The people behind the technology
Citation for published version (APA):Dolmans, S. A. M. (2013). The people behind the technology: decision making in technology commercialization.Technische Universiteit Eindhoven. https://doi.org/10.6100/IR760960
DOI:10.6100/IR760960
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The people behind the technology: Decision making in technology
commercialization
Sharon A.M. Dolmans
A catalogue record is available from the Eindhoven University of Technology
library
ISBN: 978-90-386-3496-8
Dolmans, Sharon Anna Maria
The people behind the technology: Decision making in technology
commercialization
Eindhoven: Eindhoven University of Technology, 2013.
Keywords: technology commercialization, entrepreneurship, university
inventions, technology licensing office, resource constraints, resource slack,
decision making
Eindhoven University of Technology
School of Industrial Engineering
http://www.tue.nl
Beta Ph.D. Theses Series D178
Cover design: Jeroen Frissen & Sharon Dolmans
Printed by: Proefschriftmaken.nl | | BOXPress BV
© 2013, S.A.M. Dolmans
The people behind the technology:
Decision making in technology commercialization
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magnificus prof.dr.ir. C.J. van Duijn, voor een commissie aangewezen door het College voor Promoties, in het
openbaar te verdedigen op maandag 9 december 2013 om 16:00 uur
door
Sharon Anna Maria Dolmans
geboren te Geleen
Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de promotiecommissie is als volgt: voorzitter: prof.dr. A.G. de Kok 1e promotor: prof.dr. A.G.L. Romme copromotor(en): dr.ir.arch. I.M.M.J. Reymen dr.ir. J.C. van Burg (VU University Amsterdam) leden: prof.dr. P. Mustar (Mines ParisTech) prof.dr. T. Elfring (VU University Amsterdam) dr. P.M. Le Blanc
To change the world,
Start with one step
And however small,
The first step is hardest of all
But once you get your gait,
You will walk in tall
The things you never did,
Cause you might die trying
Dave Matthews Band – You Might Die Trying (Stand Up, 2005)
VII
Acknowledgements
Albert Einstein once said that science is a wonderful thing – if one does not
have to earn one's living at it. After spending nearly four years at the ITEM
group, I can safely say that science is indeed a wonderful thing – especially if
you can earn your living with it!
Completing this dissertation would not have been possible without the
support, enthusiasm and humor of my colleagues at the ITEM group. I am
very grateful to my promotor Georges Romme. Georges, thank you for all
your support, wise advice and for giving me the freedom and confidence to
make my own decisions throughout the process. As a dean, you have one of
the busiest schedules out there, but you have always made time for me –
regardless of whether that was on short notice, during evenings or on
weekends. I truly appreciate that.
I am very grateful to my co-promotor Isabelle Reymen. Isabelle, thank
you for being so much more than a supervisor! Not only have you taught me
about research, but I also learned a great deal about teaching, industry
collaboration, project funding and university life. Thank you for being an
awesome coach, for sharing your personal experiences and for bringing out
the best in me. I hope to learn many more things from you in the future.
I am also very grateful to my co-promotor Elco van Burg. Elco, even
though we have only worked together at ITEM during the first part of my
PhD project, the impact of our discussions extends far beyond that. I have
greatly enjoyed our brainstorm sessions – always feeling a bit smarter
afterwards. Also, thank you for introducing me to the qualitative method,
you have really changed my view on research!
Moreover, I would like to thank Marion, Bianca and Frederieke for your
unconditional support, whether it concerned work or personal matters.
Without your genuine interest and care, working here would not be the
same. Further gratitude goes to Marijke and Marjan, thank you for all your
support.
VIII
Fred, thank you for giving me the opportunity to continue my work at
ITEM – I am looking forward to start my new job as assistant professor!
Lydie, who knew a PhD project would land me a new best friend! Thank you
for all the good times and for always being there for me – on the job or off
the job. Bob, without you I would have never applied for (and eventually
completed) this PhD project. Thank you for all your support over the years –
by always being one step ahead and preparing me for what is next, you have
made (and continue to make) my job much more enjoyable.
Also, many “thank you’s” go out to G-man, Mr. the Lion, Jing, Reggie,
Katrin, Le Freak, LuukELuuk, Meike, Thijs ‘P’, Mc Skipper, Jelle, Ying and
everyone else that regularly worked in or visited the AIO-tuin!
I would also like to thank the TU/e Innovation Lab for their financial
and non-financial support as well as the European Commission for their
financial support as part of the EFORCE project.
I am very grateful to Scott Shane for inviting me to work at Case
Western Reserve University for part of my PhD project. Scott, thank you for
having me over! In addition to working with and learning from a great
researcher, I learned a lot about myself. Visiting Cleveland was an invaluable
adventure, one I will never forget.
The completion of my dissertation would also not have been possible
without the support of my family. I am extremely grateful to my parents,
Rita and Rob, for raising me as they did – without your love and support I
would have never gotten this far. Thank you for encouraging me to be
curious, social and independent, and for stimulating me to be critical, but
always with respect. I am pretty sure these qualities make me a better person
but certainly also a better researcher. And, thank you Leonne, Leo, Janine
and Didier for your care, sincere interest, understanding and support.
Last – and not least, but most – I want to thank Piet (Jeroen Frissen).
Thank you for your love and patience, for unconditionally supporting me, for
always believing in me, and for (at times) pushing me to accomplish things I
would never consider possible.
Sharon Dolmans, Helmond, 2013
IX
Table of contents
Chapter 1 Introduction 1
1.1 Outline of this dissertation 4
PART I Decision making in universities 9
Chapter 2 The perceived value of inventor status 11
2.1 Introduction 12
2.2 Theory 14
2.3 Organizational Setting 16
2.4 Method 17
2.5 Results 23
2.6 Discussion 23
Chapter 3 Do technology licensing officers favor particular
inventors for start-ups? 29
3.1 Introduction 30
3.2 Theory 31
3.3 Method 36
3.4 Results 40
3.5 Discussion 44
X
PART II Decision making in new technology ventures 49
Chapter 4 Decision making in new technology ventures:
Resource positions in action 51
4.1 Introduction 52
4.2 Theory 54
4.3 Method 59
4.4 Findings 74
4.5 Discussion 90
Chapter 5 Conclusions 99
5.1 Findings 102
5.2 Implications 107
5.3 Limitations and Directions for Future Research 112
5.4 Closing Comments 115
References 117
Appendix I 135
Example invention disclosure - Inventor gender treatment 135
Appendix II 139
Interview protocol for semi structured interviews 139
Summary 147
Curriculum Vitae 153
Chapter 1
Introduction
The commercialization of new technologies, and in particular technologies
developed at universities, is one of the major drivers of economic
development by creating both economic and societal value (Lockett, Siegel,
Wright, & Ensley, 2005; Shane, 2004; Siegel, Waldman, Atwater, & Link,
2003). It is hard to image today’s world without the technological inventions
commercialized by companies like Philips or Google. Technology
commercialization processes involve transforming new technologies into
economic output, for example by establishing new ventures. As such, these
processes not only drive economic development and growth by creating jobs,
but also raise the level of welfare by providing technological advances that
improve standards of living. In this respect, the commercialization of new
technologies is becoming increasingly important in view of the major
challenges our society faces in areas such as energy and healthcare.
In parallel, technology commercialization is a phenomenon of interest
to academia. Early theory on the economics of innovation emphasized the
role of knowledge and technological progress in economic growth
(Schumpeter, 1934) and the importance of the ability to appropriate value
when investing in the commercialization of inventions under uncertainty
(Arrow, 1962). Entrepreneurship research shows that startups
commercializing new technologies have a disproportionately large economic
and societal impact (Acs, 2010; Shane, 2004) and the strategic management
literature recognizes technology commercialization as a key determinant of
firm performance (Banbury & Mitchell, 1995; Cohen & Levinthal, 1990;
Eisenhardt & Martin, 2000; Teece, 1986; Zahra & Nielsen, 2002).
Therefore, researching technology commercialization processes is
imperative from a managerial and societal as well as an academic
perspective.
INTRODUCTION
2
Despite its importance and the attention from scholars, practitioners
and also policymakers, technology commercialization remains a challenging
process, characterized by decision-making under uncertainty (Arrow, 1962;
Dew, Read, Sarasvathy, & Wiltbank, 2009; Knight, 1921). It may take years
before a new technology is improved to the extent that it constitutes an
economically viable product (Baumol, 1993) and the failure rate among new
and existing firms undertaking technology commercialization is high
(Christensen, 1997; Song, Podoynitsyna, Van Der Bij, & Halman, 2008).
This has left key stakeholders, such as universities, government agencies,
venture capital firms and new technology ventures in search of ways to
improve the process of technology commercialization (Markman, Siegel, &
Wright, 2008; Siegel, Waldman, Atwater, et al., 2003).
Successful technology commercialization entails effective decision
making under uncertainty, by the people behind the technology, along the
stages of the process. The first critical decision point involves deciding on
which technologies to commercialize (Ambos, Mäkelä, Birkinshaw, &
D’Este, 2008). This is not a straightforward decision given that the
commercial potential, possible applications and target markets of early stage
technological inventions are largely unknown at the start of the process
(Dechenaux, Goldfarb, Shane, & Thursby, 2008; Jensen & Thursby, 2001;
Markman et al., 2008; Shane, 2000). With many inventions not having
sufficient potential to justify the allocation of resources to their development
(Shane, 2001), the selection of technologies is of key importance and forms
the basis for further development decisions (Ambos et al., 2008).
Subsequently, selected technologies have to be successfully translated
into products and introduced to the market before they can generate
economic returns (Zahra & Nielsen, 2002). Yet, the future impact of
innovations is hard to ascertain even after their technical feasibility has been
established (Rosenberg, 1996), resulting in difficulties when attempting to
evaluate the benefits and costs of commercialization (Dew & Sarasvathy,
2007). Since the available options and consequences of commercializing
new technologies are largely unknown, the decision making processes in
technology commercialization are beyond systematic calculation (Baumol,
1993). Hence, key stakeholders are confronted with multiple dimensions of
CHAPTER 1 3
uncertainty, such as technological and market uncertainty, while making
decisions on the allocation of resources to development, manufacturing and
marketing (Gans & Stern, 2003).
To improve the success rate of technology commercialization, more
insight is needed in how various stakeholders make decisions in technology
commercialization. This dissertation aims to shed light on such decisions in
commercialization processes. Since technology commercialization involves
the selection of promising technologies as well as the subsequent
commercial development of such technologies, the body of this dissertation
is structured along these activities. In particular, this dissertation focuses on
two key stakeholders, universities and new technology ventures, by
addressing: (I) decision making in universities and (II) decision making in new
technology ventures.
Figure 1.1: Dissertation Outline
Selection of new technologies Commercial development
Part I - Decision making in universities
Part II - Decision making in technology ventures
Dissertation Outline
Chapter 2 and 3 Chapter 4
Technology Commercialization Process
INTRODUCTION
4
1.1 Outline of this dissertation
In line with the two main subtopics under investigation, this dissertation is
made up of two parts that are preceded by this introductory chapter and
followed by a closing chapter. Figure 1.1 presents a brief outline of the core
of this dissertation. Part I of this dissertation is about decision making in
universities on the evaluation of new technologies, involving two studies
described in Chapters 2 and 3. Part II covers decision making in new
technology ventures on commercial development and includes Chapter 4.
Chapter 5 discusses the main theoretical and practical implications of the
studies in Part I and II as well as the main limitations of this doctoral study
and directions for future research.
Chapters 2 to 4 are based on separate papers, which facilitates reading
the chapters as individual studies; as a consequence, there is some overlap
between the chapters in Part I. The next sections provide a short overview of
each of the studies in Part I and II.
1.1.1 Part I – Decision making in universities
The two chapters in Part I investigate decision making in university
technology licensing offices. In particular, the chapters focus on the decision
making of technology licensing officers with respect to the evaluation and
selection of new technologies for commercialization. Since the intellectual
property rights to inventions made by students and university employees
belong to the universities where these inventions were developed, technology
licensing officers manage the technology commercialization processes
within universities (Thursby and Thursby, 2002; Owen-Smith and Powell,
2003; Clarysse et al., 2005). That is, the decision to invest in the commercial
development of a new technology depends on the licensing officers’
evaluation of the commercial potential of the invention. Existing research
concerning the evaluation of university inventions has primarily focused on
invention characteristics to explain why technology licensing officers select
particular inventions for further commercial development. It has shown that
certain invention characteristics or technological attributes serve to assess
CHAPTER 1 5
the commercial potential of new inventions such as the pioneering nature of
the technology (Shane, 2000), the patentability (Shane, 2004; Sine, Shane,
& Gregorio, 2003) and scope of patent protection (Merges & Nelson, 1990;
Nerkar & Shane, 2007) and the ease of commercialization (Colyvas et al.,
2002; Gopalakrishnan & Damanpour, 1997). However, most university
inventions are in such an early stage of development that no one actually
knows their commercial potential, making an evaluation based on invention
characteristics a difficult task (Jensen & Thursby, 2001; Markman et al.,
2008). Anecdotal data suggest that licensing officers are also sensitive to
inventor characteristics when they consider the commercialization of
university inventions (Bunker Whittington & Smith-Doerr, 2005; Shane,
2004; Stephan & El-Ganainy, 2007), but systematic evidence of such a
relationship has yet to be established. Chapter 2 and 3 explore whether
various inventor characteristics influence technology licensing officers’
evaluations of new inventions and their decision making with respect to
patenting, commercial potential and spinoff creation.
To investigate potential causal relationships between inventor
characteristics and the evaluation of university inventions, Chapter 2 and 3
draw on a series of randomized experiments with US technology licensing
officers, where each study draws on different treatments and measures. In
these experiments, technology licensing officers were invited to evaluate real
life university invention disclosures, in which certain inventor characteristics
were manipulated. Apart from the specific treatments (inventor
characteristics), the treatment and control groups received identical
invention disclosures and inventor descriptions.
The study reported in Chapter 2 explores the influence of inventor status
on technology licensing officers’ evaluation of the commercial potential of new
inventions. Research in sociology on the evaluation of science and technology
shows that evaluators are influenced by the status of the actors associated
with new work; particularly in situations where there is uncertainty about the
quality of an invention. The results show that technology licensing officers
perceive the inventions of high status inventors to have more commercial
potential.
INTRODUCTION
6
Chapter 3 explores what the influence of various inventor characteristics is on
technology licensing officers’ support for spinoff creation. University spinoffs
require inventor involvement in the commercialization of technologies, for
example by undertaking additional technology development and acquiring
resources. Certain inventor attributes may facilitate these activities. Guided
by the existing literature, Chapter 3 investigates licensing officer sensitivity
to inventor gender, immigrant status, industry experience and ease of
working with when evaluating the spinoff potential of university inventions.
The results indicate that licensing officers are negatively disposed to
disclosures by female inventors with regard to spinoff creation and positively
disposed to disclosures by Chinese-named Asian inventors with industry
experience, who are easy to work with.
The results of Chapters 2 and 3 indicate statistically significant
differences in how technology licensing officers evaluate new inventions,
based on inventor characteristics. These chapters make several key
contributions to the literature on technology commercialization with regard
to the selection of new technologies. First, the findings in these chapters
show how inventor characteristics influence licensing officer’s evaluation of
new technologies. As such, these findings rebalance the literature’s focus on
the technological attributes of inventions as indicators of their commercial
potential by revealing how sociological factors enter the decision-making
process of technology licensing officers (Podolny & Stuart, 1995). Second,
these findings help to better understand the decisions of technology
licensing officers about university inventions by providing insight into how
their perceptions and decisions influence the process of technology
commercialization (Shane, 2004; Siegel et al., 2007). Third, these research
results serve to identify the preferences of licensing officers for particular
types of inventors; while these findings may point to biases (Bunker
Whittington & Smith-Doerr, 2005; Stephan & El-Ganainy, 2007) that should
or can be prevented, the outcomes of these studies can also help inventors to
increase the odds of commercializing their inventions.
CHAPTER 1 7
1.1.2 Part 2 – Decision making in new technology
ventures
A common mode of commercial development is exploiting technological
inventions by means of a new technology venture (Drucker, 1999; Wright,
Hmieleski, Siegel, & Ensley, 2007). Resources such as financial means,
technological capabilities, or production facilities are essential in the
development of any new business venture (Barney, 1991) and even more so
in the development process of new technology-driven ventures (Alvarez &
Busenitz, 2001). Yet, the influence of resources on the decision-making
process of entrepreneurs in ventures commercializing new technology is not
well understood. In this respect, the study in Chapter 4 sheds new light on
the on-going debate on the effects of resource constraints and resource slack
and their influence on decision making in ventures that commercialize new
technology.
While firms need resources for their survival (Pfeffer & Salancik, 2003),
growth (Penrose, 1959) and sustainable competitive advantage (Barney,
1991), large resource endowments can hinder the entrepreneurial process by
impairing firms’ ability to identify new business opportunities (Mosakowski,
2002). Resource constraints instead inhibit firm growth and lower the
probability of survival (Becchetti & Trovato, 2002; Musso & Schiavo, 2008),
but may also foster creativity (Hoegl, Gibbert, & Mazursky, 2008; Moreau &
Dahl, 2005). Thus, it is unclear how resource constraints or slack affect
decision making in new technology ventures. Previous studies of how
resource slack and constraints affect creativity and performance have
operationalized these resource positions in ways that may have concealed the
underlying complexity and dynamics. Chapter 4 draws on in-depth case
studies of three new technology ventures to explore how resource positions
influence decision making in new technology ventures.
This study makes three key contributions to the literature regarding the
effects of resource slack and constraints on decision-making in ventures that
commercialize new technology. First, the findings of the study reported in
Chapter 4 show that resource positions should be understood as perceived,
relative, transient and multidimensional. By emerging as the entrepreneur’s
INTRODUCTION
8
perception of available resources relative to demand, perceived resource
positions are not static but change over time and entrepreneurs can
experience different types of resource constraints and slack simultaneously.
Moreover, by framing resource slack and constraints as two extremes of the
spectrum of attainable resource positions, the separate literatures on
resource slack and resource constraints are combined and integrated.
Second, this study explains how perceived resource positions influence
decision-making processes in terms of individual, temporal, and resource
position dynamics. Third, the findings contribute to Austrian perspectives
on entrepreneurship by empirically demonstrating how subjective
perceptions of resource positions enter the decision-making process and
influence the entrepreneur in generating idiosyncratic options with varying
degrees of creativity.
Overall, the studies in this dissertation provide insight in how various
stakeholders make decisions in technology commercialization, particularly
regarding the selection and commercial development of new technologies.
PART I
Decision making in universities
The two chapters in Part I investigate decision making in university technology
licensing offices with respect to the evaluation and selection of new technologies for
commercialization. These studies explore whether various inventor characteristics
influence technology licensing officers’ evaluations of new inventions and their
decision making with respect to patenting, commercial potential and spinoff
creation. Chapter 2 and 3 draw on a series of randomized experiments with
technology licensing officers in the US, with different treatments and measures. In
these experiments, technology licensing officers were invited to evaluate real life
university invention disclosures, in which certain inventor characteristics were
manipulated. The study reported in Chapter 2 investigates the influence of
inventor status on technology licensing officers’ recommendation for patenting and
perceived value to industry. Chapter 3 explores the influence of various inventor
characteristics on technology licensing officers’ support for spinoff creation.
Chapter 2
The perceived value of inventor status*
Research on the evaluation of science and technology shows that when the value of
new technology is uncertain, evaluators are influenced by the status of the actors
associated with the new work. However, existing studies drawing on observational
approaches face various obstacles in attempting to isolate status effects while
controlling for quality. To assess the true causal effect of status, this study builds on
two randomized experiments to investigate how status affects evaluators’
assessments of the value of new technology in the context of university technology
licensing. In the experiments, technology licensing officers at US research
universities were invited to evaluate new university inventions in which everything
except the inventor’s status was held constant. The results suggest that technology
licensing officers perceive the inventions of high status inventors to have more
commercial potential. In addition to demonstrating the causal effect of status on
the evaluation of new technology, these findings serve to better understand the role
of inventor attributes and the decisions of technology licensing officers in the
context of university technology commercialization.
* This chapter is based on: Dolmans, S.A.M., Shane, S., Jankowski, J., Reymen,
I.M.M.J., Romme, A.G.L. (2013). Evaluating university inventions: The role of
inventor status. and has been accepted for publication in Frontiers of
Entrepreneurship Research (2013). Currently under review at Industrial and
Corporate Change.
Earlier versions of this study have been presented at the 2013 Babson College
Entrepreneurship Research Conference (Lyon, France), the 2013 Academy of
Management Annual Meeting (Orlando, FL, USA), the 2013 Technology Transfer
Society (T2S) Annual Conference (Bergamo, Italy), the 2012 High Tech Small Firms
Conference (Amsterdam, The Netherlands), the 2012 ESU European University
Network on Entrepreneurship Conference (Kolding, Denmark) and at the 2012 Beta
TRAIL Conference (Rotterdam, The Netherlands).
12 THE PERCEIVED VALUE OF INVENTOR STATUS
2.1 Introduction
Sociologists have long argued that when the future potential of novel
technologies is uncertain and its true value cannot be measured, evaluators
will rely on social factors, such as the status of the producer of the
technology, to assess its value (Azoulay, Stuart, & Wang, 2012; Stuart,
Hoang, & Hybels, 1999). Researchers have examined this proposition in a
wide variety of settings and have found support consistent with their
assertion that the status of the producers of new technology affects other
parties’ perceptions of its quality and value (Merton, 1968; Podolny & Stuart,
1995; Podolny, 1993, 1994).
However, recent research (Azoulay et al., 2012; Simcoe & Waguespack,
2011) has shown the fragility of these findings. The evidence provided by
these studies may be an artefact of the observational methods used to collect
the data. Observational approaches to investigating the effect of status on
evaluations of new technology face several obstacles in isolating status
effects while controlling for quality (Azoulay et al., 2012; Simcoe &
Waguespack, 2011). Not only is it difficult to accurately measure the quality
of new technologies, but status may also provide resources which contribute
to the actual quality of the producer’s work (Azoulay et al., 2012). As a result,
the observational evidence of the effect of inventor status on perception of
invention value may be biased by unobserved correlations between inventor
status and quality (Azoulay et al., 2012).
Biased results are problematic for both research and practice. Inaccurate
estimates of the effect of status on the evaluation of new technology makes it
difficult for researchers to determine whether status effects are merely noise
in a largely efficient system or whether these status effects cause technical
advance to diverge from an efficient path. From a practical perspective, we
do not know whether status really leads to a Matthew effect (Merton, 1968)
in which substantial gains accrue to people over the long term because their
status advantages allow them to garner disproportionate access to resources.
To assess the true causal effect of status on the evaluation of the value of
uncertain new technology, one needs to conduct an experiment in which
CHAPTER 2 13
everything except the inventor’s status is held constant. To date, no such
examination has been undertaken. We seek to fill this gap in the literature by
conducting randomized experiments to determine how status affects
evaluators’ assessments of the value of uncertain new technology in the
context of university technology licensing.
Universities often own the property rights to the technological
inventions made by their faculty, staff and students. To commercialize these
university inventions, most academic institutions seek to license their
inventions to established companies or entrepreneurs, and have established
technology licensing offices to manage this process. These offices are staffed
by professionals who must regularly evaluate technological inventions made
by faculty, staff and students to determine whether such inventions are of
sufficient commercial value to justify the expense of obtaining intellectual
property protection and marketing the technology to industry (Clarysse et al.,
2005; Owen-Smith & Powell, 2003; Thursby & Thursby, 2002). The
assessment process occurs under considerable uncertainty about the true
commercial value of the inventions because most university inventions are
in an early stage of development, making their commercial potential largely
unknown (Jensen & Thursby, 2001; Shane, 2004). This makes university
technology licensing a good setting to explore the question of how inventor
status influences evaluators’ perceptions of the (commercial) value of new
technology.
In this study we conducted two randomized experiments with 122
technology licensing officers at US Carnegie I research universities in which
we manipulated inventor status associated with otherwise identical
university inventions and inventors to see the causal effect of inventor status
on the licensing officers’ evaluation of the commercial potential of the new
technologies. Our experiments revealed that licensing officers judge
inventions to have greater commercial value if the inventor’s status was
higher.
This chapter proceeds as follows. In the next section we develop the
theory of how status affects the evaluation of technological inventions. In the
third section, we discuss the experimental research design. The fourth
14 THE PERCEIVED VALUE OF INVENTOR STATUS
section presents the results. The final section discusses the main findings,
implications and conclusions.
2.2 Theory
Ideally, evaluators would like to judge the worth of new technology on the
basis of its true commercial value. However, such estimates are difficult to
make because observable technological properties are often not reliable
indicators of commercial success and market-based information is rarely
available (Podolny & Stuart, 1995).
The literature on the evaluation of science and technology shows that
when the future potential of novel technologies is uncertain, and their true
value cannot be measured, evaluators make choices on the basis of what they
can observe. Social factors, such as the rank or status of the inventor, or their
position in a social network, are almost always available. In the absence of
observable information about the true value of new technology, this means
that evaluators often use social factors as proxies for unobserved value
(Azoulay et al., 2012; Podolny & Stuart, 1995; Podolny, 1993, 1994; Stuart et
al., 1999). Researchers have observed this pattern in many settings where
uncertain new scientific or technological discoveries need to be evaluated
(Merton, 1968; Podolny & Stuart, 1995; Podolny, 1993, 1994). One of the
most salient of these social factors is the status of the actors or inventors
associated with the new technology (Podolny, 1993). Inventors vary in status
– or the prestige with which the community views them relative to others.
Unable to know the “true” quality of new inventions, members of the
community perceive the inventions of higher status inventors to be “better”
than the inventions of lower status inventors, all other things being equal
(Azoulay et al., 2012) because the implicit and explicit promises of higher-
status actors regarding the quality of their work are more likely to be
accepted by others (Podolny, 1993). As Podolny and Phillips (1996) point
out, “status carries with it the attribution of superior quality”. This pattern is
also observed in the evaluation of scientific work; in his seminal work,
Merton (1968) found that higher status scientists receive disproportionately
CHAPTER 2 15
greater credit for their work than lower status scientists receive for
comparable contributions.
Moreover, the more uncertain the quality of invention, the more likely
evaluators are to rely on the status of the inventor in their judgment
(Podolny & Stuart, 1995; Podolny, 1993, 1994; Stuart et al., 1999). Thus, in
the assessment of cutting edge technologies, inventor status is likely to play a
particularly large role.
While much evidence shows that status has its predicted effect in
situations where the value of new technology is uncertain, Azoulay et al.
(2012: 1-2) explain that this evidence is “fragile” and unlikely to “persuade a
skeptic.” To date, the studies conducted to show the association between
status and perceptions of value of new technologies have been observational.
Because differences in inventor status are correlated with differences in
inventor quality and, consequently, the quality of the inventions they can
produce, the effect of status on outcomes should be investigated while
holding inventor quality constant (Berger, Conner, & Fisek, 1974; Berger,
Rosenholtz, & Zelditch, 1980).
However, as Azoulay et al. (2012) note, it is very difficult to accurately
control for quality in such observational studies, due to the myriad of ways in
which quality can manifest itself. Moreover, status may provide access to
resources that contribute to the actual quality of the producer’s work
(Azoulay et al., 2012; Merton, 1968). These two forces mean the more
positive evaluations of the inventions of higher status actors may arise from
greater access to resources or greater quality of the inventor or invention
(Podolny & Stuart, 1995).
As a result, most studies investigating the relationship between status
and quality suffer from the problem of unobserved heterogeneity, where an
unmeasured dimension of quality may be responsible for both the inventor’s
status and the more positive evaluations of the inventor’s output (Simcoe &
Waguespack, 2011). If correlations between status and outcome measures
are simply reflecting unmeasured differences in quality, then estimates of
the effect of inventor status on evaluations will be systematically biased
upward.
16 THE PERCEIVED VALUE OF INVENTOR STATUS
Moreover, these studies can suffer from reversed causality where the
more positive evaluations of the inventions lead to perceptions of greater
inventor status. Thus, prior studies are consistent with the idea that status
affects perceptions of the value of new technology, but are equally consistent
with the notion that status is simply a by-product of other factors (Azoulay et
al., 2012).
Since observational study designs will not be able to establish whether
status has a causal effect on perceptions of the value of new technology
(Simcoe & Waguespack, 2011), this study turns to an experimental research
design to evaluate whether variation in status affects the evaluation of new
technological inventions. By randomly assigning status to the inventors of
new technologies and examining the effect of that status on the evaluation of
their inventions, we obtain a direct test of the causal effect of status on
evaluators’ assessment of the commercial potential of the inventions.
2.3 Organizational Setting
Universities are an important source of new technology products and
services. Under the institutional regime established by the Bayh-Dole Act of
1980, US universities were given the property rights to federally funded
inventions made by their faculty, staff and students (Mowery & Sampat,
2001). Many of these inventions have had substantial commercial and
societal impact, such as the cancer drug Taxol, the Internet search engine
Google, and the sports drink Gatorade.
The impact of university technology commercialization is significant; a
recent study estimated that university licensed inventions contributed $16
billion per year to US gross domestic product (Roessner, Bond, Okubo, &
Planting, 2013). According to the Association of University Technology
Managers (2012), US universities received 4,700 new patents and entered
into just shy of 5,000 licensing agreements in 2011, bringing the number of
licenses in force to 38,600. Those agreements provided technology to
products generating annual sales estimated to exceed $120 billion.
To organize their efforts to commercialize university technology, most
research universities have established offices of technology transfer to
CHAPTER 2 17
manage disclosures of technological inventions made by faculty, staff and
students (Rothaermel, Agung, & Jiang, 2007; Siegel, Wright, & Lockett,
2007). Because obtaining intellectual property protection and marketing
inventions to industry are expensive, universities cannot pursue
commercialization of all inventions disclosed to them. The professional staff,
or technology licensing officers, employed by the offices, must therefore
assess the commercial potential of the inventions and decide which are
worth patenting and attempting to license (Clarysse et al., 2005; Jensen et
al., 2003; Owen-Smith & Powell, 2003; Thursby & Thursby, 2002).
