventions and held on to them
like chemists and mathematicians, scientific discov-
ery and technological development in the last 200
years would have been very very slow. As engineers
and scientists discover and build new tools, their
language changes and evolves. Mathematical think-
ing, which is foundational to Engineering, and
design thinking, which is also foundational to
engineering appear to operate at different speeds
and with different boundary constraints. Both are
necessary foundations for the engineering disci-
Ade Mabogunje, Neeraj Sonalkar and Larry Leifer1554
Fig. 12. Two views of engineering.
pline, and for manipulating the impact of humans
on the environment.
How can one provide a fundamental explanation
of the structure and evolution of an anthill that is
independent of the ants’ individual and collective
behaviors? This is a logic that makes design thinking
foundational to engineering. Without it, the gen-
erative and divergent side of engineering remains in
the dark, and light is only shed on the conservative,
convergent side.
11. Conclusion
We began by showing that design thinking lies at the
core of what engineers know and do. Next we
discussed various approaches that have been used
to improve the design thinking performance—from
Allan Turing’s early exploration of artificial intelli-
gence to Engelbart et al’s project on augmenting
human intellect. We discussed several limitations in
these approaches and how most of these could be
overcome, save for the need to find standard ways to
measure design process performance. Then, we
showed that by taking into account the interaction
pattern between designers and the context of their
interaction, design thinking could indeed be
measured in an integrative, comprehensive, and
meaningfully effective manner. In addition, we
demonstrated that by using the criteria for estab-
lishing the bioengineering department as prece-
dence, we could conclude that design thinking is
ready to become a new foundational science of
engineering. Furthermore, taking into account the
already immense impact of Turing, Terman, and
Engelbart’s work in improving design process per-
formance in the absence of a measurement system, it
stands to reason that we have barely scratched the
surface of what is possible with the right industry-
educational environment. We are, to quote one of
our co-authors, in the early days of the Wright
brothers and the Kitty Hawk.
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Ade Mabogunje is a Senior Research Scientist at the Center for Design Research at Stanford University. He holds a PhD,
MS and BS in Mechanical Engineering.
Neeraj Sonalkar is a Research Associate at the Center for Design Research at Stanford University. He holds a PhD, MS
and BS in Mechanical Engineering.
Larry Leifer is a Professor in the Department of Mechanical Engineering at Stanford University. He is also the director of
the Center for Design Research. Larry holds a PhD in Biomedical Engineering, an MS in Product Design and a BS in
Engineering Sciences.
Ade Mabogunje, Neeraj Sonalkar and Larry Leifer1556
