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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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https://doi.org/10.6100/IR760960

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https://research.tue.nl/en/publications/the-people-behind-the-technology(60d0dc19-98ff-446d-bfff-58a3acd28cf2).html

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


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.


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


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%)


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.


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.


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


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


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


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.


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


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


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


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



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.


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



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.


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,


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).


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).


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;


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


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


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.


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.


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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)


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)


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)


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


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


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


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



(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


0

(a) Cash (€)

0


0

1


- 70000

36000

CHAPTER 4 81


(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


5000

0

(a) Cash (€)

0

5000


0

18


2001

event #

CONSTRAINT

SLACK


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-

CHAPTER 4 83

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.


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.


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:


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:

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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.…


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


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

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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


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


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


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].

mailto:[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.