This process is not easy because most university inventions are little
more than a proof of concept at the time that technology licensing officers
must evaluate them (Jensen & Thursby, 2001). At such an early stage of
development, industry has generally not expressed interest in the new
technologies and may not even be aware of their potential value, making it
difficult to evaluate their commercial potential (Jensen & Thursby, 2001;
Siegel, Waldman, & Link, 2003). Therefore, technology licensing officers are
likely influenced by the status of the inventors who disclose inventions when
evaluating the commercial potential of inventions disclosed to university
technology licensing offices. We thus postulate a significant and positive
effect of inventor status on technology licensing officers’ perception of the
commercial potential of university inventions.
2.4 Method
To investigate the relationship between inventor status and evaluators’
perceptions of the commercial potential of inventions, while holding
constant the quality of these inventions, we conducted two randomized
experiments with a 2x1 between-subjects design (N=122, N=121). The use of
an experimental design allows for controlling the quality of the university
inventions and isolating the status effect by manipulating specific one-
dimensional conceptualizations of inventor status in otherwise identical
invention disclosures associated with otherwise identical inventors. The
experiments thus test
18 THE PERCEIVED VALUE OF INVENTOR STATUS
The invention disclosures used in our experiment were modified from
actual university invention disclosures submitted at Case Western Reserve
University (Cleveland, Ohio). The modification was done in conjunction
with the director of the technology licensing office at that university to
ensure that the disclosures were realistic and representative of the
disclosures considered by university technology licensing officers (this
licensing office did not participate in the experiment). The invention
disclosures used in our study included information on the new invention,
accompanied by background information on the inventor, such as current
academic position and educational background. The information in the
disclosure was kept completely the same except for the specific status
treatment. An example invention disclosure can be found in Appendix 1.
Each experiment was designed to test the effect of a specific
operationalization of inventor status and used a separate invention
disclosure. The status treatments were selected on the basis of an interview
with the director of the technology licensing office. In addition, we made
sure the treatments were realistically incorporated in the invention
disclosures.
2.4.1 Sample
The experiments in Chapter 2 and 3 targeted active technology licensing
officers at US universities. To obtain subjects, we contacted the technology
licensing office directors at 223 Carnegie I research universities in the
United States and asked their offices to participate in the study. All offices
that agreed to participate would receive a $50 gift card as a token of our
gratitude. Of the 223 offices contacted, 98 agreed to participate. At those
offices that agreed to participate, we asked the licensing office director for
the number of licensing professionals at their institution and the names and
email addresses of those (other) licensing officers.
In the series of experiments conducted for the studies in Chapter 2 and
3, 352 licensing officers were invited to participate in the experiments, which
were conducted online. Each licensing officer received an email that
included a password-protected link to the online experiment accompanied by
CHAPTER 2 19
a unique (anonymous) login code and password combination to gain access
to the experiment. The unique login information ensured confidentiality of
both the invention disclosures and the licensing officers’ responses.
For both experiments in this study, we randomly assigned licensing
officers to the treatment or control groups (except for the specific status
treatment, both groups received identical invention disclosures and
descriptions of inventors). Participants were required to complete the entire
experiment in a single session and were not able to modify or complete their
answers at a later point in time. Furthermore, we asked each participant to
provide us with the following information: gender, age, experience (number
of years working as a licensing officer), highest academic degree and the
technical field in which they obtained their highest degree. Table 2.1 gives an
overview of the sample of licensing officers that participated in the
experiments in this study.
Table 2.1: Sample
Experiment I II
N 122 121
Male licensing officers 86 (70.49%) 72 (59.50%)
Female licensing officers 36 (29.51%) 49 (40.50%)
Mean age (in years) 44.09 (SD 11.98) 43.36 (SD 11.91)
Mean experience (in years) 6.70 (SD 5.03) 6.83 (SD 5.33)
Highest degree
PhD 58 (47.54%) 52 (42.98%)
Master’s degree 55 (45.08%) 55 (45.45%)
Bachelor’s degree 8 (6.56%) 13 (10.74%)
Associate degree 1 (0.82%) 1 (0.83%)
Educational background
Life Sciences 58 (47.54%) 59 (48.76%)
Engineering 25 (20.49%) 23 (19.01%)
Business or Law 34 (27.87%) 38 (31.40%)
Chemistry 13 (10.66%) 13 (10.74%)
Computer science 4 (3.28%) 2 (1.65%)
Other 7 (5.74%) 5 (4.13%)
20 THE PERCEIVED VALUE OF INVENTOR STATUS
2.4.2 Treatments
Inventor status was operationalized differently in the two experiments. In
the first experiment, we operationalized inventor status with the position of
department chair. This operationalization was selected in discussion with
the director of technology licensing office at Case Western Reserve
University, who observed that department chairs were generally perceived to
have higher status than other faculty members at the university. It is also
consistent with the academic literature (Bercovitz & Feldman, 2008;
Wolverton, Ackerman, & Holt, 2005), which holds that department chairs in
science and engineering are perceived to have higher status than other
faculty members. In this experiment, licensing officers were randomly
assigned to one of two conditions: the treatment group received an invention
disclosure submitted by a department chair (full professor and department
chair), while the control group received an invention disclosure submitted by
a regular faculty member (full professor and not department chair).
In the second experiment, inventor status was operationalized as an
inventor who was elected a member of the National Academy of Sciences.
The National Academy of Sciences (NAS) is a “private, non-profit society of
distinguished scholars engaged in scientific and engineering research,
dedicated to the furtherance of science and technology and to their use for
the public good. [...] Members are elected to the National Academy of
Sciences in recognition of their distinguished and continuing achievements
in original research. Membership is a widely accepted mark of excellence in
science and is considered one of the highest honors that a scientist can
receive” (“National Academy of Sciences,” n.d.). This operationalization was
selected on the basis of an interview with the director of the technology
licensing office at Case Western Reserve University. In the experiment,
licensing officers were randomly assigned to one of two conditions: the
treatment group received an invention disclosure with an inventor that was a
member of the National Academy of Sciences (full professor and NAS
member), while the control group received the same invention but now
submitted by an inventor who was not a member of the National Academy
(full professor and not NAS member).
CHAPTER 2 21
In both experiments, we checked the random assignment of licensing
officers to treatment and control groups by comparing the treatment and
control groups on several licensing officer characteristics. Table 2.2 shows
the means, standard deviations, and t-tests for the comparison of treatment
and control groups. For most licensing officer characteristics there are only
small, non-significant differences between the treatment and control groups.
The only exceptions are the number of years of experience as a licensing
officer and the proportion of participants with a business and law
background for the National Academy of Sciences operationalization, but the
overall non significance of the differences for the other variables shows that
the randomization generally had its desired effect.
2.4.3 Measures
In consultation with the director of the technology licensing office at Case
Western Reserve University, we developed three measures of licensing
officers’ evaluation of inventions. These measures were formulated to
realistically reflect how licensing officers consider the commercial potential
of an invention as well as to be consistent with academic literature on
university technology transfer (Jensen & Thursby, 2001; Siegel, Waldman, &
Link, 2003):
(1) “How valuable do you believe this invention would be to industry?” (1=
not at all valuable, 5= one of the most valuable inventions the university has
available for licensing);
(2) “How likely are you to recommend that the university applies for a
United States patent on this invention?” (1= very unlikely, 5= very likely);
(3) “How likely are you to recommend patenting this technology in the
major markets for it outside the United States?” (1= very unlikely, 5= very
likely).
Before administration, the measures were pre-tested by licensing officers
from the technology transfer office at Case Western Reserve University and
the respondents in this pre-test indicated that the measures accurately
represented the invention evaluation decision.
22 THE PERCEIVED VALUE OF INVENTOR STATUS
Table 2.2: Randomization Check
Experiment
I
II
Treatment
Dep.
Chair
Regular
faculty
NAS
member
Regular
faculty
N 62 60
62 59
TLO characteristic Gender
1.26
(0.44) 1.33
(0.48) 1.44
(0.50) 1.38
(0.49) t-value
0.91
0.70
Age
43.61 (10.90)
44.58 (13.08)
44.12 (10.12)
42.54 (13.58)
t-value
0.45
0.73
Experience
6.89 (5.12)
6.52 (4.97)
7.68 (6.09)
5.93 (4.28)
t-value
0.41
1.81*
Education
2.34 (0.70)
2.45 (0.59)
2.24 (0.64)
2.37 (0.74)
t-value
0.95
1.04
Business or Law
0.24 (0.43)
0.32 (0.47)
0.40 (0.49)
0.22 (0.42)
t-value
0.92
2.19**
Engineering
0.24 (0.43)
0.17 (0.38)
0.23
(0.42) 0.15
(0.36) t-value
1.03
1.02
Life Sciences
0.44 (0.50)
0.52 (0.50)
0.47
(0.50) 0.51
(0.50) t-value
0.89
0.44
Other
0.05 (0.22)
0.07 (0.25)
0.02 0.13
0.07 0.25
t-value
0.43
1.43
p- values *p<0.10, ** p<0.05; ***p<0.01
CHAPTER 2 23
2.5 Results
The basic outcomes of the experiments are presented in Table 2.3. The
licensing officers who received random assignment of an inventor who was
also a member of NAS believe the invention to be significantly more
valuable compared to those who received an invention disclosure submitted
by an inventor who was not. With respect to recommending patent
application, licensing officers are thus more likely to recommend
international patent application if the inventor is a NAS member. No
statistically significant difference in the recommendation for US patenting
was present for inventions of members of the National Academy of Sciences
and non-members.
While licensing officers do not believe the inventions of department
chairs are more valuable to industry than those of other faculty members,
they are more likely to recommend those inventions for a US patent and for
patenting outside the US.
2.6 Discussion
The experiments conducted in this study provide direct evidence of the
causal effect of inventor status on technology licensing officers’ views of the
commercial value of university inventions. We found statistically significant
differences in licensing officers’ evaluation of an invention’s value to
industry and appropriateness for domestic and foreign patent application,
depending on the inventor’s status as a department chair or member of the
National Academy of Sciences. Given the role of technology licensing
officers in making decisions about which university inventions to patent and
market to industry, our findings suggest that department chairs and
National Academy of Sciences members will be more likely to see their
inventions commercialized than the technological characteristics of these
inventions alone would suggest.
24 THE PERCEIVED VALUE OF INVENTOR STATUS
Table 2.3: Comparison of the Treatment and Control Groups
Experiment I
II
Treatment Dep. Chair
Regular faculty
NAS member
Regular faculty
N 62 60
62 59
Invention valuable to industry
Mean 3 2,87 3,42 3,15 SD 0,72 0,87 0,74 0,66 t-value 0,9195 2,0883** p-value (one sided) 0,1798 0,0194
d* 0,1644 0,3878
Recommend for US patent
Mean 3,61 3,23 3,13 3,08 SD 1,19 1,12 1,12 0,97 t-value 1,7434** 0,2316 p-value (one sided) 0,0419 0,4086 d* 0,3314 0,0482
Recommend for patent outside US
Mean 2,87 2,42 2,65 2,29 SD 1,19 1,20 1,15 0,95 t-value 2,0981** 1,8618** p-value (one sided) 0,0190 0,0326 d* 0.3796 0.3433
p-values (one sided), *p<0.10, ** p<0.05; ***p<0.01 1 Cohen’s d (Cohen, 1988, 1992) as a measure of effect size, calculated using the pooled standard deviation:
√( )
( )
Small Medium Large Cohen’s d effect size (t-test difference in means)
.20 .50 .80
2.6.1 Implications
Besides demonstrating the direct effect of status on the evaluation of new
technology, our findings have several important implications for (research
on) university technology commercialization. First, this study shows how
CHAPTER 2 25
research on university technology commercialization can potentially gain
from investigating how social structure enters into the decision-making
processes of technology licensing officers (Podolny & Stuart, 1995; Podolny,
1993). By highlighting the role of inventor status, our results extend prior
findings on the importance of inventor characteristics in evaluating the
commercial potential of inventions (Jensen & Thursby, 2001; Shane, 2004;
Siegel, Waldman, & Link, 2003), thereby rebalancing the literature’s focus
on the (technological) attributes of the inventions themselves (Podolny &
Stuart, 1995). Future research on university technology commercialization
should therefore incorporate the sociological processes inherent in the
evaluation and commercialization of university inventions.
Second, by acknowledging status processes one can better understand
the decisions of technology licensing officers about university inventions.
Our results provide insight into how technology licensing officers influence
the process of technology commercialization, by demonstrating these
officers are sensitive to inventor status when they evaluate new inventions.
Licensing officers may rely on inventor status to resolve uncertainty about
the quality of a university invention (Podolny & Stuart, 1995) and use it as a
signal (Podolny, 1993) of the technical and market value of an invention. As
such, inventor status may provide a sense of credibility to those claims about
the invention that are hard to ascertain (Shane & Khurana, 2003; Shane,
2004).
Our findings suggest an important avenue for future research: should
licensing offices use mechanisms to evaluate university inventions that allow
them to ignore the inventor’s status? Our experiments show that licensing
officers perceive the inventions of department chairs and members of the
National Academy of Sciences as having greater commercial potential than
those of other academics. Whether licensing officers should take inventor
status into consideration when evaluating inventions depends on whether or
not inventor status actually improves the odds of a successful commercial
outcome.
Some research argues that higher status researchers produce inventions
of greater commercial value (Shane, 2004; Zucker & Darby, 1997),
suggesting that technology licensing officers might be accurately inferring a
26 THE PERCEIVED VALUE OF INVENTOR STATUS
greater commercial potential of inventions developed by high status
inventors. While this argument seems plausible for the effect of our member
of the National Academy of Sciences manipulation, a similar argument does
not appear to be applicable to the effect of department chairs. Unlike
membership in the National Academy of Sciences, becoming a department
chair is typically not contingent on academic achievement. Therefore, the
department chair effect suggests a status effect that is less likely to reflect the
actual commercial potential of the researcher’s invention. In this respect,
licensing officers may be biased in their evaluation of the work of high status
faculty members resulting in an inaccurate perception of quality that stems
from status. Such biases in the evaluation of high status actors may also be
the result of factors such as respect, doubts of one’s own competence to
criticize a renowned actor, or fear of offending an influential person, which
can result in less careful assessments with less strict criteria (Merton, 1968;
Zuckerman & Merton, 1971). Our results thus suggest the need for future
research that can determine whether the effects of status on evaluators’
perceptions of value are efficient or a source of bias. Future research should
therefore address to what extent various inventor status characteristics can
indeed be seen as a valid proxy or signal of underlying technological quality
or tacit knowledge.
Furthermore, inventor status may facilitate the commercialization
process by attracting the attention of potential licensees and signaling quality
to scientific and financial communities (Allen, Link, & Rosenbaum, 2007;
Audretsch & Stephan, 1996; Elfenbein, 2007). In this regard, future work
should also investigate whether status effects (in general, or such as
identified in this study) translate to industry evaluations of university
technologies.
2.6.2 Limitations
The study in this chapter has some limitations. First, our decision to conduct
a randomized experiment to examine the effect of inventor status on
licensing officer evaluation resulted in a stylized research setting. Although
we took several measures to make the experiment realistic, the licensing
CHAPTER 2 27
officers were asked to conduct a simplified and time-constrained evaluation
process rather than a more iterative, multistage selection process (Shane,
2004). Moreover, authentic invention disclosure documents may not always
contain inventor status characteristics (like National Academy of Sciences
membership). Therefore, our experimental design might have evoked a
clearer association between those attributes and the invention itself than is
the case when university licensing officers evaluate authentic disclosures of
inventions. While we have no evidence to suggest that our results are an
artifact of the stylized nature of the research design, it is possible that the
patterns observed were either over- or understated as a result of the research
design adopted.
Second, the treatments used in this experiment were selected on the
basis of interview data with the director of a technology licensing office and
prior research. Future research should explore which other inventor status
characteristics licensing officers might be sensitive to when evaluating
university inventions.
Third, our findings may not be generalizable to technology transfer
offices outside the US. Cultural differences, for instance, may lead licensing
officers elsewhere to respond differently to the status treatments employed
in this study. Although we have no evidence to suggest that these results
would not generalize to other countries, additional research is needed to
show that they can be generalized.
2.6.3 Conclusion
This study built on randomized experiments to investigate how inventor
status affects evaluators’ assessments of the value of new technology in the
context of university technology commercialization. During these
experiments, technology licensing officers at US research universities were
invited to evaluate new university inventions in which everything except the
inventor’s status was held constant. This research design serves to overcome
the typical difficulties of isolating status effects while controlling for quality.
Our results show that the status of the inventors who disclose university
inventions influences technology licensing officers’ evaluations of these
28 THE PERCEIVED VALUE OF INVENTOR STATUS
inventions. In particular, technology licensing officers believe the inventions
of high status inventors have more commercial potential, suggesting that the
inventions of high status inventors are more likely to be commercialized.
These findings provide significant ground for future research on status
processes in relation to university technology commercialization.
This chapter has shown how technology licensing officers may rely on
inventor characteristics to assess the commercial potential of new university
inventions. Chapter 3 builds on these insights by investigating the influence
of various inventor attributes on technology licensing officers’ support for
spinoff creation.
Chapter 3
Do technology licensing officers favor
particular inventors for start-ups?*
Technology licensing officers play an important role in commercializing university
inventions. Anecdotal data indicates that characteristics of inventors may
influence licensing officers’ decisions about which inventions should (not) be
commercialized. To examine the effect of faculty member characteristics on the
support that licensing officers give to spinoff company creation, this study builds on
randomized experiments with more than 200 technology licensing officers at
universities in the US. Licensing officers appear to be negatively disposed to
(disclosures by) female inventors and positively disposed to (disclosures by)
Chinese-named Asian inventors with industry experience who are easy to work
with.
* This chapter is based on: Shane, S., Dolmans, S.A.M., Jankowski, J., Reymen,
I.M.M.J., Romme, A.G.L. (2012). Which Inventors do Technology Licensing Officers
Favor for Start-ups? and has been accepted for publication in Frontiers of
Entrepreneurship Research (2012). Currently under review at The Journal of
Technology Transfer.
Earlier versions of this study have been presented at the 2012 Babson College
Entrepreneurship Research Conference (Forth Worth TX, USA), the 2012 CIR
Tilburg Conference on Innovation, (Tilburg, The Netherlands) and the 2012
Technology Transfer Society (T2S) Annual Conference (New York NY, USA).
30 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
3.1 Introduction
Technology licensing offices play an important role in commercializing
university inventions (Thursby and Thursby, 2002; Owen-Smith and Powell,
2003; Clarysse et al., 2005). Because the property rights to inventions made
by faculty, staff and students often belong to the institutions where these
inventions were developed, technology licensing officers often regulate
which inventions are commercialized through the creation of spinoff
companies.
Anecdotal data indicates that technology licensing officers are
influenced by the characteristics of the inventors who disclose those
inventions (Shane, 2004, 2005). However, systematic evidence of this
relationship has yet to be established. Whereas Chapter 2 investigated the
role of inventor status on the perceived commercial potential of new
inventions, this study explores whether inventor characteristics influence the
support that technology licensing officers give to the creation of spinoff
companies.
This study draws on experiments with 239 technology licensing officers
at 88 universities. The licensing officers were asked to evaluate identical
invention disclosures, to which we randomly assigned various inventor
characteristics. We found statistically significant differences in the rate at
which the licensing officers recommended spinoff company creation,
depending on the inventor characteristics.
Our findings are important to researchers and practitioners in several
ways. First, they provide insight into the influence that inventor attributes
have on the creation of university spinoffs, rebalancing the literature’s focus
on the attributes of the inventions themselves. Second, they help us to better
understand the decisions of technology licensing officers about university
inventions, providing insight into how these individuals influence the
process of technology commercialization (Shane, 2004; Siegel et al., 2007).
Third, this study serves to identify the preferences of licensing officers for
particular types of inventors, suggesting attributes that will help inventors to
increase their odds of founding a spinoff.
CHAPTER 3 31
3.2 Theory
Why do some university inventions, like the Google search algorithm, lead to
the creation of new companies, while others, like the sports drink Gatorade,
get licensed to existing companies? To date, researchers studying this
question have largely focused on the characteristics of the inventions
themselves. Previous research has shown that only few inventions are
sufficiently important, generic, disruptive and early stage, or with sufficient
patent protection, to be appropriate for the formation of a new company
(Pressman, 2002; Shane, 2004).
However, anecdotal data suggests that inventor characteristics also
influence whether a spinoff will be founded (Shane, 2004). Spinoffs demand
that inventors undertake additional technology development (Jensen &
Thursby, 2001), acquire resources (Shane & Cable, 2002) and establish new
organizations (Grandi & Grimaldi, 2003; Nicolaou & Birley, 2003; Burg,
Romme, Gilsing, & Reymen, 2008), all of which are facilitated by certain
inventor attributes.
Because inventor attributes affect the formation of spinoff companies,
licensing officers often assess the inventor as well as the technology when
they evaluate invention disclosures (Franklin, Wright, & Lockett, 2001;
Shane, 2004, 2005; Vohora, Wright, & Lockett, 2004). Existing research
suggests several inventor characteristics that could influence technology
licensing officer evaluations: gender, immigrant status, industry experience and
the ease of working with the inventor. Below we develop specific hypotheses
about the influence of each of these characteristics on technology licensing
officers’ recommendation of invention disclosures for spinoff company
creation.
3.2.1 Inventor gender
Female academics are less likely than their male counterparts to engage in
the commercialization of science (Bunker Whittington & Smith-Doerr, 2005;
Ding, Murray, & Stuart, 2006), whether that commercialization is measured
by number of inventions, patents, licenses, or start-up companies (Bunker
32 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
Whittington & Smith-Doerr, 2005; Ding et al., 2006; Murray & Graham,
2007). First, female professors are less likely to disclose inventions. In a
study of researchers at 11 leading research universities, Thursby and Thursby
(2005) found that 8.7 percent of male faculty members disclose inventions,
but only 6.7 percent of female academics do so, despite statistically similar
publication records.
Low levels of patenting parallel the low rate of invention disclosure
among female academics. Azoulay et al. (2007) found that the patenting rate
for female researchers was half that of their male counterparts. Similarly,
Ding et al. (2006) found that 7.8 percent of women in the life sciences have
at least one patent as compared to 25.1 percent of men. And every year after
obtaining their PhD, male academics are more likely than their female
counterparts to obtain patents on the outputs of their academic research,
leading the gender gap to increase with the number of years since the
awarding of the PhD (Ding et al., 2006). Despite producing patents with
equivalent citation counts, breadth and originality, Bunker Whittington and
Smith-Doerr (2005) found that male life scientists are more than twice as
likely as female life scientists to have ever patented an invention and
generate just less than twice as many patents per year since getting their
doctorates.
Female academics are also less likely than male academics to license
their inventions to industry. Link et al. (2007) found that male faculty
members are more likely than their female counterparts to engage in
commercial knowledge transfer, whether through licensing or consulting.
Finally, male academics are more likely than female academics to start
companies to commercialize their inventions. In a survey of 1554 university
researchers in Canada, Landry et al. (2006) found that being a man
increases the likelihood of creating a spinoff.
Researchers have offered several explanations for the gender gap in
academic patenting and licensing, including under representation of women
in senior positions, views of money and the commercialization of science,
exposure to business, research foci, and their other personal and
professional responsibilities (Murray & Graham, 2007; Ding et al., 2006;
Stephan & El-Ganainy, 2007). Fox (2005) explains that women are less likely
CHAPTER 3 33
to be at the top end of the publishing distribution and high level publications
enhance academic commercialization. Moreover, women tend to be under-
represented in those academic positions from which commercial activity is
most possible (Stephan & El-Ganainy, 2007). Murray and Graham (2006)
argue that exclusion of women from commercial science in the early days of
their careers leaves them with lesser commercial science skills than their
male counterparts. Similarly, female scientists have fewer contacts with
industry (which makes it more difficult for them to patent), because they are
more likely to believe that commercial activity would adversely affect their
careers (Ding et al., 2006). Still others argue that female faculty members
conduct different types of research than their male counterparts, which
makes it more difficult for them to engage in commercialization (Stephan &
El-Ganainy, 2007).
While these explanations may all be valid, we focus on another possible
(and complementary) explanation: the way that technology licensing officers
perceive the inventions of female faculty members. Because technology
licensing officers influence which inventions are patented, licensed and
become the basis for spinoff companies, they play a gate-keeping role, which
can lead to gender differences in spinoff company creation if their
preferences influence their recommendations.
While previous researchers have not directly addressed the question of
technology licensing officer preferences, they have discussed the possibility
that those evaluating university inventions may favor the inventions of male
academics. Stephan and El-Ganainy (2007) question whether women receive
the same support of their technology licensing offices as men. And Bunker
Whittington and Smith-Doerr (2005: 366) raise the question whether
“universities and their technology licensing offices (…) fail to support initial
commercialization for female scientists.” We thus hypothesize:
H1: Technology licensing officers favor the inventions of male faculty
members over the inventions of female faculty members for the
creation of spinoff companies.
34 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
3.2.2 Inventor immigrant status
Immigrant researchers may be more likely than other researchers to found
spinoff companies. Krabel et al. (2012) find that academic researchers who
are foreign-born and educated are more likely to start companies than native-
born researchers, perhaps because self–selection leads immigrants to be
more inventive and entrepreneurial (Hunt, 2009; Stephan & Levin, 2001).
Stephan and Levin (2001) show that foreign-born individuals are
disproportionately overrepresented among the academics that have played a
key role in launching biotechnology firms.
Immigrants are also more likely to commercialize and license
inventions. Hunt (2009) found that immigrants who originally entered the
United States on temporary work visas or on student/trainee visas
outperform native college graduates in commercializing and licensing
patents. These arguments lead to our second hypothesis:
H2: Technology licensing officers favor the inventions of immigrant faculty
members over the inventions of native faculty members for the creation
of spinoff companies.
3.2.3 Inventor industry experience
To create companies to commercialize their inventions, academics need
information and expertise from the business world (Landry et al., 2006).
However, the experience and social networks of most researchers tend to be
limited to academia (Mosey & Wright, 2007). This pattern suggests that
those academics with greater access to business information would be better
able to start companies to commercialize their inventions than other
academics.
Some research supports this argument. Industry experience helps
inventors to understand the difference between business and academia and
gives them useful skills for starting companies (Shane, 2004). Moreover,
industry experience provides insight into the workings of the industry in
which the invention would be applied, helps to position a start-up
CHAPTER 3 35
appropriately within that industry, and gives the founders information about
potential customers (Shane, 2004, 2005). By interacting with industry,
academics gain a network of potential suppliers, customers and investors
(Roberts & Malone, 1996; Shane & Cable, 2002; Shane, 2004) that is
helpful for starting a business (Grandi & Grimaldi, 2003, 2005; Nicolaou &
Birley, 2003). Moreover, Vohora et al. (2004) argue that academic
entrepreneurs without industry experience concentrate too much on
technical issues at the expense of commercial ones (Franklin et al., 2001;
Daniels and Hofer, 1993).
Prior research indicates that inventors with ties to investors or business,
or industry experience, are more likely to engage in spinoff activity. Landry et
al. (2006) show that the likelihood of launching a university spinoff
increases if researchers have consulting experience. In addition, they find
that the intensity of the researcher’s linkages with private sector
professionals increases the probability of spinoff creation. Similarly, Krabel
and Mueller (2009) find that academic scientists with close ties to industry,
in the form of experience in research cooperation with private firms, are
more likely to become spinoff company founders.
Shane (2005) finds that directors of technology licensing offices regard
spinoff companies as more appropriate when the academic inventor has
industry experience. Similarly, Franklin et al. (2001) found that licensing
officers believe that the main disadvantage of having an academic inventor
lead a spinoff company is lack of commercial experience. These arguments
lead to our third hypothesis:
H3: Technology licensing officers favor the inventions of faculty members
with industry experience over the inventions of faculty members
without industry experience for the creation of spinoff companies.
3.2.4 Ease of working with the inventor
Previous research suggests that academic inventors who are easy to work
with are more likely to start companies. To create a spinoff, researchers need
to work with many different actors, including investors, suppliers and
36 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
customers (Mustar, 1997; Walter, Auer, & Ritter, 2006). Inventors who are
difficult to work with could have problems in raising money, attracting
suppliers, and finding employees, because external stakeholders may choose
to avoid such inventors (Shane, 2005).
Interviews with technology licensing office directors indicate that they
find inventors who are easy to work with more appropriate for creating
spinoff companies (Shane, 2005). These arguments lead to our fourth
hypothesis:
H4: Technology licensing officers favor the inventions of faculty members
who are easy to work with over the inventions of faculty members who
are difficult to work with for the creation of spinoff companies.
3.3 Method
Previous studies have mainly relied on anecdotal data to suggest that
technology licensing officers are influenced by inventor characteristics. To
establish a causal relationship between inventor characteristics and the
degree of support that technology licensing officers give to spinoff creation,
we conducted experiments in which inventor gender, immigrant status,
industry experience and the ease of working with them were randomly
assigned to the same invention disclosures. We asked technology licensing
officers to evaluate the inventions on their appropriateness as the basis of a
spinoff company. These experiments were part of the series of experiments
that also served to collect data for the study in the previous Chapter, but the
studies in Chapter 2 and 3 build on different treatments and outcome
measures.
3.3.1 Sample
For the series of experiments in Chapter 2 and 3, we contacted the
technology licensing office directors at 223 Carnegie I research universities
in the United States and asked their offices to participate in the study. All
offices that agreed to participate, received a $50 gift card as a token of our
CHAPTER 3 37
gratitude. Of the 223 offices contacted, 98 agreed to participate. At those
offices that agreed to participate, we asked the licensing office director for
the number of licensing professionals at their institution and the names and
email addresses of these officers.
