Entrepreneurial risk-taking beyond bounded rationality :
risk factors, cognitive biases and strategies of new
technology ventures
Podoynitsyna, K.S.
DOI:
10.6100/IR635533
Published: 01/01/2008
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Podoynitsyna, K. S. (2008). Entrepreneurial risk-taking beyond bounded rationality : risk factors, cognitive biases
and strategies of new technology ventures Eindhoven: Technische Universiteit Eindhoven DOI:
10.6100/IR635533
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Entrepreneurial risk-taking
beyond bounded rationality:
Risk factors, cognitive biases and strategies of
new technology ventures
Ksenia Podoynitsyna
CIP-DATA LIBRARY TECHNISCHE UNIVERSITEIT EINDHOVEN
Podoynitsyna, Ksenia Sergeyevna
Entrepreneurial risk-taking beyond bounded rationality: risk factors, cognitive biases and
strategies of new technology ventures / by Ksenia Sergeyevna Podoynitsyna. - Eindhoven :
Technische Universiteit Eindhoven, 2008. – Proefschrift. ISBN 978-90-386-1280-5
NUR 801
Keywords: Entrepreneurship / New technology ventures / Success factors / Cognitive biases /
Risk and uncertainty management strategies / Performance
Entrepreneurial risk-taking beyond bounded rationality:
Risk factors, cognitive biases and strategies of
new technology ventures
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 woensdag 11 juni 2008 om 16.00 uur
door
Ksenia Sergeyevna Podoynitsyna
geboren te Moskou, Rusland
Dit proefschrift is goedgekeurd door de promotoren:
prof.dr.ir. M.C.D.P. Weggeman
en
prof.dr. X.M. Song
Copromotor:
dr. J.D. van der Bij
i
Table of contents
ACKNOWLEDGMENTS
V
CHAPTER 1
11
GENERAL INTRODUCTION
1.1 META-ANALYSIS OF SUCCESS FACTORS
12
1.2 THE MECHANISM OF ENTREPRENEURIAL RISK-TAKING
14
1.3 RISK AND UNCERTAINTY MANAGEMENT STRATEGIES
16
CHAPTER 2
19
META-ANALYSIS OF SUCCESS FACTORS
2.1 INTRODUCTION
20
2.2 DATA COLLECTION AND METHODOLOGY
21
2.2.1 Selection of studies as input for the analysis
22
2.2.2 Protocol for meta-analysis
23
2.3 ANALYSIS AND RESULTS
26
2.3.1 Success factors of technology ventures
26
2.3.2 Moderators
31
2.4 IDENTIFICATION OF HIGH-QUALITY MEASUREMENT SCALES
33
2.5 DISCUSSION AND FUTURE RESEARCH DIRECTIONS
34
2.5.1 Market and opportunity
36
2.5.2 Entrepreneurial team
37
2.5.3 Resources
38
2.6 LIMITATIONS
39
ii
CHAPTER 3
THE MECHANISM OF ENTREPRENEURIAL RISK-TAKING
41
3.1 INTRODUCTION
42
3.2 THEORETICAL BACKGROUND
44
3.2.1 Dual process theory: Definitions and theoretical foundation
44
3.2.2 Heuristics and biases stream of research
45
3.3 CONCEPTUAL MODEL AND HYPOTHESES
46
3.3.1 Relationship between biases and risk-taking propensity
47
3.3.2 Relationship between the two systems and risk-taking propensity
52
3.3.3 Relationship between the two systems and biases
54
3.4 METHODOLOGY
55
3.4.1 Sample and data collection
55
3.4.2 Measurements
56
3.4.3 Analysis
60
3.5.
RESULTS
63
3.6.
DISCUSSION
66
3.6.1 Major research findings and theoretical implications
66
3.6.2 Managerial implications
69
3.6.3 Limitations
70
CHAPTER 4
73
RISK AND UNCERTAINTY MANAGEMENT STRATEGIES
4.1 INTRODUCTION
74
4.2 THEORETICAL FRAMEWORK
77
4.2.1 Traditional risk management strategies
77
4.2.2 Real options strategy
80
4.2.3 Performance consequences of risk management strategies
82
4.2.4 Moderator: Technology standards
85
4.2.5 Moderator: Network externalities
88
4.3 METHODOLOGY
90
4.3.1 Sample and data collection
90
4.3.2 Measurements
91
4.3.3 Analysis
92
iii
4.4 RESULTS
95
4.5 DISCUSSION
97
4.6 CONCLUSION
103
CHAPTER 5
106
GENERAL DISCUSSION
5.1 DISCUSSION OF CHAPTER 2: THE META-ANALYSIS OF SUCCESS FACTORS
107
5.1.1 Conclusions
107
5.1.2 Future directions of research
108
5.2 DISCUSSION OF CHAPTER 3: THE MECHANISM OF ENTREPRENEURIAL RISK-TAKING111
5.2.1 Conclusions
111
5.2.2 Future directions of research
112
5.3 DISCUSSION OF CHAPTER 4: RISK AND UNCERTAINTY MANAGEMENT STRATEGIES 114
5.3.1 Conclusions
114
5.3.2 Future directions of research
114
5.4 FINAL REMARKS
116
REFERENCES
117
APPENDIX A: MEASURES
129
A2.1.
129
SCALES OF THE MOST IMPORTANT META-FACTORS FROM CHAPTER 2
A3.1. CONSTRUCTS, MEASUREMENT ITEMS, AND CONSTRUCT RELIABILITIES FOR
CHAPTER 3
131
A4.1. CONSTRUCTS, MEASUREMENT ITEMS, AND CONSTRUCT RELIABILITIES FOR
CHAPTER 4
136
APPENDIX B: ADDITIONAL TABLES
138
B2.1.
METHODOLOGICAL CHARACTERISTICS OF THE ARTICLES INCLUDED IN THE META-
ANALYSIS
138
B2.2.
PUBLICATION SOURCES OF THE STUDIES INCLUDED IN THIS META-ANALYSIS
141
B3.1.
LISREL RESULTS FOR THE SYSTEMS-BIASES-RISK-TAKING MEDIATION
(STANDARDIZED SOLUTION)
142
iv
APPENDIX C: FORMULAS
143
C2.1.
143
FORMULAS FOR VARIANCES CALCULATIONS
SHORT SUMMARY
145
ABOUT THE AUTHOR
149
ECIS DISSERTATION SERIES
151
v
Acknowledgments
The author would like to thank the alphabet for the letters it kindly provided.
This year is very special for me since I happen to experience a double birth: that of my
daughter and that of this thesis. Both of them can be seen as long-term projects characterized
by high risk and uncertainty. Despite some similarities, one of the most important lessons I
learned during the PhD is to never treat your work as your own child – otherwise you can
never improve on it.
I owe this and many other lessons to my supervisors. There have been a total of four
of them in different phases of my PhD and it has been an honor of working with them all. My
first first promoter, Joop Halman; I am looking back with great pleasure at the beginning of
my PhD. You sparked my interest in science and I am grateful for your enthusiasm and
insightful comments. My second first promoter, Mathieu Weggeman; thank you for
reminding me of the importance of the practitioners view on scientific research. Your
feedback allowed me to take an "outside view" on my work.
My second promoter, Michael Song; thank you for giving me the freedom to choose
the research paths I was interested in and for making sure that they were scientifically sound.
Each discussion of our papers is a challenge I immensely enjoy – they are always
unpredictable and stimulating. My daily supervisor, Hans van der Bij; I am truly thankful for
your tremendous support and for sharing your knowledge with me. You helped me dare to
make my own decisions, not the decisions that ought to be mine. Thank you for being always
open for the crazy ideas I could come with and even accepting them so now and then.
vi
I am grateful to Aard Groen, Joop Halman, Rob Verbakel, Leo Verhoef en Mathieu
Weggeman for helping me find "de proefkonijnen" for my case-studies and pre-tests of the
two surveys. I am similarly indebted to the nearly 30 entrepreneurs who agreed to share their
inspiring stories and answered my numerous questions.
My gratitude to the PhD commission for this dissertation; Anthony Di Benedetto,
Geert Duijsters and Mark Parry, thank you for evaluating this thesis and for your
understanding when I had to move its defense date due to the problems in my pregnancy.
I owe a lot of warm memories to the company of our PhD students: Ad, Bonnie,
Deborah, Elise, Jeroen, Maurice, Michael, Michiel, Mirjam, Rebekka, Stephan and Vareska. I
am thankful to the rest of our OSM colleagues who both helped me improve my Dutch during
the lunch hours and helped me out scientifically whenever I needed their advice.
The secretary room has been a social center of our group all the time, and it could not
be possible without Bianca, Marion and Marjan. Julius Caesar, famous for his multitasking,
would be jealous of your abilities to combine things!
I am endlessly grateful to my parents for raising me who I am, for tinkling my
curiosity and for being there whenever I needed their advice. Your warm words and
(sometimes not so polite) questions helped me through the most difficult times in my PhD.
Dearest Vladimir, the more I know you, the more I treasure our relationship. Thank
you for your patience and your support back home! Even more, thank you for not throwing
away my laptop in the busiest times of my PhD!
Introduction 11
Chapter 1
General introduction
Despite all the controversy about what entrepreneurship is about and who an
entrepreneur precisely is (Shane and Venkataraman, 2000), one aspect that consistently comes
back in the debates is risk-taking. Taking risk is one of the core functions of an entrepreneur
(e.g. Knight, 1921). In our exploratory in-depth interviews with entrepreneurs we came across
different risks ranging from "not having focus", "not having a proper image" to "accident in
the laboratory" and "an essential staff member leaves the firm". No matter how the various
risk factors are framed, it all tends to come back to the survival and prosperity of the firm: i.e.
to finances.
Although entrepreneurship is the driving force behind new job creation (Shane, 2003),
not all the new entrepreneurial firms become new Microsoft's or Dell's. The majority will
bleed to death: depending on the industry, only 37-54% of new firms survive the first year
(Timmons and Spinelli, 2004). The risks seem to be huge – so how can entrepreneurs improve
their risk-taking?
Despite its importance, the risk theme has been under-researched in the
entrepreneurship literature. One of the reasons may be the influential study of Brockhaus
(1980) that found no differences in risk-taking of entrepreneurs as opposed to managers in
traditional organizations. However, the recent meta-analyses of Stewart and Roth (2001,
2004) found that there is still a significant difference, no matter what measures are being used,
although these measures do influence the magnitude of the effect.
12 Introduction
Probably the most serious problem in studying risk is still of theoretical nature. Risk is
a concept that can be applied to almost any field and any theory. It is so broad that it can
easily become too limited. Each theory may have its own risks (e.g. Johnson and Van de Ven,
2002). Therefore, bringing all the possible risks under the same umbrella would mean creating
a "theory soup". A clear theoretical lens should be used for risk studies.
The empirical part of the research on risk also has a number of pitfalls. The term "risk"
has a strong negative connotation, a consequence of which is that data about risks are hard to
get from the entrepreneurs. As we have observed in our exploratory case studies,
entrepreneurs are reluctant to reveal information on risks, which may also explain why there
are so few studies explicitly studying risks. A particular risk can be framed both in positive
and negative terms. Therefore, in the studies of risk diagnostics researchers are advised to
frame the risk statements positively in order not to evoke defensive behavior from the
respondents (Keizer, Halman and Song, 2002).
We tried to avoid these pitfalls and intend to contribute to the entrepreneurship field
by answering the following three-fold main research question: (1) What kinds of risks do
entrepreneurs take? (2) How do they take the risks? (3) What kinds of strategies do they use to
manage the risks? We answer these research questions in three core chapters of this
dissertation: chapter two, three and four respectively. We answer the two "what" questions at
the firm level and the "how" question at the individual level.
1.1
Meta-analysis of success factors
"Luck is one of the key factors in entrepreneurial success."
Fern Mandelbaum (Monitor Venture Partners)
"My idea of risk and reward is for me to get the reward and others to take the risks."
An unknown entrepreneur
We started this research by asking ourselves: what kinds of risks do entrepreneurs
take? However, due to the lack of studies researching entrepreneurial risks directly, we
decided to focus on the positive side and consider risks as the opposite of success factors: i.e.
if a given factor is truly a success factor, then not possessing it would mean a risk for an
entrepreneurial firm. The higher the effect of a given factor on performance, the more severe
it becomes for a new technology venture not to possess this factor.
Introduction 13
In order to answer this question, we conducted a meta-analysis. A meta-analysis is a
method to review and integrate existing research on a given topic (Hunter and Schmidt,
1990). One aspect that clearly differentiates it from narrative reviews is its quantitative
character. Unlike primary research, in a meta-analysis the data analyzed consist of the
findings from previous empirical studies (Camisón-Zornoza et al., 2004). Just as empirical
research requires the use of statistical techniques to analyze its data, meta-analysis applies
statistical procedures that are specifically designed to integrate the results of a set of primary
empirical studies. This allows meta-analysis to pool all the existing literature on a given topic,
not only the most influential and best-known studies (Stewart and Roth, 2001, 2004). At the
same time, meta-analysis compensates for quality differences by correcting for different
artifacts and sample sizes (Hunter and Schmidt, 1990, 2004).
In this meta-analysis we subdivide the main research question into a set of these
lower-level research questions:
• What are the success factors for new technology ventures?
• Is the literature consistent on their estimates of these factors?
• In cases when the literature is not consistent, what are the potential
methodological moderators for these factors?
For our meta-analysis, we used the methodology developed by Hunter and Schmidt
(1990, 2004). We searched for studies on new technology ventures performance in ABIINFORM and on the internet using the following keywords: “new,” “adolescent,” “young,”
and “emergent“ to define the “new” axis; "technology", "high-tech", “technology-intensive,”
and “technology-based” to describe the technology domain; and “firm,” “venture,” and “startup” to define the entity. We examined past research studies where the majority of the sample
represented such “new” “technology” ventures. In general, the primary studies set the
maximum age for new technology ventures at 15 years, yet most primary studies selected cutoff values of 6 and 8 years. The last selection criterion was publication of a correlation matrix
with a performance measure, because correlation matrices serve as the main input for the
meta-analysis.
One of our conclusions from the meta-analysis is that there is a lack of
entrepreneurship studies bridging the strategic management and the deeper cognitive
mechanisms of entrepreneurial decision making. As Busenitz and Barney (1997) argued, if
certain individuals are cognitively biased in different ways, they may make strategic decisions
14 Introduction
in different ways. Past research shows that besides susceptibility to cognitive biases,
entrepreneurs differ cognitively from managers in more traditional firms on a number of
dimensions, including risk-taking propensity and reliance on intuition (Busenitz and Barney,
1997; Stewart and Roth, 2004). Thus, such cognitive mechanisms may represent sources of
competitive advantage or disadvantage of the firms (Barney, 1991). We intend to explore the
aforementioned gap on two levels: we look at the individual level how entrepreneurial risktaking propensity is formed and then we explore at the firm level the performance
consequences of various risk and uncertainty management strategies new technology ventures
pursue.
1.2
The mechanism of entrepreneurial risk-taking
''There is a fine line between confidence and arrogance. […] You have to have confidence
in order to take risks, because too many people are knocking you down and if you do not have
confidence, you are not going to keep going. But then at some point, you have success and that
confidence changes to arrogance. That's where it really gets dangerous. Arrogance indicates
that you are not listening to customers, employees and the market. So it is a fine line and you
have to stay on the good side!"
Judy Estrin (Packet Design)
"The younger the company, the more opportunity to take risks you have. Big companies do
not take risks. This is the advantage you have when you start a new company. Propensity to
take risks is what really differentiates an entrepreneur from a manager in a big company."
Randy Adams, serial entrepreneur (AuctionDrop)
In venture-related decisions, entrepreneurs have to cope on a daily basis with illstructured, uncertain sets of possibilities, while having the ultimate responsibility for each
decision (Knight, 1921; Stewart and Roth, 2001). There are a number of risks associated with
this kind of decisions and the question is: How do entrepreneurs take these risks? What is the
more precise mechanism of entrepreneurial risk-taking?
Entrepreneurship literature provides two alternative answers about how entrepreneurs
take these risks. One explanation is that entrepreneurs objectively tolerate more risks, that
they are risk-seeking and that they consciously take the risks (Stewart and Roth, 2001; 2004).
A competing, cognitive explanation is that in their intuitive decision-making, entrepreneurs
are unconscious (or at least not fully conscious) of the actual risks associated with their
decisions; they simply do not see them all due to the cognitive biases (Simon, Houghton and
Introduction 15
Aquino, 2000). These biases are in fact errors in decision-making arising from the use of
heuristics.
How can these two be brought closer to each other? Dual process theory provides an
answer. By now the entrepreneurship literature (e.g. Covin, Slevin, and Heeley, 2001; Simon,
Houghton, and Savelli, 2003) focused on intuition as the opposite side of being rational.
Entrepreneurs are seen as predominantly intuitive decision-makers. However, according to the
dual process theory, people can be intuitive and rational at the same time (Epstein, Pacini,
Denes-Raj, and Heier, 1996; Pacini and Epstein, 1999). This theory postulates that all
judgments and behavior of people are a joint output of both intuitive and rational thinking
(Epstein et al., 1996). The rational thinking monitors and eventually corrects outputs of
intuitive thinking (including the heuristics and biases). It provides an answer itself if no
intuitive judgment is available (Epstein et al., 1996; Stanovich and West, 2000). Recent
theoretical developments suggest that at least some of the cognitive biases studied until now
can be actually conceptualized as biases of human intuition (Kahneman, 2003). Thus, while
risk-seeking perspective can be related to the rational dimension in the dual process theory,
the cognitive perspective can be related to the intuitive dimension in the dual process theory.
In this individual-level study, we subdivide the main research question into a set of
lower-level research questions: we concentrate on how intuitive thinking, rational thinking,
heuristics and biases form entrepreneurial risk-taking:
• To what extent can the cognitive biases studied until now be called "intuitive",
i.e. deriving from the intuition?
• To what extent can rational thinking (i.e. without any special training) correct
the entrepreneurial cognitive biases and improve entrepreneurial decision-making?
• Do the intuitive and rational thinking also directly influence the entrepreneurial
risk-taking?
• To what extent do cognitive biases influence entrepreneurial risk-taking
propensity?
We integrate these two perspectives in a model where heuristics and biases mediate
the relationship between the intuitive and rational thinking and entrepreneurial risk-taking
propensity. We answer our research questions by testing the conceptual model using SEM
with Maximum Likelihood (ML) estimator on a sample of 289 entrepreneurs from the US.
16 Introduction
1.3
Risk and uncertainty management strategies
"There are many kinds of risk that you have when starting a company: there's technical
risk, market risk, financing risk and many other kinds of risks you can learn in a business
school. The trick is to take the risk out as early as possible. And take as few risks as possible."
