Bridging The Valley Of Death Lessons Learned From 14 Years Of Commercialization

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Within this brief outline explicate bridging the valley of death lessons learned from 14 years of commercialization.

Bridging the Valley of Death:
Lessons Learned From 14 Years
of Commercialization of
Technology Education
STEVE H. BARR
TED BAKER
STEPHEN K. MARKHAM
North Carolina State University
ANGUS I. KINGON
Brown University
We argue for the increasing importance of providing graduate students with skills in
technology entrepreneurship and the commercialization of technology. We describe the
lessons we have learned from 14 years of developing commercialization of technology
pedagogy and adapting it for use on four continents and within numerous corporations.
We demonstrate that the theory-driven approach that we use to shape the curriculum
improves our ability to learn from our mistakes and to structure small experiments to
improve the curriculum and pedagogy.
........................................................................................................................................................................
Interest in the commercialization of technology
and technology entrepreneurship has increased
significantly in the past decade. In many increas-
ingly knowledge-based economies, effective man-
agers will need better training in dealing with
technologists and in creating business growth and
advantage through commercializing technology.
Innovative new technology ventures will require
entrepreneurs who are skilled at collaborating ef-
fectively with scientists and engineers as well as
with financial managers and venture capitalists.
Technical education faces new demands as well.
For example, the National Academy of Sciences
(COSEPUP, 1995) issued a statement calling for
rethinking graduate education for scientists and
engineers to include the skills to promote the com-
mercialization of technologies that they create.
More recently, the European Commission (2008)
concluded that “the teaching of entrepreneurship
is not yet sufficiently integrated in higher educa-
tion institutions’ curricula” (European Commis-
sion, 2008: 7, emphasis in original) and that far too
little of existing entrepreneurship education efforts
target students engaged in technical and scientific
studies.
As interest in commercialization of technology
(COT) has increased, so has academic research
interest in this area. The Journal of Product Inno-
vation Management (2008) recently published a
two-issue special topic volume on technology com-
mercialization and entrepreneurship. Commensu-
rate with this increased academic interest, there
has been an increase in the number of university
education programs that provide instruction in
COT. These programs provide education and ex-
perience in using emerging technologies to start a
new business organization (new venture focus) or
to create entities within existing firms (corporate
venture focus).
There are multiple institutional reasons for uni-
versities to exhibit increased interest in new busi-
ness start-ups based on technologies created at
the host university (Jelenek & Markham, 2007).
Markham et al. (2002) describe the increasingly
This research was supported by a grant from the U.S. Depart-
ment of Education’s Fund for the Improvement of Postsecondary
Education (FIPSE). We thank Roger Debo, Michael Zapata, III,
and Raj Narayan for their program leadership and the Kenan
Institute for Engineering, Science and Technology for its gen-
erous support.
? Academy of Management Learning & Education, 2009, Vol. 8, No. 3, 370–388.
........................................................................................................................................................................
370
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vital role of university research in providing new
technology platforms and products. Kirby (2005)
discusses the development of a “dual role” model
for universities to contribute to society by educat-
ing students but also by creating research that can
be commercialized into new products and services.
Some universities are attracted to COT because of
the potential for gain due to royalty or equity po-
sitions. Breznitz, O’Shea, and Allen (2008) also note
the potential importance of university commercial-
ization in developing regional economies. Many
other studies support claims for the increased im-
portance of new business start-ups for universities’
long-term success and survival (Debackere &
Veugelers, 2005; Kirby, 2006; Kondo, 2004; Litan,
Mitchell, & Reedy, 2007; Nicolaou & Birley, 2003).
Blumenstyk (2007) reports that more than two dozen
universities had revenues in excess of $10 million
each from licensing revenue from university tech-
nologies in 2005. Siegel, Waldman, and Link (2003)
report that licensing has been the strategy most
often used by universities to commercialize univer-
sity-created technologies, but Siegel, Waldman,
Atwater, and Link (2003) also report an increase in
universities’ use of start-ups as a commercializa-
tion strategy. Even for the many universities that
do not generate large profits from commercializa-
tion, it is both a means to enhance their social
impact mission as well as to provide a forum for
interested scientists to see their research have ad-
ditional positive impact.
This increased interest in technology-based new
business ventures at universities has not trans-
lated into a defined body of knowledge that ad-
dresses the education paradigm and process of
university COT education programs. There is a
paucity of research and information directly re-
lated to the actual education of university students
in the area of commercialization of technology.
Historically, significant research funding has been
available from the National Science Foundation
(NSF) and other sources for the creation of technol-
ogy. Processes that facilitate the creation of new
technologies have been researched (e.g., Cooper,
1983, 1994) and “best practices” have been identi-
fied (e.g., Barczak, Griffin, & Khan, 2009). There is
some understanding of the process of matching
public and private funds to promising technology
start-ups with business plans and management
teams in place. Universities have created and en-
hanced technology transfer offices to facilitate this
process.
The missing link in these efforts is the transition
from an existing or emerging technology to the
creation of a compelling new market-driven busi-
ness. This institutional, financial, and skill gap is
referred to as the “valley of death” in COT (Auer-
swald & Branscomb, 2003; Markham, 2002; Marcze-
wski, 1997; Wessner, 2005). Figure 1 depicts the
“valley of death.”
1
As a result of this gap between
development of science and development of com-
mercial products, many opportunities to create
technology ventures remain undeveloped and un-
exploited (Kirzner, 1997). The remainder of this pa-
per reports on the development and lessons of a
1
Please note that all figures in this paper are versions of fig-
ures used in teaching the TEC program in the United States and
elsewhere.
FIGURE 1
The Valley of Death Bridging the Gap Between Research and Commercial Application
2009 371 Barr, Baker, Markham, and Kingon
university-level program that is designed to train
students to bridge the valley of death in COT. This
program is not designed to facilitate the creation of
technologies, a major university effort. Rather, the
goal of this program is to increase student skills in
technology entrepreneurship.
2
The article is organized as follows: First, we
briefly describe prior work on COT education.
Next, we describe a COT program we have devel-
oped over more than a decade. We then describe a
number of lessons we have learned from varying
and adapting elements of the program over time.
Finally, we assess our program against the five
criteria recently suggested by van Burg, Gilsing,
and Reymen (2008) to enhance new ventures from
university-driven science and technology. We con-
clude with lessons learned from 14 (shows longev-
ity) years of COT instruction and provide sugges-
tions for COT education.
COT EDUCATION
We differentiate between teaching general entre-
preneurship and teaching high technology-fo-
cused entrepreneurship, the latter being our focus
here. While COT education efforts are based in
part on general entrepreneurship education peda-
gogy and practices (readers are referred to the 2004
special issue of Academy of Management Learn-
ing and Education for a full discussion of topics of
interest in general entrepreneurship education),
COT education creates specific challenges given
its reliance on existing and emerging technologies
as the platform for entrepreneurship learning. A
smaller body of existing research is pertinent to
the particular challenges of teaching entrepre-
neurship within a COT framework.
COT education programs in universities are
found primarily in engineering and business
schools. Kingon et al. (2001) reviewed the curricu-
lum of both general entrepreneurship and COT
courses in engineering and business programs.
They noted an increase in the number of faculty
positions dedicated to general entrepreneurship
education and in the number of entrepreneurship
courses, consistent with results noted by Finkle
and Deeds (2001). Most of this growth was in busi-
ness schools, followed by engineering schools.