We invited 352 licensing officers to participate in the experiments,
which were conducted online. We sent each participant an email that
included a password-protected link to the online experiments accompanied
by a unique login code and password combination to gain access to the
experiments. The unique login information ensured confidentiality of both
the invention disclosures and the licensing officers’ responses. Participants
were required to complete the experiments in a single session and were not
able to modify or complete their answers at a later point in time. After
sending out the invitations and several reminders, 239 licensing officers
from 88 offices completed the experiments for this particular study (giving a
response rate of 67.9 percent).
The sample of licensing officers in this study included 155 male (64.9
percent) and 84 female licensing officers (35.1 percent), ranging in age from
25 to 78 years (M = 43.9). On average, the participants had been working 6.9
years as a university technology licensing officer. In terms of highest level of
education, 105 licensing officers hold a PhD (43.9 percent), 108 hold a
Master’s degree (45.2 percent) and 24 hold a Bachelor’s degree (10.0
percent). (Two licensing officers hold an Associates degree.) In terms of
educational background, 104 licensing officers obtained their highest degree
in life sciences (38.4 percent), 49 in engineering (18.1 percent), 44 in
business (16.2 percent), 27 in law (10.0 percent), 24 in chemistry (8.9
percent), 6 in computer science (2.2 percent) and 17 licensing officers
obtained their degree in other fields (6.3 percent).
3.3.2 Treatments
Each licensing officer was asked to look at four invention disclosures, to
examine the following inventor characteristics: gender, immigrant status,
industry experience, and how easy they were to work with. For each
disclosure, we randomly assigned licensing officers to the treatment or
38 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
control groups. Except for the specific treatment, both the treatment and
control groups received identical invention disclosures and inventor
descriptions. Our experiment included the following treatments and
controls:
Inventor gender - The treatment group received a disclosure with a
male name and male picture, while the control group received an
invention disclosure with a female name and female picture.
Inventor immigrant status - The treatment group received a disclosure
with a Chinese name and Asian picture, while the control group
received an invention disclosure with an American name and
Caucasian picture. We chose to operationalize immigrant scientists
as scientists with a Chinese name and Asian picture because our
experiment only allows for testing one type of immigrant scientist
and Asian scientists make up the largest part of the foreign-born
population of scientists in the United States (Corley & Sabharwal,
2007; Lin, Pearce, & Wang, 2008) and Chinese scientists are the
largest ethnic contributor to US domestic and international patent
applications (Kerr, 2008; Wadhwa, Jasso, Rissing, Gereffi, &
Freeman, 2007).
Inventor industry experience - The treatment group received a
disclosure where the inventor had industry experience. The control
group received a disclosure where the inventor had no industry
experience.
Ease of working with the inventor - The treatment group received a
disclosure where the inventor was easy to work with. The control
group received a disclosure where the inventor was difficult to work
with.
To check the random assignment of licensing officers to treatment and
control groups, we compared the treatment and control groups on the
following licensing officer characteristics: age, experience, gender, technical
field, and highest academic degree. As one would expect from random
assignment, there are only small, non-significant differences between the
treatment and control groups. Table 3.1 shows the means, standard
deviations, and t-tests for the check of randomization.
CHAPTER 3 39
Table 3.1: Randomization Check
Treatments
Male Female American-
named Chinese-named
Industry Exp.
No Industry
Exp.
Difficult to Work
With
Easy to Work With
N 119 120 123 116 121 118 122 117
TLO characteristics
Gender 1.39
(0.49) 1.32
(0.47) 1.31
(0.46) 1.40
(0.49) 1.39
(0.49) 1.31
(0.46) 1.30
(0.46) 1.41
(0.49)
t-value 1.13
1.42
1.31
1.87
Age 43.29 (12.55)
44.43 (11.55)
45.00 (10.90)
42.66 (13.09)
43.08 (10.63)
44.65 (13.33)
44.09 (11.98)
43.62 (12.16)
t-value 0.74
1.51
1.00
0.3
Experience 6.97
(5.46) 6.87
(4.80) 7.20
(5.48) 6.61 (4.73)
6.93 (5.32)
6.90 (4.94)
6.70 (5.03)
7.14 (5.25)
t-value 0.15
1.52
0.05
0.65
Education 2.27
(0.72) 2.38
(0.65) 2.38
(0.67) 2.26
(0.70) 2.30
(0.71) 2.34
(0.67) 2.39
(0.65) 2.25
(0.72)
t-value 1.2
1.39
0.50
1.64
Law or Business
0.28 (0.45)
0.31 (0.46)
0.30 (0.46)
0.28 (0.45)
0.31 (0.47)
0.27 (0.45)
0.28 (0.45)
0.31 (0.46)
t-value 0.53
0.28
0.73
0.49
Engineering 0.20
(0.40) 0.21
(0.41) 0.19
(0.39) 0.22
(0.42) 0.18
(0.38) 0.24
(0.43) 0.20 (0.41)
0.21 (0.41)
t-value 0.13
0.71
1.15
0.00
Life Sciences 0.49 (0.50)
0.38 (0.49)
0.47 (0.50)
0.40 (0.49)
0.51 (0.50)
0.36 (0.48)
0.48 (0.50)
0.39 (0.49)
t-value 1.63
1.17
2.31*
1.28
Other 0.17
(0.38) 0.23
(0.42) 0.17
(0.38) 0.22
(0.42) 0.17
(0.37) 0.23
(0.42) 0.20
(0.40) 0.20
(0.40)
t-value 1.11
1.04
1.23
0.00
* p<0.05; **p<0.01; ***p<0.001; ****p<0.0001
3.3.3 The invention disclosure
The invention disclosures were modified from actual university invention
disclosures submitted at Case Western Reserve University (Cleveland, Ohio).
The modification was done in conjunction with the director of the
technology licensing office at this university, to ensure that the disclosure
40 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
was realistic and representative of the disclosures assessed by university
technology licensing officers. Before administration of the experiment, it was
pre-tested by licensing officers from the technology transfer office; this office
did not participate in the study.
3.3.4 Measures
In conjunction with the director of the technology licensing office at the first
author’s university, we designed two measures to capture licensing officers’
evaluations of the invention disclosure as the basis for spinoff company
creation. The measures were formulated to realistically reflect how licensing
officers would express their support or lack of support for spinoff company
creation. The measures were designed to capture both positive and negative
approaches to spinoff creation and were both measured on a five-point Likert
scale. The first measure asks, “If the inventor wanted to start a company to
commercialize this technology, how much would you try to dissuade the
inventor?” (1= not at all, 5= as much as I could). The second asks, “How likely
would you be to recommend a startup that exploited this invention to your
university’s internal venture capital fund?” (1=very unlikely, 5= very likely).
3.4 Results
The basic results of our study are presented in Table 3.2, which gives an
overview of the expected and actual effects of our treatments. As Table 3.2
shows, our results indicate that all tested inventor characteristics influence
technology licensing officer decision-making in ways consistent with our
predictions. However, not all inventor characteristics significantly affected
both dependent variables examined.
CHAPTER 3 41
Table 3.2: Expected and Actual Effects of the Treatments
Expected Effects Actual Effects
Treatments (inventor characteristics)
Dissuade inventor from
starting a company
Recommend to university
venture capital fund
Dissuade inventor from
starting a company
Recommend to university
venture capital fund
Male - + - **
Chinese-named - + + *
Industry experience - + - **** + **
Easy to work with - + - *
* p<0.05; **p<0.01; ***p<0.001; ****p<0.0001
The results of our statistical analysis are presented in Table 3.3. Consistent
with our predictions, the licensing officers who received random assignment
of a female inventor were significantly more likely to dissuade the inventor
from starting a company (M = 2.53, SD = 1.26), compared to the officers who
received an invention disclosure with a male inventor (M = 2.09, SD =0.97),
t(237) = 3.03, p = 0.0027, Cohen’s d = 0.39. However, there was no
statistically significant difference in the recommendation of the start-up to
the university’s internal venture capital fund (female inventors M =3.37, SD
= 0.95; male inventors M =3.55, SD = 0.95, t(237) = 1.46, p =0.1456, Cohen’s
d = 0.19.)
We observed a statistically significant difference in recommendation of
the invention to the university’s internal venture capital fund based on the
inventor’s immigrant status. Licensing officers were more likely to
recommend the inventions of Chinese-named Asian inventors (M =2.42, SD
=1.09) to their university’s venture capital fund compared to inventions of
American-named Caucasian inventors (M =3.32, SD =0.97) t(237) = 2.34, p =
0.0201, Cohen’s d = 0.30. But there was no statistically significant
difference in the degree of dissuasion from starting a business between the
treatment group receiving an Chinese-named Asian inventor (M = 2.20, SD
=1.19) and the control group receiving an American-named Caucasian
inventor (M =2.42, SD =1.09), t(237) = 1.52, p = 0.1298, Cohen’s d = 0.19.
42 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
We found that licensing officers were significantly less likely to dissuade
inventors with industry experience from starting a company (M = 2.02, SD
=1.13), compared to inventors without industry experience (M =2.84, SD
=1.34), t(237) = 5.09, p = 0.0000, Cohen’s d = 0.67. Moreover, licensing
officers were more likely to recommend inventions by inventors with
industry experience (M = 3.09, SD =1.07) to their university’s venture capital
fund, compared to those of inventors without industry experience (M =2.72,
SD =1.13), t(237) = 2.60, p = 0.0099, Cohen’s d = 0.34.
Licensing officers were significantly less likely to dissuade inventors
perceived as easy to work with (M = 3.09, SD =1.07) from starting a
company, compared to inventors who are difficult to work with (M = 3.17, SD
=1.40), t(237) = 2.44, p = 0.0154, Cohen’s d = 0.32. However, there is no
statistically significant difference in the likelihood that licensing officers
would recommend a spinoff to the university’s internal venture capital fund
between inventors who are easy to work with (M =2.38, SD = 1.02) and
inventors who are difficult to work with (M =2.30, SD = 1.15), t(237) = 0.52, p
= 0.6035, Cohen’s d = 0.07.
CHAPTER 3 43
Table 3.3: Comparison of the Experimental and Control Groups
Treatments (inventor
characteristics) N
Dissuade inventor
from starting a company
t-value da
Recommend to university
venture capital fund
t-value da
Male 119 2.09
(0.97)
3.55 (0.95)
Female 120 2.53
(1.26) 3.03** 0.39
3.37 (0.95)
1.46 0.19
American- named
123 2.42
(1.09)
3.32 (0.97)
Chinese- named
116 2.20 (1.19)
1.52 0.19 3.60
(0.92) 2.34* 0.30
Industry Experience
121 2.02 (1.13)
3.09 (1.07)
No Industry Experience
118 2.84 (1.34)
5.09**** 0.67 2.72 (1.13)
2.60** 0.34
Easy to Work With
117 2.75
(1.25)
2.38 (1.02)
Difficult to Work With
122 3.17
(1.40) 2.44* 0.32
2.30 (1.15)
0.52 0.07
t p< 0.10; * p<0.05; **p<0.01; ***p<0.001; ****p<0.0001 a Cohen’s d (Cohen, 1988, 1992) as a measure of effect size, calculated using the pooled standard deviation:
√( )
( )
Small Medium Large
Cohen’s d effect size (t-test difference in means)
.20 .50 .80
3.4.1 Robustness checks
To confirm the robustness of the effects we found, we ran ordinary least
squares regression models to predict our two dependent variables with
licensing officer characteristics as control variables. Each regression model
included a treatment as the main predictor variable and licensing officer age,
experience, gender, technical field, and highest degree as control variables.
As Table 3.4 shows, the results are robust to the inclusion of these additional
controls.
44 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
Table 3.4: OLS Regressions including licensing officer controls
Model I II III IV V VI VII VIII
Dependent Variable
Diss. Invent.
Rec. for VC
Diss. Invent.
Rec. for VC
Diss. Invent.
Rec. for VC
Diss. Invent.
Rec. for VC
Treatmentsa
Male -0.43** (0.15)
0.13 (0.12)
Chinese-named
0.20 (0.15)
-0.27* (0.12)
Industry experience
-0.81**** (0.16)
0.34* (0.14)
Easy to work with
-0.37* (0.17)
0.03 (0.14)
TLO controls
Gender (male TLO)
0.17 (0.16)
0.03 (0.13)
0.17 (0.16)
0.01 (0.13)
-0.16 (0.17)
0.43* (0.15)
-0.11 (0.18)
0.28 (0.15)
Age (in years)
0.01* (0.01)
-0.01* (0.01)
0.02* (0.01)
-0.01* (0.01)
0.01 (0.01)
-0.00 (0.01)
0.01 (0.1)
-0.01 (0.01)
Experience (in years)
-0.02 (0.02)
0.01 (0.01)
-0.02 (0.02)
0.01 (0.01)
-0.02 (0.02)
0.00 (0.02)
-0.01 (0.02)
0.01 (0.02)
Educationb 0.07
(0.12) -0.04 (0.10)
0.10 (0.12)
-0.04 (0.10)
-0.08 (0.13)
0.31** (0.11)
0.20 (0.14)
0.05 (0.11)
Law or Businessc
0.03 (0.17)
0.06 (0.14)
0.03 (0.17)
0.07 (0.14)
-0.24 (0.21)
0.09 (0.17)
-0.42* (0.20)
0.26 (0.17)
Engineeringc -0.34
(0.21) 0.43* (0.17)
-0.37 (0.21)
0.43* (0.17)
-0.31 (0.23)
0.38 (0.20)
-0.36 (0.24)
0.08 (0.20)
Life Sciencesc -0.03 (0.18)
0.33* (0.15)
-0.11 (0.18)
0.36* (0.15)
0.19 (0.20)
-0.00 (0.18)
-0.18 (0.21)
0.04 (0.18)
Constant 1.73**** (0.48)
3.69**** (0.40)
1.40 (0.48)
3.86**** (0.40)
3.19**** (0.52)
1.49** (0.46)
2.21**** (0.56)
2.22 (0.46)
N 238 238 238 238 238 238 238 238
F-value 2.52* 1.86 1.66 2.35* 4.49**** 3.21** 2.51* 1.19
Adjusted R2 0.05 0.03 0.02 0.04 0.11 0.07 0.05 0.01
* p<0.05; **p<0.01; ***p<0.001; ****p<0.0001 a Dummy variable equal to 1 for treatment group b Categorical variable representing highest education level licensing officer, 0=Associate degree; 1 = BSc degree; 2 = MSc degree; 3 = PhD c Dummy variable indicating whether the licensing officer’s educational background includes this field
3.5 Discussion
Understanding how inventor characteristics influence technology licensing
officers’ support for spinoff creation is critical to the theory and practice of
CHAPTER 3 45
academic spinoff creation (O’Shea, Chugh, & Allen, 2008; O’Shea, Allen,
O’Gorman, & Roche, 2004). While previous research suggests that inventor
attributes influence technology licensing officers’ views of the
appropriateness of spinoffs as a commercialization vehicle, our experiments
provide evidence of a significant effect of inventor attributes. Specifically,
licensing officers are more positively disposed to spinoffs when the
inventions are made by male, Chinese-named Asian inventors, as well as by
inventors with industry experience and those perceived as easy to work with.
Inventors with these attributes will be more likely to create spinoff
companies than the characteristics of their inventions alone would suggest.
Our results help to explain how technology licensing officers’
preferences influence who starts spinoff companies. For example, our study
indicates that the random assignment of a female faculty member to an
invention disclosure makes licensing officers less likely to encourage the
formation of a spinoff company. This suggests university licensing officers’
preferences account for some of the underrepresentation of women among
university spinoff founders. Any efforts to address the underrepresentation
of women among spinoff company founders will therefore have to include
interventions targeting the attitudes of technology licensing officers toward
female spinoff company founders.
Universities might not want to intervene and alter all of the licensing
officers’ preferences, however. Consider the case of the inventor’s industry
experience. Our results indicate that licensing officers are more likely to
recommend (for spinoff creation) invention disclosures submitted by
inventors with industry experience. This pattern suggests that institutions
interested in boosting their output of spinoff companies should motivate
faculty members with no industry experience to team up with experienced
entrepreneurs (at an early stage), hire faculty members with industry
experience, or put in place programs that enhance the industry experience of
faculty members, such as exchange programs with industry research
laboratories and networking events (Nicolaou & Birley, 2003). Moreover, our
results underline calls for a more active role of technology licensing officers
in facilitating industry interaction for academics lacking such connections,
as a way to encourage spinoff company creation (Mustar, Wright, & Clarysse,
46 DO LICENSING OFFICERS FAVOR PARTICULAR INVENTORS
2008; Nicolaou & Birley, 2003; Vohora et al., 2004). As such, these findings
support the creation and expansion of recent (US) federal funding initiatives,
such as the “NSF Innovation Corps Program”, designed to establish
academic-to-corporate partnerships that integrate graduate students,
academic inventors and corporate/entrepreneurial mentors; and similar
academia-industry collaborative initiatives in the Netherlands, supported by
Agentschap NL, STW, and the Dutch “topsectors” programs.
3.5.1 Limitations
Our study has several limitations. First, its research design involves a
randomized experiment that serves to examine the effect of inventor
attributes on licensing officer support for spinoff creation, but also results in
a stylized research setting. Although we took several measures to make the
experiment as realistic as possible, the licensing officers were asked to
engage in a simplified and time-constrained evaluation process instead of a
more iterative multistage selection process (Shane, 2004). Moreover, the
invention disclosure document in real-life typically does not contain inventor
attributes. Therefore, our experimental design might have made a stronger
link between those attributes and the invention itself than would be the case
in reality. It therefore is possible that the patterns we observed were either
over- or understated as a result of it. Second, we operationalized immigrant inventors as Chinese. As a
result, our measure confounds race and immigrant status and we cannot be
sure which of the two characteristics accounts for the patterns we observe.
Third, for some treatments we found support for only one of the two
outcome measures examined. These partial results might reflect differences
in how licensing officers respond to inventor attributes when asked to take
positive versus negative actions (i.e. recommending a spinoff to a venture
capital fund and dissuading inventors from starting a spinoff company,
respectively). Alternatively, these results may simply represent a
measurement error that comes from the imprecision of using scale scores to
understand licensing officer recommendations.
CHAPTER 3 47
Fourth, our findings may not apply to technology transfer offices outside the
US. Although we have no evidence to suggest that our results would not
generalize to other countries, additional research is needed to show that they
would.
Finally, the study in this chapter clearly points at licensing officer
preferences for certain types of inventors, but the research method adopted
does not allow us to distinguish bias from rational decision-making.
3.5.2 Conclusion
Technology licensing officers play an important role in influencing the
commercialization of university inventions, because they often make
decisive recommendations about which inventions should be
commercialized through creating spinoff companies. Our randomized
experiment confirmed anecdotal evidence that these recommendations are
influenced by the characteristics of the inventors who disclose the
inventions. While licensing officer dispositions might be either problematic
or desirable (depending on how university officials and other stakeholders
assess these dispositions), our results clearly demonstrate the direct effect of
inventor attributes on licensing officer decisions about the
commercialization of university technology by way of forming spinoff
companies.
PART II
Decision making in new technology
ventures
The two chapters in Part I contained studies of decision making in universities.
Since technology commercialization involves not only the selection of promising
technologies but also the subsequent commercial development of such technologies,
Part II addresses decision making in new technology ventures. A common mode of
commercial development is exploiting technological inventions by means of a new
technology venture, where resources play a key role. Therefore, Chapter 4
investigates the influence of resources on decision making in new technology
ventures.
Chapter 4
Decision making in new technology
ventures: Resource positions in action*
Previous studies of the effects of resource slack and constraints on creativity and
performance offer contradictory findings. To resolve this debate, some authors
operationalize resource slack and constraints in ways that actually may have
concealed their underlying complexity and dynamics. This study seeks to
demonstrate how perceived resource positions influence entrepreneurial decision
making and creativity by drawing on in-depth case studies of three high-tech start-
ups. We show that resource positions are perceived, relative, transient and
multidimensional; that is, they reflect the entrepreneur’s perception of available
resources relative to demand. Moreover, perceived resource positions are not static
but change over time, and entrepreneurs can experience different types of resource
constraints and slack simultaneously. The influence of perceived resource positions
on decision making in turn depends on individual, temporal and resource position
dynamics. These findings link perceptions of resources to the emergence of
organizational ingenuity, by explaining how perceived resource positions influence
decision making.
* This chapter is based on: Dolmans, S.A.M., van Burg, E., Reymen, I.M.M.J.,
Romme, A.G.L. (2013), Dynamics of Resource Slack and Constraints: Resource
Positions in Action, forthcoming in the Organization Studies special issue on
“Discovering Creativity in Necessity: Organizational Ingenuity under Institutional
Constraints.” Earlier versions of this chapter have been presented at presented at the
2011 Ingenuity Conference (Burlington, Canada) and the 2011 Babson College
Entrepreneurship Research Conference (Syracuse NY, USA).
52 DECISION MAKING IN NEW TECHNOLOGY VENTURES
4.1 Introduction
Both new and established firms need resources for their survival (Pfeffer &
Salancik, 2003), growth (Penrose, 1959) and sustainable competitive
advantage (Barney, 1991); resource constraints instead hinder firm growth
and lower the probability of survival (Becchetti & Trovato, 2002; Musso &
Schiavo, 2008). However, such constraints may also foster creativity (Hoegl
et al., 2008; Moreau & Dahl, 2005) and force firms to deal with problems
promptly (Bhide, 1992). Slack resources tend to improve firms’ financial
performance (Daniel, Lohrke, Fornaciari, & Turner, 2004), buffer
environmental shocks and allow for more discretion and flexibility in
responding to competitor strategies (George, 2005). Yet, large resource
endowments also could hinder the entrepreneurial process by impairing
firms’ ability to identify new business opportunities (Mosakowski, 2002).
Thus, it is unclear when resource constraints or slack lead to organizational
ingenuity—the ability to create innovative solutions within structural
constraints using limited resources and imaginative problem solving
(Lampel, Honig, & Drori, 2011).
Several scholars have attempted to resolve these contradictory potential
outcomes of resource slack or constraints on creativity and performance, for
example in terms of inverse U-shaped relationships and context dependent
effects (Bradley, Wiklund, & Shepherd, 2011; Hoegl et al., 2008; Hvide &
Møen, 2010). A relatively less explored explanation involves the underlying
dynamics of resource constraints and slack (Nohria & Gulati, 1996) which
remain concealed in cross-sectional studies that take the firm as the primary
unit of analysis. If the entrepreneur’s perception of resource constraints and
slack is likely to affect sensemaking (Weick, 1995) and entrepreneurial
decision making, as suggested by the radical Austrian approach to
entrepreneurship (e.g., Chiles, Bluedorn, & Gupta, 2007; Chiles, Vultee,
Gupta, Greening, & Tuggle, 2010), then more objective, firm-level measures
of constraints and slack cannot serve to identify the true underlying
dynamics.
CHAPTER 4 53
Accordingly, this study considers the possibility that entrepreneurs
perceive resource constraints or slack as transient positions relative to their
start-up’s own resource demands (George, 2005; Renko, Reynolds, &
Carsrud, 2010), at any given moment. We draw on insights from both
sensemaking theory and the radical Austrian approach to explore the
question: how do resource constraints and slack influence entrepreneurial decision
making in new technology ventures? In turn, we study the influence of resource
positions at the decision making, rather than overall firm, level—such that
entrepreneurs experience different resource positions over time.
With in-depth event studies of how three high-tech start-ups develop
over time, this Chapter makes three key contributions to the literature
pertaining to the effects of resource slack and constraints on entrepreneurial
decision making. First, by studying resource positions as perceived,
anticipated and relative, we demonstrate that resource positions must be
understood as transient and multidimensional. Slack and constraints cannot
be investigated separately, at the firm level or with cross-sectional research
designs, because such measures often lead to contradictory findings. By
framing resource slack and constraints as two extremes of the spectrum of
attainable resource positions, we thus integrate research on resource slack
and resource constraints. Second, this study reveals how perceived resource
positions influence decision-making processes in terms of individual,
temporal and resource position dynamics. Third, we contribute to Austrian
perspectives on entrepreneurship by empirically demonstrating how
subjective perceptions of resource positions enter the decision-making
process, in which entrepreneurs generate idiosyncratic options with varying
degrees of creativity. These contributions advance understanding of the
emergence of organizational ingenuity, by building theory on how
constraints in a range of resource positions affect (creative) decision making
by entrepreneurs (Lampel et al., 2011).
54 DECISION MAKING IN NEW TECHNOLOGY VENTURES
4.2 Theory
An entrepreneur’s resources include all assets, capabilities, organizational
processes, information and knowledge under his or her control that may
serve to improve efficiency and effectiveness (Daft, 1983). We conceive of
resource slack and resource constraints as the two extremes of a spectrum of
attainable perceived resource positions. As Figure 4.1 illustrates, resource
positions reflect perceived resource availability, which results from the set of
actual or potential resources at one’s disposal (Bourgeois, 1981), relative to
the perceived resource demand (Cohen, March, & Olsen, 1972; George, 2005;
Mishina, Pollock, & Porac, 2004). At one end of this spectrum, the
entrepreneur experiences a shortage of resources because resource demand
is greater than resource availability. At the other end, (s)he enjoys an
abundance of resources in excess of demand, or resource slack.
Figure 4.1: Resource Position
4.2.1 The effects of resource positions
Resource positions have been linked to creativity, defined as the production
of novel and useful ideas in any domain (Amabile, 1996), such that creative
Resource Demand
Slack
Constraint
Resource Position
Resource Availability
CHAPTER 4 55
ideas differ from previously realized ideas. Accordingly, innovation is the
successful implementation of creative ideas in an organization (Amabile,
1996). Existing studies of how resource positions influence decision making,
creativity and innovation have produced mixed findings. Slack resources
might fuel innovation, by promoting experimentation and risk taking
(Bourgeois, 1981; Nohria & Gulati, 1996; O’Brien, 2003; Thompson, 1967).
In this sense, substantial resource slack relaxes internal controls and allows
firms to undertake multiple innovation projects while enabling the firm to
survive, even if a project’s outcomes are unsuccessful (Agarwal, Sarkar, &
Echambadi, 2002; Bradley, Shepherd, & Wiklund, 2011; Nohria & Gulati,
1996; Voss, Sirdeshmukh, & Voss, 2008). However, firms with abundant
resources may be less inclined to experiment (George, 2005), because the
routines they have established to exploit successful paths to market
ultimately compromise their exploration of new ideas (Levinthal & March,
1993; Mishina et al., 2004). In contrast, resource constraints might foster
creativity (Hoegl et al., 2008; Moreau & Dahl, 2005) and stimulate
innovations that are more efficient, in terms of both time and money
(Gibbert & Scranton, 2009; Hoegl, Weiss, & Gibbert, 2010).
Resource positions also influence how firms interact with their
environment. Slack resources buffer firms against environmental shocks,
stabilize the firm in times of distress (Cyert & March, 1963; Donaldson,
2001; Pfeffer & Salancik, 2003; Van Dijk, Berends, Jelinek, Romme, &
Weggeman, 2011) and provide freedom and flexibility to allow the firm to
adapt to changing competitive environments (Levinthal, 1997; Thompson,
1967). These buffers also can mask underlying problems though, or result in
overconfidence (Kahneman & Lovallo, 1993; Ross & Staw, 1993). By isolating
a firm from exogenous shocks, slack can promote managerial complacency,
induce irrational optimism (George, 2005) and allow a firm to establish
structural misfits with the environment (Litschert & Bonham, 1978).
Instead, resource-constrained firms that experience the direct effects of
environmental pressures instead are more likely to respond quickly and seek
creative ways to overcome such pressures (Hoegl et al., 2008).
56 DECISION MAKING IN NEW TECHNOLOGY VENTURES
4.2.2 Researching resource positions
Various strategies have been applied in attempts to reconcile the conflicting
findings arising from previous work on the implications of resource
availability. For example, some studies propose a curvilinear relationship
between available resources and firm performance (Hvide & Møen, 2010;
Nohria & Gulati, 1996; Tan & Peng, 2003; Zhou & Wu, 2010) or explore
mediation effects (Bradley, Wiklund, et al., 2011; Hoegl et al., 2008). Others
imply that the effects of resource constraints and slack are contingent on the
context, such as the market or competitive environment (Bradley, Shepherd,
et al., 2011; Katila & Shane, 2005), perceived environmental threats (Voss et
al., 2008), project and team characteristics (Hoegl et al., 2008) or recovery
after an adverse event (De Carolis, Yang, Deeds, & Nelling, 2009). The
actual effects of resource availability and operationalization of resource slack
and constraints continue to be subject to controversy though (Bourgeois,
1981; Marino & Lange, 1983; Mishina et al., 2004; Nohria & Gulati, 1996).
Most studies operationalize resource slack and constraints at the firm
level, using financial ratios (Greve, 2003) or measures that compare resource
availability with industry averages as a proxy for resource demand (Bromiley,
1991; Daniel et al., 2004; George, 2005; Mishina et al., 2004). However,
financial ratios often fail to reflect a firm’s resource availability or ability to
invest accurately (Bottazzi, Secchi, & Tamagni, 2012; Kaplan & Zingales,
1997, 2000; Musso & Schiavo, 2008), nor do these measures indicate the
firm’s actual resource demand (George, 2005), which is problematic if slack
or constraints depend on perceived resource demands (George, 2005;
Mishina et al., 2004; Renko et al., 2010). In addition, the majority of studies
in this area adopt a cross-sectional approach, measuring slack or constraints
at a single point in time, such that they ignore changes over time (Bourgeois,
1981; Mishina et al., 2004; Moses, 1992) and possibly conceal the
underlying dynamics (Nohria & Gulati, 1996) that might explain the mixed
results obtained from previous studies.