Jerry Kaplan, entrepreneur (Winster)
"There are risks and costs to a program of action, but they are far less than the longrange risks and costs of comfortable inaction."
John F. Kennedy, president of USA
This chapter is dedicated to the risk and uncertainty management strategies new
technology ventures may pursue. We distinguish two major types of risk and uncertainty
management strategies: the traditional risk management (Miller, 1992) and the recently
emerged real options reasoning (McGrath, 1999; McGrath, Ferrier and Mendelow, 2004).
Both types of strategies concern the mitigation of risk and management of uncertainty. An
example of the differences between them is that traditional risk management strategies
typically target immediate risk reduction, whereas real options strategy delays the full
commitment decision and provides flexibility for future decisions. In Chapter 4, we further
elaborate on the differences between traditional risk management strategies and real options
strategy using the dimensions identified by Bowman and Hurry (1993), namely risk,
uncertainty, size and timing of investments.
Despite the integrative review (Miller, 1992), the traditional risk management
strategies were hardly ever compared empirically. Recent developments in the real options
theory refined the concept of real options strategy, providing the basis for thorough empirical
tests (McGrath et al., 2004). However, real options has also received certain critique raising
doubts about the value and distinctiveness of this strategy (Adner and Levinthal, 2004a,b;
Miller and Arikan, 2004).
In this study, we aim to tackle these issues by developing a scale to measure the real
options strategy directly and comparing its performance consequences with those of the
traditional risk management strategies. We also examine how market and opportunity
characteristics influence the preference of entrepreneurial ventures for each type of strategic
risk management strategy. We consider the effects of established versus emerging technology
standards as well as effects of markets with high versus low network externalities.
Introduction 17
In this firm-level study, we subdivide the main research question into two lower-level
research questions:
• What are the performance consequences of the risk and uncertainty
management strategies?
• How is the effect of these strategies influenced by market and technology
characteristics?
We test our conceptual model by OLS regressions for three different performance
measures: return on investment, customer retention rate and sales growth rate. For this test,
we use the data from 420 new technology ventures from USA.
Meta-analysis of success factors 19
Chapter 2
Meta-analysis of success factors
New technology ventures have the lowest survival rate among all the new ventures
(Timmons and Spinelli, 2004). To get a more integrated picture of what factors lead to the
success or failure of new technology ventures, we conducted a meta-analysis to examine the
success factors in new technology ventures. We culled the academic literature to collect data
from existing empirical studies and conducted a meta-analysis. We identified 24 most-widely
researched success factors for new technology ventures. Among these 24 factors, 8 are
consistently estimated as significant success factors for new technology ventures (i.e., they
are homogeneous positive significant meta-factors that are correlated to venture
performance). They are supply chain integration, market scope, firm age, size of founding
team, financial resources, founders’ marketing experience, founders’ industry experience,
and existence of patent protection. Of the original 24 success factors, 5 were not significant:
the success of technology ventures are not correlated with founders’ R&D experience,
founders’ experience with start-ups, environmental dynamism, environmental heterogeneity,
and competition intensity. The remaining 11 success factors are heterogeneous. For those
heterogeneous success factors, we conducted a moderator analysis. Of this set, 3 appeared to
be success factors and 2 were failure factors for subgroups within the new technology
ventures’ population. To facilitate the development of a body of knowledge in technology
entrepreneurship, this study also identifies high-quality measurement scales for future
research. We conclude the article with future research directions.
20 The mechanism of entrepreneurial risk-taking
2.1
Introduction
Technology entrepreneurship is key to economic development. New technology
ventures can have positive effects on employment, and can rejuvenate industries with
disruptive technologies (Christensen and Bower, 1996).
Unfortunately, the survival rate of new technology ventures is the lowest among new
ventures in general. In our most recent empirical study of 11,259 new technology ventures
established between 1991 and 2000 in the United States, we found that after four years only
36 percent, or 4,062 companies with more than five full-time employees, had survived. After
five years, the survival rate fell to 21.9 percent, leaving only 2,471 firms with more than five
full-time employees still in operation. Given this high rate of failure, it is important to identify
what factors lead to the success and failure of new technology ventures.
Current academic literature, however, does not offer much insight. Numerous studies
focus on success factors for new technology ventures, but the empirical results are often
controversial and fragmented. For example, the data on R&D investments alone yield
ambivalent conclusions. While Zahra and Bogner (2000) found no significant relationship
between R&D expenses and new technology venture performance, Bloodgood, Sapienza, and
Almeida (1996) found a negative relationship and Dowling and McGee (1994) found a
positive relationship between R&D investments and new technology venture performance.
Similarly, although new technology ventures often develop knowledge-intensive
products and services (OECD, 1997), the research results on product innovativeness have
been ambiguous. More than two-thirds of the empirical studies have found a positive
relationship between product innovation and firm performance, while the remaining studies
have found a negative relationship or none at all (Capon, Farley, and Hoenig, 1990; Li and
Atuahene-Gima, 2001). Li and Atuahene-Gima (2001) addressed this problem by introducing
contingencies into their regression models and indeed found three moderators.
The inconsistent and often contradictory results can stem from methodological
problems, different study design, different measurements, omitted variables in the regression
models, and noncomparable samples. More than on any one methodology, entrepreneurship
theory hinges on its setting (new firms) as its common denominator. Because of that,
numerous theoretical streams run through the scholarship (Shane and Venkataraman, 2000).
To help resolve this problem, we looked for a method that would operate independently of
model composition. Meta-analysis provides a solution (Hunter and Schmidt, 1990, 2004) and
Meta-analysis of success factors 21
a lens through which we can evaluate the success factors that contribute to new technology
ventures’ performance. We based our meta-analysis on studies that explicitly focus on
antecedents of new technology venture performance.
This chapter attempts to make several contributions to technology entrepreneurship
literature: (1) our integrated quantitative evaluation of the success factors of new technology
ventures provides one step toward developing a theoretical foundation for technology
entrepreneurship, (2) it identifies universal success factors, (3) it identifies success factors that
are controversial and, by moderator analysis, offers some tentative reasons for those
controversies, (4) it reports existing high-quality scales that are important for new technology
venture performance, and (5) it proposes and provides a new theoretical framework for
studying success factors of technology ventures and a road map for future research in
technology entrepreneurship.
This chapter is organized in the following manner. First, we explain our methodology.
We then present the results of our research, including the results of the meta-analysis,
examples of high-quality scales, and the discussion of future research directions. We conclude
the chapter with a description of its limitations and some final remarks.
2.2
Data collection and methodology
Meta-analysis is a statistical research integration technique (Hunter and Schmidt,
1990). One aspect that clearly differentiates it from narrative reviews is its quantitative
character. Unlike primary research, in a meta-analysis the data analyzed consist of the
findings from previous empirical studies (Camisón-Zornoza et al., 2004). Just as empirical
research requires the use of statistical techniques to analyze its data, meta-analysis applies
statistical procedures that are specifically designed to integrate the results of a set of primary
empirical studies. This allows meta-analysis to pool all the existing literature on a given topic,
not only the most influential and best-known studies (Stewart and Roth, 2001, 2004). At the
same time, meta-analysis compensates for quality differences by correcting for different
artifacts and sample sizes (Hunter and Schmidt, 1990, 2004).
There are two main types of meta-analytic studies in the literature. The first focuses on
a relationship between two variables or a change in one variable across different groups of
respondents. In general, this type of meta-analysis is strongly guided by one or two theories
22 The mechanism of entrepreneurial risk-taking
(e.g., Palich, Cardinal, and Miller, 2000; Stewart and Roth, 2001, 2004). The second type of
meta-analytic studies examines a large number of meta-factors related to one particular focal
construct, such as performance. Such meta-analyses aim to integrate all the existing research
on that focal construct and are largely atheoretical because the research they combine rests on
heterogeneous theoretical grounds (e.g., Gerwin and Barrowman, 2002; Montoya-Weiss and
Calantone, 1994). Because the current literature teems with numerous theoretical streams
where only the setting (new firms) is the common denominator (Shane and Venkataraman,
2000), we chose the second type of meta-analysis to study the potential success meta-factors
of new technology venture performance. We selected independent ventures and collected
studies that explicitly focused on antecedents of new technology ventures’ performance.
In our study, we explore—rather than define ourselves—what “new technology
venture” means in the literature. Primary studies use such terms as “new,” “adolescent,”
“young,” or “emergent“ to define the “new” axis; and “high technology,” “technologyintensive,” and “technology-based” to describe the technology domain. We examined past
research studies where the majority of the sample represented such “new” “technology”
ventures. In general, the primary studies set the maximum age for new technology ventures at
15 years, yet most primary studies selected cut-off values of 6 and 8 years. Another important
selection criterion was the publication of the correlation matrix in the paper, because the
correlation matrices serve as the main input for the meta-analysis. All the collected studies
investigated surviving new technology ventures; consequently, we do not consider failures in
our meta-analysis.
Meta-analysis allows the comparison of different empirical studies with similar
characteristics, and thus lets researchers integrate the results. To conduct a meta-analysis it is
important to select studies as input for the analysis and follow a meta-analytical protocol to
arrive at those results.
2.2.1 Selection of studies as input for the analysis
First, we combed the literature for research that discussed the success factors of new
technology ventures, using the ABI-INFORM system and the Internet. We used keywords—
“new,” “adolescent,” “young,” “emerging” and “high-tech,” “technology,” “technologyintensive,” “technology-based”—to limit our sample’s age and domain. Finally, to assess the
type of firm, we applied the keywords “firm,” “venture,” and “start-up.” We intentionally did
Meta-analysis of success factors 23
not limit the studies to those recognized as the best in the field, as usually done in a narrative
review: this would have betrayed the spirit of meta-analysis (Hunter and Schmidt, 1990).
Instead, we collected as much research as possible, corrected later for any quality differences
and controlled for missing studies.
After we gathered papers from ABI-INFORM and the Internet, we added crossreferenced studies from them. In total, we collected 106 studies that met our search criteria.
Next, we ensured that the articles on our list (1) represented the correct level of analysis, (2)
significantly reflected new technology ventures, and (3) reported a correlation matrix with at
least one antecedent of performance and one performance measure. This procedure reduced
the number of appropriate research studies to 31 due to the absence of correlation matrices.
Appendix B2.1 details our study sample by countries of origin, industries, performance
measures, the minimum and maximum ages of the ventures, and their sample sizes. In
addition, we provide two other features. First, “sample type” indicates the particular
characteristics of the sample. This may be new technology ventures that went through initial
public offering (IPO), ventures funded by venture capitalists (VC), ventures from a general
database, ventures involved in a governmental support program, ventures that have activity
abroad, or combinations of these types. Second, “venture origin” indicates whether the
venture was actually independent. Although our meta-analysis focused primarily on
independent ventures, it also included mixed samples of independent and corporate ventures,
where most were independent, and samples where the type of venture was not specified.
Appendix B2.2 lists the journals from which the 31 papers originate.
When coding the studies, we took care to refer to the scales reported in the primary
studies, so that dissimilar elements would not be combined inappropriately, and conceptually
similar variables would not be coded separately, to compensate for the slightly different labels
that authors use to refer to similar constructs (Henard and Szymanski, 2001).
2.2.2 Protocol for meta-analysis
We used Hunter and Schmidt’s protocol (1990) for our meta-analysis. Our most
important consideration was to the ability to make comparisons across research studies. To do
this, we could draw on Pearson correlations between a meta-factor and the dependent variable
or the regression coefficient between the meta-factor and the dependent variable. Because
regression coefficients depend on the particular variables included into the model and because
24 The mechanism of entrepreneurial risk-taking
the models vary across studies, we followed the suggestions of Hunter and Schmidt (1990).
Hunter and Schmidt strongly encourage using Pearson correlations as the input, because
correlations between two variables are independent of the other variables in the model
(Hunter and Schmidt 1990). Other meta-analytic studies have made this choice, including
Gerwin and Barrowman (2002) and Montoya-Weiss and Calantone (1994).
Another advantage of Hunter and Schmidt’s method (1990) is their use of random
effects models instead of fixed effects models (Hunter and Schmidt, 2004; p.201). The
distinction is as follows: fixed effects models assume that exactly the same “true” correlation
value between meta-factor and dependent variable underlies all studies in the meta-analysis,
while random effects models allow for the possibility that population parameters vary from
study to study. Given the differences in how new technology ventures were defined in the
selected primary studies, the choice for random effects models was appropriate.
Following the procedure of Hunter and Schmidt (1990), our second step was to correct
meta-factors for dichotomization, sample size differences, and measurement errors.
1) To correct dichotomized meta-factors: we made a conservative correction by
dividing the observed correlation coefficient of the sample by 0.8, because dichotomization
reduces the real correlation coefficient by at least 0.8 (Hunter and Schmidt, 1990, 2004).
Individual correction of observed correlations for dichotomization :
roo
roi = i ,
ad
where :
ad : correction for dichotomization;
ad = 0.8 if variable is dichotimized and ad = 1 if it is not;
roo i : observed correlation of the primary study i.
2) To correct sampling error: we weighted the sample correlation by sample size
(Hunter and Schmidt, 1990, 2004).
Weighted average of correlations individually corrected for dichotomization :
n
∑N r
i oi
ro =
i =1
n
,
∑N
i
i =1
where Ni : sample size of the primary study i.
Meta-analysis of success factors 25
3) To remedy measurement errors: we used Cronbach’s alphas. We divided the
correlation coefficient by the product of the square root of the reliability of the meta-factor
and the square root of the reliability of performance. Since reliabilities were not always
reported, we reconstructed them by using the reliability distribution (Hunter and Schmidt,
1990, 2004).
Real population correlation :
ρ=
ro
=
A
ro
,
Rxx * Ryy
where :
A : compound attentuation factor;
Rxx : average of the square roots of reliabilities of indepedent variables composing
a given meta - factor;
Ryy : average of the square roots of reliabilities of depedent variables composing
a given meta - factor.
The third step in the meta-analysis protocol was to determine whether a meta-factor
was a success factor. To accomplish this, we assessed three conditions. First, the studies
should have, in essence, the same correlation. Other meta-analysis procedures often use a Chisquare test to reveal this homogeneity. However, Hunter and Schmidt (1990, 2004) argue
against it and state that this test will have a bias because of uncorrected artifacts. They suggest
a variance-based test. The total variance in the correlation coefficient has three sources:
variance due to artifacts (dichotomization and measurement errors), variance due to sampling
error, and real variance due to heterogeneity of the meta-factor. The meta-factor is assumed to
be homogeneous, if the real variance is no more than 25 percent of the total variance.
According to Hunter and Schmidt (1990, 2004), in that case unknown and uncorrected
artifacts account for these 25 percent, so that the real variance is actually close to zero. We
describe the used formulas in Appendix C2.1.
For homogeneous meta-factors, we applied two significance tests. First, we
determined whether the whole confidence interval (based on the real standard deviation) was
above zero. Second, if it was above zero, we calculated the p-value for the real correlation to
estimate the degree of significance. Both of these significance tests are necessary, because the
p-value is misleading when part of the confidence interval of the real correlation is below
zero. Only when all three conditions held did we consider a given meta-factor to be a success
meta-factor for new technology ventures.
26 The mechanism of entrepreneurial risk-taking
For those heterogeneous meta-factors, we conducted a moderator analysis. We divided
the data into subgroups according to various methodological characteristics (see Appendix
B2.1). Then, we conducted a separate meta-analysis for each subgroup, hoping to find
homogeneous meta-factors in the subgroup in two steps. First, we conducted moderator
analysis to deal with different performance measures. Second, we checked whether country,
industry, sample type, venture origin, or maximum age of the new technology ventures in the
sample were possible moderators. Second, we conducted moderator analysis for different
meta-factor measures.
Finally, we reviewed the “file drawer” in an attempt to assess any publication bias.
Because there is a general tendency to publish only significant results, insignificant results are
often abandoned in researchers’ file drawers (Hunter and Schmidt, 1990; Rosenthal, 1991).
This “file drawer” technique provides a number, XS, indicating the number of null-result
studies that when added, would make the total significance of a meta-factor exceed the critical
level of 0.05. Thus, the higher the value of XS, the more stable and reliable the results are. If
XS, is 0, it indicates that the meta-factors are already insignificant according to the p-value
criterion.
2.3
Analysis and results
2.3.1 Success factors of technology ventures
Our meta-analysis revealed 24 meta-factors related to the performance of new
technology ventures. We present the definitions of these meta-factors in Table 2.1.
Meta-analysis of success factors 27
Table 2.1. Definitions of the 24 meta-factors
Meta-factors
Definitions
Selected references
Market and opportunity
1. Competition intensity
Strength of inter-firm competition within an industry
Chamanski and Waagø, 2001
2. Environmental dynamism
High pace of changes in the firm's external environment
Zahra and Bogner, 2000
3. Environmental heterogeneity
Perceived diversity and complexity of the firm's external environment
Zahra and Bogner, 2000
4. Internationalization
Extent to which a firm is involved in cross-border activities
Bloodgood et al., 1996
5. Low cost strategy
Extent to which a firm uses cost advantages as a source of competitive
advantage
Bloodgood et al., 1996
6. Market growth rate
Extent to which average firm sales in the industry increase
Bloodgood et al., 1996; Lee et al., 2001
7. Market scope
Variety in customers and customer segments, their geographic range,
and the number of products
Li, 2001; Marino and De Noble, 1997
8. Marketing intensity*
Extent to which a firm is pursuing a strategy based on unique
marketing efforts
Li, 2001
9. Product innovation*
Degree to which new ventures develop and introduce new products
and/ or services
Li, 2001
Experience of the firm's management team in related industries and
markets
Marino and De Noble, 1997
11. Marketing experience
Experience of the firm's management team in marketing
McGee et al., 1995; Marino and De Noble,
1997
12. Prior start-up experience
Experience of the firm's management team in previous startup
situations
Marino and De Noble, 1997
13. R&D experience
Experience of the firm's management team in R&D
McGee et al., 1995; Marino and De Noble,
1997
Entrepreneurial team
10. Industry experience
28 The mechanism of entrepreneurial risk-taking
Table 2.1 (continued). Definitions of the 24 meta-factors
Meta-factors
Definitions
Selected references
Resources
14. Financial resources
Level of financial assets of the firm
Robinson and McDougall, 2001
15. Firm age
Number of years a firm has been in existence
Zahra et al., 2001
16. Firm size
Number of the firm's employees
Zahra et al., 2001
17. Firm type
The type of a firm's ownership (corporate ventures or independent ventures)
Zahra et al., 2001
18. Non-governmental financial
support
19. Patent protection
Financial sponsorship from commercial institutes
Lee et al., 2001
Availability of firm's patents protecting product or process technology
Marino and De Noble, 1997
20. R&D alliances
The firm's use of R&D cooperative arrangements. For new technology
ventures they also correspond to horizontal alliances.