Kingon et al. also note a more recent increase in
entrepreneurship courses offered by engineering
schools, primarily directed toward technology-
oriented engineering students and containing
evaluation of existing or developing technologies
as part of the pedagogy. One reason for the in-
crease of these engineering programs focusing on
COT is the desire to link the development of tech-
nology and the commercialization of technology
into a more seamless process. Engineering educa-
tion has traditionally focused on the creation or
development of technology, resulting in the cre-
ation of many technologies that, while having po-
tential application, lay dormant due to lack of
follow-through on the possible commercial appli-
cations of the technology. There is also increasing
recognition among science and engineering stu-
dents and faculty that some type of business or
management education is required to prepare
technology-based students for typical career
paths. This point was stressed in the COSEPUP
report of the National Academy of Sciences (1995).
Most of these graduates, both MS and PhDs, follow
careers in industry. In both cases, a high percent-
age of science and engineering graduates tran-
sition into management or business roles early
in their careers.
3
Although business school and engineering de-
partment entrepreneurship offerings have devel-
oped in parallel, a number of arguments support
the potential benefits of bringing together content,
faculty and students from these disciplines into
strong cross-disciplinary curricula. A major con-
clusion of the Kingon et al. (2001) review is that
both engineering and business COT education ef-
forts contain elements important to teaching stu-
dents how to develop commercially viable technol-
ogy start-ups or licenses, and both would be
appropriate for COT education programs. Simi-
larly, Wright, Piva, Mosey, and Lockett’s (2008: 13)
field study of “the challenges that [business
schools] face in relation to the development of ac-
ademic entrepreneurship in eight UK universities”
uncovered a number of issues regarding the prac-
tical value of business school faculties’ engage-
ment in efforts to promote COT. It has been ob-
served that little research conducted by business
school faculty, including entrepreneurship faculty,
is derived from practice or intended primarily to
provide insights useful to the day-to-day struggles
of creating and nurturing a thriving technology
2
In this paper, we use COT and technology entrepreneurship
interchangeably, but we acknowledge that some technology
entrepreneurship is not primarily COT and that some COT
cannot easily be labeled entrepreneurship. In our experi-
ence, the primary skills of technology entrepreneurship work
well in creating new ventures outside of or within existing
organizations.
3
Readers seeking a fuller comparison of engineering and busi-
ness school COT curriculum content and processes are referred
to Kingon et al. (2001).
372 September Academy of Management Learning & Education
venture (Drucker, 1985; Gibbs, 2002; Wright et al.,
2008). This may translate into a lack of relevance of
faculty research to the practice-oriented demands
of teaching entrepreneurship and to inappropriate
overreliance on nonresearch faculty to teach based
on personal experience and anecdote (Baker & Pol-
lock, 2007; Gibbs, 2002; Whittington, 2003). One pro-
posed corrective measure is for entrepreneurship
faculty to “significantly increase their involvement
in cross-disciplinary research and teaching, with
faculty from engineering and applied sciences”
(Gibbs, 2002, quoted in Wright et al., 2008: 10).
Such mixed groups benefit students by affording
practice in role-spanning behaviors that are likely
to be useful in organizations in which both busi-
ness and technology skills play important parts.
Another advantage is that this makes training in
fundamental business skills, known to be impor-
tant to entrepreneurial survival (Shane & Delmar,
2004), available to a broader range of students,
including scientists and engineers (European
Commission, 2008; Wright et al., 2008).
Overall, we still lack well-developed theoretical
and conceptual frameworks to direct the teaching
of general entrepreneurship, and this is a more
pointed limitation for COT. Until recently, this lack
of theory-driven approaches mirrored the status of
entrepreneurship research in general (Aldrich &
Baker, 1997; Shane & Venkataraman, 2000). Fiet
(2001) and Bechard and Toulouse (1998) have noted
the importance of “theory-based” approaches to
the teaching of entrepreneurship. Despite these
calls, and despite substantial recent improve-
ments in theory-driven entrepreneurship research,
there has been little attention given to the theoret-
ical development of entrepreneurship education,
especially in COT. We propose that a more fruitful
approach is to develop pedagogies that draw on
theoretical frameworks for design guidance, as
this provides a context for evaluating how the pro-
gram is working and for cumulative learning from
experience.
Recently, van Burg et al. (2008) proposed a sci-
ence-based design approach to creating university
spin-offs as a framework for crossing what we re-
ferred to as the “valley of death.” They note the
importance of linking scholarly knowledge in a
technology area to new venture creation using that
technology. This satisfies the university’s dual role
of knowledge creation and economic development,
which are often in competition (Bird, Hayward, &
Allen, 1993; Clark, 1998). They propose that a sci-
ence-based design approach provides a way to
enhance both future scholarly research in the area
as well as new business creation (Di Gregorio &
Shane, 2003). Van Burg et al. (2008) conclude that to
enhance start-ups from university science, five fac-
tors are critical. First, universitywide awareness of
entrepreneurship opportunities must be increased,
thus encouraging the development of entrepre-
neurial ideas. Second, a mix of technology knowl-
edge and venturing skills must be provided
through coaching and training. Third, a collabora-
tive network of advisors, managers, and investors
must be established to support start-up teams.
Fourth, spin-off processes should be separated
from academic research and teaching. Finally, a
university-level culture must be created that moti-
vates and rewards entrepreneurial behavior. Be-
low we evaluate our design and “lessons learned”
against these five factors.
In the next section we explain the education
model and processes used in the COT program
that is our focus here. We refer to this model as the
Technology Entrepreneurship and Commercializa-
tion (TEC) Algorithm. We then briefly describe the
primary theoretical perspectives that have shaped
development of the pedagogy and use this as the
context to explain the lessons we have learned.
THE TEC PROGRAM
The Technology Entrepreneurship and Commer-
cialization program (TEC) was initially developed
at North Carolina State University from 1995 to
1999. Development was supported by the National
Science Foundation (Kingon, Markham, & Zapata,
1999), the Kenan Institute for Science and Engineer-
ing, and the North Carolina State University Col-
lege of Management for total development funding
of approximately $1 million. Since this time, TEC
has been refined and adapted through trial-and-
error learning in classrooms at NC State, The Ohio
State University, University of Ljubljana (Slovenia),
Loughborough University (UK; Boocock, Frank &
Warren, 2009), a consortium of 12 universities in
Portugal, 6 universities in South Korea, the Univer-
sity of Cape Town (South Africa), and many corpo-
rate training facilities. TEC has been taught in
formats ranging from 3 full semesters to a series of
9 brief modules.
In North Carolina, TEC projects by student teams
and entrepreneurs have resulted in the creation of
over 450 new jobs and have attracted over $170
million in investments. Similar, and in the case of
Portugal somewhat superior results, are being
seen as TEC is adopted elsewhere. A recently
awarded grant from the joint US-EU “Atlantis” pro-
gram will allow creation and support of “TECnet,”
which is an international network of technology
entrepreneurship educators.
2009 373 Barr, Baker, Markham, and Kingon
Pedagogy
The baseline pedagogy (from which many adapta-
tions continue to be made for varied circum-
stances) involves a 2-semester course sequence in
which students apply a clearly structured process
model of creating businesses that sell products
and services based on novel science and technol-
ogy. The process, which is labeled “the algo-
rithm,” is designed specifically to embed sets of
skills and behaviors that allow technology com-
mercialization novices to operate as competent
technology entrepreneurs or as technology-
product champions (Markham & Griffin, 1998)
within existing firms. While appearing to be a
“technology-push” process, the algorithm is de-
signed to systematically explore connections be-
tween a wide variety of market needs and the
unique attributes and product features enabled
by new and emerging technologies.