CHAPTER 4 57
4.2.3 Dealing with resource constraints and resource
slack
Entrepreneurs have various ways to deal with a shortage or abundance of
resources. When resources fall short of demand, entrepreneurs might seek
to lower or eliminate resource demands (e.g., abandoning existing plans for
growth); cope internally and continue to operate under constrained
conditions, by making do with the resources at hand (Baker & Nelson,
2005); or alleviate constraints through external resource acquisition (Hoegl
et al., 2008). Internal coping implies a selection among the effects that can be
established with a given set of resources (Sarasvathy, 2001; Sarasvathy, Dew,
Read, & Wiltbank, 2008; Baker & Nelson, 2005), whereas external resource
acquisition generally relies on outside parties for the resources needed
(Pfeffer & Salancik, 2003). Entrepreneurs may also seek to attract external
financial capital to fund the procurement of additional resources. When
information asymmetries between capital providers and entrepreneurs
(Jensen & Meckling, 1976) and transaction costs (Williamson, 1981) make
this option expensive or unavailable, entrepreneurs search out different
options, such as bootstrapping methods (Bhide, 1992). Bootstrapping
methods aim to minimize capital requirements, optimize cash flows, and
secure resources with less cost (Winborg & Landström, 2001; Winborg,
2009; Ebben, 2009; Ebben & Johnson, 2006). Other alternatives include
reliance on social capital (Bouty, 2000; Davidsson & Honig, 2003; Hoang &
Antoncic, 2003), resource cooptation (Starr & MacMillan, 1990), or inter-
firm joint resource usages (Winborg & Landström, 2001). By building
networks of partnerships, entrepreneurs also might obtain resource
commitments from early-stage stakeholders (Sarasvathy et al., 2008;
Sarasvathy & Dew, 2005).
In contrast, when entrepreneurs believe they have slack resources, they
can redeploy them to various new uses, depending on the type of resources
available, their accessibility (Bourgeois & Singh, 1983), ease of recoverability
(Greve, 2003; Singh, 1986) and liquidity (Mishina et al., 2004; Penrose,
1959). Various types of resource slack have been identified, including
human resource (Mishina et al., 2004), financial (Nohria & Gulati, 1996;
58 DECISION MAKING IN NEW TECHNOLOGY VENTURES
Tan & Peng, 2003), operational (Bourgeois, 1981) and customer relational
(Voss et al., 2008) slack. Entrepreneurs also vary in the degree of managerial
discretion or flexibility they have to (re)deploy slack resources (George, 2005;
Nohria & Gulati, 1996; Sharfman, Wolf, Chase, & Tansik, 1988).
Entrepreneurs thus can decide differently according to their resource
availability, involving various degrees of creativity, but it is not clear how
resource positions influence their decisions.
4.2.4 Perceived resource positions
Research on sensemaking and subjectivity in entrepreneurship provides
some insights into how entrepreneurs likely determine their resource
positions, according to their past experiences and imagined futures. As
Porac, Thomas and Baden-Fuller (1989) and (Weick, 1995) recognize,
people act on the basis of the sense they make of the situation at hand.
Entrepreneurs make such sense by creating an account, together with others,
from an array of prior experiences, assessments of current conditions and of
what can be done in the future (Weick, 1995). This account, which might
manifest in an espoused strategy, also provides a means to convince others
to engage and perhaps provide resources (Cornelissen & Clarke, 2010). In a
changing environment, sensemaking involves a continuous, dynamic
practice to deal with new and adapted experiences. That is, to make sense of
the world around them, entrepreneurs relate their perceived resource
position to their (social) environment, past experiences, decisions and
actions. When an entrepreneur perceives resource constraints, (s)he may
determine that the situation demands making do with whatever resources
are available (Baker & Nelson, 2005; Weick, 1993); another entrepreneur in
the same situation might perceive some form of resource munificence and
pursue firm growth strategies (Edelman & Yli-Renko, 2010). As Bourgeois
(1981) notes, these resource perceptions include both existing and potential
resources. Overall, the entrepreneur’s perceived resource position is highly
subjective and temporary, so sensemaking processes determine how
entrepreneurs choose a particular course of action.
CHAPTER 4 59
The Austrian school of economics (see Jacobson, 1992; Kirzner, 1997)
suggests that entrepreneurs’ subjective perceptions of their resources drive
decision making. These perceptions generate heterogeneity among
entrepreneurs, often because they lack accurate data (Kirzner, 1997; Von
Hayek, 1937), but also because entrepreneurs evaluate resources and their
potential differently, depending on their varying preferences. Hence, the
value of the resources is always in the eye of the beholder (e.g., Foss &
Ishikawa, 2007; Foss, Klein, Kor, & Mahoney, 2008).
The radical subjectivist strand of Austrian economics (Lachmann, 1976,
1986) further suggests that it is not only perceptions of (potential) resource
availability, but also the imagined actions enabled by these resources that
play an important role (Chiles et al., 2007; Chiles, Tuggle, McMullen,
Bierman, & Greening, 2010; Foss et al., 2008; McMullen, 2010). If
evaluations of resource availability depend on how entrepreneurs imagine
making use of resources to support a venture (Cohen et al., 1972; George,
2005; Mishina et al., 2004), their dissimilar imagining creates heterogeneity
in perceived resource positions. The imagined action scenarios vary partly
according to how entrepreneurs make use of the resources they have at
hand, such that a similar resource base (e.g., equal amounts of available
funds) can have different implications for different entrepreneurs (Baker &
Nelson, 2005; Chiles, Tuggle, et al., 2010; Mosakowski, 2002).
Because resource positions are perceptual and dependent on imagined
action scenarios, and sensemaking processes influence decision making,
firm-level measures of constraints and slack, as used in most studies, appear
inadequate for understanding the relationship between resource positions
and decision making. Moreover, resource availability, foreseen resource
demand and imagined futures may change with time, creating a need to
consider resource positions from a process perspective.
4.3 Method
This study adopts a process research approach (Langley, 1999) to explore
resource positions and decision making over time. Using in-depth case
60 DECISION MAKING IN NEW TECHNOLOGY VENTURES
studies that incorporate multiple sources of data, we help advance theory by
studying (the underlying dynamics of) resource positions and how they
affect decision making (Lee, 1999; Locke, 2001; Yin, 2009). This qualitative
research design is appropriate, considering the (1) absence of adequate
metrics for measuring resource positions, which implies the need for an
exploratory approach; (2) perceptual and relative nature of resource
positions, which demand a method that can incorporate (real-life) contextual
conditions; and (3) ephemeral nature of resource positions, which renders
cross-sectional research largely inadequate.
4.3.1 Case selection
We selected three high-tech start-up firms in different emerging industries.
The relation between resource positions and decision making is easier to
establish for nascent than for mature firms (Renko et al., 2010), and the
creation and development processes of start-ups often involve decision
making under uncertainty (McMullen & Shepherd, 2006), such that
resources have key roles, especially for high-tech start-ups (Alvarez &
Busenitz, 2001). Therefore, we selected start-ups in the telecom and solar
energy industries, which were relatively immature industries at the time our
focal start-ups were founded (1997, 1999, and 2000). Because their
industries were marked by high degrees of uncertainty, the entrepreneurs
had substantial freedom to choose their venture’s path to market, rather than
having to conform to mature market structures (Ambos & Birkinshaw,
2010).
Each case covers the venture’s development, from idea conception,
through the founding of the venture, to commercial exploitation and market
interaction. With our objective of studying resource positions and their
influence on decision making and venture development, we needed to study
the venture from its very start, to determine how initial resource positions
affect its development and entrepreneurial decision making (Shane & Stuart,
2002; Sorensen & Stuart, 2000; Stinchcombe, 1965), and for a period long
enough to allow for some evolution (Ambos & Birkinshaw, 2010). We
selected ventures that were founded at approximately the same time and in
CHAPTER 4 61
the same country (The Netherlands), to avoid substantial variance due to
differences in national culture or economic climate. To avoid a (strong)
success bias, we selected two successful ventures (i.e., substantial growth in
staff and/or revenues) and one failure (i.e., no growth, insolvency,
bankruptcy). An overview of the three cases (SunCo, ChipCo and TextCo) is
provided in Table 4.1.
Table 4.1: Overview of Cases
SunCo ChipCo TextCo
Country of origin The
Netherlands The
Netherlands The
Netherlands Period covered in study 1997-2010 2000-2003 1999-2010 Number of events 36 30 41 Total number of interviews 9 9 10 Number of archival documents 63 54 32
4.3.2 Data collection
The data include archival and interview data. The archival data collected (149
documents) consist of annual reports, strategic planning documents,
patents, company presentations, newspaper articles, web articles and public
interviews. Interviews (28 in total) were conducted with the founders,
employees, investors and other important stakeholders of the ventures.
During the semi-structured interviews, we first invited the interviewees to
elaborate on their role in the organization and describe the development
trajectory of the venture. Subsequently, we posed questions about important
decisions during the venture’s development trajectory, especially those
related to the management team and employees, products and services,
clients, revenue models, suppliers, partners, competitors, intellectual
property protection, locations and facilities. The interview protocol can be
found in Appendix 11. We also asked about environmental shifts, such as
market dynamism or important changes in the business environment. If the
interviewee mentioned significant events, we asked follow-up questions to
obtain sufficient details. During these discussions we raised additional
62 DECISION MAKING IN NEW TECHNOLOGY VENTURES
questions, when relevant, about (initial) resource endowments, resource
needs, resource acquisition, and planning and decision making. (The
complete interview protocol is available on request.) The interviews lasted 69
minutes on average and were conducted by at least two interviewers; with
the exception of one telephone interview, all interviews were conducted face-
to-face. If necessary, we requested additional information or conducted
follow-up interviews for clarification. Each interview was digitally recorded
and transcribed.
4.3.3 Data analysis
To investigate how resource positions evolve and influence decision making,
we sought to identify resource positions at the time of the decision and
processes by which they influenced decision making. Therefore, the data
analysis consisted of three steps, using coding procedures developed by Van
de Ven & Poole (1990).
First, we analysed the interview transcripts together with archival data to
create a case-specific event list of important decisions—locations,
management team, employees, products and services, investments, clients,
suppliers, partners, and competitors—for each firm. Significant decision-
making events such as introducing a first product, contacting a potential
customer or hiring an employee involve various degrees of creativity.
Creative decision making typically entails the exploration of new ideas, areas,
products or technologies. In each case, a member of the research team
identified and coded these events, and then these initial event lists were
subjected to extensive discussions among the research team, until we
reached consensus on their identification. For each event, we recorded the
time of occurrence, to facilitate chronological ordering. We used QSR Nvivo
software to code the events, which helped us maintain a chain of evidence
across the raw interview data, archival data, and events (Yin, 2009). To
mitigate any retrospective bias, we collected data about each significant event
from at least two sources (e.g., interviews and documents), such that any
biases or memory lapses were likely offset by those of other informants
(Golden, 1992; Huber & Power, 1985). In addition, we concentrated on
CHAPTER 4 63
significant events, which are easier to remember more accurately (Chell,
2004). Finally, we sent the event lists to the interviewees for validation. The
final lists (30–41 events per case) enabled us to consider single decision-
making events, as well as their longitudinal implications (Langley, 1999).
Second, the analysis focused on determining the decision-making
process for each event. We coded these processes according to the decision
trigger (or decision-motivating tension (Zeleny, 1982) and subsequent
decision outcome, in the form of an observed action. Two types of decision
triggers demand action by entrepreneurs: organizational objectives and
environmental change. Both triggers emerged from our data analysis and
also correspond with previous research (Cheng & Kesner, 1997; Keeney,
1994; Voss et al., 2008). In addition, we coded the resource position, as
perceived by the entrepreneurs, according to the decision to be made in
relation to each particular event. The decision-making process coding began
with all three members of the research team coding the first 30 events of the
TextCo case together. Next, one team member coded the remaining events in
the first case and discussed these codes with the team. Two team members
used the refined coding rules to code the remaining two cases; that is, each
case was coded by at least two team members. The subsequent discussion
led to some minor changes, but all differences in codes assigned by different
coders were resolved through discussion. For the observed resource position
codes, we found that we needed a more elaborate discussion; so two
members of the research team worked together to identify the resource
positions perceived by the entrepreneurs and establish appropriate
categorizations. These categories thus materialized from our data, rather
than prior theory, related to the different resources available relative to
demand at the time of the decision. After carefully (re)examining all events,
we iteratively refined and aggregated the categories, which produced a
coding scheme that we applied to code all the events again. Table 4.2
displays the final coding scheme, definitions, and illustrative quotes.
64 DECISION MAKING IN NEW TECHNOLOGY VENTURES
Table 4.2: The Resource Positions Identified (Coding Scheme)
Resource Position
Description Illustrative Quote
Constraint Shortage of resource availability relative to resource demand
Financial Shortage available cash or other financial means
Everybody, every start-up, received 40 million dollars to build their own fab. And well, we raised 7 million dollars that year, and yes, that is of course way too little to build your own fab. (Founder ChipCo)
Capacity Shortage of operational or production capacity
Suddenly, demand drastically increased.… And then, I immediately started planning for a new factory, so we could expand. (Founder SunCo)
Capability Shortage of human resources or know-how
What became apparent, was that they had very strong technological capabilities, but a lot less experience in terms of product feel, and on top of that almost no commercial experience. (VC investor ChipCo)
Slack Excess of resource availability relative to resource demand
Financial Excess available cash or other financial means
So every weekend I came back with maybe 5000 guilders we charged [the nightclubs] for the text messages. So all nightclubs continuously paid upfront for these text messages … we have always been funded by our customers. (Founder TextCo)
Capacity Excess operational or production capacity
[After buying a factory] we had more volume, that’s good, but then came the crisis … we saw that there was going to be a lot of oversupply. (Founder SunCo)
Capability Excess human resources or know-how
You just have to realize you’re able to do more than just selling modules. We have a great deal of skills here and sometimes we are able to help or advise our customers, who are stuck with a project, because we do it ourselves. (Founder SunCo)
The analysis ultimately yielded six resource positions (i.e., perceived
resource availability relative to perceived resource demand) at the time of a
decision. We identified three types of constraints: financial, capacity, and
capability. In accordance with our conceptualization of resource positions,
CHAPTER 4 65
these three types of constraints mirror the three types of resource slack we
identified (financial, capacity, and capability). Financial resource positions
reflect the relative availability of cash or other financial means; capacity
resource positions refer to operational or production capacity; and capability
resource positions involve human resources or know-how (see Table 4.2).
Unlike previous research in this area that mainly draws on firm-level
measures (Daniel et al., 2004; Mishina et al., 2004), we define resource
positions as the abundance or shortage of resources perceived by the
entrepreneur. A focus on (the heterogeneity among) individual
entrepreneurs is essential, because researching firm-level phenomena must
start with the individuals constituting these firms (Abell, Felin, & Foss,
2008; Felin & Foss, 2005; Foss, 2011).
To facilitate further in-depth analyses, we created tables with
information about the decision-making processes in each start-up. These
tables include, for all events, the (decision-making) event number, year the
event took place, decision trigger, resource position at the time of the
decision, decision outcome, and illustrative quotes. For each case, Tables
4.3–4.5 show the decision-making processes for key decision-making events.
66 DECISION MAKING IN NEW TECHNOLOGY VENTURES
Eve
nt
Year
Deci
sion
Tri
gger
Reso
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e P
osi
tion
Deci
sion
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tcom
e
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stra
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319
99
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or
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l co
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nolo
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Fin
anci
al s
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&
capab
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y
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stra
int.
Th
e f
ou
nders
com
mit
man
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sou
rces
to
this
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nolo
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deve
lopm
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t, a
nd t
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est
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e n
ew
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and t
ech
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re h
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id in
vest
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deve
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o m
any
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real
ly a
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t it
is
beca
use
, to
th
e o
ther
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er
[Fou
nder
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it
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h
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is is
wh
at I
wan
t to
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ject
. I
will pu
t in
a
lot
of
mon
ey
beca
use
I b
elieve
in
it.
” (F
ou
nder
1 S
un
Co)
820
01
Addit
ion
al in
pu
t
(tech
nolo
gic
al e
xpert
ise)
for
tech
nolo
gy
deve
lopm
en
t is
needed.
Fin
anci
al s
lack
&
capab
ilit
y
con
stra
int.
Str
ategic
coopera
tion
is
star
ted w
ith
rese
arch
in
stit
ute
s in
th
e N
eth
erl
ands
and
abro
ad t
o p
erf
orm
speci
fic
tech
nolo
gic
al
deve
lopm
en
t.
We t
ry t
o g
et
the k
now
ledge w
e n
eed f
rom
an
ywh
ere
; n
ot
on
ly d
eve
lopin
g it
wit
hin
ou
r org
aniz
atio
n.
We a
re d
oin
g s
om
e c
o-d
eve
lopm
en
t ac
tivi
ties
in-h
ou
se,
beca
use
th
ey
are o
f key
import
ance
, bu
t w
e a
void
doin
g t
oo m
uch
ou
rselv
es.
At
[Un
ivers
ity
X]
kn
ew
how
to m
ake c
ryst
als.
We f
igu
red:
then
th
ey
can
als
o m
ake
sola
r ce
lls.
So w
e c
on
tact
ed t
hem
an
d s
tart
ed join
t deve
lopm
en
t, t
ogeth
er
wit
h
rese
arch
in
stit
ute
s in
th
e N
eth
erl
ands.
(C
TO
Su
nC
o)
1020
02
Du
tch
ren
ew
able
en
erg
y
subsi
die
s ar
e u
nexp
ect
edly
stopped.
An
tici
pat
ed
fin
anci
al c
on
stra
int.
Fou
nder
1 exp
lore
s doin
g p
roje
cts
acro
ss
the b
ord
er
wit
h G
erm
any,
wh
ere
sola
r
en
erg
y is
sti
ll s
ubsi
diz
ed.
Th
e s
ubsi
dy
pro
gra
ms
of
man
y re
new
able
en
erg
y in
itia
tive
s w
ere
dis
con
tin
ued.
An
d s
o t
he e
nti
re D
utc
h s
ola
r en
erg
y in
du
stry
wen
t dow
n.
Th
e g
ran
ts
dis
con
tin
ued –
th
is b
eca
me e
viden
t al
read
y in
20
01.
So w
e h
ad t
o g
o a
bro
ad a
nd
that
was
act
ual
ly t
he ju
mp w
e m
ade.
(Fou
nder
1 S
un
Co)
1220
03
Ge
rman
sola
r ce
ll s
upplier
goes
ban
kru
pt,
wh
ich
alre
ady
rece
ived 3
00
K E
uro
in p
repay
men
ts f
rom
Su
nC
o.
Cap
acit
y co
nst
rain
t
& a
nti
cipat
ed
fin
anci
al c
on
stra
int.
Fou
nder
1 se
es
the o
pport
un
ity
of
takin
g
ove
r th
e s
upplier’
s fa
ctory
, to
get
som
e
mon
ey
bac
k a
nd a
t th
e s
ame t
ime e
xpan
d
their
bu
sin
ess
by
inte
gra
tin
g a
su
pplier
and
gett
ing a
fir
mer
footh
old
in
th
e G
erm
an
mar
ket.
Th
e [
supplier’
s] f
acto
ry lay
idle
in
Mar
ch 2
00
3, w
hile I
had
a f
ew
nic
e p
roje
cts,
for
on
e o
f w
hic
h I
had
deposi
ted 3
00
,00
0 E
uro
[to
th
e s
upplier]
wh
ich
was
now
wit
h
the t
rust
ee in
ban
kru
ptc
y. Y
ou
can
no
t im
agin
e h
ow
it
got
me in
a c
old
sw
eat
an
d
how
th
is k
ept
me a
wak
e.
I im
media
tely
sai
d t
o [
Fou
nder
2]:
“w
e n
eed t
o t
ake o
ver
that
bu
sin
ess
; th
e D
utc
h m
arket
is g
oin
g d
ow
n a
nd t
hey
hav
e o
ur
300
,00
0
Eu
ro.”
(F
ou
nder
1 S
un
Co)
1420
03
As
dem
and f
or
sola
r pan
els
incr
eas
es,
pro
du
ctio
n
capac
ity
falls
short
.
Fin
anci
al s
lack
&
capac
ity
con
stra
int
Fou
nders
in
creas
e p
rodu
ctio
n in
th
e
Ge
rman
fac
tory
th
at t
hey
just
took o
ver
and
pla
n a
n a
ddit
ion
al f
acto
ry t
o p
rodu
ce
modu
les
for
the e
xpect
ed d
em
and.
We t
ook o
ver
and in
Septe
mber
the f
acto
ry w
as u
p a
nd r
un
nin
g a
gai
n.
Beca
use
of
the h
igh
dem
and,
at t
he e
nd o
f th
e y
ear
we w
ere
pro
fita
ble
agai
n,
wit
h c
utb
acks
in t
he o
rgan
izat
ion
. A
nd t
hen
I im
media
tely
sta
rted p
lan
nin
g a
new
fac
tory
,
wh
ere
we c
ou
ld e
xpan
d.
(Fou
nder
1 S
un
Co)
Tab
le 4
.3:
Dec
isio
n-M
akin
g P
roce
ss f
or
Key
Eve
nts
, S
un
Co
CHAPTER 4 67
Eve
nt
Year
Deci
sion
Tri
gger
Reso
urc
e P
osi
tion
Deci
sion
Ou
tcom
e
Illu
stra
tive
Qu
ote
22
20
08
Tech
nolo
gy
deve
lopm
en
t is
not
pro
gre
ssin
g,
maj
or
pro
ble
ms
appear
du
rin
g
pilot
pro
du
ctio
n.
Fin
anci
al s
lack
&
capab
ilit
y
con
stra
int.
Addit
ion
al in
vest
men
ts in
tech
nolo
gy
deve
lopm
en
t to
solv
e t
he t
ech
nolo
gic
al
pro
ble
ms
and k
eep t
he d
eve
lopm
en
t on
trac
k.
We a
re r
un
nin
g b
eh
ind s
chedu
le.
Bu
t th
e c
on
cept
is s
o c
on
vin
cin
g a
nd [
Fou
nder
2]
is s
o c
on
vin
ced it
will beco
me a
su
ccess
th
at h
e ju
st p
ush
es
it t
hro
ugh
. T
his
is
actu
ally
als
o e
ntr
epre
neu
rsh
ip:
just
say
th
at w
e s
hou
ld p
ers
eve
re,
believe
in
it.
(CT
O S
un
Co)
23
20
08
Sh
ort
age o
f ra
w m
ateri
al in
the m
arket
dri
ves
up t
he
pri
ces,
su
bst
anti
al
pre
pay
men
ts a
re n
eeded t
o
secu
re s
uff
icie
nt
mat
eri
al.
Su
nC
o is
un
able
to p
ay
these
lar
ge p
re-p
aym
en
ts.
Fin
anci
al
con
stra
int.
Th
ey
co-in
vest
in
a U
S s
ilic
on
pla
nt
to g
et
more
reliab
le s
upply
an
d s
table
pri
ces
for
delive
ry.
We o
nly
mad
e s
ola
r m
odu
les
and w
e d
id n
ot
hav
e t
he m
on
ey
to g
row
very
aggre
ssiv
ely
. A
nd a
t th
at m
om
en
t th
ere
was
a b
ig s
hort
age o
f ra
w m
ateri
al in
th
e
valu
e c
hai
n.
… Y
ou
had
to p
ay m
illion
s of
dollar
s in
adva
nce
in
ord
er
to g
et
delive
ry c
on
trac
ts.
We c
ou
ld n
ot
do m
uch
… w
e c
an d
o b
ett
er
ou
rselv
es,
so
there
fore
we w
ante
d t
o b
e in
th
e r
aw m
ateri
al b
usi
ness
ou
rselv
es.
(F
ou
nder
1
Su
nC
o)
Th
at's
wh
y w
e p
arti
cipat
ed in
a U
S-b
ased join
t ve
ntu
re w
ith
a r
aw m
ateri
al
pro
du
cer.
Th
ere
by
we s
ecu
red t
he b
asic
mat
eri
al f
or
sola
r m
odu
les.
(CO
O S
un
Co)
25
20
08
Tech
nolo
gy
deve
lopm
en
t is
not
pro
gre
ssin
g a
nd t
he
eco
nom
ic c
risi
s se
ts in
.
Cap
abilit
y
con
stra
int
&
fin
anci
al c
on
stra
int.
Join
t ve
ntu
re w
ith
an
Eas
t-A
sian
com
pan
y to
pro
du
ce a
rela
ted t
ech
nolo
gy,
as
a bac
ku
p
for
the o
rigin
al t
ech
nolo
gy.
Du
e t
o t
he c
rise
s an
d w
hile n
ot
bein
g m
arket-
read
y w
ith
ou
r fi
rst
gen
era
tion
[pro
du
cts]
, th
e d
eve
lopm
en
t beca
me u
nder
pre
ssu
re.
So w
e h
ad t
o d
ela
y th
e
deve
lopm
en
t, r
edu
ce t
he t
eam
to t
he c
ore
team
, an
d w
e c
on
tin
ued w
ith
th
e
chose
n t
ech
nolo
gy.
. .. [
Did
we w
ant
to]
pu
t [t
he d
eve
lopm
en
t] a
side:
neve
r. T
hin
k
how
to p
roce
ed:
yes.
Th
ere
fore
we t
ried t
o c
on
nect
wit
h [
the E
aste
rn A
sian
com
pan
y].
We s
aid:
we c
on
tin
ue t
ogeth
er
wit
h t
hem
, w
e c
an a
ccele
rate
, an
d
spre
ad t
he r
isks.
An
d n
ow
we h
ave a
sit
uat
ion
bett
er
than
eve
r im
agin
ed.
(Fou
nder
1 S
un
Co)
3020
09
Du
e t
o t
he e
con
om
ic c
risi
s,
dem
and f
or
sola
r m
odu
les
dro
ps,
cre
atin
g c
ash
flo
w
pro
ble
ms;
ban
ks
hal
t lo
ans.
Fin
anci
al
con
stra
int.
Su
nC
o s
tart
s n
egoti
atio
ns
wit
h [
a
com
peti
tor]
for
a jo
int
ven
ture
, w
hic
h is
needed t
o c
om
bin
e c
apac
itie
s an
d f
un
din
g.
Th
e a
nn
ou
nce
men
t of
the m
erg
er
also
serv
es
as a
n a
ssu
ran
ce t
o t
he b
anks
that
Su
nC
o is
still su
stai
nab
le.
An
in
tere
stin
g m
om
en
t w
as o
ur
pla
nn
ed m
erg
er
wit
h [
a co
mpeti
tor]
. In
fac
t, w
e
just
bou
gh
t ti
me w
ith
th
e b
anks,
to g
et
ou
r in
tere
st r
ates
at a
reas
on
able
leve
l
and t
o r
est
ore
ou
r tr
ust
wort
hin
ess
at
the b
anks
in 2
00
9.
(CO
O S
un
Co)
3420
10S
un
Co a
nd a
com
peti
tor
negoti
ate t
he p
oss
ible
merg
er
bu
t ca
nn
ot
agre
e
on
th
e p
rice
. S
un
Co s
tays
indepen
den
t.
Fin
anci
al s
lack
.S
un
Co w
ith
dra
ws
from
th
e m
erg
er;
th
e
(fin
anci
al)
need f
or
coopera
tion
has
dis
appear
ed a
s w
ell.
We t
alked f
or
mon
ths.
We c
ou
ld n
ot
get
on
th
e s
ame p
age a
nd m
ore
an
d m
ore
we g
ot
the f
eelin
g t
hat
it
was
not
goin
g t
o h
appen
. A
t so
me p
oin
t, t
he n
egoti
ators
lost
th
eir
belief
in t
he c
oopera
tion
. In
th
e f
irst
qu
arte
r of
20
09
, [t
he m
arket]
was
very
bad
. T
he s
eco
nd [
qu
arte
r] w
as s
ligh
tly
less
, bu
t in
th
e lat
ter
hal
f of
the y
ear
the m
arket
qu
ite r
eco
vere
d.
We e
ven
tual
ly h
ad n
et
pro
fits
in
20
09
; n
ot
very
mu
ch,
bu
t st
ill. (
Fou
nder
1 S
un
Co)
68 DECISION MAKING IN NEW TECHNOLOGY VENTURES
Eve
nt
Year
Deci
sion
Tri
gger
Reso
urc
e P
osi
tion
Deci
sion
Ou
tcom
e
Illu
stra
tive
Qu
ote
420
00
VC
in
vest
or
noti
ces
that
the p
rofe
ssor
and P
hD
stu
den
ts a
re s
tru
gglin
g
wit
h d
efi
nin
g t
heir
bu
sin
ess
.
Fin
anci
al s
lack
&
capab
ilit
y
con
stra
int.
A C
EO
is
hir
ed b
y th
e V
C in
vest
or
to k
ick
star
t th
e n
asce
nt
ven
ture
by
sett
ing u
p t
he
bas
ic o
rgan
izat
ion
al s
tru
ctu
res,
wri
tin
g a
bu
sin
ess
pla
n a
nd b
asic
ally
defi
nin
g t
heir
bu
sin
ess
.
I lin
ked t
he f
ou
r te
chn
ical
fou
nders
to [th
e C
EO
] in
ord
er
to t
alk t
o e
ach
oth
er.
Th
e t
eam
mat
ched a
nd s
tart
ed p
lan
nin
g a
bu
sin
ess
. M
y th
ou
gh
t w
as: if
th
ey
com
e w
ith
a g
ood p
lan
, th
en
we a
re g
oin
g t
o f
inan
ce it.
(V
C I
nve
stor
Ch
ipC
o)
Th
e p
rofe
ssor
did
not
see it,
an
d h
ad n
o idea
how
to s
tart
a c
om
pan
y. S
o t
hey
had
man
y dis
cuss
ion
s on
wh
at t
hey
cou
ld d
o, w
here
to f
ocu
s on
, pro
du
cts,
etc
. S
o [th
e
VC
in
vest
or]
tri
ed t
o c
oac
h t
hem
, bu
t it
resu
lted in
noth
ing. T
hen
th
ey
called m
e:
“Go
tal
k t
o t
he p
rofe
ssor
and h
is P
hD
stu
den
ts.”