Zahra and Bogner, 2000; McGee et al.,
1995
21. R&D investment
Intensity of the firm's investment in internal R&D activities
Zahra and Bogner, 2000
22. Size of founding team
Size of the management team of the firm
Chamanski and Waagø, 2001
23. Supply chain integration
A firm’s cooperation across different levels of the value-added chain, for
example suppliers, distribution channel agents, and/or customers
George et al., 2001; George et al.,
2002; McDougall et al., 1994
24. University partnerships
The firm's use of cooperative arrangement with universities
Zahra and Bogner, 2000; Chamanski
and Waagø, 2001
* - these two factors are called marketing differentiation and product differentiation in the stream of research stemming from the work of Porter (1980)
Meta-analysis of success factors 29
Table 2.2 reports the meta-analytic results on the antecedents, or the success metafactors of new technology ventures’ performance. To be concise and limit the sensitivity of
the results to studies not included in our meta-analysis, Table 2.2 presents only the metafactors found in three or more research studies. The table presents ρ, an estimate of the real
population correlation; total N, the aggregate sample size; and K, the number of correlations
that build a given meta-factor. Both N and K are conservative: we counted each study only
once. Ninety-five (95) percent confidence interval is the spread of the real correlation
variance. XS is the critical number of null-results studies.
To make the analysis of the meta-factors more transparent and interpretable, we
generate appropriate categories grounded in the literature’s existing frameworks (Chrisman,
Bauerschmidt, and Hofer, 1998; Gartner, 1985; Timmons and Spinelli, 2004). These
categories are: (a) Market and Opportunity, (b) the Entrepreneurial Team, and (c) Resources.
After three researchers reviewed those categories for completeness and appropriateness, we
conducted content analysis, a classification technique that assigns variables to a particular
category. Two researchers independently assigned each variable to a category. The two
researchers agreed on variables' categorizations in 91.2 percent of the cases across 306
variables. A third researcher resolved any disagreements, making the final categorization. At
the same time, variables were combined to form meta-factors.
Reflecting the primary studies, the Market and Opportunity category typically
described either the market characteristics, such as environmental dynamism, environmental
heterogeneity, and competitive strategies based on Porter’s (1980) typology. The
Entrepreneurial Team category included characteristics of the new technology venture team,
including experience and capabilities, both as individuals and as a team. The Resources
category united a broad scope of factors, comprising resources, capabilities, and
characteristics of the new technology ventures as firms. Such resources included financial
resources, firm size, patents, and university partnerships.
The meta-factors were unevenly distributed across the three categories. The majority
fell into the Resources category; the smallest number, into the Entrepreneurial Team category.
The Resources category consisted of heterogeneous meta-factors for 55 percent and the
Market and Opportunity category for 56 Percent. Only the Entrepreneurial Team category was
completely homogeneous.
30 The mechanism of entrepreneurial risk-taking
Table 2.2. Results of the meta-analysis
Meta-Factor
Total
N
K
ρ
95 %
Explained ModeConfidence Variance
rators
a
Interval
Xs
MARKET and OPPORTUNITY
1
Competition intensity
634
7
0.01
100%
0
2
Environmental dynamism
637
5
0.05
100%
0
3
Environmental heterogeneity
287
3
0.10
100%
0
4
5
6
7
8
9
Internationalization
Low cost strategy
Market growth rate
Market scope
Marketing intensity
Product innovation
523
286
505
7
4
4
(*)
(-0.21,0.37)
38%
Yes
6
(**)
(-0.13,0.49)
70%
Yes
10
(***)
(-0.26,0.72)
16%
Yes
12
0.08
0.18
0.23
***
1046 10 0.21
622
6
(***)
0.42
100%
(-0.19,1.00)
(-0.48,0.56)
78
23%
Yes
55%
b
Yes
64
702
8
0.04
423
4
0.11*
100%
2
*
0
ENTREPRENEURIAL TEAM
10 Industry experience
11 Marketing experience
381
3
0.11
100%
2
12 Prior start-up experience
114
3
0.00
100%
0
13 R&D experience
329
3
0.09
100%
0
638
6
0.12**
100%
14
157
RESOURCES
14 Financial resources
15 Firm age
16 Firm size
17 Firm type
18
Non-governmental
financial support
19 Patent protection
20 R&D alliances
21 R&D investments
22 Size of founding team
***
(0.08,0.23)
87%
(***)
(-0.31,0.83)
10%
Yes
b
1890 15 0.16
1360 11 0.26
715
4
0.09
(-0.15,0.33)
31%
Yes
0
405
4
0.20(***)
(-0.15,0.55)
31%
Yes
16
453
5
0.11*
571
863
332
5
9
5
0.03
(*)
0.05
100%
31%
Yes
0
(-0.49,0.60)
19%
Yes
3
**
0.13
***
1
100%
6
41
604
6
0.23
(0.12,0.35)
89%
24 University partnerships
330
3
-0.04
(-0.25,0.17)
50%
Yes
– explained variance lower than 75% means that the meta-factor has moderator(s)
– see Table 2.3 for suggested moderators
*
p < 0.05; ** p < 0.01; *** p < 0.001. 1-tailed test statistic. Direction depends on the sign of ρ
p-values indicated by (*), (**) or (***) mean that the meta-factor is heterogeneous
b
b
(-0.52,0.58)
23 Supply chain integration
a
197
0
Meta-analysis of success factors 31
Results in Table 2.2 reveal eight universal success factors (i.e., they are homogeneous
positive significant meta-factors that are correlated to venture performance):
•
•
•
•
•
•
•
•
supply chain integration (ρ = 0.23, p<0.001)
market scope (ρ = 0.21, p<0.001)
firm age (ρ = 0.16, p<0.001)
size of founding team (ρ = 0.13, p<0.01)
financial resources (ρ = 0.12, p<0.01)
marketing experience (ρ = 0.11, p<0.05)
industry experience (ρ =0.11, p<0.05)
patent protection (ρ =0.11, p<0.05)
One success factor represented Market and Opportunity, five success factors
represented Resources, and two success factors were part of the Entrepreneurial Team
category.
Results in Table 2.2 also suggested that the following five factors have no significant
effects on technology venture performance: 1) R&D experience, 2) prior start-up experience,
3) environmental dynamism, 4) environmental heterogeneity, and 5) competition intensity.
Three of these meta-factors represented Market and Opportunity and two represented the
Entrepreneurial Team category.
2.3.2 Moderators
As Table 2.2 indicates, 11 of the 24 meta-factors had heterogeneous correlations (i.e.,
the importance of the factors depend on situations). Therefore, we conducted moderator or
subgroup analysis for differences in performance measures, meta-factor measures, venture
origin, maximum age of venture in the sample, sample type, country, and industry.
Table 2.3 presents those results from the moderator analysis, including ρ, an estimate
of the real population correlation; total N, the aggregate sample size1; K, the number of
correlations that build a given meta-factor; the 95 percent confidence interval of the real
variance; and XS, the critical number of null-results studies.
1
Since some studies used multiple measures of performance, sum of performance moderator
subgroups sample sizes may be greater than total N of a meta-factor.
32 The mechanism of entrepreneurial risk-taking
Table 2.3 also presents the variance explained by dichotomization of meta-factors,
measurement, and sampling error. This variance must be more than 75 percent to yield a
homogeneous factor. In that case, the real variance is less than 25 percent of the total variance
of correlations from the primary studies. The remaining variance is likely due to other
unknown and uncorrected artifacts, and therefore it can be neglected (Hunter and Schmidt,
1990, 2004). To keep overview, for the moderator, or subgroup analysis, we only report the
meta-factors with at least two subgroups that have no overlapping confidence intervals; each
subgroup consists of at least two studies.
Table 2.3. Suggested moderators
Metafactor
Moderator a
ρ
Total
K
N
95 %
Confidence
Interval
Explained
XS
Variancea
RESOURCES
Firm type
0.09
715
4 (-0.15,0.33)
31%
Performance operationalization
Profit based
-0.01
572
3 (-0.03,0.01)
98%
Sales based
0.27***
464
2
100%
R&D alliances
0.03*
571
5 (-0.52,0.58)
31%
Venture origin
Independent ventures
-0.36***
262
2
100%
Mixed origin
0.37***
309
3
100%
MARKET and OPPORTUNITY
Product innovation
0.04
702
8 (-0.71,0.79)
12%
Venture origin
263
3 (-0.52,-0.27)
80%
Independent ventures
-0.39***
Mixed origin
0.44***
300
2 (0.23,0.65)
43%
a
- explained variance lower than 75% means that the meta-factor has moderator(s)
*
p < 0.05; ** p < 0.01; *** p < 0.001. 1-tailed test statistic. Direction depends on the sign of ρ
0
0
18
0
10
28
0
23
23
The results reported in Table 2.3 suggest that of the 11 heterogeneous factors, 3 metafactors (firm type, R&D alliances, and product innovation) had distinct moderator subgroups
(i.e., the effect of these factors on venture performance depends on situation). The relationship
between firm type and performance depended on the way performance was measured. Firm
type was insignificantly related to the profits of new technology ventures, but significantly
and positively related to the sales of new technology ventures. No other (methodologically
oriented) moderators affected the firm type.
R&D alliances were negatively associated with performance for independent ventures.
However, for ventures of a mixed origin, R&D alliances were positively associated with
performance.
Meta-analysis of success factors 33
Product innovation was moderated by venture origin. For independent new technology
ventures, product innovation has a significantly negative association with performance.
However, for samples with mixed firm type, product innovation has a significantly positive
association with performance.
By examining the results in Table 2.2, eight meta-factors proved inconclusive:
internationalization, low-cost strategy, market growth rate, marketing intensity, R&D
investments, firm size, non-governmental financial support, and university partnerships. Of
these eight meta-factors, market growth rate and non-governmental financial support have
only one subgroup with two or more studies when differences in meta-factor measurements
are considered. We also found only one suitable subgroup for internationalization when
looking at sample type, for marketing differentiation when looking at the country, and for
university partnerships when either looking at the sample type or the industry. Further
research is needed to validate or disprove these potential moderators. Finally, no
methodological moderators were found for R&D investments, low cost strategy, and firm
size.
2.4
Identification of high-quality measurement scales
Our high-quality scale is either a ratio/interval measure or a Likert-type scale with a
Cronbach’s alpha of at least 0.7 (Nunnally 1978) that consists of at least three items. The last
condition ensures that Likert-type scales will be reliable and that they will still hold a certain
reserve for future studies in case one of the items does not load. Identification of such scales
can assist the work of future researchers in the technology entrepreneurship and alert them to
poor operationalization practices. Consequently, one of our study goals was to report on
scales from meta-factors that were stable and reliable success factors for new technology
ventures.
We selected only significant homogeneous (unmoderated) meta-factors from Table 2.2
or homogeneous subgroups from Table 2.3. This selection resulted in 11 strongly supported
new technology venture success factors. To ensure that individual scales would perform well
in further studies, within each meta-factor, we selected only scales with an observed
correlation significant at the 0.05 level. Marketing experience did not have a significant highquality scale in the previous studies. Therefore, we report high-quality scales found for 10
new technology venture success factors in Appendix A2.1. Further research should be
34 The mechanism of entrepreneurial risk-taking
conducted on other potentially significant success factors (see moderated meta-factors from
Table 2.2) before valid conclusions can be drawn.
2.5
Discussion and future research directions
In this study, we conducted a meta-analysis on antecedents of new technology
ventures performance and tried to identify success factors for new technology ventures. To
the best of our knowledge, this is the first systematic, quantitative effort to integrate the
existing research on this topic. Our study sought to contribute to a more homogeneous theory
of technology entrepreneurship. We summarize the results of our meta-analysis in Figure 2.1.
In the spirit of meta-analysis, we present the results in four main blocks: significant and
insignificant homogeneous factors, heterogeneous factors with moderators and heterogeneous
factors without moderators. The latter two blocks are shown by the dotted lines. We also
show within each block from which category a given meta-factor originates.
Meta-analysis of success factors 35
Figure 2.1. Summary of success factors in new technology ventures
36 The mechanism of entrepreneurial risk-taking
The results are compelling: eight of the 24 meta-factors remain heterogeneous even
after we searched for methodological moderators. They are evenly distributed across Market
and Opportunity, and Resources categories. Five of the 24 meta-factors were homogeneous,
but not significant. Three of them are from Market and Opportunity, and two meta-factors are
from Entrepreneurial Team category. Only eight meta-factors are homogeneous and
significant, suggesting that they are the only universal success factors for the performance of
new technology ventures. The majority of them belong to the Resources group. Two metafactors are success factors for sub-groups in the population of new technology ventures and
one works only for sales and not for profit-based performance. Therefore, more research is
necessary on the heterogeneous, moderated meta-factors listed in Table 2.2. While we have
identified some moderators in Table 2.3, future research should also explicitly test the effects
of these moderators. To help build the body of the knowledge in technology entrepreneurship,
we have also identified high-quality scales of the success factors and presented the
measurement scales in Appendix A2.1.
2.5.1 Market and opportunity
Nine success factors represented the market and opportunity category in our metaanalysis. One was homogeneous and significant, five were heterogeneous, and the other three
were insignificant. Therefore, we can conclude that based on extant research only one general
factor, market scope, clearly enhances new technology venture performance. Moreover, we
found only one success factor within the moderator subgroups. Product innovation improves
new technology venture performance of corporate ventures, but it is detrimental for
independent new technology ventures. A radical innovation strategy may be too risky for
independent ventures, while corporate ventures can share risks with their parent companies.
Examining the number of heterogeneous meta-factors, one might conclude that the
new technology venture population is generally too heterogeneous to examine the success
factors.
This idea was supported by the fact that for a number of meta-factors, no
methodological moderators were found, suggesting that there may be other moderators that
have not been reported in published research studies. As a follow-up of this research, we
conducted 16 case studies of new technology ventures. We found a striking difference in the
strategies used by these entrepreneurs dependent on their background—technical or business.
In the first scenario, the entrepreneurs were usually the inventors of the venture technology
Meta-analysis of success factors 37
and focused on it rather than on its market. In the second scenario, entrepreneurs paid close
attention to financials and the product market, while their ventures could do little or even no
R&D, and yet these new technology ventures still produced high-technology products by
following a sort of “me too“ strategy. Thus, the background of entrepreneurs leading to
different involvement in the technological development of the products may be a missing
moderator.
Another worthy direction for future studies is further contingency research. Until now,
scholars in technology entrepreneurship have focused on product differentiation strategy and
its interaction with different environmental characteristics, such as competition intensity and
environmental dynamism (Li, 2001; Li and Atuahene-Gima, 2001; Zahra and Bogner, 2000).
Other competitive strategies have received considerably less attention in studies of
environmental contingencies.
Existing meta-factors describe opportunity in a rather indirect way. Thus, another
possible direction of future research could focus more closely on opportunity, the key concept
of entrepreneurship (Shane and Venkataraman, 2000). For example, opportunity sources vary
in the amount of uncertainty and thus have different degrees of success predictability
(Drucker, 1985; Eckhardt and Shane, 2003). Future researchers may want to consider a
greater range of opportunity dimensions. The question of how to measure an opportunity
remains open. In general, the technology entrepreneurship research does not address the
multiple dimensions of the entrepreneurial opportunity concept and generally overlooks the
interaction effects of the strategies followed by new technology ventures, opening these two
themes to future researchers.
2.5.2 Entrepreneurial team
In our study, four types of experience described the characteristics of the
Entrepreneurial Team: marketing, R&D, industry, and prior start-up experience. Only
experiences in marketing and industry were significant, suggesting that acquiring more
experience in these areas may lead to higher new technology venture performance. However,
both prior start-up experience and R&D experience were insignificant at the 0.05 level. The
former finding may be further evidence of overestimation of the role of prior start-up
experience, ironically one of the most profound venture capitalist evaluation criteria (Baum
and Silverman, 2004). It should be noted that the latter finding might have been caused by
38 The mechanism of entrepreneurial risk-taking
lack of variance in the samples of new technology ventures, since new technology ventures
are often defined by having a certain amount of R&D expenses.
Two ways present themselves as means for resolving the weak results of the
Entrepreneurial Team factors. First, these findings may be due to the tendency to limit
experience to the number of years the founder(s) spent in a certain area, without measuring
the quality, variety, and complementarity of both joint and individual experiences (Eisenhardt
and Schoonhoven, 1990; Lazear, 2004). Moreover, certain aspects of the Entrepreneurial
Team have been overlooked in the literature on new technology ventures. In particular,
researchers have identified a variety of cognitive characteristics that make entrepreneurs
distinctive, such as psychological traits (Gartner, 1985; Stewart and Roth, 2004), cognitive
biases, and thinking styles (Baron, 1998, 2004).
An alternative explanation applicable to all these meta-factors is that their influence
manifests itself through a more subtle, indirect mechanism. Researchers have concentrated
their efforts on direct links between personality characteristics of entrepreneurs and the
performance of new technology ventures. However, recent research has found support for
their indirect influence on the performance of ventures (Baum, Locke, and Kirkpatrick, 1998);
for example, human capital factors influence performance by directing the competitive
strategies entrepreneurs choose (Baum, Locke, and Smith, 2001) or channeling the
opportunities they recognize (Shane, 2000). Future research should investigate these
alternative explanations.
2.5.3 Resources
More than half of the identified success factors in our meta-analysis were in the
Resource category. Although a significant amount of research has been conducted within this
category, results have not been conclusive. We found five success factors within this
category: supply chain integration, firm age, size of founding team, financial resources, and
patent protection. So investing in supply chain integration seems to yield higher returns.
However, except for supply chain integration, most factors may not be fully controllable ones.
One may control the size of the founding team and collect more experience in the team
(indicating that this factor is close to the Entrepreneurial Team factors), while enlarging
communication requirements and facing power problems. In any case, the meta-analysis
results indicated that enlarging the team may improve new technology venture performance.
Meta-analysis of success factors 39
The financial resources, however, may be more difficult to control. Even though our study
results suggest that more financial resources may improve performance, not all firms can
absolutely control their financial resources. Nevertheless, setting up new technology ventures
may need to wait until required financial resources have become available. Finally, when a
possibility of patent protection exists firms should take the opportunity.
Our analysis also found six heterogeneous meta-factors within this category. In our
moderator analysis, we showed that firm type has a positive influence on sales performance.
Moreover, in ventures of mixed origin, R&D alliances improved performance, while for
independent ventures these alliances worsened performance. Perhaps equity conditions could
better be negotiated in corporate ventures, having more power than independent ventures.