The process begins with the creation of multidis-
ciplinary teams of 5–8 graduate students from
business and engineering/science disciplines.
Teams frequently include graduate students from
other fields such as technical writing or design.
Teams are given access to a portfolio of technolo-
gies that have been disclosed to any of 20 univer-
sity technology transfer offices or corporate R&D
offices. Each team chooses at least two (and no
more than five) technologies to begin the 5-phase
process. An overall summary of the phases in the
process is presented in Figure 2. In the following
sections we review each phase.
The first 4 weeks are dedicated to the “ideation”
phase. The objective of this phase is to develop a
set of prioritized product concepts with strong hy-
pothesized linkages between the unique capabili-
ties of the technologies and customer/market
needs, with these linkages described in terms of
initial product concepts. The ideation phase is de-
scribed below and summarized in Figure 3. Ideas
are generated, prioritized, slightly refined, and
written into preliminary initial statements describ-
ing the product and the markets they might serve.
Students first investigate the technology and dis-
cover how it works and what unique capabilities it
may create or enable. They engage in structured
rounds of “creative imagination” during which
they are taught to use individual and joint creativ-
ity tools to imagine solutions to problems or needs
that might be achievable through products or ser-
vices based on the technology. Recently, we have
relied heavily on the “nominal group technique,”
which we have found to be particularly useful for
diverse, interdisciplinary groups (Van de Ven,
2007). Students are introduced to Pauling’s notion
that the best way to develop good ideas is to gen-
erate numerous ideas and learn which ones to
discard (Crick, 1996). They are encouraged to use a
wide variety of sources to generate ideas, includ-
ing written documents, the large network of local
FIGURE 2
The TEC Algorithm
374 September Academy of Management Learning & Education
executives eager to help the teams, and their own
social networks. We validate the role of “prior ex-
perience” and “knowledge corridors” (Hayek, 1945;
Shane, 2000) by introducing students to academic
work in this area and encouraging them to make
use of what they know from prior experience about
needs they might address.
The key construct we introduce to generate and
capture “lots of ideas” is called “T-P-M,” which
refers to “technology–product–market” linkages.
Student teams are required to generate multiple
product ideas that might be developed for each
technology and multiple markets for each product
(or service). For example, students recently applied
T-P-M to a single patented chemical compound to
describe product ideas as diverse as a fluid to
prolong the useful life of transplant organs, an
antiaging skin cream, and an energy drink. Then
students are asked to identify multiple market op-
portunities for each product idea. Identifying di-
verse market needs guides the process of further
specifying product attributes and—if the initial
technology appears incomplete or inadequate—
guides the search for technologies with the needed
performance characteristics.
A major benefit of the pedagogy is that students
become more comfortable with interdisciplinary
tasks and demands. Science and engineering stu-
dents are typically comfortable with the “concrete”
nature of the science and technology and are ini-
tially less comfortable with the “made-up” nature
of the product and market needs they envision. By
the end of the program, products and market needs
are more concrete and relatively more important to
these students. Similarly, during the program MBA
students become more comfortable dealing with
novel technologies and discussing and evaluating
their intricacies with the scientists and engineers
who have created the technology. We have found it
is useful to address these patterns of student com-
fort up front, thereby making both science/engi-
neering and business students’ initial discomfort
feel more “normal” to them.
The remainder of the first semester is dedicated
to “Phase 1” and to the first iteration of “Phase 2”
(see Figure 4). These phases are described below.
During Phase 1 and Phase 2 students improve and
select among their product concepts by grounding
and challenging in market and technical realities
what was previously mostly “imagination.”
Phase 1 and Phase 2 are elements of opportunity
evaluation structured around series of questions
and analytical tools that guide technology com-
mercialization neophytes to ask fundamental
questions about a variety of topics covering tech-
nology, legal, marketing, organization, manufac-
turing, financial, industry and competitive issues.
We refer to this as the functional and strategic
assessment. These guiding questions distill for in-
vestigation the primary issues considered by ex-
perts to be important to entrepreneurial success.
The questions are updated through periodic re-
views of new literature, through the regular input
of members of the local entrepreneurship commu-
nity, and through our experience with the courses.
We also adapt standard business analysis tools to
the evaluation process. For example, students are
required to apply standard industry analysis tools
based on Porter’s (1980, 1985) work.
In both Phases 1 and 2 students use the guide
questions and analytic tools to direct their re-
search into whether they have identified a valu-
able opportunity. There are three primary differ-
ences between the two phases. First, the primary
purpose of Phase 1 is to identify “fatal flaws” of
any sort that would warrant setting a technology or
product idea aside at least for the time being (Paul-
ings’ “which ones to throw away”), while the pri-
mary purpose of Phase 2 is to begin building the
business case and becoming expert in the technol-
ogies, products, and markets that have survived
Phase 1 (the presumptively good ideas). Most “fatal
FIGURE 3
Ideation Phase
2009 375 Barr, Baker, Markham, and Kingon
flaws” discovered during Phase 1 fall into one of
two categories: technology flaws or market flaws.
In a technology flaw, the students discover that the
technology cannot do what has been claimed or
will not be able to do it without massive and un-
economical infusions of research funds. For exam-
ple, students recently discovered a fatal flaw when
an outside expert they contacted pointed out that
the video signal data reduction and recovery tech-
nology they hoped to exploit violated a law of
physics and could never achieve the intended per-
formance. Flaws may also be judged fatal when
early stage technologies might work but appear
highly unlikely to do so, or when intellectual prop-
erty the students need is legally protected and the
students appear unable to gain the license they
require on reasonable terms. In market flaws, the
students sometimes discover that superior technol-
ogies and products are about to be introduced,
obviating the need for their innovations. For exam-
ple, this happened recently when a team excited
about a set of substantial advancements over gog-
gles then available for viewing videos privately
learned about a new product introduction that
leapfrogged their own advancements. In every
case, categorizing something as a fatal flaw is
based on the student team’s judgment that there is
no opportunity for them worth considering or de-
veloping further from the T-P-M linkages they have
created between a technology and a particular
market.
As a second difference between the first two
phases, Phase 1 consists of a few dozen questions
and some cursory analytic tools, while Phase 2
consists of several hundred questions and requires
rigorous application of a variety of standard
tools—e.g. “five forces” analysis—with which the
MBA students are in principle familiar but which
the science and engineering students must learn
in the context of the project. The purpose of having
so many questions is to guide inexperienced busi-
ness and technical students to gather the wide
variety of information needed to make informed
decisions.
Third, while Phase 1 requires some limited inter-
action with external experts, Phase 2 requires stu-
dents to interact with and begin building relation-
ships with dozens of external parties, including
scientists, managers at potential competitor
firms, suppliers, and especially customers. In
Phase 2 students make heavier use of product
development and market research tools such as
“voice of the customer” (Griffin & Hauser, 1993) and
“lead user” analysis (von Hippel, 1986). By the end
of the first term, groups have typically reduced
their portfolios to no more than two technologies
and three sets of related product ideas, most tar-
geted at several market segments. They are aware
that by early in the second term they will be
forced—typically against their will as they have
“fallen in love” with more than one set of T-P-M
linkages—to choose one technology “platform”
and one initial set of start-up product ideas.