Th
ese
ch
ips
are d
esi
gn
ed f
or
the t
ele
com
mar
ket
and m
y bac
kgro
un
d is
in t
he t
ele
com
mar
ket.
(C
EO
Ch
ipC
o)
720
01
Ph
Ds
and C
EO
look t
o s
tart
com
pan
y an
d f
un
din
g is
secu
red b
y m
ean
s of
a V
C
con
trac
t.
Fin
anci
al s
lack
.C
hip
Co is
fou
nded, th
e f
ou
ndin
g t
eam
is
com
pose
d, an
d o
ffic
e s
pac
e is
hir
ed t
o h
ave
som
e s
pac
e o
uts
ide t
he u
niv
ers
ity
faci
liti
es.
Wh
en
th
e s
ign
atu
re f
or
the m
on
ey
was
th
ere
, w
e c
ou
ld leav
e o
ur
jobs
[at
Un
ivers
ity
X]; a
t le
ast
for
som
e t
ime t
here
was
sal
ary.
… W
hen
we w
ere
real
ly
separ
ated f
rom
th
e u
niv
ers
ity,
we c
ou
ld t
hin
k in
a d
iffe
ren
t w
ay. T
his
was
th
e
mom
en
t w
e r
eal
ly s
tart
ed t
hin
kin
g a
bou
t th
e f
irst
form
of
a bu
sin
ess
pla
n.
(Fou
nder
1 C
hip
Co)
1020
01
Fou
ndin
g t
eam
needs
to
dete
rmin
e t
he (
scope o
f
the)
firs
t pro
du
ct.
Cap
abilit
y sl
ack &
fin
anci
al s
lack
.
Th
ey
inve
st in
pro
du
ct d
eve
lopm
en
t of
a
hig
h-e
nd s
yste
m-in
tegra
tin
g c
hip
for
the
tele
com
mar
ket,
wh
ich
wou
ld d
em
on
stra
te
the c
utt
ing-e
dge t
ech
nolo
gy
and c
apab
ilit
ies
of
Ch
ipC
o.
On
th
e o
ne s
ide, w
e h
ad t
he V
C’s
, pu
shin
g u
s to
pro
ceed d
eve
lopin
g t
he h
oly
gra
il n
ot
to f
ocu
s on
sim
ple
su
b-p
rodu
cts.
We t
hou
gh
t: w
e m
ake f
irst
som
e
hyb
rid m
odel fo
r a
cou
ple
of
sim
ple
pro
du
cts
to g
et
alre
ady
som
e r
eve
nu
e w
hile
we c
on
tin
ue t
he d
eve
lopm
en
t of
more
com
ple
x pro
du
cts.
[T
he V
C in
vest
or]
was
tota
lly
agai
nst
th
is, w
e h
ad t
o c
om
ple
tely
focu
s on
th
e h
oly
gra
il.…
Th
is w
as n
ot
a
real
pro
du
ct, it
was
a d
em
on
stra
tor
… w
hic
h s
how
ed a
ll a
spect
s of
ou
r
tech
nolo
gic
al c
apab
ilit
ies.
(F
ou
nder
1 C
hip
Co)
1220
01
Th
e t
ele
com
mar
ket
cras
hes
and a
nti
cipat
ed
dem
and d
rops
sign
ific
antl
y.
Fin
anci
al s
lack
.D
esp
ite t
he m
arket
chan
ges,
Ch
ipC
o
con
tin
ues
the d
eve
lopm
en
t of
its
pro
du
ct.
Th
en
th
e c
risi
s ca
me a
nd s
o y
ou
cou
ld s
ee t
he e
nti
re s
em
icon
du
ctor
indu
stry
collap
se. A
s a
con
sequ
en
ce, so
me p
arti
es
open
ed u
p t
heir
pro
du
ctio
n f
acilit
ies,
wh
ich
we c
ou
ld u
se [to
deve
lop o
ur
pro
du
ct]. (
CE
O C
hip
Co)
1520
01
Ch
ipC
o n
eeds
to e
stab
lish
dedic
ated p
rodu
ctio
n
faci
liti
es
(a ‘fa
b’)
for
their
inte
gra
tin
g c
hip
.
Fin
anci
al
con
stra
int,
capab
ilit
y co
nst
rain
t
& c
apac
ity
con
stra
int
Ch
ipC
o s
ear
ches
for
par
tners
to o
uts
ou
rce
the p
rodu
ctio
n, si
nce
it
does
not
hav
e
en
ou
gh
mon
ey
for
its
ow
n p
rodu
ctio
n
faci
liti
es.
[Th
e C
EO
] an
d I
were
lookin
g t
o b
uild a
fab
ou
rselv
es,
an
d in
par
alle
l w
e looked
wh
eth
er
we c
ou
ld ju
st u
se e
xist
ing f
acilit
ies.
Th
at w
as a
ctu
ally
un
usu
al in
th
is
bu
sin
ess
. E
very
body,
eve
ry s
tart
-up, re
ceiv
ed 4
0 m
illion
dollar
s to
bu
ild t
heir
ow
n
fab. A
nd w
ell, w
e r
aise
d 7
million
dollar
s th
at y
ear
, an
d y
es,
th
at is
of
cou
rse w
ay
too lit
tle t
o b
uild y
ou
r ow
n f
ab. B
ut
that
mad
e u
s re
aliz
e t
hat
we h
ad t
o d
o t
hin
gs
in a
dif
fere
nt
way
. S
o w
e s
tart
ed lookin
g f
or
pro
du
ctio
n p
artn
ers
. A
nd t
hat
is
exa
ctly
th
e p
ath
we e
nded u
p t
akin
g. (F
ou
nder
1 C
hip
Co)
Tab
le 4
.4:
Dec
isio
n-M
akin
g P
roce
ss f
or
Key
Eve
nts
, C
hip
Co
CHAPTER 4 69
Eve
nt
Year
Deci
sion
Tri
gger
Reso
urc
e P
osi
tion
Deci
sion
Ou
tcom
e
Illu
stra
tive
Qu
ote
1920
02
Pro
du
ct d
eve
lopm
en
t an
d
pro
cess
im
pro
vem
en
t
requ
ire a
ddit
ion
al
em
plo
yees
and p
rodu
ctio
n
run
s
Fin
anci
al c
on
stra
int
(an
tici
pat
ed),
capac
ity
con
stra
int
Th
e C
hip
Co t
eam
deci
des
to in
creas
e t
he
nu
mber
of
test
ru
ns,
to e
nab
le q
uic
ker
adju
stm
en
ts o
f th
e p
rodu
ct w
ith
th
e a
im t
o
keep d
eve
lopm
en
t on
tra
ck.
At
on
e p
oin
t w
e h
ad t
hat
7 m
illion
. In
itia
lly,
we d
id o
ne d
esi
gn
ru
n e
very
mon
th.
We im
pro
ved o
ur
ow
n p
roce
ss e
very
ru
n, as
we w
ere
in
ven
tin
g s
om
eth
ing n
ew
.
On
e r
un
tak
es
a 10
0.0
00
Eu
ro, as
all p
arti
es
hav
e t
o p
erf
orm
th
eir
tas
ks
eve
ry
run
. B
ut
we a
lso h
ad o
ur
dai
ly c
ost
s of
keepin
g t
he b
usi
ness
goin
g; w
e h
ad t
o p
ay
20
sta
ff m
em
bers
, th
e r
en
tal fe
es
et
cete
ra. It
is
a ve
ry c
ost
ly b
usi
ness
. T
hen
we
incr
eas
ed t
o t
wo r
un
s a
mon
th a
nd w
e b
urn
ed o
ur
mon
ey
eve
n f
aste
r. (
CE
O
Ch
ipC
o)
26
20
02
Cu
stom
er
feedbac
k o
n
sam
ple
s of
the in
tegra
tin
g
chip
in
dic
ates
the p
rodu
ct
can
not
be in
corp
ora
ted in
the e
xist
ing d
esi
gn
s of
pote
nti
al c
ust
om
ers
.
Cap
abilit
y sl
ack &
anti
cipat
ed f
inan
cial
con
stra
int.
In v
iew
of
futu
re m
on
eta
ry c
on
stra
ints
,
Ch
ipC
o s
tart
s exp
lori
ng o
pti
on
s fo
r an
alte
rnat
ive p
rodu
ct (
bas
ed o
n its
exi
stin
g
tech
nolo
gy)
th
at c
ust
om
ers
can
im
ple
men
t
more
eas
ily
in t
heir
desi
gn
s.
Wh
en
it
beca
me c
lear
th
at t
he r
oad
map
for
inte
gra
ted p
rodu
cts
was
act
ual
ly
mu
ch f
urt
her
ahead
th
an w
e t
hou
gh
t, t
hey
[th
e C
hip
Co t
eam
] defi
ned f
or
exa
mple
a n
ew
pro
du
ct w
hic
h u
sed t
he s
ame f
un
ctio
ns
in a
com
ple
tely
dif
fere
nt
way
, in
th
is c
ase a
mon
itori
ng c
hip
. ... It
was
more
a n
ich
e m
arket
than
[in
tegra
tin
g c
hip
], b
ut
at t
hat
tim
e, th
e f
eelin
g t
hat
we n
eeded t
o g
en
era
te
reve
nu
e b
eca
me s
tron
ger
and s
tron
ger…
Th
e lon
g-t
erm
vis
ion
did
not
chan
ge,
bu
t th
e q
uest
beca
me: ok, w
hat
is
needed f
or
tom
orr
ow
? (V
C I
nve
stor
Ch
ipC
o)
27
20
02
Ch
ipC
o w
ants
to s
tart
pro
du
cin
g a
n a
ddit
ion
al
pro
du
ct b
ut
has
no r
eve
nu
e
from
th
e in
tegra
tin
g c
hip
.
Fin
anci
al
con
stra
int.
Fac
ilit
ated b
y th
e e
xist
ing V
C in
vest
ors
, th
e
fou
ndin
g t
eam
vis
its
more
th
an 4
0 V
Cs
to
attr
act
addit
ion
al f
un
din
g.
I vi
site
d 4
0 in
tern
atio
nal
in
vest
ors
in
hal
f a
year
an
d p
rese
nte
d t
he b
usi
ness
pla
n.
An
d I
tri
ed t
o s
um
mar
ize t
he e
nti
re c
om
pan
y in
a f
ew
Pow
erP
oin
t pag
es.
Tw
elv
e
of
them
were
in
tere
sted in
th
e c
om
pan
y, b
ut
dro
pped o
ut
on
e b
y on
e.
(CE
O C
hip
Co)
28
20
03
Ch
ipC
o e
xperi
en
ces
the
rein
forc
ing e
ffect
s of
a
collap
sed t
arget
mar
ket,
no
pro
du
ct s
ales
and f
ew
fin
anci
ng o
pti
on
s.
Fin
anci
al
con
stra
int.
To g
en
era
te o
pti
on
s to
con
tin
ue t
he
com
pan
y, C
hip
Co’s
fou
nders
an
d e
ngin
eers
star
t exp
lori
ng e
ven
more
alt
ern
ativ
e
applica
tion
s of
Ch
ipC
o’s
tech
nolo
gy,
eve
n in
very
dis
tan
t fi
eld
s, s
uch
as
mobilit
y an
d
defe
nce
applica
tion
s.
At
the m
om
en
t th
at e
very
body…
, th
at t
he t
ele
com
mar
ket
collap
sed, th
at c
lien
ts
told
us
not
now
, n
ot
at t
his
mom
en
t, t
hen
we s
tart
ed lookin
g a
t al
tern
ativ
es.
Yes,
beca
use
, st
ill w
e w
ere
able
to b
uild a
bou
t an
yth
ing. S
o w
e looked a
t a
pro
ject
to
mak
e a
su
perc
om
pu
ter.
…W
e looked a
t m
ilit
ary
applica
tion
s. A
ll s
ort
s of
com
mu
nic
atio
ns,
su
ch a
s bac
k-u
p f
acilit
ies
for
ban
ks.
... W
e a
lso looked a
t oth
er
applica
tion
s, lik
e m
oti
on
sen
sors
. W
e looked a
t th
e S
egw
ay, beca
use
we c
ou
ld
inte
gra
te t
he g
yrosc
opes
that
keep t
he t
hin
g u
pri
gh
t on
a s
qu
are m
illim
etr
e. W
e
hav
e looked a
t m
any
opport
un
itie
s w
here
we c
ou
ld h
ave c
reat
ed o
ther
applica
tion
s. (
Fou
nder
1 C
hip
Co)
70 DECISION MAKING IN NEW TECHNOLOGY VENTURES
Eve
nt
Year
Deci
sion
Tri
gger
Reso
urc
e P
osi
tion
Deci
sion
Ou
tcom
e
Illu
stra
tive
Qu
ote
219
99
Th
e f
ou
nders
com
e u
p
wit
h t
he idea
to u
se S
MS
tech
nolo
gy
for
nig
htc
lub
adve
rtis
ing.
Cap
acit
y sl
ack,
capab
ilit
y sl
ack &
fin
anci
al c
on
stra
int
Th
e f
ou
nders
sta
rt e
xperi
men
tin
g w
ith
SM
S.
Th
ey
collect
ph
on
e n
um
bers
an
d s
en
d
mess
ages
con
tain
ing t
he w
eekly
pro
gra
mm
e o
f th
e n
igh
tclu
b.
Th
ey
use
exi
stin
g c
on
tact
s, a
nd t
he u
niv
ers
ity’
s an
d
par
en
ts’
reso
urc
es.
As
stu
den
ts,
we ju
st h
ad lit
tle e
xpen
ses.
… T
he f
irst
tw
o y
ear
s w
e w
ork
ed w
ith
ou
t
sala
ry.
We s
tart
ed f
airl
y eas
y, e
ach
tim
e t
akin
g s
mal
l st
eps;
we n
eve
r h
ad a
n
inve
stm
en
t re
qu
irin
g a
big
ste
p..
... W
e h
ad a
modem
an
d a
[te
leph
on
e p
rovi
der]
ph
on
e t
o u
plo
ad a
nd p
ost
th
e m
ess
age t
o a
nu
mber.
Th
is w
as d
on
e f
or
a list
of
100
nu
mbers
, eac
h n
um
ber
separ
ately
. F
or
a h
un
dre
d n
um
bers
, th
is w
as
feas
ible
. S
o,
we a
sked o
ur
par
en
ts if
we c
ou
ld u
se t
heir
tele
ph
on
e lin
e a
nd s
om
e
sock
ets
for
plu
ggin
g in
th
e lap
top.
(Fou
nder
1 T
ext
Co)
1020
00
Fou
nders
get
seve
ral
inve
stm
en
t off
ers
.
Fin
anci
al s
lack
.T
he f
ou
nders
refu
se s
eve
ral in
vest
men
t
off
ers
to k
eep f
ull c
on
trol ove
r th
eir
bu
sin
ess
. T
hey
deci
de t
o g
o f
or
cash
-flo
w
fin
anci
ng a
nd d
o n
ot
need in
vest
men
t to
fin
ance
th
eir
sm
all deve
lopm
en
t st
eps
at
the m
om
en
t.
For
us,
it
was
a c
hoic
e w
heth
er
or
not
we w
ante
d t
o h
ave e
xtern
al f
inan
cers
.
Man
y co
mpan
ies
in t
his
sect
or
hav
e a
t so
me p
oin
t ch
ose
n f
or
ven
ture
cap
ital
an
d
oth
er
inve
stors
. W
e h
ave c
on
scio
usl
y ch
ose
n n
ot
to u
se e
xtern
al f
inan
cin
g,
thou
gh
we h
ad t
wen
ty t
imes
the c
han
ce t
o d
o s
o,
if w
e w
ante
d.
Th
is is
a st
rate
gic
choic
e w
e m
ade:
“Can
you
pay
it
you
rself
to f
acilit
ate g
row
th?.
.. O
r w
ou
ld y
ou
gro
w m
ore
if
you
wou
ld h
ave m
ore
mon
ey
and w
ou
ld it
be m
ore
pro
du
ctiv
e if
you
get
shar
eh
old
ers
cap
ital
?” W
e d
elibera
ted o
ver
this
ch
oic
e o
ver
and o
ver
agai
n.
(Fou
nder
2 T
ext
Co)
1420
01
A s
peci
fic
SM
S s
erv
ice,
Pre
miu
m S
MS
(vo
tin
g in
TV
sh
ow
s) is
intr
odu
ced t
o
the m
arket
by
com
peti
tor
[Sm
artT
ext
].
Fin
anci
al s
lack
.T
he f
ou
nders
deci
de n
ot
to p
urs
ue
pre
miu
m S
MS
; th
ey
do n
ot
believe
in
th
e
con
cept.
Th
ey
con
tin
ue t
heir
focu
s on
bu
lk
SM
S (
sen
din
g m
ult
iple
mess
ages
at o
nce
).
In 2
00
1, a
com
peti
tor
[Sm
artT
ext
] …
sta
rted w
ith
th
is idea
of
Pre
miu
m S
MS
,
togeth
er
wit
h [
a popu
lar
TV
-sh
ow
were
con
test
ants
com
pete
to s
tay
in a
hou
se].
Th
e w
hole
idea
was
th
at y
ou
can
vote
by
mean
s of
SM
S f
or
wh
o s
hou
ld leav
e t
he
hou
se,
and y
ou
need t
o p
ay a
gu
ilder
[for
voti
ng].
So w
e r
eal
ly m
ade a
bad
deci
sion
at t
hat
mom
en
t, b
eca
use
we d
id n
ot
believe
in
it.
(F
ou
nder
2 T
ext
Co)
1720
02
Fou
nders
wan
t to
exp
and
their
bu
sin
ess
in
th
e
nig
htc
lub s
ect
or,
an
d o
ne
of
the f
ou
nders
lik
es
to
exp
eri
men
t w
ith
intr
odu
cin
g d
iffe
ren
t
pro
du
cts.
Cap
abilit
y sl
ack.
Fou
nder
1 in
trodu
ces
a n
ew
pro
du
ct:
reco
rdin
g v
ideos
of
par
ties
at n
igh
tclu
bs
and
sellin
g t
he v
ideos
on
DV
D.
More
ove
r, h
e
star
ts (
amon
g o
ther
thin
gs)
im
port
ing a
nd
sellin
g w
hit
e g
love
s.
Seve
ral ti
mes
I re
ally
tri
ed t
o b
rin
g o
ther
pro
du
cts
to m
arket,
ju
st b
eca
use
I lik
e
it.
We d
id r
eal
ly q
uit
e b
izar
re t
hin
gs
in t
he c
lub m
arket.
I s
et
up ‘
[DV
D p
rodu
ct]’
for
clu
bs.
We w
en
t w
ith
fiv
e p
eople
an
d m
e a
s ca
mera
man
to c
lubs
and r
eco
rded
videos,
wh
ich
we t
hen
bu
rned o
n D
VD
an
d p
eople
cou
ld b
uy
them
for
35 D
ollar
s
or
Eu
ros.
It
was
so m
uch
fu
n,
bu
t ve
ry lab
ou
r in
ten
sive
. It
was
not
real
ly a
gre
at
succ
ess
. ..
. In
Pola
nd e
very
body
was
wear
ing w
hit
e g
love
s; t
hey
love
bla
ck lig
ht.
So,
I th
ou
gh
t, let’
s im
port
wh
ite g
love
s fr
om
Ch
ina.
Gre
at f
un
an
d w
e c
an
exp
eri
men
t w
ith
th
em
in
th
e c
lub m
arket.
We w
ill se
e w
heth
er
we w
ill m
ake
[su
ccess
ful] b
usi
ness
ou
t of
it.
Bu
t it
was
not
a m
ega
bu
sin
ess
model. S
o,
we t
ried
seve
ral th
ings.
(F
ou
nder
1 T
ext
Co)
21
20
03
Th
e f
ou
nders
wan
t to
gro
w
their
com
pan
y by
off
eri
ng
more
serv
ices.
Fin
anci
al s
lack
&
capab
ilit
y sl
ack.
Th
e f
ou
nders
in
vest
in
rese
arch
an
d
deve
lopm
en
t an
d f
ile a
pat
en
t (f
or
100
K
EU
R)
on
a n
ew
pro
du
ct.
We s
tart
ed t
hin
kin
g:
if w
e s
en
d a
text
mess
age t
o a
file [
of
ph
on
e n
um
bers
], w
e
can
als
o s
en
d a
n e
-mai
l. S
o w
e e
xten
ded o
ur
syst
em
wit
h e
mai
l. W
e h
ave a
pat
en
t
on
[m
ail se
rvic
e]
and o
n [
fire
wal
l se
rvic
e].
(F
ou
nder
2 T
ext
Co)
T
able
4.5
: D
ecis
ion
-Mak
ing
Pro
cess
fo
r K
ey E
ven
ts,
Tex
tCo
CHAPTER 4 71
Eve
nt
Year
Deci
sion
Tri
gger
Reso
urc
e P
osi
tion
Deci
sion
Ou
tcom
e
Illu
stra
tive
Qu
ote
22
20
04
A n
igh
tclu
b r
eco
mm
en
ds
exp
lori
ng t
he P
olish
mar
ket
and in
vite
s
fou
nders
to P
ola
nd.
Fin
anci
al s
lack
&
capab
ilit
y sl
ack.
Th
e f
ou
nders
acc
ept
the in
vita
tion
, beca
use
they
perc
eiv
e t
hat
th
ey
can
not
gro
w in
th
eir
Du
tch
hom
e m
arket
anym
ore
. T
hey
open
an o
ffic
e in
Pola
nd, fa
cilita
ted b
y lo
cal
con
tact
s th
ere
.
In 2
00
4, on
e o
f ou
r cu
stom
ers
in
th
e c
lub m
arket
had
fam
ily
in P
ola
nd a
nd a
sked
us
to c
om
e a
lon
g t
o P
ola
nd. S
o, w
e w
en
t to
Pola
nd a
nd looked a
rou
nd. T
here
were
als
o n
igh
tclu
bs
that
wan
ted t
o d
o s
om
e a
dve
rtis
ing. S
o, w
e c
opie
d t
he m
odel
we h
ad in
Th
e N
eth
erl
ands
[to P
ola
nd]: g
oin
g t
o t
he c
lubs,
tal
kin
g t
o p
eople
, etc
..
We f
ou
nd a
nic
e c
leve
r la
dy,
we r
en
ted o
ffic
e s
pac
e, an
d w
en
t th
ere
eve
ry m
on
th.
Th
at a
ll w
en
t w
ell. (F
ou
nder
1 T
ext
Co)
24
20
04
As
it s
tart
s opera
tin
g
ou
tsid
e n
igh
tclu
bs,
speed
and d
elive
ry s
tati
stic
s
beco
me v
ery
im
port
ant
to
cust
om
ers
in
logis
tics
an
d
ban
kin
g s
ect
ors
.
Fin
anci
al s
lack
&
capab
ilit
y
con
stra
int.
Th
e f
ou
nders
in
itia
te t
he d
eve
lopm
en
t an
d
intr
odu
ctio
n o
f th
eir
mon
itor
serv
ice, a
SM
S p
lan
nin
g a
nd m
on
itori
ng s
yste
m (
that
can
als
o b
e u
sed b
y oth
er
mar
ket
par
ties)
.
Th
ey
[a logis
tics
com
pan
y] h
ave in
tegra
ted S
MS
in
th
eir
pla
nn
ing s
yste
m. W
e s
aw
imm
edia
tely
th
at t
his
serv
ice ju
st h
as t
o w
ork
; if
a m
ess
age is
late
, th
ey
arri
ve lat
e
at a
cu
stom
er.
.... S
o t
hen
we w
en
t th
rou
gh
a lear
nin
g p
roce
ss: h
ow
we a
re g
oin
g
to m
on
itor
and p
arti
cula
rly
how
are
we g
oin
g t
o s
en
d t
hose
text
mess
ages
and
[ch
eck
] h
ow
well t
hat
goes?
Very
qu
ickly
it
was
tak
en
to a
mu
ch h
igh
er
leve
l. …
Th
is is
a pro
du
ct t
hat
tak
es
the s
erv
ice t
o a
mu
ch h
igh
er
leve
l. (
Fou
nder
1
Text
Co)
26
20
06
Text
Co n
oti
ces
the h
uge
succ
ess
of
[Sm
artT
ext
] w
ith
Pre
miu
m-S
MS
.
Fin
anci
al s
lack
&
capab
ilit
y sl
ack.
Text
Co s
tart
s deve
lopin
g P
rem
ium
-SM
S
tech
nolo
gy.
Th
is d
eci
sion
was
a s
trat
egic
move
; th
e s
ame t
ech
nolo
gy
cou
ld b
e s
old
to
oth
er
com
peti
tors
as
well.
[Sm
artT
ext
] an
d t
he T
V r
an o
ff w
ith
pre
miu
m S
MS
. A
t a
cert
ain
mom
en
t, I
thou
gh
t, w
e s
hou
ld a
ctu
ally
do s
om
eth
ing w
ith
it.
We t
ook t
he d
eci
sion
. It
was
qu
ite a
siz
eab
le in
vest
men
t, b
ut
mon
thly
reve
nu
es
are h
igh
as
well. A
nd w
e w
en
t
on
th
e m
arket
wit
h a
revo
luti
on
ary
new
pri
cin
g m
odel fo
r th
at m
arket.
Ou
r
com
peti
tor
[Sm
artT
ext
] ear
ned p
erh
aps
5 ce
nt
per
text
mess
age; w
e s
aid: ju
st
giv
e u
s 50
0 E
uro
per
mon
th. T
he t
ele
visi
on
sh
ow
pro
du
cers
lik
ed t
his
idea
very
mu
ch s
ince
it
low
ere
d t
heir
cost
s w
ith
som
e 1
00
k E
uro
. (F
ou
nder
1 T
ext
Co)
3520
08
A lar
ge D
utc
h b
ank
exp
eri
en
ces
pro
ble
ms
wit
h
its
curr
en
t S
MS
serv
ice
pro
vider.
Cap
abilit
y sl
ack.
Text
Co’s
fou
nders
appro
ach
th
e b
ank a
nd
off
er
their
serv
ices.
Wit
h t
heir
exp
ert
ise,
Text
Co s
tart
s S
MS
serv
ice f
or
ban
kin
g (
a
new
mar
ket)
, at
a n
ew
serv
ice leve
l.
Ou
r co
mpeti
tor
exp
eri
en
ced p
roble
ms
wit
h t
he t
ech
nolo
gy
beca
use
th
ey
had
had
man
y pers
on
nel ch
anges,
so n
o o
ne k
new
how
th
e s
yste
m w
ork
ed. S
o t
his
was
ou
r ch
ance
.... W
e t
ook o
ver
two y
ear
s ag
o. T
his
was
very
exc
itin
g, beca
use
th
ey
actu
ally
sen
d a
lmost
fiv
e t
ext
mess
ages
eve
ry s
eco
nd o
f eve
ry w
eek-d
ay. S
o t
hat
mean
s th
at if,
for
just
on
e m
inu
te, th
ey
are n
ot
pay
ing a
tten
tion
, th
en
30
0 t
ext
mess
ages
go w
ron
g, an
d b
eca
use
it
con
cern
s a
ban
k, it
mean
s th
at f
or
eve
ry t
ext
mess
age a
cu
stom
er
is m
issi
ng p
aym
en
ts. (F
ou
nder
1 T
ext
Co)
3720
09
Indu
stry
sta
ndar
ds
are
movi
ng t
o 2
4-h
ou
r
cust
om
er
support
.
Fin
anci
al s
lack
&
capac
ity
slac
k.
Text
Co s
tart
s 24
-hou
r su
pport
to c
on
trol
SM
S t
raff
ic a
nd s
erv
ice. T
hey
star
t w
ith
a
few
people
to s
et
up t
he 2
4-h
ou
r su
pport
cen
tre.
Th
e lig
ht
has
not
been
tu
rned o
ff s
ince
Jan
uar
y 20
09
. S
o t
here
are
alw
ays
people
here
. ... W
e r
eal
ly d
eci
ded n
ot
to o
uts
ou
rce [th
is 2
4h
serv
ice], b
ut
to d
o it
ou
rselv
es.
A r
eas
on
able
in
vest
men
t, b
ut
there
are
als
o m
any
people
usi
ng t
he
[ban
k]. I
t co
nce
rns
a te
xt m
ess
age t
hat
you
get
wh
en
you
log in
to y
ou
r [b
ank]
acco
un
t. P
eople
log o
n t
o t
heir
acc
ou
nt
at n
igh
t, s
o it
shou
ld a
lway
s w
ork
. A
nd
then
it
is r
eal
ly n
ice if
you
get
to t
ell y
ou
r cl
ien
ts: w
e a
re lookin
g a
t yo
ur
syst
em
.
Th
is in
clu
des
all S
MS
or
oth
er
pro
du
cts.
(F
ou
nder
1 T
ext
Co)
72 DECISION MAKING IN NEW TECHNOLOGY VENTURES
Finally, we conducted in-depth analyses of the decision-making processes.
To trace patterns in resource positions over time, we created a graphical
overview of the resource positions per event per case (see Figure 4.2). By
combining these overviews with detailed descriptions of the events in the
decision-making process tables (e.g., Tables 4.3–4.5), the nature of the
resource positions could be studied. Noting the large variety in resource
position configurations, involving combinations of different types of slack
and constraints, we grouped events with similar resource positions across
cases, to analyse their relation with decision making. In so doing, we drew
on several tabular representations to group events by the types of resource
slack, resource constraints and their particular combinations. However, in
grouping the events and establishing a link between resource positions and
decision making, we did not identify any direct, consistent effects of resource
positions. Therefore, we redirected our attention toward the underlying
dynamic complexity that appears to influence the relationship between
resource positions and decision making.