A remarkable finding of our study was that the R&D investments were not a success
factor (much like product innovation, mentioned earlier). Generally, when looking at all
resource factors, we did not find any particularly technological resource factors. Within the
population of new technology ventures, these factors generally have a high level and there
was insufficient variation in these factors. However, in line with a resource-based view of the
firm, the focus may need to be on the quality of the resources rather than the quantity. Barney
(1991) posited that the value, rareness, non-imitability, and non-substitutability of
resources—instead of the amount of resources—led to competitive advantage. We advise
future research consider that direction.
2.6
Limitations
As with all research, this meta-analysis had several limitations. First, the Pearson
correlations we used are primarily intended for measurement of the strength of a linear
relationship between two variables. In the case of zero correlation, a chance existed of
observing a vivid curvilinear relationship between variables. Second, the primary studies used
in the meta-analysis based their samples on surviving new technology ventures because of the
difficulties in accessing new technology ventures that failed. Therefore, any meta-analysis in
this topical area must be inherently biased toward more successful, surviving firms. This bias
has two implications: (1) meta-factors that influence the success and mortality of a new
technology venture could conceivably be substantially different (Shane and Stuart, 2002) and
(2) strategies (meta-factors) that seem to deliver the best performance can be misleading. The
greater the potential a particular strategy has, the greater the risks associated with it. Finally,
40 The mechanism of entrepreneurial risk-taking
the last limitation of the study was the sample size of the meta-analysis itself, which included
31 studies reflecting the emerging nature of this research domain as well as the generally poor
standards of descriptive statistics publication. However, the 31 studies provided a sufficient
sample size for a preliminary meta-analysis (Gerwin and Barrowman, 2002; Montoya-Weiss
and Calantone, 1994). Thus, this meta-analysis should not and must not preclude future
research, but rather stimulate and direct it.
The mechanism of entrepreneurial risk-taking 41
Chapter 3
The mechanism of entrepreneurial risk-taking
In this study, we use the dual process theory to link two streams of research on
entrepreneurial risk-taking: the "conscious" risk-seeking perspective and the "unconscious"
cognitive perspective. We model the mechanism of entrepreneurial risky decision-making by
looking at how the cognitive biases mediate the relationship between the intuitive and
rational thinking of entrepreneurs and their risk-taking propensity. Hereby we reveal the
more stable nature of the cognitive biases, as opposed to the context-driven one. In our
research, we focus on seven important biases: hindsight bias, illusory correlation, and
overconfidence originating from availability heuristic; and base-rate fallacy, illusion of
control, sample size fallacy, and regression fallacy deriving from the representativeness
heuristic. We show that rational and intuitive thinking have two distinct effects on
entrepreneurial risk-taking propensity. While intuition has only indirect, mediating effect on
risk-taking, rational thinking has only direct effect on risk-taking. Cognitive biases mainly
derive from intuition and the rational system can correct some of them, thereby bringing the
subjective experience of risks of the entrepreneur closer to objective reality. While all the
biases have a significant effect on risk-taking propensity, some of them have a positive and
some have a negative effect. Finally, although entrepreneurs are generally treated by the
literature as predominantly intuitive decision makers, our results also highlight the
importance of the rational side of entrepreneurs.
42 The mechanism of entrepreneurial risk-taking
3.1
Introduction
This study concentrates on the effects of intuitive and rational thinking on
entrepreneurial risk-taking behavior. In venture-related decisions, entrepreneurs have to cope
on a daily basis with ill-structured, uncertain sets of possibilities under high time pressures. At
the same time, they have the ultimate responsibility for each decision (Knight, 1921; Stewart
and Roth, 2001). There are a number of risks associated with this kind of decisions and the
question is: How do entrepreneurs take these risks? Entrepreneurship literature holds two
alternative perspectives on how entrepreneurs take risks: cognitive perspective assuming
intuitive thinking and risk-seeking perspective assuming rational thinking. The former
perspective postulates that in their intuitive decision-making, entrepreneurs are unconscious
of the actual risks associated with their decisions; they simply do not see them due to
cognitive biases (Simon, Houghton and Aquino, 2000). The latter, competing, perspective
postulates that entrepreneurs objectively tolerate more risks, that they are risk-seeking and
that they consciously take the risks (Stewart and Roth, 2001; 2004).
Do these two perspectives really exclude each other or are they complementary? The
cognitive perspective postulates that entrepreneurs do not see the risks. The reason for that are
heuristics that speed up and simplify their predominantly intuitive decision-making, but also
inevitably lead to cognitive biases (Kahneman and Frederick, 2002). Researchers found that
entrepreneurs as a group score higher on the biases than other people (Busenitz and Barney,
1997), while entrepreneurs vary in the degree to which they are susceptive to certain biases
(Forbes, 2005). The core concept in the risk-seeking perspective is risk-taking propensity.
According to Sitkin and Pablo (1992), risk-taking propensity is a general tendency of the
decision maker to take or avoid risks. A higher risk-taking propensity is associated with risky
behavior. Risk-taking propensity is a predisposition that can change over time and thus is an
emergent, although persistent property of the decision maker (Sitkin and Weingart, 1995).
After the influential study of Brockhaus (1980), who found no difference in risk-taking
propensity between entrepreneurs and other people, risk-taking propensity largely disappeared
from the studies of entrepreneurship. However, the meta-analyses of Stewart and Roth (2001;
2004) brought it back. They found that entrepreneurs on the whole have a higher risk-taking
propensity than other people, and they also found that entrepreneurs as a group vary in their
risk-taking propensity.
The mechanism of entrepreneurial risk-taking 43
The dual process theory provides a way to bridge the risk-seeking and the cognitive
perspective. According to the dual process theory, people can be intuitive and rational at the
same time (Epstein, Pacini, Denes-Raj, and Heier, 1996; Pacini and Epstein, 1999). This
theory postulates that all judgments and behavior of people are a joint output of both intuitive
and rational thinking (Epstein et al., 1996). The rational thinking monitors and eventually
corrects outputs of intuitive thinking (including the heuristics and biases). It provides an
answer itself if no intuitive judgment is available (Epstein et al., 1996; Kahneman, 2003;
Stanovich and West, 2000).
By now the cognitive and the risk-seeking streams within entrepreneurship research
have never been compared. Moreover, the cognitive stream has only focused on three
cognitive biases, overconfidence, illusion of control and law of small numbers, and their
influence on entrepreneurial decision-making in different situations involving risk (e.g. Simon
et al., 2000; Simon and Houghton, 2003). By following this research line studies in the
cognitive stream chose for the view that the cognitive biases are evoked by specific contexts
rather than emanate from certain personality characteristics. However, both views are valid
(Forbes, 2005). Moreover, although the cognitive stream puts emphasis on intuitive decisionmaking, intuition has been rarely explicitly studied. The risk-seeking stream has mainly
focused on entrepreneurs-managers comparisons. Only outcome history, inertia and risk
preferences have been identified as antecedents of risk-taking propensity (Sitkin and Pablo,
1992). Despite the more conscious, systematic character of determinants in this stream,
rational thinking has never been related to risk-taking propensity. Finally, up till now the
primarily conceptual studies in the dual process theory have not focused on risk-taking
propensity.
In this study, we will try to fill these gaps. We will examine the impact of intuitive and
rational thinking on risk-taking propensity. Moreover, we will explore whether seven
cognitive biases – hindsight bias, illusory correlation, overconfidence, base-rate fallacy,
illusion of control, law of small numbers, and regression fallacy – have a mediating role in
this relationship. We empirically test our model using structural equation modeling with
maximum likelihood estimation on a sample of 289 entrepreneurs from the US.
Our theoretical framework and empirical model allow us to critically assess the
different statements and assumptions about entrepreneurial decision-making made by both the
cognitive and risk-taking stream of research, such as the highly intuitive nature of
44 The mechanism of entrepreneurial risk-taking
entrepreneurs, the origin of cognitive biases and the ways to correct them as well as the
effects of these biases on risky decision-making. Moreover, by examining risk-taking
propensity as a dependent variable, we consider the more stable part of cognitive biases, thus
contributing to the cognitive stream, and to the dual process theory. By explicitly taking into
account the intuitive and the rational system, we contribute to the cognitive stream and the
risk-seeking stream respectively. By empirically testing the relationship between intuitive and
rational thinking and cognitive biases, we contribute to the dual process theory. Finally, we
contribute to the entrepreneurial field by applying dual process theory, and by studying the
largest set of biases ever examined. Moreover, our study emphasizes the importance of the
rational side of entrepreneurs.
We build the chapter as follows. In the theoretical background section, we give
definitions and review the dual process theory and the concepts of heuristics and biases. Then
we develop our conceptual model and research hypotheses. In the following two sections, we
describe the methodological grounds of the study, and the results. We finish with the
discussion how our results contribute to the entrepreneurship, and dual process theory.
3.2
Theoretical background
3.2.1 Dual process theory: Definitions and theoretical foundation
The recent reviews of dual process theory established that despite the different names
and focuses the streams in the dual process theory converged towards a unifying view on the
functions and properties of the two systems (Epstein, 1994; Kahneman, 2003; Stanovich and
West, 2000). Dual process theory postulates that individuals have two fundamentally different
systems, intuitive and rational, that are responsible for two modes of information processing
(Epstein et al., 1996). All behavior is seen as the product of operation of both intuitive and
rational systems. The main assumption of the dual process theory is that the two systems
operate simultaneously, independently and interactively (Kahneman, 2003). Probably the
most important one is the simultaneous contradictory belief, when people ask themselves a
question and get two different answers at the same time. For example, what kind of animal is
a whale? The first impression most people get is that a whale is a fish, because it looks like
fish. At the same time, technically, a whale is a mammal. In this example the answer "whale
is a mammal" comes from the rational system and the answer "whale is a fish" comes from
the intuitive system. Because of the simultaneous contradictory belief, judges are often forced
The mechanism of entrepreneurial risk-taking 45
to ignore their intuitive sense of justice in order to mete out punishment according to the law,
rationally (Sloman, 2002). Such contradictory beliefs result in "the conflict between the heart
and the head" (Epstein, 1994).
In this study we will follow the definitions of cognitive-experiential self-theory
(CEST) (Epstein, 1994), which is one of the most elaborated dual process theories. According
to CEST, the experiential system (also known as intuitive) is a system that operates in an
automatic, holistic, associationistic manner (Denes-Raj and Epstein, 1994). It is primarily
non-verbal and intimately associated with affect. The rational system is a primarily conscious
analytical system that functions by a person's understanding of conventionally established
rules of logic and evidence (Denes-Raj and Epstein, 1994). It is intentional, analytic, primarily
verbal and relatively affect-free.
Below we will elaborate on the role of heuristics and biases in the dual process theory.
3.2.2 Heuristics and biases stream of research
Similar to the experiential and the rational system, heuristics and biases influence our
decision-making. Research on heuristics and biases started with biases and only recently
delivered a formal definition of what drives the biases: namely, heuristics. A judgment is said
to be influenced by a heuristic when people do not use the target attribute of the object or
subject for their judgment, but substitute it by a related heuristic attribute that comes more
readily to mind (Kahneman, 2003). There are two main heuristics described in literature:
representativeness and availability. Representativeness is the degree to which the heuristic
attribute is similar to or resembles the target attribute. Availability is the ease with which
instances or associations related to the target attribute could be brought to mind (Kahneman
and Frederick, 2002; Kahneman and Tversky, 1973). For example, a professor has to assess
whether her candidate will be suitable for a tenure position in her department. She asks the
candidate to give a presentation and makes her judgment on the basis of this presentation.
Actually, she uses the representativeness heuristic since she assumes that the quality of the
presentation is representative for, or resembles the suitability of a tenure position. Suppose the
candidate is married and his wife has a job 600 miles away. The professor may ask herself
what is the probability that this distance causes trouble in the new job within one year. In
finding an answer, she may look for troublesome instances from her own experience that
come readily to mind. In that case, she would use the availability heuristic, since she makes
46 The mechanism of entrepreneurial risk-taking
the probability estimates on the basis of available (known) instances of troublesome and less
troublesome situations.
Biases (or fallacies) are errors in judgment where a heuristic is applied. They appear
because the target attribute and the heuristic attribute are not the same (Kahneman and
Frederick, 2002). In our research, we focus on seven important biases: hindsight bias, illusory
correlations, overconfidence, base-rate fallacy, illusion of control, sample size fallacy, and
regression fallacy. Literature suggests that the first three biases primarily originate from the
availability heuristic, while the other four primarily originate from the representativeness
heuristic (e.g., Tversky and Kahneman, 1974, Kahneman, 2003, Russo and Schoemaker,
1992). We elaborate more on these seven biases while developing hypotheses in the following
section.
3.3
Conceptual model and hypotheses
Based on the dual process theory, we present the conceptual framework of the
relationship between the two systems of thought, heuristics and biases, and risk-taking
propensity. Our model essentially suggests that heuristics and biases (partially) mediate the
relationship between the experiential and rational system and risk-taking propensity.
Figure 3.1. The mechanism of risk-taking propensity formation
The mechanism of entrepreneurial risk-taking 47
3.3.1 Relationship between biases and risk-taking propensity
In this sub-section we will concentrate on the influence of the heuristics and biases on
risk-taking propensity. Below we will first explain this link for the three biases that are
primarily deriving from the availability heuristic and then we elaborate on four biases that
primarily derive from representativeness heuristic.
The first bias is hindsight bias. Hindsight bias appears when, after a certain event
occurs, subjects tend to remember their predictions about the event as being more accurate
than they actually were. So, there is an inconsistency in the prediction of an outcome before
and after knowing the actual outcome of the event (Slovic and Fischhoff, 1977). The Polish
proverb "A Pole is wise after damage occurred", is an example of this hindsight bias. In
hindsight, people tend to assign higher likelihoods to outcomes that actually had occurred and
exaggerate what they could have anticipated in foresight. They tend to view events as having
appeared "relatively inevitable" before they happened (Fischhoff, 1982).
Let us consider entrepreneurs choosing a certain strategy or developing a business
model to promote their product. If the product turns out to be a success, entrepreneurs that
score high on hindsight bias may think that this success was inevitable because of their
actions. In meanwhile only a limited number of factors could have been truly controlled by
the entrepreneurs. Similarly, if the product turns out to be a failure, entrepreneurs that score
high on hindsight bias may think that this failure was inevitable and that even those they did
their best, they could not have done better for the product. This is also not quite true because
to a certain extent they could have interfered in the process. Thus, in both cases such
entrepreneurs would have a wrong impression about the factors that lead to success or failure.
Therefore the following time when they would try to launch a product, they will probably try
to influence wrong factors. Objectively, it would mean that they are taking more risks and
have higher risk-taking propensity than entrepreneurs with a low level of hindsight bias.
Therefore, we hypothesize:
Hypothesis 1a. The level of hindsight bias will be positively associated with the
level of entrepreneurial risk-taking propensity
A second bias is illusory correlation. Illusory correlation is the phenomenon of seeing
a co-occurrence between two events in a set of data, when no such co-occurrence exists
(Tversky and Kahneman, 1974). Tversky and Kahneman (1974) describe an experiment with
experienced clinicians and students. They both get information about a number of mental
48 The mechanism of entrepreneurial risk-taking
students. From each patient they get a diagnosis statement and a drawing of a person made by
that patient. Then the clinicians and students had to recall from memory how often diagnostic
statements such as paranoia or suspiciousness were associated with various features of the
drawing such as peculiar eyes of the person in the drawing. Both the clinicians and the
students markedly overestimated the frequency of co-occurrence of natural associates, for
example between suspiciousness and peculiar eyes.
Let us consider entrepreneurs who come to the conclusion, when remembering stories
from professional journals and newspapers that high technology innovativeness is associated
with high venture performance. This association does not exist in reality. Eisenhardt and
Schoonhoven (1990) found that technology innovativeness and venture performance are
uncorrelated. However, entrepreneurs with high level of illusory correlation would belief in
this association and invest in technologies that are highly innovative. As we know, because of
high investments before launch and no strong relationship with existing markets, this may
lead to high risks and failure rates, so (probably without knowing) these entrepreneurs would
be very risk seeking. In general, when entrepreneurs have this type of prejudice they
objectively govern their venture on the basis of wrong incentives, and therefore (without
knowing) take more risks than entrepreneurs without this prejudice, and thus they have a
higher risk-taking propensity.
We hypothesize:
Hypothesis 1b. The level of illusory correlations will be positively associated
with the level of entrepreneurial risk-taking propensity
The third bias is overconfidence. Overconfidence bias is the failure to know the limits
of one's knowledge (Russo and Schoemaker, 1992), resulting in unjustified confidence in
one's own judgments. To put it simple: it is not what you know, but whether you know what
you know and what you do not know. Examples of overconfidence are numerous, and the
phenomenon seems to become almost universal when one (over)estimates the likelihood that
one's favored outcome will occur (Griffin and Varey, 1996). Even for neutral general
knowledge questions people show stable overconfidence (Fischhoff, Slovic and Lichtenstein,
1977). The general procedure is to ask respondents for their answers on the questions and ask
them to rate their confidence that these answers are indeed correct. Overconfidence bias
occurs therefore when respondents are extremely sure that they are right when they are
The mechanism of entrepreneurial risk-taking 49
actually wrong. In the study of Fischhoff et al. (1977) percent of respondents with extreme
overconfidence ranged from 5% to 77% depending on the type of question.
Let us consider entrepreneurs busy with a new product introduction. Entrepreneurs
with a high level of overconfidence bias will tend to be more certain of the product success
than their actual judgment accuracy would allow. When entrepreneurs are overconfident, they
feel that the additional risk reduction measures are unnecessary and therefore will be less busy
with undertaking actions to mitigate potential risks. This means that objectively, they will be
taking more risks than entrepreneurs with a low level of overconfidence and thus have a
higher risk-taking propensity. Therefore, we hypothesize:
Hypothesis 1c. The level of overconfidence will be positively associated with
the level of entrepreneurial risk-taking propensity
The four biases primarily emanating from the representativeness heuristic are baserate fallacy, illusion of control, sample size fallacy, and regression fallacy (Tversky and
Kahneman, 1974). We will also describe these biases below.
The fourth bias is the base-rate fallacy. It occurs when irrelevant information is used
to make a probability judgment, ignoring available statistical information about prior
probabilities (the base-rate frequency) (Tversky and Kahneman, 1974). For example, in an
experiment of Tversky and Kahneman (1974) subjects had to make a judgment whether a
person named Dick is an engineer or a lawyer based on both statistical and specific
information. Dick is part of a population of 70% lawyers and 30% engineers. The specific
information about Dick was designed to be worthless for the probability judgment and be
equally applicable to both a lawyer and an engineer:
Dick is a 30 year old man. He is married with no children. A man of high
ability and high motivation, he promises to be quite successful in his field. He
is well liked by his colleagues.