The second term of the 2-semester sequence be-
gins first by deepening the Phase 2 research and
analyses, resulting in the choice of “which com-
pany we are going to start,” including, importantly,
the industry within which it will be started (Shane,
2005). The choice of venture/industry does not fol-
low automatically from the Phase 2 analysis. In-
stead, the teams are required to develop a set of
criteria that they consider most important for mak-
Phase 1 Objectives:
– To eliminate product ideas (not
technologies)
based on fatal flaws
Phase 2 Objectives:
– To build the business case
Idea
Phase II
Functional
Assessment
Strategic
Assessment
Product
Redefinition
Decision
Product
Definition
Confirmation
Reject or Retain for
Further Refinement
FIGURE 4
Phase 1 & 2 Assessments
376 September Academy of Management Learning & Education
ing this fundamental decision, to assign weights to
the criteria, and then to assess each opportunity
for which they have developed T-P-M linkages
against these criteria, resulting in a quantitative
ranking. This ranking then becomes the primary
basis for the selection of what opportunity will be
carried forward. Typical criteria include not only
industry attractiveness, but also the degree to
which the students are passionate about the ven-
ture idea, the degree to which the team has the
skills to meet early milestones, and, on the nega-
tive side, the projected capital intensity of the ven-
ture, the required further development of the un-
derlying technology, and the projected time to
profitability. Also of interest, although many of the
criteria the teams adopt fit with those that scholars
identify as appropriate (e.g., Shane, 2005), some of
the criteria adopted are instead expressions of
idiosyncratic team desires. For example several
teams have recently placed a high value, beyond
“market” consideration, on “green” technologies
and products.
Phase 2 is followed by development of a com-
mercialization/start-up strategy. Throughout the
course, students are challenged to develop “value
propositions” for their products using a simple pre-
scriptive format. This forces the students to answer
“what is this product, who will buy this product,
why will they buy it instead of doing nothing, and
why will they buy it instead of buying something
from the (inevitable) competitors?” In addition,
they build a “business model” that answers ques-
tions about how the business will bring the prod-
uct to market and how it will do so profitably,
including initial financial projections. Finally the
students propose answers to strategic questions of
where and when the business will operate and
market its products and services. The result is a
modular “business proposal” that can be easily
updated and which students subsequently adapt
to create proposals to recruit executives, raise fi-
nancing, and market their services to early cus-
tomers. The actual launch of the business typically
takes place after the end of the formal coursework.
Two additional integrated courses are also offered
to interested students, “Launching the Technology-
Enabled Growth Venture” and “Managing Venture
Growth.”
Teaching Format
Primary course instruction is provided by two or
more full-time tenured faculty, one with entrepre-
neurship research and new venture creation expe-
rience and one with significant science or technol-
ogy research experience. Numerous additional
“technology” and “new venture” creation experts
are available as needed. These include academ-
ics, entrepreneurs, and venture capital experts. In
its basic format, the course meets once a week for
3 hours. A little less than half this time is spent on
lecture/discussion, introducing students to each el-
ement of the algorithm in a “just-in-time” manner.
The remainder of the class is spent in group meet-
ings. These are run by the students, but are facil-
itated by “executives in residence,” volunteer
coaches from the business community, who work
with the student teams through both terms. Often,
for those student teams that launch businesses
after graduation, the coaches become either mem-
bers of the entrepreneurial team or stakeholders in
the business. Student teams are required to meet
in-person as a group at least one other time each
week. The teams are required to produce a variety
of documents throughout both terms, including
many “worksheets” that provide step-by-step guid-
ance to each element of the algorithm and also
provide evidence of team progress and decision
making.
It is essential to note that the entire TEC process
is explicitly “iterative” (see Figure 2), based on the
epistemological assumption drawn from entrepre-
neurship theory that some of what teams need to
know to make appropriate decisions is uncertain
and unknowable (Knight, 1929) or simply undiscov-
ered (Kirzner, 1997). When a team learns anything
that makes a prior decision appear suspect, they
are required to “iterate back” to the appropriate
part of the algorithm and enact the process again.
Indeed, the typical team experience is one of re-
peated iteration as new discoveries are made,
which helps to explain why at the end of the year
most students are well-practiced at the algorithm
and have internalized many of the skills and prac-
tices it requires. Finally, the entire “group” ele-
ment of the grade for both terms is based on dis-
cipline and explicitness in applying the TEC
algorithm and the resultant quality of decision
making. Failure to iterate and “go back through
the process” upon the late discovery of contradic-
tory information or of a substantially superior op-
portunity is the primary error that teams can make.
As we make clear from the beginning, “Nobody
said this would be easy or quick.” Two normal
outcomes of the algorithm, besides starting a new
firm, are reject (from further consideration at the
current time) and retain for further development
and licensing (see Figure 4). In the former case,
scientists and engineers associated with the tech-
nology are given guidance as to what develop-
ments might make their work more commercially
valuable, and the door is left open for future
2009 377 Barr, Baker, Markham, and Kingon
engagement. In the latter case, technology trans-
fer and research and development offices are
given leads on firms likely to want to license a
technology if it proves inappropriate for a
start-up venture.
LESSONS LEARNED
In the following sections, we first describe two
primary theoretical frameworks guiding continued
development of the pedagogy and then describe
some of the most important lessons we have
learned. Major pedagogical design lessons are
presented first, followed by more specific issues.
Theory and Design
Because we take seriously the potential benefits of
cross-disciplinary mixing of students, the design of
our program needs to accommodate widely vary-
ing student backgrounds and experiences. Our
students include both full- and part-time MBA stu-
dents, along with master’s and PhD students from
a wide range of science and technical disciplines.
Most important, our students vary in the extent to
which they already intend to become entrepre-
neurs, in the extent to which they feel they are
capable of becoming entrepreneurs, in the extent
to which they have already developed various
skills useful for engaging in technology entrepre-
neurship, and in the extent to which they have
been conditioned by prior education (Gibb, 1987)
and employment to think of entrepreneurship as
an option (Sorensen, 2007). While a wide variety of
genetic (Nicolaou et al., 2008); family (Aldrich &
Cliff, 2003); and other factors in students’ pasts may
affect their desire and capabilities to engage suc-
cessfully in entrepreneurship, we operate on the
assumption that for most students, we can provide
learning experiences which open up entrepreneur-
ship as a reasonable option.
Our primary goal is not to turn the greatest num-
ber of our students into entrepreneurs. It is, in-
stead, to get our students to understand that entre-
preneurship is an option for them and to increase
their confidence and self-efficacy in regard to mak-
ing this career choice (Boyd & Vozikis, 1994; Chen,
Greene, & Crick, 1998; Tabak & Barr, 1999). We
provide them with skills, knowledge, and behav-
iors, that is, entrepreneurial management prac-
tices (Drucker, 1985), that will help them to succeed
if they choose to engage in technology entrepre-
neurship. Prior research provides evidence that en-
trepreneurship training can substantially increase
cognitive and motivational precursors to entrepre-
neurial activity, which suggests that the training
may open up entrepreneurship as a choice to stu-
dents who would otherwise remain closed to it.