CHAPTER 4 73
Figure 4.2: Transient and Multidimensional Resource Positions (Perceived)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Resource Positions Events SunCo
CO
NS
TR
AIN
TS
S
LA
CK
1997 2010
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Resource Positions Events TextCo
CO
NS
TR
AIN
TS
S
LA
CK
1999 2010
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Resource Positions Events ChipCo
CO
NS
TR
AIN
TS
SL
AC
K
2000 2003
74 DECISION MAKING IN NEW TECHNOLOGY VENTURES
4.4 Findings
In this section we start with the case descriptions of the three start-ups. Next,
several key findings on the transient and multidimensional nature of
perceived resource positions are discussed. Finally, we synthesize the
outcomes of the process analyses in terms of the underlying dynamics of
resource positions and decision making.
4.4.1 Case descriptions
4.4.1.1 SunCo
In early 2000, the founder of a small energy company and the founder of a
multinational glass company combined forces and started a new company in
the solar panel industry. The two entrepreneurs had different ideas about
how to develop the company, so they adopted a dual strategy: The founder
from the small energy company set out to build a project-based business in
The Netherlands and neighbouring countries, focused on selling and
integrating solar panels already available on the market, while the other
founder committed large upfront investments to developing a radically new
solar technology. In the first few years, the project-based business expanded
internationally (Europe and the United States) while the technology
development process ran increasingly behind schedule and depleted the
initial budget, as a result of several major problems. When the economic
crisis hit in 2008, demand for SunCo’s products and services dropped, and
major liquidity problems emerged. The company’s leadership developed
various alternative strategies to get through the crisis and finally chose to
cooperate with an experienced partner to develop the new solar technology,
so that it could get the product to market.
4.4.1.2 ChipCo
After being approached by a venture capital (VC) firm in 2000, a professor
and doctoral student from the electrical engineering department of a Dutch
university realized the potential of starting a company based on the optical
CHAPTER 4 75
chip technology they had invented. Noting the growing use of broadband
telecommunication, the VC firm offered the researchers substantial funding,
provided that they would develop a commercial proposition for the global
telecom market. The team was unable to translate its findings into a
business case though, so an outside CEO with extensive telecom experience
was hired to start the business. After obtaining the VC funding, the start-up
team set out to develop its first product, a demonstrator chip that
incorporated the expertise of three doctoral dissertations on optical
integration. In 2000, during the product development phase, the telecom
industry crashed with the collapse of the dot.com bubble, and major telecom
operators faced severe losses. Confident in their abilities, ChipCo’s team
continued the product development process and hired additional employees.
Although potential customers were impressed by the team’s abilities, they
remained unwilling to purchase the new chip, because its implementation
demanded an extensive system redesign. In need of cash, the entrepreneurs
quickly developed a second, more marketable product and started looking for
additional funding but were unsuccessful in the rapidly declining industry.
This crisis motivated the team to look for other applications and markets for
the technology, but without success. With no other options left, the company
filed for bankruptcy in June 2003.
4.4.1.3 TextCo
In 1999, two industrial engineering students explored new ways of making
money by advertising for nightclubs. They noted the growing use of mobile
telephones and thus decided to use text messages (SMS) as an advertising
tool for their local nightclub. After collecting the mobile numbers of people
entering Belgian nightclubs, the students used their university laptops and
their parents’ Internet connections to send advertising messages. When this
experiment proved successful, they started a company to offer SMS services
to other types of Dutch and Belgian businesses, such as logistics firms. The
founders also tried to develop and introduce new products in the market and
opened an office in Poland, though they were forced to close this foreign
office when their products failed to catch on locally. In response to customer
76 DECISION MAKING IN NEW TECHNOLOGY VENTURES
feedback, the founders successfully developed a new service, a 24-hour
monitoring system for SMS traffic. In 2002, a key competitor launched a
new concept, in which it charged customers extremely high fees to receive
text messages that allowed them to participate in televised SMS voting.
TextCo’s founders thought little of this new use and stuck to their existing
operations, but the competitor’s concept proved to be a huge success. Four
years later, TextCo’s founders sought to claim some share of this
opportunity, while expanding their operations to other European countries.
4.4.2 Perceived and transient resource positions
We identified different types of resource positions, as shown in Table 4.2. A
closer inspection of the perceived resource positions in each case and across
cases, in Figure 4.2 and the decision-making process tables (key events in
Tables 4.3–4.5), reveals several interesting observations. In particular,
entrepreneurs’ perceptions of resource positions are not static but transient
and changing over time. Perceived resource positions can change any time a
situation involves some reflection on (anticipated) available resources relative
to (anticipated) resource demand. Both perceived resource availability and
perceived resource demand can shift easily, such that the resource position
perceived by the entrepreneur becomes a transient imagination. For example,
by the end of 2002, the founders of SunCo believed they had sufficient
financial resources to buy an existing solar development project, but this
view changed when a key supplier (which they already had prepaid
thousands of Euros) was about to go bankrupt. This anticipated financial
constraint stimulated the entrepreneurs to come up with an idea to prevent
severe losses. This example also illustrates how anticipated resource
positions arise from imaginations of the future and influence
entrepreneurial decision making: anticipated financial constraints (i.e.,
expected bankruptcy of supplier) led the entrepreneurs to act to prevent
future losses. These findings highlight how resource positions may enter
subjectively imagined futures (Chiles et al., 2007). The constantly changing
positions and configurations of the bars in Figure 4.2 reflect the ever-
changing perceptions of (anticipated) resources relative to demand,
CHAPTER 4 77
demonstrating the transient nature of perceived resource positions.
Therefore, the time-invariant or annual measures of slack and constraints
used in previous studies (Daniel et al., 2004; Nohria & Gulati, 1996; Tan &
Peng, 2003) appear to generate situational snapshots, with limited
longitudinal reliability.
We also explore how understanding resource positions as perceived and
transient might relate to conventional, firm-level measures of resource
positions. In Figures 4.3-4.5, we depict different operationalizations of
financial slack and constraints for all three cases, including three common
firm-level measures of financial resources (relative to demand) obtained
from annual reports: cash (George, 2005; Voss et al., 2008), current assets
divided by current liabilities or current ratio (Bourgeois, 1981; Bromiley,
1991; Daniel et al., 2004), and the difference between current assets and
current liabilities (Bradley, Wiklund, et al., 2011; Mishina et al., 2004). A
comparison of longitudinal patterns reveals that the firm-level financial
measures convey different, and at times inconsistent, information with
respect to the level of financial slack. The three firm-level measures produce
graphs with different shapes, implying opposite conclusions. Consider, for
example, Figure 4.3(a) versus Figure 4.3(c) for TextCo: The cash measure (a)
indicates substantial excess financial resources during 2006–2008, whereas
the difference measure (c) implies significant financial constraints in the
same period, because current liabilities exceed current assets. This important
finding sheds some new light on why previous studies offer conflicting
results regarding the effects of slack and constraints.
Figure 4.3(d) further illustrates the difference between these objective
financial measures and perceived, transient resource positions. Consider
2007 for TextCo. The firm-level financial measures indicate the company is
experiencing either financial slack or financial constraints for the entire year;
our approach reveals a more fine-grained and dynamic picture. Thus, firm-
level measures appear poorly suited for capturing and incorporating the
subjective nature of resource positions (Chiles et al., 2007; Chiles, Tuggle, et
al., 2010; Foss et al., 2008; Kor, Mahoney, & Michael, 2007). Researchers
must attend to the heterogeneity among individual entrepreneurs to
understand firm-level outcomes (Felin & Foss, 2005; Foss, 2011), particularly
78 DECISION MAKING IN NEW TECHNOLOGY VENTURES
with regard to their subjective perceptions of resource positions (Foss et al.,
2008).
A micro-level perspective of dynamic perceived resource positions over
time entails both subjective and volatile resource availability and subjective
and variable imagined resource demands. For example, TextCo’s
entrepreneurs experienced both financial slack and constraints during 2007,
depending on their perceptions of their financial resources available relative
to the amount they needed. Its founders were looking to expand the
company, but financial constraints limited their ability to do so, so the
founders made the decision to start looking for investors, as TextCo Founder
2 explained:
First, that did not work really well, because we didn’t have a track record.… Then we hired someone to make a business plan and slides and then we went to visit 10 investors.
Yet in the same year, TextCo’s founders experienced enough excess financial
resources to take over entire divisions of competing companies. Founder 2
further noted,
[In] 2007, there were so many acquisitions. And we took part in that too.… And we did that twice, successfully. So we just told competing companies: “We are buying your division!”… We told them: “Yes, you will sign over your customers to us and we will give you money in return.”
In summary, considering resources positions as perceived and transient
offers an appropriate approach, because it provides a more accurate, fine-
grained representation than do firm-level measures. These observations also
correspond well with events that can be tracked in firm-level measures.
Returning to Figure 4.2, we find that the solid bars representing TextCo’s
financial resource positions do not exhibit a particular pattern but move
almost randomly up and down over time, in line with the company’s internal
cash flow financing strategy and organic growth. SunCo’s financial resource
positions in Figure 4.2 instead indicate a wave-like pattern, representative of
its initial large financial commitments to product development, followed by
the constraining effects of the economic downturn. ChipCo’s financial
resource positions in Figure 4.2 also display a pattern consistent with its
(anticipated) venture capital rounds and subsequent bankruptcy: financial
slack during the first rounds of venture capital, followed by a series of
CHAPTER 4 79
constraints related to costly production. Later, in anticipation of new
funding, it made investments to speed up the development process, but the
inability to attract additional funding led ChipCo to declare bankruptcy.
Figure 4.3: Financial Slack: Firm-Level vs. Perceived Resource Positions
(TextCo)
2007
(c) Current assets – current liabilities (€)
0
- 1600
(a) Cash (€)
0
50
(b) Current assets / current liabilities
0
1
28 29 30 31 32 33 34
CONSTRAINT
SLACK
(d) Perceived resource positions
event #
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
80 DECISION MAKING IN NEW TECHNOLOGY VENTURES
Figure 4.4: Financial Slack: Firm-Level vs. Perceived Resource Positions
(SunCo)
2004 2005 2006 2007 2008 2009 2010 2011
2004 2005 2006 2007 2008 2009 2010 2011
2004 2005 2006 2007 2008 2009 2010 2011
17 18 19 20
2007
event #
SLACK
CONSTRAINT
(c) Current assets – current liabilities (€)
0
(a) Cash (€)
0
(b) Current assets / current liabilities
0
1
(d) Perceived resource positions
- 70000
36000
CHAPTER 4 81
Figure 4.5: Financial Slack: Firm-Level vs. Perceived Resource Positions
(ChipCo)
2000 2001 2002 2003 2004 2005
2000 2001 2002 2003 2004 2005
2000 2001 2002 2003 2004 2005
6 7 8 9 10 11 12 13 14 15 16 17 18
(c) Current assets – current liabilities (€)
5000
0
(a) Cash (€)
0
5000
(b) Current assets / current liabilities
0
18
(d) Perceived resource positions
2001
event #
CONSTRAINT
SLACK
82 DECISION MAKING IN NEW TECHNOLOGY VENTURES
4.4.3 Multidimensional resource positions
Different types of resource constraints and resource slack can be perceived
simultaneously, as also illustrated in Figure 4.2. In contrast with one-
dimensional measures that indicate firms experience either resource
constraints or slack, we observe simultaneous combinations of constraints
and slack. Therefore, resource positions appear multidimensional, in contrast
with the conventional wisdom that implies constraints or slack are absolute
positions in time (Bradley, Wiklund, et al., 2011; Nohria & Gulati, 1996; Tan
& Peng, 2003). By identifying financial-, capacity- and capability-related
resource positions (see Table 4.2), we observe that entrepreneurs can
experience constraints and slack capabilities at the same time, as illustrated
by ChipCo’s event 14 in Figure 2. In 2001, the founders of ChipCo
anticipated severe financial constraints; they needed to secure a second
round of VC funding but also received a complaint from another company
claiming patent infringement. ChipCo’s CEO explained:
During the second round funding we faced a blocking patent, … where we would have to pay royalties of about 20 percent on everything we sold and a sign-up fee of, I believe, half a million, really ridiculous.…We were in the middle of [securing] that investment round and our [potential VC investor] told us: “This is a major event, so we will need to see. This changes the entire situation.”
At the same time, ChipCo employed top-notch scientists, with plenty of
underutilized (slack) capabilities. According to its CEO,
The brainpower of our guys, I mean, we had about four or five PhD’s from [university X], super smart guys, real beta’s—tremendous amount of respect for those guys who all got their PhD at the intersection of physics and electrical engineering, real eggheads with international status.… They worked to see if they could come up with a re-design to work around the patent. Within two weeks they came up with seven re-designs…!
In this example, financial constraints arising from patent infringement and
slack capabilities jointly characterized the resource position at the time of the
decision; together they spurred creative solutions. The finding that
entrepreneurs can (simultaneously) perceive different types of constraints
and slack reflects early theorizing about the role of resources in
organizations (e.g., Hannan & Freeman, 1993; Scott, 1987; Thompson,
1967), and it signals that attempts to attribute particular effects to one-
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dimensional measures are highly problematic. The use of one-dimensional
measures can result in outcomes driven by unobserved factors, which may
explain the mixed prior findings regarding the effects of resource slack and
constraints. Resource slack or constraints thus cannot be scrutinized in
isolation; instead, they must be examined in more comprehensive ways.
4.4.4 Mixed effects of constraints and slack: Underlying
dynamics
We now turn to exploring how perceived, anticipated and relative resource
positions pertain to decision making. Previous research has not been able to
provide consistent insights regarding the effects of resource constraints or
slack. We grouped events with similar resource positions in tables to
establish a link between resource positions and decision making; however,
we did not detect a recurrent or systematic pattern at the event level. The
perception of constraints in some instances motivates entrepreneurs to
engage in some creative explorations (e.g., Table 4.4, event 28, ChipCo);
whereas entrepreneurs perceiving constraints in other settings do not
pursue that direction (Table 4.3, event 25, SunCo). Similar results arise with
regard to the effects of slack resources (Table 4.5, events 14 versus 21,
TextCo). At times, constraints encourage the production of creative ideas; at
other times, slack resources stimulate such ideas; and in still other instances,
neither constraints nor slack induce creative solutions. But why do
constraints and slack not have univocal effects?
In line with our theoretical argument, our findings show that a decision
outcome is unlikely to relate directly to an observed resource position. The
relationship between resource positions and decision making instead
depends on several underlying dynamics, including those at the individual,
temporal, and resource position levels. In the remainder of this subsection,
we ground these key factors and relationships in the data.
84 DECISION MAKING IN NEW TECHNOLOGY VENTURES
4.4.4.1 Individual dynamics
Analysis of the relation between resource positions and decision outcomes
suggests entrepreneur-specific effects. Different entrepreneurs have unique
backgrounds and perceptions of their ventures’ resource availability and
demands that lead them to construct specific ideas about how to make their
decisions; the decision outcome observed likely arises from the interaction
between the entrepreneurs leading the venture. For example, both SunCo
and TextCo started with two founders, each with their own perceptions of
resource availability, resource demands, and options to develop the venture.
SunCo’s Founder 1, who previously had started and operated a small energy
company, explained how his background influenced his view of the
situation:
I wasn’t born rich. At one time, I started in a chicken barn of 500 square meters.… That’s where I started with, to first prove that it works, so I put in the small amount of money I owned.
Then, after he met SunCo Founder 2:
So we started SunCo. I owned half of the company and [Founder 2] the other half.… When you talk about starting, it was very small scale, facilitated by another small company I owned. It was just me, with one other guy. We did not start with huge investments. But built up very slowly … dipping your toes in the water to feel how warm it is, to find out whether to proceed or hold back.
SunCo Founder 2 came from a multinational company and believed there
were no financial constraints, which resulted in a different approach to the
venture. The company’s chief technology officer, one of the first employees,
explained:
[Founder 2] came in and said: “I want to make solarpanels, go figure it out!”… In addition to the entrepreneurship style of [Founder 2], the beginning of the story is: this founder wants this specific technology, and that’s what we started with.
SunCo Founder 1 added:
We did invest a great deal in development. Compared to many other companies, really a lot. But it is because, for [Founder 2], it was worth the money. He told us: “this is what I want to do, this is my project. I will put in a lot of money because I believe in it.”
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TextCo Founder 1 also recognized the differences between his and Founder
2’s perceptions:
You need to grow every dimension of your company. At first, this was difficult to get used to. Because it meant one had to make large investments which were, at that time, somewhat excessive, but needed in future. And this is where an important difference between me and [Founder 2] becomes visible. [Founder 2] is more of a visionary, he is able to foresee the future, what will be needed, and invest. I’m more conservative: Should we really do this? Why not focus on minimal investment? This is a very interesting and healthy tension. Whose plan of action is taken, comes down to who has the most convincing arguments.
These examples effectively show how prior experiences influence
entrepreneurs in making sense of their perceived resource availability and
demand (Weick, 1995). Not only individual perceptions play a role (Chiles et
al., 2007; Chiles, Vultee, et al., 2010), but interactions within the
organization also affect the relationship between perceived resource
positions, decision making and creativity (Ford & Gioia, 2000; McMullen,
2010). Many decisions made by SunCo and TextCo arose from negotiated
compromises, based on inter-subjectivity or joint sensemaking by pairs of
entrepreneurs with different attitudes (Weick, 1995), which influenced the
overall creativity in these decisions (Ford & Gioia, 2000). However, the
founders also actively engaged in perspective taking, allowing their partners
sufficient resources and time to experiment.
4.4.4.2 Temporal dynamics
Regarding the connection among multiple events within a case, our findings
suggest that the influence of resource positions on decision making is not
consistent over time but rather is subject to temporal dynamics. Past
experiences (paths) influence the decision-making process and thus the
relationship between resource positions and decision outcomes. Such path-
dependent effects occur when entrepreneurs only see options along their
existing path, despite possible changes in their perceived resource position.
In this case, entrepreneurs decide according to routine first (i.e., choosing
the familiar path) rather than according to their current, changed resource
position.
86 DECISION MAKING IN NEW TECHNOLOGY VENTURES
The product development phase at the VC-backed ChipCo clearly
revealed path-dependent effects. Its initial financial slack facilitated a
development trajectory, free of short-term financial and environmental
pressures; later ChipCo persisted with the development of its product, even
in the face of severe resource constraints. Founder 1 explained that, at first,
he believed all necessary resources were available:
When we started, both VC investors told us: “don’t bother about attracting subsidies. It is a lot of effort and a lot of hassle. If you need more money, then just ask for more money and you will get more money” … they [VC investors] pushed us: “Continue the development of the Holy Grail, don’t focus on simple sub-products [to generate cash-flow].”
The CEO added:
…it was just invested based on the needs of the technology. This has that much potential; this will turn out just fine. Something will come out of this: that has been the starting point.
Because the founders of ChipCo believed their resource demands would
always be met, they set out to develop a cutting-edge product that would
incorporate all the technical expertise available to them.
However, by the final stages of the product development process,
ChipCo’s target (telecom) market appeared on the brink of collapse, creating
vast uncertainty. ChipCo’s founding team still perceived ample financial
slack, in the form of substantial (existing and potential) VC funding.
Founder 2 reflected on the decision to proceed with product development:
Actually, it was the wrong time to … when you look back; it was really a very odd period to start a company. Actually, it is just not possible. A shrinking market and a completely new technology.
As ChipCo continued with its product development activities, the costs
increased drastically. Despite these (anticipated) financial constraints—a
significant change in resource position—the team still decided to continue
with the initial idea and even increased the number of design runs. The CEO
explained why ChipCo persisted, even when the perceived resource position
changed from financial slack (7 million Euro of VC funding) to financial
constraints (high development costs):
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At one point we had that 7 million. Initially, we did one design run every month. We improved our own process every run, as we were inventing something new. One run takes a 100.000 Euro, as all parties have to perform their tasks every run. But we also had our daily costs of keeping the business going; we had to pay 20 staff members, the rental fees, et cetera. It is a very costly business. Then we increased to two runs a month and we burned our money even faster.
In addition to high operating fees and a collapsing telecom market, another
problem surfaced: Customers were not willing to buy ChipCo’s products
because they were not able to integrate them into existing systems. Faced
with even greater financial constraints, ChipCo’s leadership saw few
alternatives other than continuing to develop products for the telecom
market. According to its VC investor:
At that time, the feeling that we needed to generate revenue became stronger and stronger.… The long-term vision did not change, but the quest became: ok, what is needed for tomorrow?
The entrepreneurs did not want to give up on ChipCo’s long-term (product
development) goals, so they conceived of an intermediate product for the
telecom market that would be more marketable but still based on the
developed technology (i.e., slack capabilities). Founder 1 indicated:
But then we thought about another application, a monitoring application, as this does not demand a significant redesign of the system. This can be plugged in [existing systems of telecom providers], and then we can at least sell something. It is based on the same technology we are already using, but then with [more] channels.… And yes, I think we were a little too late with that.
In the end, after confronting the consequences of a collapsed market,
ChipCo’s investors resigned themselves to failure. At that point, the
founding team of the insolvent ChipCo finally saw how the routine had
failed: ChipCo’s path had constrained the options that the founders could
imagine, even when the situation (and resource position) kept changing. As
soon as the founders were no longer able to follow the existing path, they
acknowledged the need to explore alternative ideas. Founder 1 explained:
88 DECISION MAKING IN NEW TECHNOLOGY VENTURES
At the moment that everybody … that the telecom market collapsed, that clients told us not now, not at this moment, then we started looking at alternatives. Yes, because, still we were able to build about anything.… The time to develop something to be used in a different market; this takes time and money. Both we did not have.
ChipCo’s product development process thus illustrates that when
entrepreneurs decide to stick to an existing path, unaffected by changes in
resource positions, it impedes the timely imagination and exploration of
creative solutions.
The SunCo case revealed a similar path dependency in its response to
resource constraints. Such path dependency affects the relationship between
resource position and decision making: Past experiences constrain the
options entrepreneurs are able to imagine, even when changes in their
resource position give them a reason to become creative. That is,
unconstrained, forward-looking imagination can drive the creative decision-
making process more effectively (as Austrian argument states, e.g., Chiles et
al., 2007; Chiles, Tuggle, et al., 2010; Foss et al., 2008; McMullen, 2010),
whereas approaches that rely on previous paths and experiences can
constrain creativity (Keeney, 1994; Vergne & Durand, 2011; Weick, 1979,
1995) by making entrepreneurs less receptive to changes in resource
positions.
4.4.4.3 Resource position dynamics
Our findings suggest different types of resource constraints and slack are
perceived simultaneously; together they make up the overall resource
position perceived at the time of the decision. Perceived constraints and
slack jointly influence the way entrepreneurs make decisions, so perceptions
of different resource configurations have different effects. In 2008, SunCo
set out to establish international sales offices for its modules, which led it to
assess its resource position (Figure 4.2, event 19). According to SunCo’s
chief operating officer:
CHAPTER 4 89
I think we were more a module producer. So, we built modules. But, as we grew, only building modules was not sufficient to create enough volume [to make profit].… When I arrived, there was a kind of organization that had a track record and a number of people who had, say, expertise [of project management] in their heads.… You just have to see you are capable of doing much more than just selling modules. So there is a lot of capability here and sometimes we can use those capabilities to help customers who are stuck with a project, we can help those customers because we are used to doing it too.… So we have an additional channel, an additional outlet to bring products to the market. Because we also add value, not just [deliver] a module, but a complete system, you generate an interesting margin.
Thus, SunCo established a second line of business, in view of both
(anticipated) financial constraints and slack capability. This example
suggests it was the perception of this specific combination of constraints and
slack that stimulated the novel idea of a second line of business, rather than
the need to generate higher margins or underutilized existing capabilities.
When entrepreneurs perceive a combination of various resource positions,
this specific combination affects their decision-making process, which
makes it impossible to trace observed effects back to single resource
positions. Moreover, studying resource positions in isolation may result in
an incomplete picture of the entrepreneur’s perceived situation, which fails
to account for any combination-specific effects of resource positions.
In the ChipCo case, we also observed an influence of perceived resource
combinations (e.g., Figure 4.2, event 15). In 2001, when ChipCo’s founders
started planning the production of their first product, they experienced for
the first time all three types of resource constraints, because of the resources
demanded: they did not have a cleanroom to develop their product (capacity
constraint), nor did they have sufficient financial resources to build their
own cleanroom facility (financial constraint), and the team also lacked the
proper experience (capability constraint). The founders faced tough
challenges, as ChipCo’s CEO recalled:
With respect to operations, it is highly complex and incredibly expensive, it is a nightmare. So I almost developed a stomach-ache because of this, apart from the fact that I had absolutely no idea what it [building a cleanroom] was about. I cannot build such a thing.…
90 DECISION MAKING IN NEW TECHNOLOGY VENTURES
Because, unlike other start-ups in the industry, ChipCo lacked sufficient
financial resources to build a cleanroom, Founder 1 noted their production
decision had to involve creative elements:
Everybody, every start-up received 40 million dollars to build their own fab [cleanroom]. And well, we raised 7 million dollars that year, and yes, that is of course way too little to build your own fab. But that made us realize that we had to do things in a different way. So we started looking for production partners. And that is exactly the path we ended up taking.
This particular combination of constraints pushed ChipCo’s founding team
to come up with the idea for production partners. What would they have
done, though, had they experienced fewer resource constraints or a different
combination of constraints and slack? The way entrepreneurs make sense of
their context and the options they imagine appears to depend on the
(situation- and time-specific) perceived combination of resource positions.
These findings demonstrate how perceived combinations of different types
of resource constraints and/or slack enter the decision-making process and
influence the entrepreneur, generating idiosyncratic options with varying
degrees of creativity. Therefore, the results of the study in this chapter extend
entrepreneurship theory, in particular with regard to the process of resource
(re)combination (Chiles et al., 2007; Chiles, Vultee, et al., 2010; Foss et al.,
2008; Schumpeter, 1934), by showing how entrepreneurs not only engage in
imaginative (re)combinations of existing resources but also can be guided by
imaginative (re)combinations of perceived resource slack and resource
constraints.
4.5 Discussion
In this chapter, we have explored the characteristics of resource positions
and how they influence entrepreneurial decision making and creativity.
Viewing resource slack and resource constraints as two extremes on a
spectrum of resource positions constitutes an important step toward
integrating the resource slack and resource constraints literature, which
represent core discourses on organizational ingenuity. Perceived resource
positions reflect the entrepreneur’s imagination of available resources
relative to demand including anticipated resources or resource demands.
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Furthermore, resource positions are transient imaginations, allowing the
entrepreneur to move along the constraint–slack spectrum over time.
Resource positions are multidimensional constructs too; our findings show
that entrepreneurs perceive different types of constraints and slack
simultaneously (e.g., capacity constraints and financial slack), in line with
prior work that has acknowledged the multidimensional nature of resources
(e.g., Hannan & Freeman, 1977; Voss et al., 2008).
Such perceived, anticipated and relative resource positions influence
creative decision making, but not systematically. Constraints and slack do
not have univocal effects, but rather lead to idiosyncratic decisions by
entrepreneurs. Constraints sometimes encourage inventive behavior, or
slack resources might induce innovative activities; in other cases, neither
constraints nor slack results in creative decisions. The relationship between
resource positions and (creative) decision making thus is highly complex,
influenced by underlying dynamics that tend to remain hidden in firm-level
studies that rely on cross-sectional measures (Felin & Foss, 2005). By
studying perceived, anticipated, and relative resource positions over time at
the decision-making level, we demonstrate that the processes by which
resource positions influence decision making depend on individual,
temporal, and resource position dynamics. These results have notable
implications for research and theory about the relationship between
resources and creativity in decision making.
4.5.1 Perceived resource positions and individual
dynamics
Resource positions reflect an entrepreneur’s perception of available
resources relative to demand. Unlike previous research that has tended to
overlook the role of individuals in organizations (Abell et al., 2008; Felin &
Foss, 2005; Foss, 2011) because it adheres mainly to firm-level measures
(Daniel et al., 2004; Voss et al., 2008), we conceptualize resource positions
as the abundance or shortage of resources perceived by the entrepreneur.
Perceived resource availability and demand are entrepreneur-specific and
92 DECISION MAKING IN NEW TECHNOLOGY VENTURES
highly subjective, in line with sensemaking (e.g.,Cornelissen & Clarke, 2010;
Weick, 1995) and Austrian economics (e.g., Foss & Ishikawa, 2007; Foss et
al., 2008) research. An entrepreneur’s imagination influences the subjective
evaluation of available resources (Chiles et al., 2007; Chiles, Tuggle, et al.,
2010; McMullen, 2010), so firm-level measures cannot address the
heterogeneously perceived value of available resources in relation to
imagined action scenarios. The commonly used, financial, firm-level
measures, which result in contradictory characterizations of a start-up’s
resource position, thus are less appropriate for describing the effects of
resource slack and constraints.
The idea that resource positions are transient imaginations has
important implications for related studies, because the relationship between
resource positions and decision making is subject to individual-level
dynamics. Different entrepreneurs perceive resource availability relative to
imagined demand in distinct ways—as clearly exemplified by the two SunCo
founders—and therefore make different decisions. Our findings thus extend
prior research that suggests that founders likely engage in creative and
innovative activity by nature, by habit, or in response to certain resource
positions (Baker & Nelson, 2005; Bundy, 2002; Woodman, Sawyer, &
Griffin, 1993). For example, effectuation theory implies that the way
entrepreneurs make decisions depends on their individual expertise and the
degree of uncertainty (Sarasvathy, 2001; Sarasvathy et al., 2008; Read &
Dolmans, 2012). As the degree of uncertainty may shift for each event, it is
impossible to find a direct or generalizable effect of resource availability,
because individual perceptions and decision making drive firm-level
behavior and outcomes.