The subjects judged the probability of Dick being an engineer to be 50%, just as much
as being a lawyer. By doing so they neglected the statistical information about the population
and followed the principles of the base-rate fallacy. According to Tversky and Kahneman
(1974), people properly utilize prior probabilities when no specific evidence is given;
however, they ignore prior probabilities when worthless evidence is given.
Let us consider a situation when an entrepreneur gets a chance to take over a
machinery facility. The machines are approximately five years old. Statistically 85% of
50 The mechanism of entrepreneurial risk-taking
machines of this type need a major repair in the 6th year. Entrepreneurs may ask an
independent engineering firm to check the machines and buy only the ones that have no
problems in operation. Entrepreneurs with high level of base-rate fallacy would think that the
chance that their new machinery would get a problem within one year is close to zero. In this
case they are ignoring statistical information and paying too much attention to the judgment
of the engineering firm that is only applicable to the current moment of time and not to the
next year. In general, entrepreneurs with high level of base-rate fallacy neglect existing
statistical information that is in fact the most accurate indication of the level of risk. It means
that objectively they are taking more risks than they perceive and thus have a higher risktaking propensity than entrepreneurs with the low level of base-rate fallacy. Therefore, we
hypothesize:
Hypothesis 1d. The level of base-rate fallacy will be positively associated with
the level of entrepreneurial risk-taking propensity
The fifth bias primarily emanating from the representativeness heuristic is illusion of
control, or unrealistic control. Note that it is different from illusory correlation although it is
sometimes confused with it. People have illusion of control when they perceive that
objectively chance determined, or uncontrollable events are within their control (Langer and
Roth, 1975; Zuckerman, Knee, Kieffer, Rawsthorne and Bruce, 1996). Illusion of control
refers to an overestimation of one's skills, and consequently his or her ability to cope with and
predict future events (Simon et al., 2000). This phenomenon has to do with the difference
between skills and luck. In principle this distinction is clear: in skill situations there is a causal
link between behavior and outcome, thus success in skill tasks is controllable; luck, on the
other hand, is a fortuitous happening, thus success in luck or chance activities is
uncontrollable. When one has a high level of illusion of control, this distinction between skills
and luck is not recognized (Langer, 1975). In studies of dice players, researchers found that
these players clearly behave as if they were able to control the outcome of a toss. They threw
the dice softly when they wanted to have low numbers and threw it hard when large numbers
were needed. Moreover, they believed that effort and concentration would pay off (Langer,
1975).
In the entrepreneurial setting it means that entrepreneurs with a high level of illusion
of control perceive that they can influence factors that only are partially controllable or not
controllable at all. For example, they may feel they can accurately foresee and influence their
The mechanism of entrepreneurial risk-taking 51
sales. However, there are always some exogenous factors that they can not control and on
which they do not anticipate. Because entrepreneurs with a high level of illusion of control
think that they can influence more than they actually can, they are taking actions that are
riskier than those of entrepreneurs with a low level of illusion of control. Therefore, we
hypothesize:
Hypothesis 1e. The level of illusion of control will be positively associated with
the level of entrepreneurial risk-taking propensity
The sixth bias, sample size fallacy, is also known as belief in the law of small
numbers. Sample size fallacy concerns the phenomenon that people make judgments on the
basis of sample proportion, and erroneously do not take into account the size of the sample
that crucially influences the reliability of the sample proportion outcome (Tversky and
Kahneman, 1974). Suppose an urn is filled with balls, 2/3 of one color and 1/3 of another
color, however it is unknown which colors are those. One individual draws 5 balls from the
urn, and finds 4 red and 1 white balls. Another individual draws 20 balls, of which 12 are red
and 8 white. Which sample provides strongest evidence that 2/3 of the balls are red and 1/3
white (and not the opposite)? Most people feel that the first sample provides more evidence,
because the proportion of red balls in the first sample is larger than in the second. However,
sampling theory shows that this is not true. People's intuitive judgments are dominated by
sample proportion, and not by sample size.
This insensitivity to the size of the sample can take many forms. Let us consider
nascent entrepreneurs that make their decision to start a venture being inspired by stories of a
few successful “college drop-out” entrepreneurs (e.g., Bill Gates and Michael Dell). Success
stories of entrepreneurs and their ventures are widely covered in journals and newspapers
while publications about failures of similar ventures and risks associated with their business
strategies are relatively unknown (Simon and Houghton, 2003). Entrepreneurs with high level
of sample size fallacy would generalize from the small samples without being aware of the
confidence intervals that information based on these small samples has. Thus they are
unaware of the risks surrounding their decisions and objectively are taking more risks than
entrepreneurs with low level of sample size fallacy. Therefore, we hypothesize:
Hypothesis 1f. The level of sample size fallacy will be positively associated
with the level of entrepreneurial risk-taking propensity
52 The mechanism of entrepreneurial risk-taking
The last, seventh, bias is regression fallacy. Regression fallacy is an erroneous causal
interpretation of regression to the mean (Tversky and Kahneman, 1974). Statistically,
regression to the mean is a phenomenon taking place when one looks at two related
measurements. The first measurement is either extremely high or extremely low and therefore
naturally attracts attention. In this case, the second measurement is likely to move closer to
the mean than the first measurement. As such, regression to the mean is a statistical
phenomenon, caused by chance. However, when one erroneously tries to explain this
phenomenon by a causal mechanism, we speak about regression fallacy. There is a number of
regression fallacy examples. For instance, the test of a certain stress-reducing drug, intended
to increase reading skills of poor readers. Students get a reading test and 10% of the students
with the lowest scores get the drug. After some time, these 10% are tested again with a
different, but similar reading test. The general finding is that their reading scores improved
significantly. People with a high level of regression fallacy would conclude that the drug has
been a great success. However, this is an unwarranted conclusion: even without the drug, the
principle of regression to the mean would have predicted the same outcome.
Let us consider a situation when a product of the entrepreneurial firm has performed
especially well in a given year. Most entrepreneurs would probably try to stimulate and
support further success of the product for example by an extensive advertising campaign.
However, the next year due to the regression to the mean the product will most likely perform
worse or at least deliver a marginal increase in the product performance. The natural
conclusion for an entrepreneur with the high level of regression fallacy is that the advertising
campaign did not work or was at least not worth it. Thus, the higher the level of regression
fallacy by entrepreneurs, the easier they will be making a wrong causal link between their
actions and performance of their venture by either abandoning a working strategy or using an
actually non-working strategy. Applying a wrong strategy means that the venture has a higher
risk of failure. Thus, objectively these entrepreneurs will be taking more risks than
entrepreneurs with low regression fallacy. Therefore, we hypothesize:
Hypothesis 1g. The level of regression fallacy will be positively associated with
the level of entrepreneurial risk-taking propensity
3.3.2 Relationship between the two systems and risk-taking propensity
The relationship between experiential system and risk-taking propensity can be
substantiated on a number of grounds. While earlier studies tended to use the intuitive and
The mechanism of entrepreneurial risk-taking 53
risk-taking as synonyms (Miller and Toulouse, 1986) or characterize risk-taking as an
intuitive activity (McGinnis, 1984), recent theoretical developments allow us to build a more
refined relationship between these two concepts.
According to the dual process theory, experiential thinking is based on a network of
learned associative pathways (Sloman, 2002). So, for a high level of experiential thinking a
lot of personal experience is required. Despite the personal experience, there is no guarantee
that problems and questions are addressed in their full complexity. In fact, professional
judgments are likely to have a very comprehensive view of a restrictively defined problem
(Fischhoff, Lichtenstein, Slovic, Derby, and Keeney, 1981: 75). Because of this narrow
definition of situation entrepreneurs may perceive that they fully know and have control over
the situation. They feel they can safely take risks in such situations, thus demonstrating a high
risk-taking propensity.
Moreover, even if the decision situation is observed in its full complexity, experienced
entrepreneurs feel they are more able to evaluate risks inherent in the situation, and are more
inclined to feel safely and accept them, thus objectively showing a higher risk-taking
propensity. Therefore, we hypothesize:
Hypothesis 2a. The more the experiential system is used the higher the level of
entrepreneurial risk-taking propensity will be
Due to the adherence to the formal and abstract rules of logic the rational system is a
relatively slow system (Kahneman, 2003). Entrepreneurs relying on this system take time to
think the problem thoroughly through. Because of this, potential actions are considered more
carefully, which means that there is a higher chance that a potential problem or risk will be
discovered. Realizing the additional risks of the potential actions means that concrete actions
on these issues become postponed and that eventually some of them will not be preceded. In
that case fewer risks are taken, so there is a lower risk-taking propensity.
Therefore, we hypothesize:
Hypothesis 2b. The more the rational system is used the lower the level of
entrepreneurial risk-taking propensity will be
54 The mechanism of entrepreneurial risk-taking
3.3.3 Relationship between the two systems and biases
The mechanism of influence of experiential and rational system on heuristics and
biases can be clarified via the dual process theory (Epstein et al., 1996; Kahneman and
Frederick, 2002). Epstein et al. (1996) also found a positive association between the
experiential system and heuristic thinking, and negative association between the rational
system and heuristic thinking. However, they focused on a different vision of heuristics than
the heuristics and biases in our study, which are proposed by Kahneman and Tversky. In
particular, Epstein et al. (1996) examine emotional (typically angriness or feeling foolish) vs.
rational answers to vignettes on various real-life situations.
We first explain how the experiential system influences heuristics and biases. As we
mentioned previously, experiential system is automatic, holistic and makes associationistic
connections (Denes-Raj and Epstein, 1994). Source of knowledge in the experiential system is
personal experience (Sloman, 2002). Personal experience makes instances of different events
available, which means that they may come automatically and readily to mind. For example,
entrepreneurs may find instances of how technical problems were solved in other apparently
similar situations, and will try to use these solutions in the new situations. Similarly,
entrepreneurs may fall back on former experiences in treating potential clients during sales
negotiations. In general, the more experience is built up, the more experienced events come
readily to mind, and thus the more likely an entrepreneur will use the availability heuristic,
which leads to the aforementioned availability biases.
In the same way, with an increased level of experience, more apparent regularities
amongst various features within the venture come to mind and can be used. For instance, an
entrepreneur that used to work in the aircraft construction and is now developing accessories
for cars will have a lot of associations with airplanes in his work. The more associations are
built up, the more likely it is that they are used to replace a target attribute by a seemingly
similar heuristic attribute, thus the more likely will entrepreneurs use the representativeness
heuristic, that leads to the aforementioned representativeness biases.
The experiential system is also a holistic system (Denes-Raj and Epstein, 1994),
because it delivers answers to the question as a whole, not trying to differentiate between
various aspects and features of the question and thus staying on a more aggregate, rough level.
On this level there is a greater chance that issues appear to have more similarities, because the
The mechanism of entrepreneurial risk-taking 55
particular details are omitted. Having more similarities creates a fertile ground for the
representativeness heuristic. This means that the more individuals use the experiential system,
the greater is the likelihood that they will use the representativeness heuristic, and that the
representative biases will occur.
Recapitulating the former discussion, we can hypothesize:
Hypothesis 3a. The more the experiential system is used, the higher the level of
heuristics and biases of entrepreneurs will be
The rational system is a primarily conscious analytical system that functions by a
person's understanding of conventionally established rules of logic and evidence. (Denes-Raj
and Epstein, 1994). It is slow, effortful and rule-governed (Kahneman, 2003). On the other
hand, the core characteristic of heuristics is that they provide answers that are automatically
computed and come immediately to mind, allowing for quick and efficient decision making.
Decision making based on heuristic thinking is characterized as a simplifying strategy
(Busenitz and Barney, 1997; Kahneman and Frederick, 2002; Kahneman, 2003). When
working according to the thorough, rule-based way of the rational system, it is unlikely that
target attributes are substituted by heuristic attributes that simplify matters and quickly come
to mind. The logic reasoning within the rule-based system simply does not allow for that type
of replacements. Thus, the more a person uses the rational system, the less he or she will be
involved in heuristic thinking, and therefore he or she will not be exposed to biases.
Therefore, we can hypothesize the following:
Hypothesis 3b. The more the rational system is used, the lower the level of
heuristics and biases of entrepreneurs will be
3.4
Methodology
3.4.1 Sample and data collection
Several sources were utilized to identify entrepreneurs for this study: (1) A list of the
one hundred entrepreneurs, compiled by the venture capitalist, David Silver (Silver, 1985); (2)
The list of national winners of the Entrepreneurs of the Year awards, compiled by Ernst and
Young; and (3) a list of 6,359 founders of venture-backed firms provided by VentureOne, a
leading VC research company based in San Francisco. VentureOne began tracking equity
investment in 1992. It collects data by surveying VC firms for recent funding activities and
56 The mechanism of entrepreneurial risk-taking
portfolio updates, gathering information through direct contacts at venture-backed companies,
and investigating various secondary resources such as company press releases and IPO
prospectuses from VentureOne 2001. Together, these sources drew their members from a pool
that included virtually every enduring company created by an entrepreneur in the US from
1960 till 2001.
1,500 randomly selected entrepreneurs with complete contact information were
selected for the survey. In administering the survey, we followed the total design method for
survey research (Dillman, 1978). The first mailing packet included a personalized letter, a
project fact sheet, the survey, a priority postage-paid envelope with an individually-typed
return-address label, and a list of research reports available to participants. The package was
sent by priority mail to 1500 selected entrepreneurs. 324 mailing packages were returned due
to undeliverable addresses or names. Thus, the adjusted sample was 1,176 entrepreneurs.
To increase the response rate, we sent four follow-up mailings to the companies. One
week after the mailing, we sent a follow-up letter. Two weeks after the first follow-up, we
sent a second package with same content as the first package to all non-responding
companies. After two additional follow-up letters, we received completed questionnaires
from 289 entrepreneurs, representing a response rate of 24.6% (289/1176).
3.4.2 Measurements
In our survey we used existing cases and scales from the literature if possible. We
strengthened the measurements by developing additional entrepreneurial cases based on the
existing cases. Due to the survey length limitations we had to select the items from the scales.
We chose items with the highest factor loadings. Moreover, when there were no scales
available we deduced items from the definitions, examples and ideas in the existing literature.
Because of these changes we had to pre-test the survey.
We conducted a pre-test by extensively interviewing twelve entrepreneurs. In the
beginning of each interview entrepreneurs told us about the background of their ventures, how
they started, how they discovered the opportunity and how the business idea developed over
the time. This allowed us to break the ice and better interpret their answers on the
questionnaire. In the last part of the interview we used the protocol method and asked the
entrepreneurs to "think aloud" as they filled out the English questionnaire (Hunt, Sparkman,
Jr., and Wilcox, 1982). The interviews were recorded and two researchers made careful notes
The mechanism of entrepreneurial risk-taking 57
of the verbalizations and the thinking process of the entrepreneurs. The analysis of interviews
led to changes in wording of instructions and wording of some cases and items.
Appendix A3.1 provides the construct reliabilities, the response format employed in
the questionnaire, and the details of the measurement items used in this study.
Dependent variable. We measure risk-taking propensity using the certainty equivalent
approach, which aims to plot the individual form of the entrepreneur's utility curve
(Kahneman and Tversky, 1979; Mullins, Forlani and Walker, 1999; Schneider and Lopes,
1986). In this approach entrepreneurs receive a number of scenarios with two possible
options: one certain and one risky with the same expected value. Risk aversion is thus
preference for certain outcomes to gambles of equal expected value, while risk-seeking is the
opposite. In the measure taken from Mullins et al. (1999) and Schneider and Lopes (1986)
entrepreneurs get five scenarios, each scenario measuring risky actions on a different level of
the expected value. Entrepreneurs are completely risk averse if they choose for certain options
and are completely risk-seeking if they choose for risky options in all the five scenario's. The
scores between 0 and 5 represent varying degrees of risk-taking propensity.
We supplemented this measure by another two risk-taking propensity measures based
on the certainty equivalent approach. However, in these two measures entrepreneurs have to
choose between one certain option and nine risky options. All the 10 options have the same
expected value. The risky options are ranked according to their riskiness manipulated by the
percent of the total sum that entrepreneurs can win and loose. As a result, we get a very
precise estimate of a point on the entrepreneur's utility curve. This point is enough to estimate
the form of the utility curve of the entrepreneur using the approximation by the exponential
function (e.g., Walls and Dyer, 1996). The Cronbach's α for all three measures of risk-taking
propensity is 0.87.
Mediating variables. Hindsight bias occurs when entrepreneurs remember their
predictions about a former event more accurately than they actually were. In the beginning of
our survey we asked entrepreneurs to answer a number of questions and rate with what
probability their answers on these questions were correct. The questions were difficult general
knowledge questions with two answer options: correct and incorrect. At the end of the survey,
we gave respondents the correct answers on the knowledge questions and asked them to
remember their estimates for correctness in hindsight (without looking at the first page of the
survey). The hindsight bias manifests itself when respondents originally gave the incorrect
58 The mechanism of entrepreneurial risk-taking
answer and lowered their estimate for correctness in hindsight. The larger the difference
between the original estimate and estimate in hindsight, the bigger the bias. We follow
Bukszar and Connolly (1988) and Slovic and Fischhoff (1977) with this procedure. According
to Campbell and Tesser (1983), there should be at least 30 min. between an original judgment
and the hindsight judgment. It took the participants about 40-45 min. to fill out our
questionnaire, therefore the potential memory bias is not a problem in our study. We used a 3item scale for hindsight bias (α=0.75).
Illusory correlation takes place when entrepreneurs see a co-occurrence between two
events, when no such co-occurrence exists. Our items are based on the ideas of Tversky and
Kahneman (1974). We used common myths about co-occurrences between for example, the
fact that a cat has been spayed or neutered and its weight, and between university licenses and
the larger size of a company. The 3-item scale has a Cronbach α of 0.67.
We also used the aforementioned general knowledge questions and the estimates of
the probability that the answers are correct, to measure overconfidence. We followed the
procedure of Forbes (2005), and Brenner, Koehler, Liberman, and Tversky (1996) to develop
a 3-item scale (α=0.82) for overconfidence, however we used other knowledge questions,
because questions from literature are somewhat out-dated. The more certain respondents are
that they gave the correct answer when in fact they are wrong, the higher level of
overconfidence they have.