For example, in an interesting set of arguments,
Gibb (1987) suggests that entrepreneurs may be
characterized according to a set of useful personal
attributes. Rather than arguing that this constrains
entrepreneurship to only a limited group of people
with specific personality types, he instead argues
and adduces evidence in support of three claims:
First, that the task demands of entrepreneurship
are so varied that few people, regardless of their
personal attributes, are likely to be incapable of
entrepreneurial activity; second, that the task de-
mands of entrepreneurial endeavors may them-
selves stimulate and strengthen particular useful
attributes and behaviors among individuals en-
gaged in entrepreneurship (Busenitz & Barney,
1997); and third, that many of the attributes and
behaviors useful to entrepreneurship are amena-
ble to development through experience and train-
ing. Focusing specifically on the effects of entre-
preneurship training on undergraduate science
and engineering students in the UK and France,
Souitaris, Zerbinati, and Al-Laham (2007) found
that the training increased both attitudes toward
and intentions to engage in entrepreneurship. Also
of interest, the most important trigger to these
changes appeared to be the degree to which the
students were emotionally aroused and inspired
by the entrepreneurship modules.
In general, it remains unclear how tightly later
entrepreneurial activity is linked to entrepreneur-
ial attitudes and intentions at the end of a curric-
ulum, in part because there are often substantial
time gaps between the completion of an entrepre-
neurship program and attempting to engage in
entrepreneurship (Gibb, 1987; Luthje & Franke,
2003; Ronstadt, 1990). However, Charney and Libe-
cap’s detailed analysis of the effect of entrepre-
neurship curricula at the University of Arizona sug-
gests that the effects of entrepreneurship curricula
may be lasting, as it showed that over the period
from 1985 to 1999, compared to members of a con-
trol group of nonentrepreneurship graduates of the
college, graduates of the entrepreneurship pro-
gram were more likely to start ventures, more
likely to grow them successfully, more likely to
commercialize technologies, and more likely to
create technology-based firms.
We have shaped the ongoing development of our
curriculum and pedagogy and, therefore, our de-
sign of the student educational experience primar-
ily in accordance with two fundamental theories:
cognitive theory and the theory of planned action.
Both theories are consistent with our assumption
that technology entrepreneurship can be usefully
378 September Academy of Management Learning & Education
taught. Social cognitive theory (Bandura, 1977,
1986) explains learned behavior as a reciprocal
interaction of cognitive, behavioral, and environ-
mental factors. The primary ideas we take from
Bandura are the notions of self-efficacy and enac-
tive mastery. Self-efficacy refers to one’s beliefs
regarding “how well one can execute courses of
action required to deal with prospective situa-
tions” (Bandura, 1982: 122), and it is shaped pri-
marily by one’s prior experience with similar
situations. Numerous studies have shown that
“enactive mastery experiences,” experiences in
which success required persistence and learning
from failure and setback, increase self-efficacy
and make it more robust, thereby allowing individ-
uals to maintain their self-efficacy in the face of
future hurdles (Bandura, 1997, 2000). In our program
development, enactive mastery experiences have
to be perceived as authentic and real to have the
desired effects.
It is worth noting that the need for enactive mas-
tery experiences is also consistent with many pub-
lished observations that effective entrepreneur-
ship education needs to be “hands-on.” For
example, the European Commission Expert Report
(2008) suggests that traditional teaching methods,
such as lectures, tend to be ineffective in entrepre-
neurship teaching and that “there is a need for
more interactive learning approaches, where the
teacher becomes more of a moderator than a lec-
turer” (2008: 8). Gibb (1987:19), adopting a critical
perspective on both business school research and
teaching, calls for entrepreneurship education to
utilize “learning by doing—gaining insight as well
as knowledge by involving students in problem
solving in real-world situations right up to, and
through, the solution and action component.” A
similar insight underlies Ronstadt’s (1990: 80) sug-
gestion that entrepreneurship programs should
proceed “from being more structured to extremely
unstructured—to the point that individual initia-
tive ultimately becomes the critical variable shap-
ing the project and the outcomes.” Consistent with
this insight, very loosely structured hands-on en-
gagement with trying to move a project forward,
which may provide both skill development and
enactive master experiences, becomes the core of
later stages of the TEC curriculum. The last several
weeks of the program are designed on the fly to
accommodate and respond to questions and de-
mands that arise in the students’ work on their
projects. As Ronstadt emphasized, this approach is
in strong contrast to many traditional pedagogical
approaches built around the often largely reflec-
tive, analytical and highly structured construction
of a “business plan.”
The second primary theoretical framework that
affects our program design is the theory of planned
behavior (Ajzen, 1987), which builds on classic
work on attitude formation and behavior (Fishbein
& Ajzen, 1975; Ajzen & Fishbein, 1977). At the core of
the theory of planned behavior is the notion of
“perceived behavioral control,” which in many
ways resembles Bandura’s notion of self-efficacy.
This theory explains behavior that is calculative
and planned to be consistent with the relative
likelihoods (probabilities) and consequences
(outcomes) associated with each behavior under
consideration. It undergirds the more calculative
and predictive elements of our program (see
Souitaris et al., 2007 for a complementary recent
application of the theory of planned behavior to
entrepreneurship).
Together, social learning theory and the theory
of planned behavior suggest a number of prescrip-
tive elements for design of a program to teach
students the skills, behaviors, attitudes, motiva-
tions, and self-efficacy required for the sorts of
entrepreneurial undertakings that graduate stu-
dents in both business and science/engineering
often find highly daunting. Through almost 14
years of developing this program, and more re-
cently, through helping people in other institutions
and nations adapt it to their environments and
students, we have engaged in a great deal of trial-
and-error learning: We have made a number of
mistakes and attempted to learn from them. In
addition, we have engaged in intentional manip-
ulation and variation of the curriculum in order to
see what works. The “lessons” learned and de-
scribed below are the results of these learning
processes.
Four Fundamental Elements
We have learned that the pedagogy falters when
any of four key elements is weakened. The pro-
gram must be real, intensive, interdisciplinary,
and iterative.
Real
As we noted above, “enactive mastery” experi-
ences must be perceived as “authentic” if they are
to strengthen self-efficacy. The only way we have
found to let students experience the program as a
real and authentic experience of technology entre-
preneurship is to have the real and explicit end-
point of the program focused on creating real com-
panies. Not every student wants to or will follow an
entrepreneurial path. Each year, students in the
U.S. program start 2–4 new ventures that involve
2009 379 Barr, Baker, Markham, and Kingon
about 25% of the enrolled students and projects.
The percentage of class projects transitioning or
transitioned to commercial ventures is similar in
Portugal, at around 33%.
This history of starting ventures is enough to
make most students experience the entire year-long
program as “real.” Indeed, each year, the current
students are interested in knowing about prior start-
ups and in meeting the students behind them. Even
with this history, however, students are still highly
attuned to any statement or action that might trigger
doubt or concerns such as “we are working this hard
for an academic exercise?” Running the course “as
if” it were focused on creating companies is not good
enough to engage the students fully.
This lesson was reinforced recently when we
reflected on the fact that some students are simply
not interested in starting a business at the mo-
ment; they just want to learn some skills. We de-
cided to stop pressing the issue and instead to
share and discuss with students why it was impor-
tant that we “act as if” we were in the process of
starting businesses in order to get them to “act as
if” they were starting businesses. Several of the
students who were most eager to engage in trying
to start new ventures (or taking on different chal-
lenges with an employer) clearly felt somewhat
“duped,” while other students displayed evidence
that they were just “going through the motions.”
During the last 2 years, we have returned to and
strengthened the message that the class focus is
on developing opportunities and actually enabling
and launching new ventures. The trade-off, which
we accept, is that while most students appear to be
very engaged, a small group of students that
would prefer a less “authentic” experience and are
unhappy when “going through the motions” is not
enough to generate a good start-up. We have also
learned to provide a highly realistic preview of the
courses in order to allow students who know they
do not want to do this sort of work to opt out early.