Various decision outcomes also arise from the interaction between
entrepreneurs who team up for a particular venture. Both individual
perceptions and interactions between individuals thus influence the
relationship between perceived resource positions and decision making
(Chiles et al., 2007; Chiles, Vultee, et al., 2010; Ford & Gioia, 2000;
McMullen, 2010). In this sense, our findings extend research on collective
creativity by showing how interactions between entrepreneurs, perceiving
distinct resource positions, can affect the production and implementation of
CHAPTER 4 93
creative ideas (Ford & Gioia, 1995; Hargadon & Bechky, 2006; Kurtzberg &
Amabile, 2001; Sawyer & DeZutter, 2009; Sawyer, 2008; Woodman et al.,
1993). This study also extends previous work on perspective taking and
creativity (McMullen, 2010) as antagonistic perspectives might generate
underlying tensions, and founders who cannot converge on a shared
perspective on resource positions may nix their partners’ creative ideas.
Future work in this area should incorporate these individual and collective
effects and gather the perceptions of all entrepreneurs (and perhaps their
stakeholders) about their resource availability and imagined resource
demand.
4.5.2 Transient resource positions and temporal
dynamics
Perceived resource positions are not static, but change over time (George,
2005; Mishina et al., 2004). On an event basis, perceived resource positions
can shift easily, such as when the founders of SunCo perceived that they had
sufficient resources to buy an existing solar development project but shortly
thereafter recognized significant financial constraints due to the expected
bankruptcy of a key supplier. Time-invariant or annual measures of slack
and constraints thus may not capture precisely how resource positions affect
decision making (Daniel et al., 2004; Nohria & Gulati, 1996; Tan & Peng,
2003). Capturing resource positions with a single observation only provides
a situational snapshot, whereas both perceived resource availability and
imagined resource demand are variable. The underlying temporal dynamics
offer an important explanation of the mixed effects of resource constraints
and slack, as they can blur the causal relationship between resource position
and observed outcome. Depending on when resource positions, decisions
and outcomes get measured, different conclusions emerge regarding the
effects of constraints or slack. Measuring resource positions at a single point
in time thus ignores the possibility that a follow-up measure would produce
a completely different result. Therefore, subjective and longitudinal
94 DECISION MAKING IN NEW TECHNOLOGY VENTURES
representations of resource positions (related to decision-making events) are
necessary.
From the Austrian economics perspective, scholars have argued that the
perceived nature of a firm’s resources reflects the heterogeneity of
entrepreneurs and their dynamic perceptions of resources over time (e.g.,
Chiles et al., 2007). We extend these insights by showing that micro-level
dynamics, including subjective and variable resource availability and
subjective and variable imagined resource demand, determine the transient
resource position (Figure 4.1).
Although perceived resource positions are variable, path-dependent
effects dampen the variation in decisions made on the basis of resource
positions (Hannan, 1998; Romme, 2004; Stinchcombe, 1965). The ChipCo
case reveals how entrepreneurs can grow accustomed to a routine for dealing
with problems, and that routine regulates their future behavior, regardless of
their resource position (Heiner, 1983; March & Simon, 1958). Even when
confronted with resource shortages, they may seek to exploit their past
successes by engaging in local learning and optimization, rather than
learning from distant places or exploring new opportunities (Levinthal &
March, 1993; March, 1991). Entrepreneurs can become trapped in an
exploitative learning cycle, such that they simply fail to take into account
their actual resource position. These findings extend existing research by
showing that the influence of both resource slack and constraints is subject
to organizational routines (Cheng & Kesner, 1997; Cyert & March, 1963;
Nelson & Winter, 1982). Unconstrained forward-looking imagination can
drive creative decision-making processes (e.g., Chiles, Tuggle, et al., 2010),
whereas approaches relying on past paths and experiences can constrain
creativity (Keeney, 1994; Vergne & Durand, 2011; Weick, 1979, 1995), which
makes entrepreneurs less receptive to changes in resource positions. This
happens when entrepreneurs see few alternatives besides the obvious ideas
they have implemented before (Keeney, 1994; Lubart, 2001). The notion of
path dependency thus helps explain entrepreneurial decision making (in
view of resource positions) and the level of creativity in entrepreneurial
decisions.
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Furthermore, entrepreneurs do not make decisions in a vacuum. We
have focused on the individual entrepreneur, to elucidate the micro-
foundations of the effect of resources (Felin & Foss, 2005; Foss, 2011), but
the strategy and organizational design of the firm also can create path
dependency. Major commitments to capital providers and employees make it
hard, if not impossible, to change decisions radically (cf. Hannan &
Freeman, 1984), which may reduce the level of creativity in key decisions.
Such path-dependent effect is evident in the failure case, in that ChipCo’s
founders had such strong commitments to their current path that only after
the team ran out of alternatives did it decide to explore new options. In the
two other cases, the founders were more responsive and proactive in
approaching change.
4.5.3 Multidimensional resource positions and resource
position dynamics
Simultaneity of resource constraints and slack challenges the conventional
wisdom that constraints or slack take absolute positions in time (e.g.,
Bradley, Wiklund, et al., 2011; Tan & Peng, 2003). Our results imply that
resource positions are multidimensional, such that different types of slack
and constraints occur at the same time. Early theory about the role of
resources in organizations suggested this simultaneity (e.g., Hannan &
Freeman, 1993; Thompson, 1967), but recent studies tend to overlook these
insights.
These simultaneous perceptions can create problems for researchers
who want to attribute particular effects to one-dimensional interpretations of
resource positions, whereas firms, such as SunCo, might establish a second
line of business in view of both financial constraints and slack capability.
Moreover, the relationship between resource positions and decision making
appears subject to such combinations in resource positions; because
different configurations of resource constraints and slack jointly influence
entrepreneurs’ decisions, it is difficult to attribute any specific decision
outcomes to a single type of resource constraint or slack. Investigating
96 DECISION MAKING IN NEW TECHNOLOGY VENTURES
constraints or slack in isolation produces an incomplete picture of the
resource position, which may explain mixed effects in previous studies
(Hoegl et al., 2008; Mellahi & Wilkinson, 2010). Our case studies also
illustrate how perceived combinations of different types of resource
constraints and slack enable entrepreneurs to generate idiosyncratically
creative options, which extends existing theory about the process of resource
(re)combination (Chiles et al., 2007; Chiles, Vultee, et al., 2010; Foss et al.,
2008; Schumpeter, 1934). Entrepreneurs not only engage in imaginative
(re)combinations of existing resources, but also are affected by imaginative
(re)combinations of perceived slack and constraints in resources.
4.5.4 Conclusion
The study in this chapter sheds new light on the ongoing debate about the
effects of resource constraints and slack and the circumstances under which
organizational ingenuity may emerge. We show how the relationships of
resource positions, decision making and creativity depend on underlying
dynamics that remain concealed in cross-sectional studies at the firm level.
By conceiving of resource positions as perceived, anticipated and relative, we
clarify how perceived resource positions influence organizational ingenuity
in terms of decision making and creativity—not systematically, but
according to individual, temporal, and resource position dynamics.
Individual-level dynamics relate to how different entrepreneurs, even those
working in the same venture, may perceive resource availability relative to
demand in distinct ways; individual-level dynamics also relate to how
interactions between entrepreneurs affect decisions. Temporal dynamics
imply that the influence of resource positions on decision making is not
consistent over time, as past experiences can influence the decision-making
process and hence the relationship between resource positions and decision
outcome. Finally, resource position dynamics pertain to how combinations
of different types of resource constraints and/or slack enter the decision-
making process and lead to unique outcomes.
Our finding that resource constraints and slack are transient with the
entrepreneur’s perception of available resources and resource demands has
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important implications for further investigations of the effects of resource
positions. To link resource positions to outcomes of interest, researchers
should assess both resource availability and perceived resource demands,
preferably on an event-specific basis. Future work also needs to acknowledge
that resource positions are multidimensional, and moreover that the
entrepreneur’s sensemaking of complex situations explains his or her
decisions. To understand the effects of resource positions, one cannot
examine resource constraints and slack in isolation. Because entrepreneurs
perceive resource availability in relation to demand, while individually and
collectively making sense of the present, past and future, firm-level
operationalizations are insufficient as well. Future research must build on
individual and collective interpretations of resource positions.
4.5.5 Limitations and directions for future research
Several limitations of this study offer directions for research. First, we
focused on short-term implications of resource positions for entrepreneurial
decision making and ignored longer-term effects. The potential value of
investigating long-term (performance) effects may be somewhat
questionable, considering that resource positions are transient imaginations.
Nonetheless, longer-term implications, such as those associated with
deployments of slack resources or new strategies implemented in response
to resource constraints, need to be studied and assessed. Second, our results
are based on data pertaining to more than 100 events involving three
companies, one of which was unsuccessful. The research design thus is not
perfectly balanced; in-depth longitudinal studies, using larger samples with a
more balanced research design, are likely to provide further insights. Third,
our data pertain to high-tech start-ups in emerging industries. More research
should explore whether similar patterns can be observed in other types of
start-ups and in large corporations, in both emerging and mature industries.
With in-depth longitudinal studies, future research can also derive
higher-level implications of resource positions and generate testable
propositions. The use of process methods and individual- and collective-level
interpretations of resource positions should clarify the causal relationships
98 DECISION MAKING IN NEW TECHNOLOGY VENTURES
among resource positions, decision making, and creativity. Alternatively,
more quantitative studies likely will be only as effective as the
operationalizations adopted. Without appropriate measures, studies using
quantitative methods may be ineffective; because resource positions are
transient imaginations grounded in individual and collective sensemaking,
we suggest that additional work addresses the possibility of capturing,
characterizing, and quantifying resource positions systematically. Finally,
further research should expand understanding of the inner workings of, or
interplay among, the underlying dynamics of resource positions. For
example, future work can investigate how different levels of inter-subjectivity
in perceived resource positions relate to creative imagination and decision
making, or how organizational routines influence collective sensemaking
within organizations.
We have empirically demonstrated how subjective perceptions of
resource positions enter the entrepreneurial decision-making process that
generates idiosyncratic options with varying degrees of creativity. As such,
research exploring the relationships among entrepreneurship, resource
positions, decision making and organizational ingenuity needs to
incorporate such micro-foundational dynamics.
Chapter 5
Conclusions
In addition to the surge in technology commercialization activities at
research universities (Mowery, Nelson, Sampat, & Ziedonis, 2001; Nelsen,
1998), private, public, and even non-profit organizations are increasingly
mobilizing their unexploited discoveries, inventions and innovations into the
open market (Markman et al., 2008). While the potentially powerful
consequences of technology commercialization (Lockett et al., 2005; Shane,
2004; Siegel, Waldman, Atwater, et al., 2003) have captured the attention of
practitioners, policymakers and scholars, many new technologies fail to live
up to their commercial potential (Christensen, 1997; Song et al., 2008).
Technology commercialization continues to be a challenging process; it
involves the selection and commercial development of new technologies
under uncertainty (Ambos et al., 2008; Zahra & Nielsen, 2002), implying
that the outcome of the process depends on the stakeholders who make
decisions and allocate resources under these conditions of uncertainty
(Arrow, 1962; Baumol, 1993; Dew et al., 2009; Knight, 1921). To improve
the success rate of technology commercialization processes, more insight is
needed in how various stakeholders make decisions in technology
commercialization. This dissertation aimed to shed light on the black box of
decision making in technology commercialization processes.
“Why is it that the founders of Google, Genentech, Netscape and Yahoo!
are all men?” and “Why did Jobs and Wozniak decide to start Apple out of a
garage?” The studies in this dissertation provide important insights that can
help in answering such questions. The studies contribute to our
understanding of decision making in technology commercialization in two
100 CONCLUSIONS
ways. First, they demonstrate that the evaluation and selection of new
technologies for commercial development is not only contingent on
technological features. And second, they show how technology
entrepreneurs rely on their perception of resources when making decisions.
The core of this dissertation was organized in two parts along the stages
of the commercialization process, in line with the two stakeholders under
investigation (Markman et al., 2008): universities (technology licensing
offices) in Part I and new technology ventures in Part II. Whereas
universities frequently act as a supplier of technological inventions by
selecting research with commercial potential (Shane, 2004), new technology
ventures are a typical mode of commercial development of technological
inventions (Drucker, 1999; Wright et al., 2007). Therefore the key findings
and implications of Chapters 2, 3 and 4 will be mainly discussed according
to this structure, followed by limitations and directions for future research.
To guide the discussion of this dissertation’s findings, Figure 5.1
summarizes the studies, research questions, methods and key findings of
each study.
CHAPTER 5 101
Figure 5.1: Overview of dissertation
Part II Decision making in technology ventures
Selection of new technologies Commercial development
Part I Decision making in
universities
Chapter 4 Dynamic slack and constraints: Resource
positions in action
Chapter 2 The perceived value of inventor status
Chapter 3 Do technology licensing officers favor
particular inventors for spinoffs?
Research Question How do resource slack and resource
constraints influence decision making of entrepreneurs in new technology
ventures?
Method Case studies of three new technology
ventures, drawing on event-based process analysis
Findings Chapter 4 Resource slack and constraints can be
seen as two extremes of the spectrum of attainable resource positions.
Resource positions emerge as the entrepreneur’s perception of available
resource relative to demand in transient imaginations where different types of
resource constraints and slack are perceived simultaneously.
These perceived resource positions influence decision-making processes in
terms of individual, temporal, and resource position dynamics and help
entrepreneurs in generating idiosyncratic options with varying degrees of creativity.
Research Question How do inventor characteristics influence technology licensing officers’ evaluation
and selection of new technological inventions?
Method Randomized experiments with technology licensing officers
Findings Chapter 2
Technology licensing officers perceive inventions by high status inventors
(department chairs and NAS-members) to have more commercial value.
Findings Chapter 3
Technology licensing officers are negatively disposed to inventions by
female inventors with regard to spinoff creation and positively disposed to those of Chinese-named Asian inventors with
industry experience, who are easy to work with.
Technology Commercialization Process
102 CONCLUSIONS
5.1 Findings
5.1.1 Part I
The studies in Part I, reported in Chapter 2 and 3, investigated decision
making in universities; more specifically, decision making of technology
licensing officers. These studies complement existing research that has
primarily focused on technological features to explain why technology
licensing officers select particular inventions for further commercial
development (Colyvas et al., 2002; Sine et al., 2003) by addressing the role of
inventor characteristics. By drawing on randomized experiments with
technology licensing officers, these studies revealed important insights
regarding the influence of inventors on the evaluation and selection of
university inventions for commercialization. In this respect, the findings of
Chapter 2 and 3 enhance our understanding of decision making in
universities with respect to the commercialization of new technologies.
5.1.1.1 Chapter 2 – The perceived value of inventor status
This chapter addressed how inventor status influences the evaluation of new
technologies, by exploring the influence of inventor status on technology
licensing officers’ evaluation of the commercial potential of new inventions.
Previous work on the evaluation of science and technology has shown that
when there is uncertainty about the underlying value of new technology,
evaluators will rely on social factors to judge the potential of such work
(Azoulay et al 2012, Stuart et al, 1999). In particular, the status of the
producers of new technology has been found to influence evaluators
perceptions of the underlying quality and value (Merton, 1968; Podolny &
Stuart, 1995; Podolny, 1993, 1994). Yet, studies investigating the effect of
status on these evaluations are faced with various obstacles in trying to
isolate status effects while controlling for quality (Azoulay et al., 2012;
Simcoe & Waguespack, 2011). The key obstacle is that status is often
associated with unobserved quality in ways that are impossible to control;
not only is it difficult to accurately measure quality for uncertain
CHAPTER 5 103
technological developments, but status, in turn, may provide resources
which likely contribute to the actual quality of the producer’s work (Azoulay
et al., 2012). Since observational study designs will not be able to overcome
these problems (Simcoe & Waguespack, 2011), an experimental design was
required. Given the need for university technology licensing officers to
evaluate the commercial value of new technologies, despite considerable
uncertainty about their true value, this setting lends itself to study the effect
of inventor status on perceptions of the value of technology. To assess the
true causal effect of status on the evaluation of the value of uncertain new
technology, the study in Chapter 2 built on two randomized experiments in
which everything except the inventor’s status was held constant. By
operationalizing inventor status as an inventor holding the position of
department chair and an inventor who is member of the National Academy
of Sciences, the experiments revealed that licensing officers judged
inventions to have greater commercial value and were more likely to
recommend patenting if submitted by a high status inventor. The results
indicated that licensing officers are likely to rely on inventor status to resolve
uncertainty about the quality of a university invention (Podolny & Stuart,
1995). On the other hand licensing officers may have been biased in their
evaluation of the work of high status faculty members, which can result in
less careful assessments with less strict criteria (Merton, 1968; Zuckerman
& Merton, 1971).
Overall, the results of Chapter 2 demonstrate how social structure
enters into the decision-making processes of technology licensing officers
(Podolny & Stuart, 1995; Podolny, 1993), implying that future work should
incorporate these sociological processes inherent in the evaluation and
commercialization of university inventions.
5.1.1.2 Chapter 3 – Do technology licensing officers favor particular
inventors for spinoffs?
Why do some university inventions lead to the creation of a new spinoff
company, and others do not? To understand how technology licensing
officers decide on which inventions are suitable for spinoff creation, Chapter
104 CONCLUSIONS
3 built on the insights of Chapter 2 with respect to the role of inventor
characteristics in technology licensing officer decision making. This chapter
explored the influence of various inventor characteristics on technology licensing
officers’ support for spinoff creation. Given the key role of inventors in
commercializing university technology by means of a spinoff company
(Grandi & Grimaldi, 2003; Nicolaou & Birley, 2003; Jensen & Thursby,
2001; Shane & Cable, 2002), technology licensing officers are likely to rely
on inventor characteristics when they evaluate inventions for spinoff
potential (Franklin et al., 2001; Shane, 2004, 2005; Vohora et al., 2004). In
this respect, existing research points to several inventor characteristics as
conducive to spinoff creation:
Gender; female academics are less likely than their male counterparts
to engage in the commercialization of science (Bunker Whittington
& Smith-Doerr, 2005; Ding et al., 2006).
Immigrant status; foreign born researchers are more likely to start
companies than native-born researchers (Krabel et al., 2012).
Industry experience; inventors with ties to investors or business, or
industry experience, are more likely to engage in spinoff activity
(Krabel & Mueller, 2009; Landry et al., 2006).
Ease of working with the inventor; to start a spinoff researchers need to
work with many different actors, including investors, suppliers and
customers (Mustar, 1997; Walter et al., 2006).
To investigate the influence of these particular inventor characteristics on
licensing officers’ recommendation for spinoff creation, Chapter 3 drew on
randomized experiments with 239 technology licensing officers. The
licensing officers were asked to evaluate invention disclosures, in which
characteristics were manipulated, by indicating how much they would try to
dissuade the inventor if the inventor wanted to start a company to
commercialize the invention, and how likely they would recommend a
spinoff that exploited the invention to their university’s internal venture
capital fund. The experimental results indicated that technology licensing
officers are negatively disposed to (disclosures by) female inventors and
positively disposed to (disclosures by) Chinese-named Asian inventors with
industry experience who are easy to work with. These findings highlight the
CHAPTER 5 105
role of inventor characteristics, thereby rebalancing the literature’s focus on
the attributes of the inventions themselves. In addition, the results offer
insight in how technology licensing officers’ preferences may influence who
starts spinoff companies. In this respect, university licensing officers’
preferences may account for some of the underrepresentation of women
among university spinoff founders.
5.1.2 Part II
Part II investigated decision making in new technology ventures, which
plays an important role in technology commercialization. Technology
ventures not only have a disproportionately large impact on society by
producing far more jobs than other entrepreneurial ventures (Seifert, Leleux,
& Tucci, 2008) but by engaging in the commercialization of disruptive
breakthroughs, these ventures are also able to shift the wealth creation curve
(Acs, 2010; Schumpeter, 1934). Yet, these ventures typically also require a
disproportionately large amount of resources to undertake the commercial
development of new technologies and are more likely to fail in the process.
Chapter 4 therefore addressed decision making of entrepreneurs in new
technology ventures, and particularly how decisions on commercializing
new technologies are subject to perceived resource positions.
5.1.2.1 Chapter 4 – Dynamics of slack and constraints: Resource
positions in action
To develop an in-depth understanding of decision making in new ventures
and to shed new light on the ongoing debate about the effects of resources,
Chapter 4 explored how resource positions influence decision making in new
technology ventures. To investigate how resource positions evolve and
influence decision making, this study sought to identify resource positions at
the time of decision making and the processes by which they influenced the
entrepreneurs. Unlike previous research in this area, that has tended to
overlook the role of individuals in organizations (Abell et al., 2008; Felin &
Foss, 2005; Foss, 2011) by mainly drawing on firm-level measures (Daniel et
106 CONCLUSIONS
al., 2004; Mishina et al., 2004), in Chapter 4 we conceptualize resource
positions as the abundance or shortage of resources perceived by the
entrepreneur.
Data was collected on three new technology ventures; interview
transcripts and archival data were used to create a case-specific event list of
important decisions for each venture. In subsequent analysis the decision-
making process inherent in each event was coded according to a decision
trigger, decision outcome as well as the resource position, as perceived by the
entrepreneurs at the time of decision making. This yielded six resource
positions at the time of a decision; three types of constraints: financial,
capacity, and capability, mirrored by similar types of resource slack. Further
in-depth analyses resulted in important findings on the nature of resource
positions, their dynamics and relation to decision making.
Perceived resource positions reflect the entrepreneur’s imagination of
available resources relative to demand including anticipated resources or
resource demands. Because these resource positions are transient
imaginations, entrepreneurs move along the constraint–slack spectrum over
time. Moreover, entrepreneurs perceive different types of constraints and
slack simultaneously, making resource positions multidimensional
constructs.
Chapter 4 showed that perceived, anticipated and relative resource
positions influence (creative) decision making, but not systematically.
Constraints and slack do not have univocal effects, but lead to idiosyncratic
decisions by entrepreneurs influenced by underlying dynamics. The findings
demonstrated that the processes by which resource positions influence
decision making depend on individual, temporal, and resource position
dynamics. Individual-level dynamics relate to how different entrepreneurs,
even those working in the same venture, may perceive resource availability
relative to demand in distinct ways; individual-level dynamics also relate to
how interactions between entrepreneurs affect decisions. Temporal dynamics
imply that the influence of resource positions on decision making is not
consistent over time, as past experiences can influence the decision-making
process and hence the relationship between resource positions and decision
outcome. Finally, resource position dynamics pertain to how combinations of
CHAPTER 5 107
different types of resource constraints and/or slack enter the decision-
making process and lead to unique outcomes.
5.2 Implications
5.2.1 Theoretical implications
This dissertation contributes to the literature in several ways. The findings in
Chapter 2 and 3 have shown how inventor characteristics influence the
decision-making processes of technology licensing officers. In doing so, the
findings contribute to the body of research on university technology
commercialization, which thus far has tended to abstract from examining
the role of the technology licensing office in the process (Jensen et al., 2003).
By making the evaluation and selection decision of technology licensing
officers explicit, the studies in this dissertation help to understand the
decisions of technology licensing officers by providing insight into how their
perceptions and decisions influence the process and outcome of technology
commercialization.
The findings arising from Chapter 2 and 3 also rebalance the existing
literature’s focus on technological attributes (Colyvas et al., 2002; Merges &
Nelson, 1990; Shane, 2004; Sine et al., 2003) by showing how sociological
factors affect the evaluation and selection of new technologies (Podolny &
Stuart, 1995). In doing so, these studies exposed technology licensing officer
preferences (or biases) regarding particular types of inventors, that may help
scholars better understand and explain the under- or overrepresentation of
certain types of inventors in the population of scientists commercializing
technology. Future research will need to address whether the licensing
officer preferences identified in Chapter 2 and 3 are in fact well-grounded
selection criteria, or alternatively, biases that are better avoided or
suppressed; future work in this area can answer this fundamental question
by relating such patterns to technology commercialization success.
In addition, Chapter 2 contributes to the literature on status. By
demonstrating how experiments can serve to isolate status effects while
108 CONCLUSIONS
controlling for quality, this study is one of the first to uncover the true
influence of status on the evaluation of new technologies, opening up ways
to investigate the effect of status beyond observational studies.
The findings in Chapter 4 indicate that the effects of resource
constraints and slack are not univocal, but subject to underlying dynamics,
shedding new light on the debate on the effects of resource positions. The
results show how entrepreneurial decision making is influenced by
perceived resource positions. In doing so, these results contribute to theory
on creativity, innovation and the radical subjectivist strand of Austrian
economics. They extend research on collective creativity and perspective
taking by showing how interactions between entrepreneurs, perceiving
distinct resource positions, can affect the production and implementation of
creative ideas (Ford & Gioia, 1995; Hargadon & Bechky, 2006; Kurtzberg &
Amabile, 2001; McMullen, 2010; Sawyer, 2008; Woodman et al., 1993). The
findings also extend current insights on Austrian economics (Chiles, Tuggle,
et al., 2010; Chiles, Vultee, et al., 2010) by empirically demonstrating how
subjective perceptions of resource positions enter the decision-making
process, in which entrepreneurs generate idiosyncratic options with varying
degrees of creativity. Researchers must attend to the heterogeneity among
individuals making decisions to better understand firm-level outcomes
(Felin & Foss, 2005; Foss, 2011), particularly with regard to their subjective
perceptions of resource positions (Foss et al., 2008).
In addition, Chapter 4 offered several implications on the
conceptualization, measurement and interpretation of resource slack and
constraints. First, since perceived resource positions reflect entrepreneurs’
imagination of available resources relative to demand, firm-level measures
cannot address the heterogeneously perceived value of available resources in
relation to imagined action scenarios. Second, because resource positions
are transient imaginations, they should not be investigated using cross-
sectional research designs. And third, as resource positions are
multidimensional constructs, resource slack and constraints should never be
studied in isolation. In this respect, by viewing resource slack and resource
constraints as two extremes on a spectrum of resource positions, the study in
CHAPTER 5 109
Chapter 4 constitutes an important step toward integrating the resource
slack and resource constraints literature.
The findings presented in this dissertation also contribute to the
broader entrepreneurship literature. According to Shane and Venkataraman
(2000), the field of entrepreneurship research can be seen as the scholarly
examination of how, by whom, and with what effects opportunities (to create
future goods and services) are discovered, evaluated, and exploited. Figure
5.2 maps this dissertation’s findings in terms of the entrepreneurship
process, starting with opportunity and followed by evaluation and
exploitation.
The main contributions of this dissertation are in terms of the
evaluation and exploitation of new technology, at the right-hand side of the
entrepreneurship process. The findings of Part I on the selection of new
technological inventions show that the external evaluation of such
opportunities, by technology licensing officers, is subject to inventor
characteristics. These findings thus enhance our understanding of the
evaluation phase of the entrepreneurship process. The findings of Part II, on
the commercialization of technology by new technology ventures, show that
decision making on the exploitation of new technology is subject to the
perception of resources. As such, these findings provide valuable insights
with respect to the exploitation phase of the entrepreneurial process.
At the left-hand side, Figure 5.2 is adapted from Shane and
Venkataraman (2000) to also include alternative – but not necessarily
mutually exclusive – views on where entrepreneurial opportunities may
originate from. Entrepreneurial opportunities may be regarded as the
discovery of objective opportunities, caused by exogenous shocks, to create
new products or services (Shane & Venkataraman, 2000); as the
endogenous creation of opportunities by the actions of individuals exploring
ways to produce new products or services (Alvarez & Barney, 2007); and
opportunities may take shape as imagined future means-ends relationships
(Klein, 2008). The studies presented in this dissertation do not make explicit
assumptions about the nature of opportunities, nor do they make explicit
contributions to the part of entrepreneurship research that studies
opportunities. However, the studies in this dissertation can be classified
110 CONCLUSIONS
according to their compatibility with the existing views on opportunities and
the studies contribute to each of the perspectives. In this respect, the studies
in Part I take place in a setting that starts with a new technological invention
from which opportunities may originate. Therefore the studies in Part I are
compatible with a discovery view of entrepreneurial opportunities. In this
respect, Part I helps to understand the processes of evaluating technological
inventions as entrepreneurial opportunities. The study in Part II, is
compatible with all three views on entrepreneurial opportunity and explains
how entrepreneurial imagination influences the exploitation of
entrepreneurial opportunities.
Figure 5.2: Dissertation findings in context of entrepreneurship research
Opportunity
Evaluation
Exploitation Discovery
(Shane & Venkat., 2000)
Creation
(Alvarez & Barney, 2007)
Imagination
(Klein, 2008)
Part I & II Part II Part II
Part I Selection
Part II Commercialization
5.2.2 Practical implications
The findings in this dissertation also have practical implications for
policymakers and various stakeholders involved in technology
commercialization. While the following implications may also be of
importance to various other stakeholders involved in technology
commercialization, such as large technology-driven corporations or
government agencies, the practical implications discussed in this section are
limited to the stakeholders researched in this dissertation.
The studies in Part I point out how the evaluation of new inventions is
subject to sociological factors, in support of anecdotal evidence in this area.
Entrepreneurship process (Shane & Venkataraman, 2000)
CHAPTER 5 111
In doing so, the findings shed light on the mechanisms technology licensing
officers use to value commercializable research (Markman 2008) and how
this affects their selection decision and intention to allocate resources. These
findings have practical implications for technology licensing officers,
university policy makers and for researchers aiming to commercialize their
inventions.
First, technology licensing offices should take notice of the patterns
found in the studies in Part I to (re)consider the (implicit) methods used to
assess the commercial potential of university inventions. The findings in
Chapter 2 show that technology licensing officers have a preference for
inventions submitted by department chairs and very prominent professors,
and Chapter 3 indicates a preference for male inventors, immigrant
scientists, faculty members with industry experience and inventors who are
easy to work with. If technology licensing officers are not aware of their
preferences for certain inventors, this can be detrimental to the process of
technology commercialization. As many university inventions are believed to
be of questionable value (Jensen et al., 2003) and require substantial
investments in efforts to commercialize them (Roberts & Malone, 1996;
Siegel, Waldman, Atwater, & Link, 2004; Siegel, Waldman, & Link, 2003;
Thursby & Kemp, 2002), the contribution and justification of selection
criteria should not be overlooked.