Base-rate fallacy occurs when irrelevant case information is used to make judgments
in favor of available statistical information. We used two cases to measure the base-rate
fallacy (α=0.73), both are based on Lynch and Ofir (1989). The first case is about high-tech
firms. Respondents have to make an estimate of the probability that a given high-tech firm
will fail within the first five years. We start the case description by giving statistical
information (the base-rate) about high-tech firms' failures (60%). We also give irrelevant
information about the founder's hobbies and social life. When respondents deviate in their
predictions from 60%, they exhibit base-rate fallacy. The second case is about purchasing a
five-year old car. Similarly, we start by statistical information: "Consumer Reports" suggest
that there is a 50% probability that such a car will require major repairs in the 6th year. We
also give irrelevant case information concerning the color and the interior of the car.
Respondents are asked to predict the likelihood that the car requires major repairs during the
The mechanism of entrepreneurial risk-taking 59
next year. When respondents deviate from 50%, they show a base-rate fallacy. The more they
deviate, the higher the bias.
Illusion of control means that people perceive that objectively uncontrollable events
are within their control. We measured this construct by a 5-item scale (α=0.88) based on
Simon et al. (2000) and Zuckerman et al. (1996). Items concern, for instance, the accuracy of
predictions of future market developments and the perception that everything that happens is a
result of the respondent's own doing. The more respondents think that they can accurately
predict the market, or that what's happening is always a results of their own doing, the higher
level of illusion of control they exhibit.
Law of small numbers bias arises when people make their judgments on the basis of a
(small) sample, while not taking into account the actual size of this sample. The 3-item scale
(α=0.86) is based on Simon et al. (2000) and Mohan-Neill (1995). Items concern basing
strategic decisions on the opinion of closest friends and colleagues, on only one source of
information, or not basing such decisions on large scale market research. The higher
respondents score on these items, the greater law of small numbers bias they exhibit.
Regression fallacy concerns an erroneous causal interpretation of regression to the
mean. In such situation, there are always two related measurements: one that is extreme and
therefore attracts attention, and another that is closer to the mean. We measured regression
fallacy by a case that is based on an example of Kahneman and Tversky (1973). The case
describes a stable economic environment, which is not likely to grow naturally. The firm's
sales increased by 15% two years ago and decreased by 5% one year ago, thus bringing the
sales closer to the mean. In order to grow further, the firm increased its advertising budget last
year by 25%. As we know, despite that, its sales decreased by 5% due to regression to the
mean. Without the advertising campaign, the firm's sales could have decreased by even more
than 5%. When respondents conclude that advertising was not effective, they give a causal
interpretation of sales decrease in the last year and therefore exhibit regression fallacy.
In Appendix A3.1 we also describe the coding algorithm used to recode the hindsight
bias, overconfidence, base-rate fallacy and regression fallacy measurements for further
analysis.
Independent variables. We measure the rational system (α=0.94) by the "Need for
cognition" scale (Epstein et al., 1996; Pacini and Epstein, 1999) The rational system scale taps
the extent to which entrepreneurs are good at and rely on in-depth, hard, and logical thinking.
60 The mechanism of entrepreneurial risk-taking
The experiential system (α=0.98) is measured by the "Faith in intuition" scale (Epstein et al.,
1996; Pacini and Epstein, 1999). The experiential system scale taps the extent to which
entrepreneurs rely on their gut feelings and instincts, and believe in their hunches. We
selected the items for our systems scales from the latest version of the "Need for cognition"
and "Faith in intuition" instrument based on the highest factor loadings and correspondence to
the conceptual domains listed in the definitions of the rational and experiential system (Pacini
and Epstein, 1999).
3.4.3 Analysis
We used a number of procedural remedies to diminish potential common method bias
(Podsakoff, MacKenzie andLee, 2003). In our study applying archival data or having different
respondents was not possible. Therefore, we used a second best option by varying the types of
measures. Besides Likert scales, we also used cases for some biases and more objective utility
curve-based measures for risk-taking propensity. We shuffled Likert-scale items as well.
Finally, we tested for the common method bias statistically. The second-smallest positive
correlation among the manifest variables provides a conservative estimate for the common
method variance (Malhotra, Kim and Patil, 2006). In our data it was the correlation between
the age of the entrepreneur and the number of times the entrepreneur was involved as an
early-stage employee (r2=0.01, p=0.75). As another statistical check, we did the Harman's
single-factor test (Podsakoff et al., 2003). We forced all constructs into one factor model in a
confirmatory factor analysis. The χ2/df=16.8, indicating an extremely bad fit and significantly
worse than the fit of our measurement model, reported in Table 3.2. Therefore, we can
conclude there is no significant common method bias in our data.
In Table 3.1 we present descriptive statistics and Cronbach α's. Cronbach α's range
between 0.73 and 0.98, except for one, which suggests good reliabilities (Nunnally, 1978).
Note that overconfidence and hindsight bias have a high correlation, since we intentionally
linked both constructs in our measurements3.
2
Kendall's τ correlation coefficient
Neither the VIF-based nor the condition index-based tests indicated any substantial multicollinearity
effects (Hair et al.,1998). The VIF for overconfidence and hindsight bias was 4.64 and 4.59
respectively (while the cut-off value is >10). The highest condition index was 27.7. In this case the
variance proportion for overconfidence and hindsight bias was 0.21 and 0.18 respectively (while the
cut-off value is >0.5).
3
The mechanism of entrepreneurial risk-taking 61
Table 3.1. Means, standard deviations, correlations, and reliabilities
Construct
1. Risk-taking propensity
2. Hindsight bias
3. Illusory correlation
4. Overconfidence
5. Base-rate fallacy
6. Illusion of control
7. Law of small numbers
8. Regression fallacy
Mean
St.Dev.
1
4.45
2.35
0.87
1.04
0.94
.27**
1.25
**
.20**
**
**
.87
.17**
**
.05
-.01
4.65
5.80
2.62
5.11
4.71
5.62
2.42
0.99
1.24
1.37
2.49
.34
.35
.22
**
.50
**
.35
**
.40
2
3
5
6
7
8
9
10
0.75
**
.28
**
.39
**
.35
0.67
.31
**
.24
**
.06
1.39
.31
.30
.29
10. Rational system
3.13
1.19
-.34**
-.38**
-.11*
Figures on the diagonal line represent Cronbach's α
*
p<0.05
**
p<0.01
.10
**
.23
**
.35
.40
4.46
**
0.82
**
9. Experiential system
**
4
**
0.73
.30**
0.88
-.08
.22**
-.04
**
.29**
**
**
.24
0.85
-
**
.32
.06
.29
.38
.17**
0.98
-.35**
-.04
-.37**
-.18**
-.20**
-.30**
0.94
62 The mechanism of entrepreneurial risk-taking
Table 3.2. Confirmatory factor analysis
Construct
Item
Factor
Loading
T-value
Hindsight bias
HIN1
0.68
11.80
HIN2
0.74
13.32
HIN3
0.70
12.71
Illusory correlation
COR1
0.63
11.12
COR2
0.54
9.01
COR3
0.77
11.71
Overconfidence
OV1
0.83
15.88
OV2
0.85
16.49
OV3
0.67
12.29
Base-rate fallacy
BAS1
0.54
6.61
BAS2
1.07
8.53
Illusion of control
IC1
0.79
15.52
IC2
0.87
17.88
IC3
0.65
11.92
IC4
0.73
14.00
IC5
0.82
16.53
Law of small numbers
SN1
0.86
16.90
SN2(R)
0.71
13.16
SN3(R)
0.85
16.50
Experiential system
ES1
1.00
24.00
ES2
0.80
16.40
ES3
0.99
23.51
ES4
0.99
23.55
ES5
0.99
23.48
Rational system
RS1(R)
0.74
14.78
RS2
0.65
12.47
RS3
0.99
23.65
RS4
0.98
23.14
RS5(R)
0.94
21.38
(R) – the item is reversed
χ2=586.09, df=346, RMSEA=0.049, DELTA2=0.98, CFI=0.98, NFI=0.95, NNFI=0.97
The mechanism of entrepreneurial risk-taking 63
Prior to testing the hypotheses, we conducted confirmatory factor analysis on the
independent and mediating variables with a metric measurement scale (Hair, Anderson,
Tatham and Black, 1998) via Maximum Likelihood estimation in LISREL 8.54. We reviewed
each construct and deleted items that loaded on multiple constructs or had low item-toconstruct loadings. The measurement model is presented in Table 3.2.
Results in Table 3.2 indicate a good fit of the model; χ2/df is 1.69, while RMSEA is
0.049, DELTA2 is 0.984, CFI is 0.98, NFI is 0.95 and NNFI is 0.97 (Hair et al., 1998). All
loadings on the respective constructs are highly significant (p<0.001), while the standardized
loading of each item was greater than 0.5, demonstrating that our scales have convergent
validity (Fornell and Larcker, 1981). Moreover, no inter-factor correlations have a confidence
interval that contains a value of one (p<0.01) and all item-level correlations between
constructs are insignificant, thus we conclude that our scales possess discriminant validity
(Bagozzi, Yi and Philips, 1991).
The mediating nature of our study strongly favors the use of structural equation
modeling (SEM) to test our hypotheses. We base our full latent variable model on the
measurement model from CFA with an addition of the one-item construct of regression
fallacy and risk-taking propensity as our final dependent variable (Anderson and Gerbing,
1988). We also allow for covariance between overconfidence and hindsight bias and between
their error terms, because of the related measures of these two constructs. The model revealed
a reasonable fit between the theoretical model and the empirical covariances provided by the
sample (Hair et al., 1998); χ2/df is 2.02, RMSEA is 0.059, DELTA2 is 0.97, CFI is 0.96, NFI
is 0.93 and NNFI is 0.96.
3.5.
Results
Results are presented in Figure 3.2 and in the Appendix B3.1. Our overall hypothesis
was that heuristics and biases mediate the relationship between the experiential and rational
system and risk-taking propensity. Roughly, our results show that heuristics and biases fully
mediate the experiential system risk-taking propensity relationship, while they do not mediate
the rational system risk-taking propensity relationship. We compared a full model with direct
paths between the experiential and rational system and risk-taking propensity, and without
4
We follow the recommendations of Gerbing and Anderson (1993) in reporting this index
64 The mechanism of entrepreneurial risk-taking
-0.03
0.25***
Hin
1
ES1
-0.73**
Hindsight bias
-0.31***
Hin
2
Hin
3
ES2
ES3
Experiential
system
0.38***
0.21**
Illusory correlation
0.01
ES4
Cor
1
ES5
Cor
2
Cor
3
0.29***
0.68**
Overconfidence
-0.26***
Ov1
Ov2
Ov3
RP1
0.09
0.19**
Base-rate fallacy
0.04
Bas
1
Risk-taking
propensity
RP2
RP3
R
Bas
2
0.22***
IC1
RS1
R
0.36***
Illusion of control
-0.28***
IC2
IC3
IC4
IC5
RS2
0.38***
RS3
Rational system
Law of small
numbers
-0.02
0.26***
RS4
SN1
RS5
R
SN2
R
SN3
R
0.25***
0.13*
-0.12*
Regression fallacy
Reg
1
-0.17**
Figure 3.2. LISREL results for the Systems-Biases-Risk-Taking mediation (standardized
solution)
The mechanism of entrepreneurial risk-taking 65
such paths. The model with the extra paths was significantly better (∆χ2= 10.91, df=2,
p<0.01), due to a significant path from the rational system to risk-taking propensity. The
indirect effect of the rational system on risk-taking propensity along the biases is
insignificant, due to the high path coefficients of hindsight bias. When we delete this bias
from our model, there is a partial moderating effect of the rational system. We will now
consider all hypotheses in more detail.
The majority of our results is consistent with hypothesis 1 suggesting a positive
relationship between biases and risk-taking propensity. All cognitive biases we studied,
except for the hindsight bias, show a significant, positive relationship with risk-taking
propensity. In particular, the level of illusory correlation (β=0.21, p<0.01), the level of
overconfidence (β=0.68, p<0.01), the level of base-rate fallacy (β=0.19, p<0.01), the level of
illusion of control (β=0.36, p<0.001), the level of law of small numbers (β=0.26, p<0.001),
and the level of regression fallacy (β=0.25, p<0.001) all have positive impact on risk-taking
propensity. However, hindsight bias has a negative relationship with risk-taking propensity
(β=-0.73, p<0.01).
Hypothesis 2 concerns the relationships between the experiential system and rational
system and risk-taking propensity. The rational system has a negative relationship with risktaking propensity (β=0.-0.17, p<0.01), while the relationship between the experiential system
and risk-taking propensity is insignificant. Thus, we can conclude that hypothesis 2 is partly
confirmed.
Hypothesis 3 suggests a positive relationship between the experiential system and the
biases and a negative relationship between the rational system and the biases. Figure 3.2
shows that the experiential system has positive impact on the level of hindsight bias (β=0.25,
p<0.001), the level of illusory correlation (β=0.38, p<0.001), the level of overconfidence
(β=0.29, p<0.001), the level of illusion of control (β=0.22, p<0.001), the level of the law of
small numbers (β=0.38, p<0.001), and the level of regression fallacy (β=0.13, p<0.05). The
rational system has negative impact on the level of hindsight bias (β=-0.31, p<0.001), the
level of overconfidence (β=-0.26, p<0.001), the level of illusion of control (β=-0.28,
p<0.001), and the level of regression fallacy (β=-0.12, p<0.05). Contrary to hypothesis 3, the
relationships between the experiential system and the level of base-rate fallacy is
insignificant. This also holds for the relationship between the rational system and the level of
illusory correlation, the level of base-rate fallacy and the level of the law of small numbers.
66 The mechanism of entrepreneurial risk-taking
3.6.
Discussion
3.6.1 Major research findings and theoretical implications
In this study, we explore how reason and intuition of entrepreneurs form their
cognitive biases that in return influence their risk-taking propensity. Using the dual process
theory, we link the cognitive and risk-seeking streams of research. The first stream assumes
that in their intuitive decision-making, entrepreneurs are unconscious of the actual risks
associated with their decisions; they simply do not see them due to cognitive biases (Simon,
Houghton and Aquino, 2000). The second stream assumes that entrepreneurs objectively
tolerate more risks, that they are risk-seeking and that they consciously take the risks (Stewart
and Roth, 2001; 2004). Using the dual process theory, we show that both perspectives do not
exclude, but rather complement each other. In this study, we also follow the recent theoretical
developments in other fields by conceptually separating heuristics from biases. These
developments suggest that heuristics are the drivers of intuition, while biases are the errors
arising due to the use of such heuristics in intuitive thinking (Kahneman, 2003; Kahneman
and Frederick, 2002). These refinements allow us to critically assess the different statements
and assumptions about entrepreneurial decision-making made by both the cognitive and risktaking stream of research. As a result, this study improves our understanding of the
entrepreneurial decision-making in the following four ways.
First, recent theoretical developments in the behavioral decision-making suggested
that the traditional cognitive biases are an output of intuition (Kahneman, 2003; Kahneman
and Frederick, 2002). However, the results of our study show that it is not always true.
Among the seven biases we studied, one bias – base-rate fallacy – is independent of intuitive
thinking. Base-rate fallacy occurs when qualitative, often irrelevant information is used to
make a probability judgment, ignoring available statistical information about prior
probabilities (the base-rate frequency) (Tversky and Kahneman, 1974). It is an interesting
case for the dual process theory because our results show that base-rate fallacy is associated
with neither experiential nor rational system use, but is still significantly related to risk-taking
propensity. A possible explanation is that this bias is not driven by heuristics. At the same
time, the base-rate fallacy has many shapes and forms, therefore this finding should not
preclude future research. Thus, base-rate fallacy is a unique bias that is just there, influencing
entrepreneurial risk-taking irrespectively of how intuitively or rationally entrepreneurs think.
The mechanism of entrepreneurial risk-taking 67
Therefore, the intuitive nature of the cognitive biases should be assumed with certain
precautions.
Second, in line with the previous statement, previous studies postulated that cognitive
biases are a product of non-rational, intuitive thinking, thereby implicitly suggesting that
entrepreneurs only have to take their time and think the matter thoroughly through in order to
make the biases disappear (Busenitz and Barney, 1997). By introducing rational thinking into
our theoretical model, we were able to test this assumption. In fact, thinking rationally can
lower the level of only four of the seven biases we studied. This means that illusory
correlation, base-rate fallacy and law of small numbers require special debiasing procedures
in order to be corrected. Thus, further research should explore which debiasing procedures
work for entrepreneurs and what their input is into the quality of decision-making and
ventures' performance.
Table 3.3.
Evaluation of alternative full models in LISREL
Chi-square
(df)
p
RMSEA
DELTA2
CFI
NFI
NNFI
Hypothesized Model
943.48 (468)
0.000
0.059
0.97
0.96
0.93
0.96
Alternative Model (1):
A path was suggested from
overconfidence to
regression fallacy
896.92 (467)
0.000
0.057
0.97
0.97
0.94
0.96
Alternative Model (2):
An extra path was suggested
from hindsight bias to law
of small numbers
859.86 (466)
0.000
0.054
0.97
0.97
0.94
0.96
In the main model, the illusory correlation and the law of small numbers biases can
not be directly corrected by the rational system. However, our alternative models suggested
by the Langrangian multiplier indexes (see Table 3.3) show that there may be a direct positive
relationship between hindsight bias and the law of small numbers. In this model, there is a
significant negative indirect effect of the rational system on the law of small numbers, which
means that potentially there is an indirect correction of the law of small numbers via the
hindsight bias. This suggests that there may be a more refined certain internal structure
between the biases than previously acknowledged. Some biases may be "deeper", evoking
68 The mechanism of entrepreneurial risk-taking
other biases. The existence of an internal structure between the biases may also explain the
insignificant effect of the rational system on illusory correlation. Future empirical research is
necessary to check for such internal structure, and if it exists, explain it theoretically.
Third, previous studies found that cognitive biases lead to greater risk-taking, e.g. in
terms of decision to start a new venture or to launch a pioneering product (Simon et al., 2000;
Simon and Houghton, 2003). Strikingly, our results show that higher levels of biases do not
always mean more risk-taking: hindsight bias has a negative effect on risk-taking propensity
instead of the hypothesized positive effect. It means that the more entrepreneurs tend to
correct a posteriori their mental representation of their a priori judgment, the fewer risks they
tend to take. This relationship suggests that risk-takers among entrepreneurs do not tend to
have the hindsight bias. Risk-takers, on the contrary, accept their errors and learn better from
their mistakes than non risk-takers. Hindsight bias is the only bias we researched that
explicitly concerns the past, suggesting that entrepreneurs treat the future and the past
differently. They may have the rose-colored glasses when they discover an opportunity, but
they become sober-minded when exploiting it. Hindsight bias distorts interpretation of and
learning from mistakes. Previous studies suggested that such distortions make strategic
decision-making in general and risk management in particular problematic (e.g. McGrarth,
1999). The negative effect of the hindsight bias on risk-taking propensity is also the reason
why the total indirect effect of the rational system on risk-taking propensity is insignificant.