Intensive
Self-efficacy is enhanced by the experience of
working hard against obstacles, overcoming them,
slipping back, staying with the effort, and eventu-
ally succeeding. We have experimented with “how
hard” we make the course (varying things like the
size and composition of the technology portfolio,
how quickly different tasks need to be accom-
plished, how much directive guidance we provide,
etc.) based on student self-reports of spending exces-
sive numbers of hours in the courses and a general
sense of the program as overwhelming, especially
during the first 6 weeks. Experiments with making
the course easier, such as forcing teams to carry only
two (rather than their more typical choice of 3–5)
technologies through Phase 1, have backfired. Easier
pedagogical demands seem to undermine the enac-
tive mastery experience and result in students re-
sponding less positively to setbacks later in the
course. When students have lived through the expe-
rience of the very difficult first 6 weeks, however, it
appears that the gains in self-efficacy become robust
against the more serious technological and business
setbacks that could otherwise surprise them much
later in the year.
One function of the “modular” nature of the
course (Ideation, Phase 1, Phase 2, etc.) is to modu-
larize the enactive mastery experiences. That is,
students experience success with one module and
gain self-efficacy on those tasks before they move
on to the next. In practice, this also means that
when students “iterate” back to an early step in the
algorithm on the basis of “surprise” information,
they are remarkably better at it the second time
than the first time. For example, the first time stu-
dents run through the questions on the Phase 1
functional and strategic analyses, the tasks typi-
cally require 25–35 person hours. By the second or
third time the teams do a Phase 1 analysis, time
spent is often reduced to 6–10 person hours. From
an initial daunting task, it has become just another
tool they can apply with increasing efficiency and
confidence.
Interdisciplinary
There are myriad benefits to interdisciplinary
teams. The most obvious are that the students learn
to work well with people from different backgrounds
and that the projects are “staffed” with many differ-
ent skills. We discuss the value of diverse teams,
including our next planned experiment, below. But
the most important element of the interdisciplinary
teams, based on our experiments with creating dis-
ciplinary-focused ones, is that teams that include
graduate scientists, engineers, and managers create
a situation in which everyone is ignorant about
something. They therefore find it easier to admit
to their ignorance in the group setting. Individu-
als’ shock at the level of their own ignorance in
the face of the size and scope of the tasks to be
accomplished creates an openness to learning
and cross-disciplinary cooperation.
Iterative
The algorithm attempts to put a somewhat linear
framework around what is otherwise a seemingly
chaotic process. However, the technology entrepre-
380 September Academy of Management Learning & Education
neurship process is not inherently linear. This is
very difficult for many students to understand. The
modularity of the curriculum provides a structure
in which students can understand this nonlinearity
specifically through experiencing the need to “go
back” one or more steps as surprises occur. During
a typical year students strongly resist “iterating”
early on, they iterate fluidly through the middle
part of the process, and by the end, iteration has
become largely second nature. These multiple it-
erations and experiences of enactive mastery cre-
ate a high level of self-efficacy in which students
develop a profound sense that “I can do this” and
an equally profound sense that they should not
expect it to be easy.
More Specific Issues and Recommendations
In addition to the four primary design consider-
ations above, we have also experienced other ped-
agogical issues and tried various experiments
around several other design features.
Create Temporal Checkpoints
The modules and stages described above provide
important checkpoints to assess project status and
process. We have been surprised to find that these
are not adequate, especially in the early stages of
student learning. We have found that most stu-
dents view the process (ideation through develop-
ment of a commercialization strategy and business
plan) as a daunting task with high uncertainty and
many complexities. Breaking this long-term task
into smaller tasks with regular deliverables due on
specific dates is an effective way to keep teams’
attention focused on the task at hand, and is con-
sistent with guidance from social cognitive theory
on the need to actively focus “attention.” Moreover,
the combination of modular and temporal check-
points allows students to set and achieve a series
of challenging but attainable goals, which is con-
sistent with recommendations from the goal-set-
ting and performance appraisal literatures
(Latham & Wexley, 1981; Locke & Latham, 1990;
Bretz, Milkovich, & Read, 1992).
De-Emphasize Business Plans
Many entrepreneurship curricula use development
of a business plan effectively as the central orga-
nizing principle and primary outcome of one or
more courses. Consistent with Ronstadt’s (1990) cri-
tique of business plan-focused curricula, we have
observed that this can lead to a form of goal sub-
stitution in which writing a business plan, which is
a useful tool for some but not all businesses (Bhide´ ,
2000), becomes the primary learning target. We
have experimented with having students develop
a business plan as a year-long process in which a
loose and somewhat impressionistic early plan
gets revised many times as the students learn new
skills and do research. This structure caused sub-
stantial problems. Students began to view the de-
velopment of a business plan as an “investment”
that should produce a rate of return. They devel-
oped rationales for the existing business plan and
felt like they should try to “win” in their defense of
the plan rather than viewing the plan as part of the
process. As important, when students had in hand
a business plan they wanted to defend, they exhib-
ited a corollary resistance to iterating, to going
back and revisiting earlier steps in the process,
even when it was apparent that their opportunity
needed further development (see also Alvarez &
Barney, 2007, on the restricted place of business
plans in the process of “creating” opportunities).
As a result, we observed better performance in
writing finely crafted business plans and poorer
performance in following a disciplined approach
to opportunity development and exploitation.
More generally, reliance on a premature busi-
ness plan is likely to produce poor overall deci-
sions. Consistent with behavioral decision-making
literatures, early adoption of a business plan may
result in an “anchoring” effect. Research consis-
tently demonstrates that there is insufficient ad-
justment from initial anchoring in decision mak-
ing. Decision makers seek and process information
that supports the initial anchor and rationalize
disconfirming information. Similar effects are
noted frequently in the escalation of commitment
literature (Staw, 1981; Bobocei & Meyer, 1994).
When we encouraged students to write a business
plan early and to revise it during the rest of the
course sequence, we found that they were too an-
chored on the early version and that we needed to
apply substantial pressure to induce the more rad-
ical revisions that were often warranted. More
troubling, once the students have anchored on a
written plan, they think they are close to the “right
answer” and become less eager to continue to
work hard on developing an opportunity.
Consistent with the iterative nature of the TEC
algorithm, students learn that the business con-
cept continues not just to evolve but sometimes to
change radically as the process continues. Al-
though by the end of the program we require every
team to write and present a plan, we do not even
introduce this topic until students have been work-
ing on an opportunity for approximately 18 weeks.
We therefore encourage students to consider the
2009 381 Barr, Baker, Markham, and Kingon
business plan as a “sunk cost” that they want to
avoid taking on until they must. Eventually, they
learn that if they have done all of the work we have
asked them to do throughout the program, actually
writing the business plan becomes a very straight-
forward and (for some) enjoyable “last minute”
task of writing what is basically the core of a
document to “market the company,” different ver-
sions of which they might use to attract stakehold-
ers ranging from technology transfer offices, to
early employees, to investors and to customers.
Structure Large Blocks of Time
Individual students and student teams need large
blocks of time to gather and process information.
We prefer that classes meet once weekly for 3
hours, rather than multiple shorter sessions. The
Ideation, Phase1, and Phase 2 activities require
significant amounts of uninterrupted time for in-
depth consideration of the technology–product–
market linkages, functional area assessments, and
commercialization strategy development. As dis-
cussed earlier, the TEC algorithm process is highly
recursive with multiple interdisciplinary relation-
ships. Thus, it is a complex decision task. This
process is similar to the demands of academic
research, in that it often requires intensive time
commitments to become immersed in the specifics
of the research under investigation. Mitchell (2007),
in a discussion of academic values, notes the im-
portance of hours of uninterrupted time for the
thinking, reading, and writing necessary for high-
quality research. For many people, longer blocks of
time are more effective in producing quality re-
search than more frequent smaller blocks of time,
even when total time is the same.