Depending on the value judgments of university policymakers,
universities may choose to encourage or discourage the technology licensing
officer preferences found in Part I. For example, the findings in Chapter 3
show inventors with industry experience are more likely to receive support
from their technology licensing office for spinoff creation. This suggests that
institutions interested in boosting their output of spinoff companies should
hire faculty members with industry experience or motivate faculty with no
industry experience to incorporate an experienced (e.g. sequential)
entrepreneur in the spinoff creation team.
Correspondingly, university faculty, students and staff wishing to
commercialize their inventions may use the findings in Part I to increase the
odds of getting their research commercialized. Going back to the example of
the study in Chapter 3, if researchers with industry experience are more
112 CONCLUSIONS
likely to be supported in spinoff creation, then inventors aspiring to start a
spinoff can influence their chances of success by interacting with industry.
The findings of Part II offer several suggestions for technology
entrepreneurs, as well as other stakeholders (such as investors) involved in
ventures commercializing new technology. Chapter 4 showed how
entrepreneurs might grow accustomed to a routine for dealing with
problems during the commercialization of new technologies and that this
can inhibit the timely production of solutions when faced with challenges.
Major commitments to technology development trajectories, capital
providers and employees can hinder thinking out of the box, which may
reduce levels of creativity in attempting to overcome problems in the
technology commercialization process. Entrepreneurs and investors should
therefore be aware of the risks of strong commitments to existing paths and
be mindful of alternative paths besides the obvious ideas that were
implemented before.
In this respect, the findings in Chapter 4 point to the importance of
perspective taking when making decisions on the allocation of resources in
technology commercialization. Antagonistic perspectives, for example
regarding the availability and usage of resources, may generate unproductive
tensions and conflicts; and stakeholders who cannot converge on a shared
perspective may risk disregarding and overlooking their partners’ creative
ideas and solutions.
5.3 Limitations and Directions for Future
Research
While the studies in this dissertation provide valuable insights from both a
practical and theoretical perspective, there are some limitations to the
findings. These limitations also point to areas open for further investigation.
As the study-specific limitations and future research directions have been
discussed in detail in the foregoing chapters, this section will focus on
limitations and directions for future research that cut across the entire
dissertation.
CHAPTER 5 113
In order to understand technology commercialization processes at the
decision-making level, the studies in this dissertation focused on decision
making from the perspective of two key stakeholders: universities
(technology licensing offices) in Part I, and new technology ventures in Part
II. Although these perspectives provide much needed insights to understand
the micro-foundations of technology commercialization outcomes or success
(Felin & Foss, 2005; Foss, 2011), the generalization of findings to other
stakeholders, such as private research institutions, government agencies and
large corporations, may be limited. Future work will need to investigate
decision making processes from the perspective of various other
stakeholders involved in technology commercialization to see whether the
identified patterns are consistent across institutions.
Since the studies in this dissertation have mainly focused on single
stakeholder decision making, the main findings are severely limited from an
inter-stakeholder or network perspective. As technology commercialization
processes typically involve many agents (Garud & Karnøe, 2003; Markman et
al., 2008; Rothaermel et al., 2007), future research needs to address decision
making beyond individual stakeholders. In this respect, scholars may
approach technology commercialization processes from an ecosystem
perspective (Dolmans & Reymen, 2013; Iansiti & Levien, 2004), to gain
understanding of inter-stakeholder dynamics.
The studies in this dissertation have been conducted in The United
States (Part I) and The Netherlands (Part II). For the two studies in Part I
The United States was chosen for two reasons. First, US universities own the
intellectual property rights to university inventions (unlike some other
countries), making technology licensing officers responsible for the
commercial development of university inventions. Second, The United
States has the largest population of active and experienced technology
licensing officers. The study in Part II was conducted in The Netherlands,
as the data collection phase for exploratory case study research requires
frequent face-to-face contact as well as a thorough understanding of the
situational context. While there are no immediate reasons to assume why the
findings presented in this dissertation would not translate to other
geographical areas, cultural differences may limit the generalization of the
114 CONCLUSIONS
results. Future studies, preferably with larger samples, should address
decision making in other geographical areas and cultural contexts.
As suggested in previous sections, this dissertation makes both
theoretical and practical contributions which provide ground for future
research. Chapter 2 and 3 demonstrated how randomized experiments in
natural environments can effectively uncover real-life decision-making
patterns, opening up the way for future investigations of decision making in
technology commercialization beyond observational studies. While
randomized experiments conducted in real-life organizational settings have a
higher probability of being affected by unobservable noise, they are essential
in generating meaningful scientific knowledge as it is virtually impossible to
create fully controlled laboratory conditions that approximate authentic
organizational environments (Romme, 2011; Starbuck, 2004, 2006).
The studies in Chapter 2 and 3 also show the value of research on
sociological processes and decision making in (university) technology
commercialization. Moreover, future work may target the thought processes
and decision-making heuristics of technology licensing officers in ways that
can be more readily implemented by practitioners, for example by
establishing normative principles that can guide decision making by
technology licensing officers.
By revealing how resource positions emerge as the entrepreneur’s
imagination of available resources relative to demand, the findings in
Chapter 4 suggest several areas for future research on resources,
imagination and entrepreneurial decision making. For example, the
dynamics uncovered in Chapter 4 lend themselves for further investigation
by drawing on system dynamics modeling (Sterman, 2000). By validating
and extending the findings in this Chapter with other methods such as
system dynamics modeling, future work can enhance our understanding of
the effects of resources on decision-making in new technology ventures.
While the studies in this dissertation investigated decision making from
various perspectives, future work can benefit from including psychological
perspectives on decision-making processes in technology commercialization.
Such research might, for example, build on existing psychological work on
CHAPTER 5 115
heuristics and biases in decision making under uncertainty (Kahneman,
Slovic, & Tversky, 1982; Tversky & Kahneman, 1981).
5.4 Closing Comments
The studies presented in this dissertation investigated decision making in
technology commercialization from the perspective of two key stakeholders,
universities and new technology ventures. The studies give insight in the
decision making processes of these stakeholders by pointing to specific
factors that guide decision making in universities and new technology
ventures along the stages of the commercialization process. Given that the
commercialization of new technologies is becoming increasingly important
in view of the major challenges our society faces, the findings presented in
this dissertation provide several valuable insights.
The findings in Part I show that the evaluation and selection of new
technologies for commercial development is not merely a process of
selecting among technological features. That is, sociological aspects enter the
decision making process of evaluators and serve as a heuristic in
determining the commercial potential of uncertain early stage technological
inventions. If policymakers, university officials and commercialization
professionals are not aware of such patterns in the evaluation and selection
of new technologies, this can have detrimental consequences – not only for
the success rate of technology commercialization but also for our society at
large. For example, the findings in Chapter 3 point to a potential gender bias
in the selection of technological inventions. Such biases in the early selection
stage of the technology commercialization process may have large
consequences if groundbreaking or lifesaving technologies remain
undeveloped.
The findings of Part II have demonstrated that entrepreneurs
undertaking the commercial development of new technologies are
influenced by their perceptions of available resources. In particular, having
too many resources may create path dependency in the commercialization of
new technologies. Major resource commitments to technology development
116 CONCLUSIONS
trajectories may hinder the flexibility and creativity necessary to overcome
problems in the technology commercialization process. These findings have
important implications for stakeholders looking to improve the success rate
of technology commercialization. For example, startup funds or subsidy
programs should be aware of how entrepreneurs’ perception of resource
availability may stimulate or hinder creative decision making.
In sum, the findings presented in this dissertation enhance our
understanding of decision making under conditions of uncertainty by
shedding light on parts of the technology commercialization process that
have mostly been treated as a black box.
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Appendix I
Example invention disclosure - Inventor gender
treatment
Background
You have been assigned the following 4 invention disclosures which were
submitted to your university’s technology transfer office by faculty inventors.
Your technology transfer office consists of 8 full time equivalent
professionals and receives an average of 160 disclosures per year. Although
each invention is assessed on its own merits, the office seeks patents on
about 50 percent of disclosures per year. The university operates an internal
investment fund that can invest up to $250,000 in start-ups founded by
entrepreneurs who license university inventions. Please read through the
information about the invention and its inventor and then answer the
questions that follow. Because this is a controlled experiment to see how
licensing officers make decisions about technologies, we ask that you not use
any resources other than those we provide you with to make your decisions.
Please be aware of the fact that when continuing to the next invention
disclosure it is not possible to navigate back. If you experience any
difficulties in accessing the experiment or if you have any questions, please
contact Sharon Dolmans at [email protected].
136 APPENDIX I
Invention: Polymer Nanocomposites with
Endodontic/Dental Applications
Professor Ken Anderson/Kim Anderson has been with
the university since 1993, when he/she received his/her
doctorate from the Harvard School of Dental Medicine.
Professor Anderson has authored or co-authored 120
articles in field of endodontics and has authored the
books Introduction to Endodontic Treatments and
Endodontic Microbiology. Currently Professor
Anderson serves as an associate editor of the International Endodontic
Journal, and is a board member of the American Association of
Endodontists, the European Society of Endodontology and the International
Association of Dental Research. Professor Anderson has received 20
research grants for research in the area of endodontic fillers, bacteriology of
endodontic infections and antimicrobial medicaments.
The professor’s invention improves the material used in endodontic surgery.
During root canal treatment – the commonly used name for an endodontic
therapy that removes decayed nerve and pulp from a tooth’s cavity – dentists
remove the microbial ecology in the root canal system through
biomechanical cleaning and shaping, after which the root-end will be
hermetically sealed. Because of the complex anatomy of the root canal
system, this preparation sometimes fails to break the microbial ecosystem,
which can lead to chronic infection and, ultimately, tooth loss. To prevent
such infections, dentists typically undertake a surgical resection of the apical
end of the “infected” root and seal it with a root-end filling (retrofill)
material. Most current retrofill materials, although effective, have several
disadvantages, including shrinkage, technique sensitivity, and moisture
contamination. Additionally, none of the current materials demonstrates any
form of drug-releasing capability.
Inventor
picture omitted
for privacy
reasons
APPENDIX I 137
Recent research has shown that these disadvantages can be overcome by the
use of novel nanocomposites – a new class of polymeric materials – which
both improve sealing ability and offer controlled release of an antimicrobial
drug. Professor Anderson has invented a new nanocomposite-based dental
material with drug-eluting capabilities that can be used in endondontic
surgery. The new material overcomes the disadvantages of current
alternatives. The addition of nano-material enhances the properties of the
polymer matrix, increasing sealing ability, which is important because the
success of endodontic procedures depends (to a large extent) on the ability to
seal the accessory canal. It also has drug-eluting capabilities, which in
combination with antimicrobial drugs can reduce the risk of infection
following surgery. Research conducted by the professor and others has
shown that the rate of drug release can be controlled through the addition of
the nano-material. In addition, drug efficacy studies have demonstrated the
minimum dose strength needed, with 4 μg of eluted drug being found to be
sufficient for an antimicrobial effect. Furthermore, in-vitro data show a
decrease in bacterial leakage from the filling compared to conventional
materials, demonstrating effective sealing capability. These preliminary
studies have demonstrated that the polymer-nanocomposite based material
outperformed currently available polymer- based retrofill materials. The use
of the new polymer-nanocomposite loaded with the antimicrobial drug
chlorhexidine (an already approved antimicrobial drug currently used in oral
rinses to treat gingivitis and in skin cleansers) during endodontic procedures
has been shown to decrease the likelihood of post procedure infection.
The invention could potentially be used for several dental procedures,
including pulp capping for mechanical pulp exposure, subgingival
restorative for fractured roots, repair of root resorption lesions, repair of root
perforations, and retrograde filling material.
The US dental product and material industry, in which this product would
be used, is currently $8.8 billion, with demand estimated to increase at 4.5
percent annually for the next five years. The materials used in endodontic
surgery that this product would replace, account for $2 billion in annual
138 APPENDIX I
sales. Moreover, solid growth in demand for these materials is expected
since growth in the over 55 population will increase the need for these
materials, as older individuals are more likely than other segments of the
population to require repair and restorative dental products, such as crowns,
bridges or dentures.
At present, six companies have been contacted about the invention, three of
which showed interest, and two of which have entered into a CDA.
Appendix II
Interview protocol for semi structured interviews
Interview protocol
Date: Location: Time:
Interviewee: Interviewer:
Introduction
Background of the project
Explaining the case method
Feed-back: quotes will be checked individually, case will be approved by the
organization’s representative.
Personal
Can you tell us about your background and your current position? (for all the
founders/and all ‘important’ employees)
What previous experience do you have (marketing, managing, technical)
Can you tell about the personal background of the other founders and other
‘important’ people in the new venture?
Venturing process
Can you narrate the development of the company from its ultimate start
until now (from “intention to start” to now)?
How did the idea/opportunity of your business come about?
How did you evaluate the idea (market, competition, key elements to
success)?
140 APPENDIX II
What type of planning did you do prior to starting up?
What has been changed since the start of the company? What was the start
situation and what is the current situation?
What have been the most crucial events in the company’s history till now?
What were your ambitions when you started? Have these ambitions changed
over time?
Management/founding team and employees
How is this business organized? (sole proprietorship, partnership,
corporation )
How is the management/founding team composed?
How did the composition evolve over time?
How did the organization of the business develop over time? (all important
changes)
How many employees do you have? How many people does the organization
employ? (expressed in full-time equivalents and absolute numbers).
When did you decide to hire people for your company?
How did you find people to bring into your organization that truly care about
the organization the way you do?
What personal attitudes, characteristics, and skills were necessary for the
success of your enterprise?
How do you motivate your employees?
Product/service and innovation
How did you develop your product or service?
What customer need do you address with your product/service? What value
does it deliver for your client?
What different versions did you have?
What were the important changes made to the product/service?
How did you arrive at the current version(s)?
How did customers shape the offered product/service?
What is the origin of the technology or the opportunity base of the venture
(e.g., university spin-off, corporate spin-off)?
What kind of technology is involved (e.g., biotech, micro-electronics)
APPENDIX II 141
How do you generate new ideas, products and services?
Could you please take me through the reasoning that you make when you
evaluate whether or not to introduce a new or improved process, product or
service?
Which factors do you usually consider?
What kind of criteria do you apply whether or not to introduce a change?
Do you usually consider the time-frame in which the innovation might pay
off?
How do you make the decision to launch it? Can you walk me through the
process?
How much do you approximately spend on new product/technology/service
development?
How was this in the past?
How many people within your organization work (exclusively) on developing
new products services or processes?
How was this in the past?
Clients
Who are your clients/customers? Are they local or international?
How did you get your first client(s)?
How did you scale up to attract more clients? (how many clients do you have
/ what’s your market share?)
What ‘channels’/means do you use to attract clients?
How are your prices established?
Revenues
Where does the company get revenue from? (what revenue sources)
Where is the revenue used for (salaries, investments in research,
development, marketing, etc.)?
How did the turnover and/or revenues develop over time? Can you provide
the numbers? (annual reports, balance sheets?)
How long did it take to reach a positive cash flow position (break-even point)
How long did it take for your company to show a profit?
142 APPENDIX II
Investments
What did you personally invest in the enterprise (financial, social, time)?
Did you consider investing more in the enterprise yourself? (if not, why not?)
How did you obtain the necessary funds to start up your enterprise?
Public funding (subsidies, public pre/seed funds), business angels, venture
capitalists, other investors.
How much capital was invested in your enterprise? (at what moments?)
How did you convince investors? How did the financing come about?
What returns/paybacks are asked for the funding (in-kind, equity)?
How do you evaluate these contract terms?
Where there terms under which this support was provided (both financial
and non-financial)?
Competition and industry
Who are your competitors? How strong is competition (and on what aspects,
e.g., price, quality, service)?
What are the main differences between your company and the direct
competitors?
How did the competition evolve over time? (all important changes; growing,
declining, stable)
How can you characterize the market you are operating in? (stable, highly
dynamic)
What is the industry/field the venture is operating in (e.g., biotech,
chemistry, information tech.)?
Did you experience important changes in your business environment?
How did you deal with the changes in your environment?
Cooperation
With whom did you cooperate to startup your company?
Are these typically for-profit, non-profit, government or other
organizations/people?
Are the organizations you cooperate with typical from your region, your
country, or are these international organizations?
And what is your relation with them, i.e. are they your clients, suppliers,
APPENDIX II 143
consultants, competitors, i.e. other organizations in your field?
Also how would you describe the nature of the relationship?
What are the main reasons for experiencing problems, if any, when
collaborating with these actors?
What type of outside help were you able to obtain? (Lawyers, accountants, tax
experts, patent and trademark specialists, etc.) (international?)
What were the terms under which this support was provided?
Can you explain how these experts were able to help you in your enterprise?
What was your personal network while you started, which you used for your
company? (for all the founders and key people)
Can you tell about the personal network of the other founders and other key
people in the new venture?
How did the network around your company evolve over time? (all important
changes)
How did you build this network, using your personal contacts or other
contacts?
How have others helped to build your network? (e.g., network around
venture, reputation effect)
What is the value of your network for your company?
Can you provide an example in which network relationships clearly
contributed to the development of the venture?
Can you provide an example in which an appropriate network / the right
contacts were lacking?
Supply
What suppliers do you have? How many? (international?)
How important are these suppliers for you?
How have you organized the supply process?
How did the supplier relations develop over time? (all important changes)
How did you choose the suppliers?
How did you do the negotiations with the suppliers? (did you cooperate, did
they do ‘in-kind’ investments or postponed payments?)
144 APPENDIX II
Intellectual property (if applicable)
Who owns the Intellectual Property?
In case of spin-off: How is the cooperation with technology owner
(corporate/university) at the moment?
What was the procedure with regard to intellectual property transfer to your
venture?
What agreement do you have with regard to licensing/the patent? What are
the terms of the agreement?
What is the procedure/policy with regard to the distribution of shares and
potential revenues of the Intellectual Property (e.g., between university and
spin-off)?
What is the effect of the distribution of shares and revenues on you?
(motivation?)
Can you sketch the negotiation process you had about the intellectual
property?
Who did the negotiations? (is there an external party involved?)
Is there a (standardized) procedure? (decision tree?)
What did you expect before the negotiations started?
Was there enough correct information?
Was the process clear at the beginning?
Did frictions/tensions occur? What tensions? (potential tensions: multiple
roles, valuation of the invention, etc...)
Did you perceive mutual trust?
How do you evaluate the outcome of the negotiations?
How do you maintain your Intellectual Property (coverage of the patents,
infringement)?
How did the IP (positions) develop over time during the venturing process?
Location and facilities
How did you decide on your location?
Has the location of the venture changed over time? If so how, and with what
impact?
What is the influence of the direct geographical context on your venture?
What kind of facilities do you have (and did you gather over the process of
APPENDIX II 145
starting)? (e.g., office space, cleanrooms and labs, tools, instruments,
computers)
Who has provided these facilities? (especially important for spin-offs)
What has been the policy of the facility-owner with regard to providing these
facilities and paying them?
What has been the result of providing these facilities? How does this
contribute to the venture’s development?
Future and sustainability
How do you protect the sustainability of the company?
What do you see as the strengths of your enterprise?
What have been and what are the main threats for the company?
What would you do differently if you were to start all over again?
What do you expect with regard to the development of the company:
In two years
In ten years
What important decisions have to be taken in the near future?
Concluding
What aspects have not been mentioned yet, that are important to the
venture’s development?
Documents available (balance sheets, annual reports, memo's, reports,
brochures, etc.)?
Other interviewees we should talk with?
Summary
Organizations in the private and public sectors are increasingly engaging in
efforts to commercialize their unexploited technological inventions.
Technology commercialization involves transforming new technologies
into economic output; it not only drives economic growth and development,
but also creates societal value by providing technological advances that
improve standards of living. While these potentially beneficial outcomes
have captured the attention of practitioners, policymakers and scholars,
technology commercialization remains a challenging process. This is
because the commercial potential, possible applications and target markets
of early stage technological inventions are largely unknown at the start of the
process. It may take years before a new technology is improved to the extent
that it constitutes a viable product, after which it has to be successfully
introduced to the market before generating economic returns. As a result,
many new technologies fail to live up to their commercial potential.
Since the available options and consequences of commercializing new
technologies are largely unknown, the decision making processes in
technology commercialization are beyond systematic calculation. The
selection and commercial development of new technologies under
uncertainty implies that the outcome of the process depends on the
stakeholders who make decisions and allocate resources under these
conditions of uncertainty. Given the challenges and high failure rates, key
stakeholders such as universities, government agencies and new technology
ventures are still in search of ways to improve the process of technology
commercialization.
To increase the success rate of technology commercialization processes,
this doctoral dissertation describes how various stakeholders make decisions in
technology commercialization. Since technology commercialization involves
both the selection of promising new technologies as well as the subsequent
commercial development of such technologies, the body of this dissertation
is structured along these activities. In particular, this dissertation focuses on
148 SUMMARY
two key stakeholders, universities (selection of new technologies) and new
technology ventures (commercial development), by addressing: (I) decision
making in universities in Chapter 2 and 3 and (II) decision making in new
technology ventures in Chapter 4.
The studies presented in Chapter 2 and 3 focus on decision making in
universities. Central in these chapters is the decision making of technology
licensing officers. Since the intellectual property rights to scientific
inventions belong to the universities where these technologies were
developed, technology licensing officers manage the technology
commercialization processes within universities. That is, the decision to
invest in the commercial development of a new technology depends on the
licensing officers’ evaluation of the commercial potential of the invention.
Existing research has primarily focused on invention characteristics or
technological attributes to explain why technology licensing officers select
particular inventions for further commercial development. Yet, anecdotal
data suggest that licensing officers are also sensitive to inventor
characteristics when they consider the commercialization of university
inventions. In particular, most university inventions are in such an early
stage of development that no one actually knows their true commercial
potential, making an evaluation purely based on technological attributes a
difficult task. Therefore, Chapter 2 and 3 explore whether various inventor
characteristics (e.g., gender, status, etc.) influence technology licensing
officers’ evaluations of new inventions and their decision making with
respect to patenting, commercial potential and spinoff creation. These
chapters draw on a series of randomized experiments with US technology
licensing officers, where each study builds on different treatments and
measures. In these experiments, technology licensing officers were invited to
evaluate real life university invention disclosures, in which selected inventor
characteristics were manipulated.
Chapter 2 explores the influence of inventor status on technology licensing
officers’ evaluation of the commercial potential of new inventions. Research in
sociology on the evaluation of science and technology shows that evaluators
are influenced by the status of the actors associated with new work;
particularly in situations where there is uncertainty about the quality of an
SUMMARY 149
invention. Yet, studies investigating the effect of status on these evaluations
are faced with various obstacles in trying to isolate status effects while
controlling for quality. To overcome these obstacles and assess the true
causal effect of status on the evaluation of the value of uncertain new
technology, the study presented in Chapter 2 builds on two randomized
experiments in which everything except the inventor’s status is held
constant. Inventor status is operationalized as an inventor holding the
position of department chair and an inventor who is member of the National
Academy of Sciences. The experiments reveal that licensing officers judge
inventions to have greater commercial potential and are more likely to
recommend patenting if the invention is submitted by a high status
inventor.
The results suggest that licensing officers are likely to rely on inventor
status to resolve uncertainty about the quality of a university invention. On
the other hand, licensing officers may have been biased in their evaluation of
the work of high status faculty members, which can result in less careful
assessments with less strict criteria. Nonetheless, by demonstrating how
experiments can serve to isolate status effects while controlling for quality,
this study is one of the first to uncover the true influence of status on the
evaluation of new technologies, opening up ways to investigate the effects of
status beyond observational studies. Moreover, the findings demonstrate
how social structure enters into the decision-making processes of technology
licensing officers. In this respect, future work should incorporate these
sociological processes inherent in the evaluation and commercialization of
university inventions.
Chapter 3 explores the influence of various inventor characteristics on
technology licensing officers’ support for spinoff creation. This study builds on the
insights of Chapter 2 with respect to the role of inventor characteristics in
technology licensing officer decision making. Given the key role of inventors
in commercializing university technology by means of a spinoff company,
technology licensing officers are likely to rely on inventor characteristics
when they evaluate inventions for spinoff potential. In this respect, existing
research points to several inventor characteristics as conducive to spinoff
creation:
150 SUMMARY
Gender; female academics are less likely than their male counterparts
to engage in the commercialization of science.
Immigrant status; foreign-born researchers are more likely to start
companies than native-born researchers.
Industry experience; inventors with ties to investors or business, or
industry experience, are more likely to engage in spinoff activity.
Ease of working with the inventor; to start a spinoff, researchers need to
work with many different actors, including investors, suppliers and
customers.
To investigate the influence of these particular inventor characteristics on
licensing officers’ recommendation for spinoff creation, Chapter 3 draws on
randomized experiments, in which the above characteristics were
manipulated. Technology licensing officers were asked to evaluate invention
disclosures by indicating how much they would try to dissuade the inventor
if the inventor wanted to start a company to commercialize the invention,
and how likely they would recommend a spinoff that exploited the invention
to their university’s internal venture capital fund.
The results indicate that technology licensing officers are negatively
disposed to (disclosures by) female inventors and positively disposed to
(disclosures by) Chinese-named Asian inventors with industry experience
who are easy to work with. These findings highlight the role of inventor
characteristics in technology licensing officer decision making – thereby
rebalancing the literature’s focus on the attributes of the inventions
themselves. The results offer insight in how technology licensing officers’
preferences concerning inventor characteristics influences who starts spinoff
companies. By exposing these preferences (or biases) regarding particular
types of inventors, Chapter 3 may help scholars better understand and
explain the under- or overrepresentation of certain types of inventors in the
population of scientists commercializing technology. In this respect,
university licensing officers’ preferences may account, for example, for some
of the underrepresentation of women among university spinoff founders.
Chapter 4 addresses decision making in new technology ventures. A
common mode of commercial development is exploiting technological
inventions by means of a new technology venture. Resources such as
SUMMARY 151
financial means, technological capabilities, or production facilities are
essential in the development of any new business venture but even more so
in the development process of new technology-driven ventures. Yet, the
influence of resources on the decision-making process of entrepreneurs in
ventures commercializing new technology is not well understood. Chapter 4
draws on in-depth case studies of three new technology ventures to explore
how resource positions influence decision making in new technology ventures. This
study reveals important findings on the nature of resource positions, their
dynamics and relation to decision making. Perceived resource positions
reflect the entrepreneur’s imagination of available resources relative to
demand, including anticipated resources or resource demands. Because
these resource positions are transient imaginations, entrepreneurs move
along the constraint–slack spectrum over time. Moreover, entrepreneurs
perceive different types of constraints and slack simultaneously (for example,
financial constraints and slack capabilities), making resource positions
multidimensional constructs. The data shows that perceived, anticipated and
relative e resource positions influence (creative) decision making, but not in
a systematic way. Resource constraints and slack do not have univocal
effects, but lead to idiosyncratic decisions by entrepreneurs influenced by
underlying dynamics. The processes by which resource positions influence
decision making depend on individual, temporal, and resource position
dynamics. These findings contribute to Austrian perspectives on
entrepreneurship by empirically demonstrating how subjective perceptions
of resource positions enter the decision-making process and influence the
entrepreneur in generating idiosyncratic options with varying degrees of
creativity.
Chapter 4 offers several implications on the conceptualization,
measurement and interpretation of resource slack and constraints. First,
since perceived resource positions reflect entrepreneurs’ imagination of
available resources relative to demand, firm-level measures cannot address
the heterogeneously perceived value of available resources in relation to
imagined action scenarios. Second, because resource positions are transient
imaginations, scholar should be careful with using cross-sectional research
designs. And third, as resource positions are multidimensional constructs,
152 SUMMARY
resource slack and constraints should never be studied in isolation. In this
respect, by viewing resource slack and resource constraints as two extremes
on a spectrum of resource positions, the study in Chapter 4 constitutes an
important step toward integrating the resource slack and resource
constraints literature. The results show how entrepreneurial decision
making is influenced by perceived resource positions.
Overall, the studies in this dissertation provide insight in how various
stakeholders make decisions in technology commercialization, particularly
regarding the selection and commercial development of new technologies.
The studies point to specific factors that guide decision making in
universities and new technology ventures along the stages of the
commercialization process. In addition, this dissertation also offers valuable
insights for the many stakeholders searching for ways to improve the process
of technology commercialization. The findings show that the evaluation and
selection of new technologies for commercial development is not merely a
process of selecting among technological features. That is, sociological
aspects enter the decision making process of evaluators and serve as a
heuristic in determining the commercial potential of uncertain early stage
technological inventions. Furthermore, entrepreneurs or managers
undertaking the commercial development of new technologies are
influenced by perceptions of resources in their decision making, in ways
beyond what previous research has been able to demonstrate. As such, the
findings in this dissertation enhance our understanding of decision making
under conditions of uncertainty by shedding light on parts of the technology
commercialization process that have mostly been treated as a black box.
ABOUT THE AUTHOR 153
Curriculum Vitae
Sharon Dolmans was born in Geleen, the Netherlands, on April 20, 1985.
After earning a bachelor degree (with distinction) in Business Studies and a
master degree in Investment Analysis at Tilburg University, she pursued a
premaster in Econometrics at Erasmus University Rotterdam. In 2010 she
started her PhD project at Eindhoven University of Technology of which the
results are presented in this dissertation. Her work has been published in,
among others, Organization Studies (forthcoming), Frontiers of
Entrepreneurship Research and the International Review of
Entrepreneurship. From December 2013 onwards, she is working as
assistant professor at Eindhoven University of Technology in the field of
Technology Entrepreneurship and Technology Commercialization.