Further research should considerer these explanations.
Finally, our study supports the statement that entrepreneurs are predominantly
intuitive decision-makers (Busenitz and Barney, 1997; Simon et al., 2000). Consistent with
this statement, entrepreneurs in our sample also score significantly higher on the experiential
system than on the rational system (see Table 3.1). However, our findings also emphasize that
entrepreneurs are not purely intuitive "creatures. The results showed that the total
standardized effects (i.e. including the indirect effects via the biases) of the experiential and
rational system on risk-taking propensity are of comparable magnitude (0.28 and -0.25
respectively). This means that although entrepreneurs tend to rely more on their intuition and
on rational thinking, both types of thinking have strong impact on entrepreneurial risk-taking.
Therefore, the rational side of entrepreneurs is as important as their intuitive decision-making
and should not be left out of entrepreneurship research.
The mechanism of entrepreneurial risk-taking 69
Our findings have several important theoretical implications. First, we used the dual
process theory to show that rational and intuitive thinking have two distinct effects on
entrepreneurial risk-taking propensity. Intuitive and rational thinking are not two extremes of
the same concept, but rather two independent systems of thought that can be both extensively
used (or not) by entrepreneurs. Second, past studies concentrated on three biases with a
special focus on overconfidence (e.g. Forbes, 2005; Simon et al., 2000; Simon and Houghton,
2003). By more than doubling the scope of cognitive biases studied in the entrepreneurship
literature simultaneously, we show that limiting such scope may lead to missing the bigger
picture. Biases do not have to have similar effects on entrepreneurial actions – they may
actually have opposite effects as we show on the example of hindsight bias. We also show
that biases are not equally easy to correct, that some biases "lie deeper" than the others do and
that there is potentially an internal structure between the biases. Third, by adding a new set of
antecedents for risk-taking propensity we revealed the more stable part of cognitive biases.
We therefore contribute to the longstanding debate on the context vs. trait nature of the
cognitive biases (Baron, 1998; Busenitz and Barney, 1997; Forbes, 2005). Finally, we
developed new highly reliable measures for cognitive biases that can be used in future
research.
3.6.2 Managerial implications
Entrepreneurs can use this model for self-assessment in order to determine to what
extent biases contaminate their decision-making. The cognitive biases create a considerable
difference between the level of riskiness the entrepreneur himself experiences, and the level
of riskiness someone else with similar background and knowledge would observe in these
entrepreneur's actions (e.g. Simon et al., 2000). In other words, it creates a difference between
the objective risky "reality" and the subjective risky "reality" of the entrepreneur. As a result,
entrepreneurial risk-taking propensity becomes unbalanced. Risk-taking propensity has a
significant influence in a variety of domains: for example, it determines to the largest extent
the marketing program creativity (Andrews and Smith, 1996), influences whether companies
choose for acquisitions or licensing to acquire technologies (Steensma and Corley, 2001) and
whether they introduce pioneering new products (Simon and Houghton, 2003). Thus,
balancing and purifying risk-taking propensity means improving the decision-making in these
domains.
70 The mechanism of entrepreneurial risk-taking
Scoring high on the level of biases in such an assessment would mean that
entrepreneurs have to de-bias their decision-making. This study has simple yet powerful
implications on how to get rid of the cognitive biases and balance entrepreneurial risk-taking.
The traditional literature on such "debiasing" has focused on the context and task
characteristics to design a specific bias-dependent intervention and these strategies vary a lot
(e.g. Fischhoff, 1982). However, our results indicate that the more entrepreneurs use the
rational system, the less problematic are the cognitive biases. This is true for the hindsight
bias, overconfidence, illusion of control, regression fallacy and potentially for law of small
numbers. Thus, in order to get rid of these biases, entrepreneurs should simply take the time
and think. At the same time, this does not by any sense mean that intuitive thinking should be
avoided. Intuition still provides the most efficient way to facilitate the entrepreneurial
decision making. What is necessary are the corrective actions from the rational thinking
applied to the intuitive thinking.
There is however one exception among the biases: hindsight bias. Scoring high on
hindsight bias means that the entrepreneur has difficulties learning from the past mistakes.
The higher entrepreneurs score on hindsight bias, the fewer risks they take. The results are
disastrous: due to this bias, entrepreneurs will fail to learn properly from their mistakes, while
taking fewer and fewer risks – eventually this will likely lead to failure of the venture.
Because of the lower propensity to take risks, ex-entrepreneur will not likely start a new
company and a potential serial entrepreneur will disappear. Thus, hindsight bias is probably
the most serious of the cognitive biases.
Finally, entrepreneurs may use our model as a guiding tool for their decisions to hire
personnel, in particular when hiring new members of the management team. Knowing the
driving forces behind their risk-taking propensities may help avoid making unrealistically
optimistic decisions regarding new product development, introduction and promotion
(Andrews and Smith, 1996; Simon and Houghton, 2003).
3.6.3 Limitations
The measurement scale for a given construct should cover the main conceptual
domains for this construct (Churchill, 1979). We measured the rational system by the Need
for Cognition scale (Cacioppo, Petty, Feinstein and Jarvis, 1996; Epstein et al., 1996).
Although this scale is extensively used within the dual process theory, it originally comes
The mechanism of entrepreneurial risk-taking 71
from a different field and does not cover all the conceptual domains set by the rational system
definition. An extensive list of these domains can be found for example, in Epstein et al.
(1996) and Kahneman (2003). Similarly, Faith in Intuition scale used to measure the
experiential system does not fully cover the conceptual domains of this system. Therefore,
future research should bridge this gap between theory and measurement.
In our research design, we intentionally linked the questions about overconfidence and
hindsight bias to the same context in order to be able to directly compare their effects. In
particular, answers on the hindsight bias are anchored to answers on the overconfidence bias
(see Appendix A3.1). However, we did not find any significant multicollinearity effects
between the two constructs. Moreover, we put the overconfidence and hindsight bias
questions as far away from each other as possible within the questionnaire. Since it took our
respondents about 40-45 minutes to fill in the questionnaire, the time lag between answering
the overconfidence questions and answering the hindsight bias questions should be sufficient
(Campbell and Tesser, 1983). However, future research with a measure of the hindsight bias
with a greater time lag is necessary in order to validate our findings. Alternatively, completely
independent measures of overconfidence and hindsight biases may be used.
Finally, our dataset consisted of relatively experienced entrepreneurs. In particular,
their mean number of years of entrepreneurial experience was 14.5, about 90% are serial
entrepreneurs and about 70% were involved in two or three ventures at the same time. On
one hand, it means that the problems we identified with the influence biases on
entrepreneurial risk-taking are even more profound and that they do not disappear with
experience. On the other hand, it is interesting to explore whether these findings hold for less
experienced entrepreneurs as well. Moreover, it becomes essential for further research to
account for entrepreneurial experience when fine-tuning the cognitive appraisal dimensions of
emotions to the population of entrepreneurs.
Risk and uncertainty management strategies 73
Chapter 4
Risk and uncertainty management strategies
In this study, we show that the general intuition that it is better to manage risks and
uncertainty than not to manage at all is not true. We do so by exploring the performance
consequences of traditional risk and uncertainty management and recently emerged real
options reasoning. First, we elaborate on how exactly the strategies from these perspectives
manage risk and uncertainty. Then we use real options theory to develop hypotheses about
performance consequences of these strategies and changes in their effects in case of markets
with established (vs. emerging) technology standards and high (vs. low) network externality
effects. We test our hypotheses on a sample of 420 new technology ventures. The results
strongly indicate that new technology ventures should avoid being excessively cautious when
entering new markets. New technology ventures should also think twice before imitating other
firms. Strategies that do generally improve performance are strategic control, strategic
cooperation and real options strategy. While existing technology standards appear to have
predominantly direct effect on performance of new technology ventures, risk and uncertainty
management strategies do perform differently in markets with high direct and indirect
network externality effects. Our study contributes to the network externality literature by
showing that the effects of direct and indirect network externalities can be actually of
opposite direction. We contribute to the real options theory by falsifying one of its main
assumptions: real options work better under the conditions of higher uncertainty. Our results
show that it is clearly not the case when uncertainty arises from the absence of established
technology standards or from presence of direct network externality effects on the firm's
market. Finally yet importantly, our results support the theoretical distinction between the
74 Risk and uncertainty management strategies
traditional risk management and the real options reasoning by showing that these two types
of strategies can have opposite effects in certain circumstances.
4.1
Introduction
If entrepreneurship is about creating new ends or means-ends relationships (Shane and
Venkataraman, 2000), then risk and uncertainty are an essential part of all entrepreneurial
actions. Thus, the question is not so much about how to get rid of risk and uncertainty, but
how to manage them in more general terms. For example, risk management may among
others include increasing the level of potential downside loss, i.e. risk, in order to make
greater revenues possible. Uncertainty management may include both decreasing uncertainty
or leaving it unchanged while cutting it into pieces. So how should new technology ventures
manage risk and uncertainty?
Although they are often tied together, risk and uncertainty are distinct concepts. Risk
is one of the most controversial concepts in the strategic management literature, often
criticized for having conceptualizations different from the ones used in the real world (Ruefli,
Collins and Lacugna, 1999). Using the insights of the studies of managerial and
entrepreneurial risk-taking, we define risk as the downside loss – the maximum possible
amount that an entrepreneurial firm may loose. Probability is sometimes included in the
definition of risk, however prior studies have shown that the risk in terms of amount of
potential loss is more salient to managers and entrepreneurs (March and Shapira, 1987;
Sarasvathy, Simon and Lave, 1998). The definitions of uncertainty suffer from their
divergence focusing on different drivers such as dynamism, complexity and lack of
knowledge instead of the core phenomenon: inability to predict future outcomes. As opposed
to risk, uncertainty involves unpredictability: for entrepreneurs, it is unpredictability of the
venture payoffs. Uncertainty can be categorized into different types and sources, such as
technology, customer, competitor and operational uncertainty (Atuahene-Gima and Li, 2004;
Bstieler, 2005; Huchzermeier and Loch, 2001). In this study we will mainly focus on risks
and uncertainties arising from the external environment of the venture. Because risk and
uncertainty are two different although related concepts, managing risk does not necessarily
mean managing uncertainty. Moreover, several ways to manage risk and uncertainty are
known.
Risk and uncertainty management strategies 75
Strategies may target both risk and uncertainty at the same time. There are two main
streams of strategic management literature studying risk and uncertainty management: one
investigating traditional risk management strategies and the other investigating the real
options strategy. The strategies from the first stream focus on risk mitigation. For example,
risk is immediately reduced in case of strategic avoidance and strategic imitation with little or
no additional investments, while strategic control and strategic cooperation involve an
additional investment before the level of risk or its probability will decrease (Miller, 1992).
The second stream involves the real options strategy, which is also known as the real options
reasoning, thinking or logic (Miller and Shapira, 2004; McGrath, 1997,1999). It is a
qualitative way to use the real options theory. Under this strategy, firms pursue multiple
product options with high growth potential and thus high uncertainty, and invest further in a
product option only if uncertainty has been resolved and conditions are favorable. By doing
so, firms take calculated risks and create strategic flexibility (McGrath, 1999; McGrath,
Ferrier and Mendelow, 2004). Despite the different foci of these two streams, they provide a
way to manage both risks and uncertainties simultaneously.
There are also things we do not know yet about entrepreneurial risk and uncertainty
management. First, the two main streams of research produced five dominant strategies to
manage risk and uncertainty. All of these strategies should treat risk and uncertainty
differently (Miller, 1992). However, the precise conceptual differences between these
strategies are not well elaborated in the literature. The second void refers to the relationship
between the risk and uncertainty management strategies and performance: not all strategies
are empirically proven to enhance performance. Within the area of real options, a noticeable
theoretical effort has been made recently to advance our understanding of the qualitative way
to use the real options theory (e.g. Adner and Levinthal, 2004a,b; McGrath et al., 2004).
However, the empirical tests of these developments still lag behind. The third gap follows
from the previous one and concerns environmental contingencies: under which market and
technology development conditions should entrepreneurs choose for which strategies?
In our study, we focus on new technology ventures, i.e. spending at least 3% of their
total sales on R&D and not older than 15 years (Li and Atuahene-Gima, 2001; Song,
Podoynitsyna, van der Bij, Halman, 2008). In order to fill the aforementioned gaps, we first
conceptually investigate the differences between the two streams of research: one focusing on
the traditional risk management strategies and the other focusing on the real options strategy.
We elaborate on them using the dimensions suggested by Bowman and Hurry (1993) as
76 Risk and uncertainty management strategies
important in terms of firm performance consequences, i.e. approach to risk, approach to
uncertainty and size and timing of investments. Second, we empirically test the new venture
performance consequences of five different strategies to manage risk and uncertainty. Third,
we investigate the potential contingencies by focusing on two market conditions: pursuing an
opportunity in a market with well-accepted industry standards vs. a situation when no
standards are known, and pursuing an opportunity in a market with high vs. low network
externalities. Both of these market and industry characteristics are crucial for new technology
ventures, which research, develop and promote technology-intensive products. Bidding on the
right technology that will later on comprise the dominant design in the industry determines to
a great extent success of the product (Warner, Fairbank and Steensma, 2006). Network
externalities facilitate dominant design selection and increase the likelihood that a given
technology standard will be selected as part of the dominant design (Schilling, 2002).
Ignoring these two factors can be lethal for a new technology venture, requiring that the
venture adjusts its strategies according to these market and industry conditions.
Thus, we intend to make a three-fold contribution to the strategic management and
entrepreneurship fields. First, we conceptually clarify the differences between the various
traditional risk management strategies and real options strategy. Second, we empirically
validate performance consequences of both types of strategies. Third, we empirically explore
under which circumstances each of the two major types of risk and uncertainty management
strategies performs best.
This chapter is organized as follows. In the theoretical background section, we present
our conceptual model, give background information about the two main streams of research
on risk and uncertainty management and describe the differences between the streams. Then
we formulate hypotheses regarding the impact of both major types of risk and uncertainty
management strategies on new venture performance and the moderating effect of market
conditions on these relationships. In the following two sections, we describe the
methodological grounds of the study and our results. We finish with a discussion of
theoretical and managerial implications of this study, its limitations, and future research
directions.
Risk and uncertainty management strategies 77
4.2
Theoretical framework
The core topic in our conceptual model are risk and uncertainty management strategies
of entrepreneurial firms. We see ventures choose different strategies. In this study, we
compare two major ways to manage risks and uncertainties with respect to their performance
consequences: the real options strategy and the more traditional risk and uncertainty
management strategies. For simplicity reasons we will call the latter traditional risk
management strategies.
Traditional risk management strategies target risk and uncertainty mitigation by
strategic avoidance, strategic imitation, strategic control, or strategic cooperation (Miller,
1992). Real options strategy is a firm's strategy to manage risk and uncertainty by pursuing
multiple product options with high growth potential and high uncertainty, while further
investments into a product option are only made if uncertainty has been resolved and
conditions are favorable (McGrath, 1999; McGrath et al., 2004). The real options strategy is
also known as the real options reasoning, thinking or logic (Miller and Shapira, 2004;
McGrath, 1997, 1999).
In this study, we examine under which circumstances each of the two ways of
managing risk and uncertainty performs best for entrepreneurial firms. We consider two types
of circumstances. First, we examine the effects of the existence or non-existence of
technology standards in the firm's primary served market. Second, we study the presence of
high network externalities in the firm's primary served market. We present our conceptual
model in Figure 4.1.
Below, we will first discuss the two types of risk management strategies in more detail
and then cover the performance effects of these strategies. Afterwards, we will present the
two market conditions and their expected moderator effects.
4.2.1 Traditional risk management strategies
The approach to risk management has evolved over time. The traditional approach
concentrates on strategies for direct reduction of risk and uncertainty, although there are also
more refined differences in how these strategies approach risk and uncertainty. The traditional
78 Risk and uncertainty management strategies
Figure 4.1. The conceptual model
risk management strategies consist of four main approaches: strategic avoidance, strategic
imitation, strategic control, and strategic cooperation5. For all these four strategies, eventual
uncertainty is resolved only after the decision to follow a particular strategy is chosen. These
strategies assume investments irreversibility and full commitment. Decisions are made at the
beginning of the project.
Strategic avoidance occurs when management considers risks and uncertainties
associated with operating in a given product or geographic market to be unacceptable. For
firms already active in the market, avoidance may involve exiting the market. For firms that
are not yet active on the market or have recently entered it – as is the case with new ventures
5
We decided to exclude Miller's (1992) flexibility strategy from the list of traditional risk
management strategies in order to ensure content and discriminant validity of the strategy constructs,
because flexibility is also an essential part of the real options strategy (McGrath et al., 2004).
Risk and uncertainty management strategies 79
– this strategy may include postponement of operations for which risks are considered
unacceptable, initial product introduction to low uncertainty market niches or growth from
small scale (Miller, 1992; Shane, 2003). This strategy allows avoiding risk by reducing the
marketing and variable production costs. However, its disadvantage is that it decreases
potential returns as well. Strategic avoidance decreases market uncertainty by choosing the
alternative, more predictable markets. An advantage of this strategy is that it does not require
any additional investments.
Strategic imitation refers to copying rivals' strategies and technologies (Miller, 1992).
Although firms can pursue either differentiation or imitation strategy, they often choose for
matching the behavior of rivals in an effort to reduce the risk (Lieberman and Asaba, 2006).
Imitation occurs when the firm's products are similar to the main competitors' products, or
when manufacturing techniques or product technologies are adopted from other firms. This
strategy decreases the risk by reducing the R&D costs and decreases both technology and
market uncertainty by copying the actions of competitors. The additional investment
necessary for imitation are the costs for information gathering.
Strategic control refers to strategies targeted on controlling important environmental
contingencies. Strategic control may comprise creating entry barriers to make entry of new
competitors problematic. It also may include attempts to increase influence on customers
through advertising, and to control supplier relationships by using contractual agreements.
Finally, mergers may be negotiated with competitors. This strategy is distinct from strategic
avoidance and strategic imitation because it assumes direct interaction with the firm's
environment. Prior studies suggest that managers prefer to control environmental variables
over considering them as constraints within which they have to operate (Miller, 1992). The
control strategy is different from the other three traditional risk management strategies (i.e.,
strategic avoidance, strategic imitation, strategic cooperation) because it is the only strategy
that actually increases the level of risk by the costs necessary to realize this strategy. At the
same time, it decreases the probability that the risk will occur and increases the probability of
greater returns by controlling the environment. Control strategy decreases market uncertainty
by making the actions of the market players more predictable. It involves additional
investments in the form of advertising, merger realization and contract arrangement costs.