We encourage a small number of regularly
scheduled “long” (minimum 2 hours) weekly team
meetings outside of class and now require at least
one such meeting weekly. Over the period that we
have been teaching this program, students have
become more resistant to structuring work this way
because they are accustomed to multitasking and
cycling quickly between activities. However, once
they are forced to a structure requiring sustained
attention, many students become converts to the
value of uninterrupted time for improving their
focus and creativity. Supporting this approach, our
courses have been recently changed from 3 credit
hours to 4. Our science/engineering students had
noted that the course demands were easily equal
or greater than the 4 credit hour format (3 hours
course plus 1 hour lab) found in many engineering
and science courses.
Emphasize and Balance Team Diversity
Projects requiring both management and science/
engineering talent require a certain minimum
level of functional diversity to work effectively.
Beyond this requirement, our most extensive ex-
perimentation has been focused on trying to figure
out the trade-offs in other forms of skill and back-
ground diversity. For example, for several years,
we attempted to create teams in which the scien-
tists and engineers had overlapping technical
backgrounds (e.g., biologists, biochemists, chemi-
cal engineers, etc.) and in which the business stu-
dents had experience in relevant industries (e.g.,
life sciences companies, agricultural chemical
companies, clinical test firms, etc.). The primary
goal was to create a team with strong interlocking
complementary skills such as might be found in a
good small firm in the relevant industry. We found
that the teams were particularly good, once they
had identified and initially developed an opportu-
nity, at figuring out how to exploit it.
We then experimented with creating teams that
were intentionally heterogeneous in terms of tech-
nical backgrounds and industry experience. In
comparison with the earlier groups, the new
groups were not as strong in their abilities to cre-
ate strong paths to exploitation. However, they
were substantially better in generating the T-P-M
linkage ideas, the sets of opportunities that feed
into the evaluation and exploitation processes. On
balance, it became clear to us that the quality of
opportunities that the teams develop is a much
more important determinant of the quality of the
student learning experience and of the firms that
are created through the program. Therefore, we
now construct teams around the primary criterion
of skill diversity. Our next experiment involves
creating a structure that allows students to recon-
figure team membership during the second term,
to see whether we can optimize for creativity and
the quality of opportunities during the first term
and optimize for opportunity exploitation during
the second term. We anticipate that one problem
will be the strong bonding that takes place as
teams work closely together during the first term,
and therefore, the personal reluctance of students
to move from one team to another.
Generate Technology Flow
An important success factor in university COT ed-
ucation is the presence of significant volume and
quality of technical assets or technologies to be
examined. As noted, our approach initially in-
volves “weeding out” technologies based on use of
382 September Academy of Management Learning & Education
the algorithm. Most technologies are discarded for
one or more reasons at this point. Thus a flow of
new technologies is required for a COT program to
sustain itself. Having access to a portfolio of tech-
nologies keeps the teams engaged in evaluating
multiple options and helps keep them from prema-
turely foreclosing on one opportunity. We have
found that settling on one technology too soon re-
sults in an overly optimistic bias toward the team’s
perceived one and only opportunity, similar to the
optimism well documented in the research litera-
ture as entrepreneurs pursue one venture (Aldrich,
1999; de Meza & Southey, 1996).
Beware of Idiosyncratic Heuristics
Decision makers in general and entrepreneurs in
particular operate in complex environments with
high levels of uncertainty and often with time con-
straints. In situations like this, decision makers are
likely to use heuristics or rules of thumb to reduce
uncertainty and enable themselves to reach a de-
cision (Tversky & Kahneman, 2004). This is true of
entrepreneurs, who are especially likely to rely on
heuristics and cognitive biases to maneuver
through uncertainty (Busenitz & Barney, 1997).
These heuristics develop based on prior experi-
ence and are exhibited strongly by experienced
entrepreneurs. Most students in our program have
limited or no prior entrepreneurial experience.
Thus, we utilize the TEC algorithm to provide the
students with a common platform as a basis for
future entrepreneurship experience.
The goal is to have the students’ repeated, inten-
sive, iterative experience with the process steps of
the algorithm create both explicit tools and useful
heuristics that they will carry with them in their
careers. A serious issue sometimes arises in the
interactions between the students and the experi-
enced entrepreneurs (i.e., the “executive in resi-
dence”) serving as mentors to the teams. Each
team is assigned two such volunteer mentors, who
meet with the teams at least once a week for two
academic semesters. As experienced members of
the entrepreneurial community, these entrepre-
neurs “know” from gut sense and heuristics what
the teams should do. They are tempted to pass
their insights and implicitly their personal heuris-
tics along to the students. This interferes with our
ability to embed the skills and structured, process-
based heuristics we are trying to teach. The prob-
lem is not so much that the mentors are “wrong” in
their gut reactions (although, of course, they some-
times are) but rather that simply “hearing the right
answer” from a mentor does not teach students
approaches to figuring out an answer themselves.
Handing an answer to the students undermines the
applied, hands-on benefits of the pedagogy and
turns learning-by-doing back into a “minilecture.”
We have found therefore that it is necessary both
to put a strong effort into teaching new mentors the
algorithm, and also to team novices with experi-
enced mentors to minimize these issues. The rule
of thumb we insist on is that a mentor should only
violate the structured process of the algorithm
when she or he sees that the students are com-
pletely spinning their wheels or in the unlikely
event that they are about to do something poten-
tially disastrous. When the students are them-
selves more experienced entrepreneurs or manag-
ers, the algorithm serves as a “checklist” to ensure
a more thorough decision process.
OVERALL POSITIONING AND ASSESSMENT
Although we have worked from a basic theoretical
perspective in learning to shape and adapt our
program, we note that its design is also largely
consistent with the five high-level design elements
suggested for creating university spin-offs de-
scribed earlier (Van Burg et al., 2008). A major ele-
ment of the Van Burg et al. perspective is that a mix
of technology knowledge and venturing skills must
be provided through coaching and training. The
TEC algorithm combines an in-depth understand-
ing of the technologies being evaluated coupled
with development of potential technology–
product–market combinations. The course content
also includes functional analysis skills in areas
like marketing, financial, and intellectual prop-
erty.
A second proposed element is a collaborative
network of advisors, managers and investors. The
use of classroom instruction by multiple faculty,
significant time in teams outside class, availabil-
ity of the pool of executives in residence (content
experts in both technology and venturing special-
ties), and presentation of final business proposals
to leading members of the local entrepreneurial
community provide multiple levels of support for
the teams as they move through Ideation, Phases 1
and 2, and development of the commercialization
strategy. A new experiment, which we have imple-
mented this year, is to require that each team meet
(preferably in person, virtually otherwise, involv-
ing each student member in at least four meetings)
with at least 12 members of the long list of mem-
bers of the local and national entrepreneurship
communities who have expressed interest in sup-
porting our students and program, choosing their
contacts based on apparent match between con-
tact background and project needs. This is in ad-
2009 383 Barr, Baker, Markham, and Kingon
dition to the several hundred contacts students are
expected to make as part of their ongoing project
development and research. It is our hope that by
implementing this requirement, we will give some
students who appear shier in meeting new people
the opportunity to learn that doing so is within
their capabilities, while simultaneously improving
every student’s embeddedness in useful entrepre-
neurial communities.
A third design element of the Van Burg article
suggests that spin-off processes should be sepa-
rated from academic research and teaching. To a
limited extent, our program violates this recom-
mendation, because we are interested not only in
optimizing the creation of spin-offs, but more im-
portant, in using this process to teach students
technology entrepreneurship. We are also cur-
rently experimenting with ways to provide a better
feedback loop to technologists by revisiting previ-
ously “rejected” technologies in subsequent years
in order to provide continued guidance as to how
the research might become more commercially
useful. Nonetheless, the ultimate decision about
any commercialization of technology (licensing,
new business start-up) is controlled by the univer-
sity through the offices governing technology
transfer. Neither technology-creating faculty nor
the TEC faculty influences this process in a sub-
stantial way beyond the individual projects on
which we cooperate with technology transfer of-
fices and through occasional mutual training sem-
inars. However, as Wright et al. (2008) and Lockett
and Wright (2005) demonstrate, the very challenges
our students face and the very skills we try to help
them develop are also some of the primary chal-
lenges faced by university technology transfer pro-
fessionals. We believe that our efforts complement
the ongoing efforts of the many technology transfer
offices with which we work, and the directors of
these offices have provided us with strong support.
The last two elements of the science-based
design perspective for university COT efforts in-
clude development of university awareness of
entrepreneurial opportunities and the creation of
a university-level culture to motivate and reward
entrepreneurial behaviors. These are organization
(university) wide design prescriptions and are be-
yond the scope of the TEC pedagogy. However, the
interdisciplinary nature of the student teams, the
instructional faculty, and the executives in resi-
dence provide needed multidisciplinary expertise
and perspectives, and have contributed to the cam-
puswide and multicampus awareness of entrepre-
neurship opportunities for science and engineer-
ing faculty involved in the creation of new
technologies.
CONCLUSIONS
A major advantage of the pedagogical approach
we have described is that it addresses the factors
that cause technology and innovation to languish
in the Valley of Death, a critical problem in tech-
nology commercialization. The program is de-
signed to bridge this gap between the creation of
technologies and the commercialization of these
technologies (see Figure 1). The use of real tech-
nologies in a team environment with content and
functional experts that support the teams allow the
students to be more fully engaged in the early
stages of the COT process than does more tradi-
tional case-based education or the creation of
business plans around an existing business con-
cept. The added emphasis on these early stages
through the identification and evaluation of possi-
ble technology–product–market linkages in a pro-
cess-based comprehensive model provides signif-
icantly more value creation to the early stages of
COT. The T-P-M construct allows students to begin
with a technology but move quickly to under-
standing the decisive role in commercialization
of product and market forces, thereby effectively
integrating “technology push” and “market pull”
commercial logics.
Earlier, we proposed that experience-based
teaching (reliance on experience of the instructor)
may not be the most effective pedagogy for COT
education, although it is common. Neither theory,
nor cases, nor engagement is sufficient. All are
useful, but at the core, there needs to be a process
to guide the COT education effort. We propose that
process-based instruction, the modules of which
represent significant steps in the commercializa-
tion process and also represent coherent sets of
skills, provides a better means for students to un-
derstand and master the complexities of COT.
Given the current centrality in the entrepreneur-
ship literature of debates about the nature of op-
portunities and whether they are “discovered” or
“created,” it may be worth noting how we have
adapted our program to this scholarly discussion.
At the most abstract levels, the debate involves
fundamental ontological considerations: In simple
terms, are opportunities objective, out there and
waiting to be discovered and then exploited, or are
they created through entrepreneurial action (Al-
varez & Barney, 2007; Eckhardt & Shane, 2003;
Sarasvathy et al., 2003)? However, the debate also
embraces highly practical implications for prac-
tice, contingent on whether entrepreneurs are in a
discovery or creation “context” and whether they
rely on discovery or creation “assumptions” about
how to behave in that context (Alvarez & Barney,
384 September Academy of Management Learning & Education
2007). At the level of ontology, our curriculum re-
mains agnostic (although we never seem to run
into opportunities just waiting to be plucked), but
generally in agreement with synthesizing claims
that the subjective and objective characteristics of
opportunities are resolved through entrepreneurial
action in the form of “enactment” (Baker & Nelson,
2005; Sarasvathy et al., 2003).
In terms of curriculum development, in response
to the development of “creation” theory, our peda-
gogy has continued to reduce emphasis on “dis-
covery” of opportunities, and has therefore, supple-
mented the attempt to teach appropriate search,
analysis, and forecasting skills with behaviors
more useful to “creation” opportunities such as the
ability to persuade others to visions of products
and markets that may not yet exist (Aldrich & Fiol,
1994; Alvarez & Barney, 2007). In addition to our
traditional focus on teaching skills to attract and
manage professional equity investments, we are
now attempting to help students also learn how to
make do through effective use of bricolage and
bootstrapping behaviors (Baker & Nelson, 2005;
Bhide´ , 1992, 2000). Our curriculum has always em-
phasized the importance of “iterative” decision
making, which has recently been identified as an
important practical element of creation theory (Al-
varez & Barney, 2007). More generally, we have
found that incorporating “creation” behaviors and
skills into the curriculum is both interesting and
challenging, because there are fewer published
tools and approaches available than exist for tra-
ditional “discovery” approaches and because to
many students, creation behaviors seem more like
the characteristics of “struggle,” than like the char-
acteristics of “analysis,” to which they are more
accustomed. Overall, our pedagogical approach
presages recent conjectures that the behaviors re-
quired for creation opportunities encompass the
full set of behaviors and skills required for entre-
preneurship more generally (Alvarez & Barney,
2007; Sarasvathy et al., 2003).
Much prior research has examined factors that
may affect COT in universities (Agrawal, 2006;
DiGregorio & Shane, 2003; Siegel, Waldman, &
Link, 2003; Shane & Stuart, 2002: Thursby, 2004).
We complement these efforts, by studying the
pedagogy and content of the education experi-
ence for the university’s students. We believe
this research is critical both in supporting uni-
versities’ increasingly important roles in entre-
preneurship and economic development and,
more important, in providing students with the
skills they need to create and lead technology-
rich entrepreneurial ventures.
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Steve H. Barr is professor of management in the Department of Management, Innovation and
Entrepreneurship at NC State University. Barr received his PhD from the University of Iowa.
Current research interests include decision making in high uncertainty environments and
new business start-ups.
2009 387 Barr, Baker, Markham, and Kingon
Ted Baker is associate professor in the Management, Innovation & Entrepreneurship Depart-
ment at the NC State University College of Management, where he is the lead instructor in the
TEC program. Baker earned his PhD (sociology) from UNC–Chapel Hill. His current research
focuses on entrepreneurial resourcefulness and bricolage.
Stephen Markhamis associate professor of management in the College of Management at NC
State University. Markham received his PhD from Purdue University. He is an active member
of the Product Development Management Association (PDMA) and a cofounder of the North
Carolina chapter. His research and teaching interests include technology-based start-ups and
management of innovation.
Angus Kingon is the Barrett Hazeltine Professor of Entrepreneurship and Organizational
Studies at Brown University, and professor of engineering. Kingon is director of the Com-
merce, Organizations and Entrepreneurship Program. He was previously at NCSU where he
cofounded the TEC Program. He earned his PhD (chemistry) from the University of South
Africa.
388 September Academy of Management Learning & Education

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