Strategic cooperation refers to involvement in multilateral agreements with other firms
(Miller, 1992). Strategic cooperation is relatively well elaborated in prior studies (e.g. Li and
80 Risk and uncertainty management strategies
Atuahene-Gima, 2001; Das and Teng, 1998a, 1998b). Risks under strategic cooperation
strategy are reduced by sharing with partner(s), resulting in behavioral interdependence
between the partners and a reduction of the autonomy of each partner. Cooperative strategies
may include joint R&D, joint manufacturing, or joint marketing and sales (Li and AtuaheneGima, 2001). The cooperation strategy may reduce both technology and market uncertainties.
This strategy typically cuts the NPD risk in half, but adds an additional risk of the partner's
opportunistic behavior (Das and Teng, 1998a, 1998b). Cooperation decreases the risk by
sharing it with the partner(s), however it may also increase returns if there is a synergy
between partners' competences allowing to improve the product quality. Strategic cooperation
strategy may decrease both technology and market uncertainty by tapping into the expertise of
the partner(s). The additional investments necessary for this strategy consist of costs for
arranging and maintaining the cooperation.
4.2.2 Real options strategy
Real options theory offers an alternative for traditional risk management (Miller and
Arikan, 2004). Our use of the real options theory for the exploration of risk management
strategies originates from the stream of literature focusing on real options reasoning, logic or
thinking (McGrath, 1999; Miller and Arikan, 2004), what McGrath et al. (2004) also called
"options reasoning as a heuristic for strategy" and Bowman and Hurry (1993) called
"flexibility options". Real options strategy preaches choosing new products with high revenue
potential and inherent high payoffs uncertainty and is therefore most useful in case of high
technology and/or market uncertainty. At the same time, this strategy involves waiting until
uncertainty resolves (or decreases below a certain threshold) before fully committing
resources. The main motivation for the real options strategy is increasing final returns. Real
options theory allows the decisions to be made both before and after the uncertainty
surrounding these decisions has been resolved (Huchzermeier and Loch, 2001). Real options
strategy is stricter in this sense because it has an assumption of actions and decision making
only after the uncertainties are resolved and the conditions are favorable (McGrath, 1999;
Adner and Levinthal, 2004a; 2004b).
The core idea of a real option can be formulated as a limited commitment that creates
future decision rights (McGrath et al., 2004). Similar to options on financial securities, real
options involve discretionary decisions or rights, with no obligations, to acquire or exchange
Risk and uncertainty management strategies 81
the value of the underlying asset for a specified price (Panayi and Trigeorgis, 1998). One
characteristic common for new technology ventures is that they have to develop at least one
product with a considerable amount of R&D. Thus, the underlying assets for our real options
strategy are the new products of the venture.
There are six types of real options: option to defer, option to abandon, option to
expand or contract, switching option and option to improve (Huchzermeier and Loch, 2001;
Trigeorgis, 1993). Defer option refers to waiting until more information becomes available.
Abandonment option refers to investment in stages, deciding at each stage, based on newest
information, whether to proceed or stop. Expansion/contraction option refers to the possibility
to adjust the scale of investment depending on whether market conditions turn out favorably.
Switching option refers to changing the mode of operation of an asset, depending on factor
prices (e.g. switching the energy source of a plant, or switching raw material suppliers).
Improvement option refers to midcourse actions during R&D projects to improve the
performance of the products; or to correct their targeting to market needs.
For the real options strategy, the final decision delay is essential because a real option
gives decision rights in the future and conveys access to future opportunities (Huchzermeier
and Loch, 2001; McGrath et al., 2004). There is always a certain time period between option
creation and option exercise, otherwise the full commitment would be immediately possible
and option creation would be redundant. This time period is necessary in order to receive new
information about the developments of the asset underlying the option (new products in our
case), which allows for better decision-making. However, even more important, if the option
exercise stage was not already conceived at the time of the initial investment, such strategy
cannot be characterized as a real options strategy and cannot be distinguished from other less
structured investment activities (Adler and Levinthal, 2004a,b). A simulation study by Miller
and Arikan (2004) arrived at similar conclusions. Therefore, a real options strategy is a
deliberate strategy and there should be a number of in advance formulated criteria as to what
outcomes merit continuation with the option. As a result, firms that use real options strategy
should show a higher percent of paused and stopped NPD projects than other firms (Adler and
Levinthal, 2004a,b). In this study, we will take the above-mentioned considerations into
account.
The real options strategy limits downside risk (the maximum is the investment to
create the option) and increases returns by staged investments. It manages uncertainties by
82 Risk and uncertainty management strategies
breaking down the total uncertainty into different, manageable parts at each investment stage.
The real options strategy further allows to manage both technology and market uncertainty by
creating flexibility, so that how uncertainty is resolved becomes less important or not
important at all: the venture has a solution to tackle it. Under this strategy, firms choose new
products with high revenue potential and inherent high uncertainty of payoffs. The
investments necessary for the real options strategy are additional R&D costs to ensure design
flexibility, costs for additional equipment or equipment with more capacity, etc.
4.2.3 Performance consequences of risk management strategies
Any new venture may choose from five different risk and uncertainty management
strategies described above. In Table 4.1, we summarize the main differences between these
strategies in terms of how they approach risk and uncertainty, as well as size and timing of
investments necessary for each strategy. These dimensions follow the ones proposed by
Bowman and Hurry (1993) to discuss the performance advantages of the real options strategy.
We elaborate further on them and extend them for the domain of traditional risk management
strategies.
Starting a venture comes together with embracing a priori irreducible uncertainty,
stemming from the fact that entrepreneurs cannot precisely predict what the market demand
for their products will be (Huchzermeier and Loch, 2001; McGrath, 1999; Shane and
Venkataraman, 2000). Market payoff determines the extent to which a venture may be called
a success and it is therefore crucial to tackle this uncertainty. Besides operational and timing
aspects, market payoffs from new products are determined by both their price and
characteristics. While price is relatively easy to change and can be experimented with to
maximize the payoffs, the characteristics of the products are fixed at the moment of the
product launch. Moreover, changing these characteristics and adapting the performance range
of the products usually requires additional financing and increases time-to-market. This
means that it is crucial for the venture that the product characteristics fit the actual market
requirements.
How can this risk be managed? In the previous sections we discussed the two major
ways to manage risk and uncertainty: the traditional risk management strategies and the real
options strategy.
Risk and uncertainty management strategies 83
Table 4.1. Characteristics of risk and uncertainty management strategies
Uncertainty
Risk
Dimensions
Approach towards
risk (downside
loss)
Approach towards
uncertainty
(unpredictability of
payoffs)
Risk management strategies
Avoidance
Imitation
Control
Cooperation
Real options strategy
Decreases the risk
by reducing the
marketing and
variable production
costs. Also
decreases potential
revenues
Decreases the
risk by reducing
the R&D costs.
May decrease
potential
revenues
Increases the level of risk
by the costs necessary to
realize this strategy, but
decreases the probability
of risk and increases the
probability of greater
revenues by controlling
the environment
Decreases the risk by
sharing it with the
partner(s), but may also
increase revenues if
product quality
improves
Risk is the price of the real
options creation.
Limits risk and increases
revenues by staged
investments
Decreases technology
and market uncertainty
by tapping into the
expertise of the
partner(s)
Does not directly change the
level of any uncertainty.
Allows to manage both
technology and market
uncertainty by making
decisions in stages and
creating flexibility, so that
how uncertainty is resolved
becomes less important.
Chooses new products with
high revenue potential and
inherent high uncertainty
Decreases market
uncertainty by
choosing the
alternative, more
predictable markets
Decreases
technology and
market
uncertainty by
copying the
actions of
competitors
Decreases market
uncertainty by making the
actions of the market
players more predictable
84 Risk and uncertainty management strategies
Table 4.1. Characteristics of risk and uncertainty management strategies (continued)
Size and timing of investments
Dimensions
Risk management strategies
Avoidance
Imitation
Control
Cooperation
Real options strategy
Additional
investments
No additional
investments.
Investments are
assumed to be
irreversible
Costs for
information
gathering.
Investments are
assumed to be
irreversible
Advertising, merger
realization, contract
arrangement costs, etc.
Investments are assumed
to be irreversible
Costs for arranging and
maintaining the
cooperation.
Investments are
assumed to be
irreversible
Price for option creation:
additional R&D costs, costs
for additional equipment or
equipment with more
capacity, etc. Price for option
exercise: costs to make the
decision plus eventually
investments due to full
commitment
Number of
decisions
1 decision
1 decision
1 decision
1 decision
2 decisions: option creation
and option exercise
Type of
commitment
Timing of the full
commitment
decision
Full
Full
Full
Full
Partial until option exercise,
after that – full. Investing
further only if the previous
stage meets certain criteria
Before uncertainty
is resolved
Before
uncertainty is
resolved
Before uncertainty is
resolved
Before uncertainty is
resolved
After uncertainty is resolved
Risk and uncertainty management strategies 85
We first focus on traditional risk management strategies. These strategies are applied
immediately after the risk has been detected. They all impact performance, but in a different
way. Strategic avoidance and strategic imitation save marketing, variable product and R&D
costs, while they increase the predictability of the financial payoffs. Therefore, these
strategies will have positive impact on new venture performance. Strategic control does not
allow any savings and only involves additional investments; thus, this strategy actually
increases downside loss. However, it also allows to significantly increase the revenues.
Therefore, we expect a positive impact of this strategy on new venture performance. Finally,
strategic cooperation will have positive impact on new venture performance, since it may
allow both to save costs and increase revenues. George, Zahra, Wheatley and Kahn (2001),
George, Zahra and Wood (2002), and McDougall, Covin, Robinson and Herron (1994) also
found a positive relationship between cooperation and new venture performance.
Therefore, we hypothesize:
Hypothesis 1. Applying traditional risk management strategies – strategic
avoidance, strategic imitation, strategic control and strategic cooperation – have a
positive impact on new venture performance.
Real options strategy allows for small initial investments to create a potential
performance range of the products that fits with perceived market requirement variability.
After uncertainty resolves, ventures may choose options with best perspectives and fully
invest in them. Not all of the created options will be exercised, but such options create the
flexibility necessary to realize a better, thorough match between product specifications and
the ultimate market requirements. Therefore, we expect that real options strategy has a
positive impact on new venture performance.
Thus, we hypothesize:
Hypothesis 2. Applying real options strategy has a positive impact on new
venture performance.
4.2.4 Moderator: Technology standards
In markets where technology standards are well-accepted or formalized,
entrepreneurial ventures may act differently compared to markets with emerging or unknown
standards. When technologies compete within a particular market the entrepreneurial venture
may face a lot of uncertainty. It is hard to predict which technology will prevail and selecting
86 Risk and uncertainty management strategies
the wrong technology may result in a huge downside loss (Christensen, Suarez and Utterback,
1998). When a particular technology standard wins over the other alternative standards, the
technological uncertainty drastically reduces. A venture wanting to develop and introduce a
new product for this market will not have to choose between competing standards anymore.
Moreover, the customer requirements will also become clearer. As a result, the potential
payoffs will become more certain. Assuming that comparable investments are necessary for
alternative technologies and that an independent venture is not likely to fully commit to more
than one technology at the same time, the risk (downside loss) will not change. However, the
probability of such a risk will decrease, because one of the possible reasons why a venture
may fail – betting on the wrong technology – is eliminated.
This view is consistent with the evolutionary perspective. Tushman and Anderson
(1986, 1997) introduced technology cycles in their evolutionary perspective. These cycles are
composed of technological discontinuities that trigger periods of technological and
competitive ferment. These turbulent periods are closed with the emergence of a technology
standard or a dominant design in the industry as a synthesis of a number of proven concepts.
Then a period of diminished ferment comes up. During this period incremental changes in the
technology or the dominant design are developed, resulting in a relatively low uncertainty.
When technology standards are well known, in general the traditional risk
management strategies will have less positive performance consequences. Most of these
strategies require extra costs, while part of their effect, in terms of risk and uncertainty
reduction, already has been realized autonomously by the selection of the technology
standards within the industry. Below, we will explain this for each strategy, strategic
avoidance, strategic imitation, strategic control, and strategic cooperation, separately.
Strategic avoidance mainly targets to reduce market uncertainty and marketing costs.
In markets with known (as opposed to unknown) technology standards, customer
requirements become clearer, decreasing the market uncertainty. Choosing for avoidance
strategy in this relatively certain market means that the new venture will choose for even more
certain market segments within this market and grow from small scale. As a result, avoidance
strategy will be less useful in such markets since the market uncertainty is already reduced.
Therefore, we expect less positive performance consequences of this strategy when
technology standards are known, compared to the situation when technology standards are
unknown.
Risk and uncertainty management strategies 87
Strategic imitation is targeted to reduce technology and market uncertainty and R&D
costs. Extra costs for information gathering are spent. In a situation with known technology
standards, spending of these extra costs is a waste, since technology and market uncertainty
already decreased by selection of technology standards, while R&D costs only have to be
spent to develop the technology selected. So, we expect that compared to a situation with
unknown technology standards, also this strategy has less positive performance consequences.
Strategic control targets to reduce market uncertainty by controlling market players
through advertising, merging, contracting etc. Of course, these actions need some investments
and in circumstances of known technology standards, these investments are (partly) a waste
since market uncertainty already has been decreased by selection of the technology standards,
thus resulting in less positive performance consequences of this strategy, compared to a
situation with unknown technology standards.
Strategic cooperation targets to decrease technology and market uncertainty, as well as
risk by sharing it. Time investments have to be made to search a partner and build a
partnership. Again in a situation of known technology standards, these investments are
(partly) wasted because of the resolution of technology and market uncertainty by selection of
the technology standards. Therefore, we expect less positive performance consequences of
this strategy when technology standards are known, compared to the situation when
technology standards are unknown.
Thus, we hypothesize:
Hypothesis 3. Well-accepted technology standards will negatively moderate
the relationship between the traditional risk management strategies strategic
avoidance, strategic imitation, strategic control, and strategic cooperation and new
venture performance.
When technology standards are not known yet, real options strategy offers the
opportunity to make small investments in more than one technology, thus experimenting with
the technologies and learning from it (Bowman and Hurry, 1993). However, when standards
are accepted, from a technological perspective it is more effective to make large investments
in the technology at once. In such situation the performance advantage of the real options
strategy will decrease.
88 Risk and uncertainty management strategies
Therefore, we hypothesize:
Hypothesis 4. Well-accepted technology standards will negatively moderate
the relationship between real options strategy and new venture performance.
4.2.5 Moderator: Network externalities
The economic perspective suggests that there are two types of network externalities:
direct and indirect externalities. Direct externalities come into existence through a direct
physical effect of the number of users on the quality of the product. There is an increase in
product value associated with having additional users in the network (Katz and Shapiro,
1986). The benefits that a customer derives from buying a telephone or fax machine, clearly
depends on the number of other users of the telephone or fax network. Indirect network
externalities come into existence when complementary products or services are of importance
for the value of the product (Schilling, 2002). One can think of traffic requiring highways,
DVD players requiring movies on DVD, and computer software requiring hardware (Rohlfs,
1974; Katz and Shapiro, 1986).
New technology ventures act differently in markets with network externality effects as
opposed to markets without these effects. Network externality effects in a market enhance the
potential value of the products in that market, and, thereby, the likelihood of higher revenues
of the firm. However, sales of a product have to reach a critical mass point in the first place,
before the mechanism of network effects will be activated. This is exactly the problem that
every new venture in such a market will experience. Only from the critical mass point will the
value obtained from the good or service be greater or equal to the price paid for it (Wärneryd,
1990).
In markets with network externality effects, the initial market uncertainty will be
higher: the customers have to rely on primary features of the product, while it remains
uncertain whether they will eventually get the additional benefits from the network effect.
There is a chance that a venture's product(s) will not reach the critical mass point, while its
competitors' product will. This in turn makes the customers hesitate before choosing for a
particular product and raises the market uncertainty level. At the same time, assuming that the
investments would be comparable, only the probability of failing in such markets will be
higher while the potential downside loss will remain the same as in markets without network
externality effects.
Risk and uncertainty management strategies 89
Considering the high level of market uncertainty in markets with network externality
effects, not all of the strategies we researched will be equally attractive. Some traditional risk
management strategies will become more useful, others will become less useful.
Among the traditional risk management strategies, the less useful ones are strategic
avoidance and strategic imitation. Following the avoidance strategy will simply not let a
venture enter such a market. A signal that a product is worth imitating in such a market is the
fact that it reached the critical mass point and started benefiting from network externality
effects. This means that the imitator's actions will always be lagged. The lag becomes even
bigger since the imitator has to develop its product. Finally, the imitator also has to reach the
critical mass point. This three-fold lag makes it less probable that the imitating venture ever
benefits from network externality effects.
The more useful strategies are strategic control and strategic cooperation. They
directly influence both customers and competitors on the market. The control strategy tries to
influence through establishing market entry barriers or through advertising. The cooperation
strategy focuses on influencing customers through better promotion and distribution of the
product. By cooperating in these activities, firms can afford bigger budgets and cover more
potential customers, which allow them to reach the critical mass point faster.
Therefore, we hypothesize:
Hypothesis 5a. The higher the network externalities, the weaker the effect of
strategic avoidance and strategic imitation on new venture performance.
Hypothesis 5b. The higher the network externalities, the stronger the effect of
strategic control and strategic cooperation on new venture performance.
In markets with high network externalities (as opposed to low), there is a higher level
of market uncertainty, as we explain below. Although the first intuition would suggest that in
case of higher uncertainties the real options strategy would become more useful, we
hypothesize otherwise for this moderator. Real options are created under the conditions of
high uncertainty and exercised (or not) after the uncertainty has resolved. It is valuable
because it creates flexibility to invest in a better option. However, the (additional) uncertainty
due to high network externalities can be only resolved after the new products have been
developed and launched – i.e. all the major investments have been made. This uncertainty
emanates from the fact that it is not clear whether the product can be embedded and accepted
90 Risk and uncertainty management strategies
in an already existing network of products, or whether the critical mass point will be reached
in case a new network around the product must be created (Wärneryd, 1990). Therefore, the
required number of sales is less easily guaranteed in markets with high network externalities
as opposed to markets without network externalities. Extensive marketing efforts are required
in order to overcome this additional uncertainty, otherwise the product may not gain the
additional network benefits. These extra marketing efforts are not required in markets without
network externalities. Therefore, we expect that real options strategy will be less positively
related to new venture performance in markets with high network externalities, compared to
markets with low network externalities.
At the same time, a real option constitutes a limited commitment. Decisions are split
in two phases – first, when an option is created and second, when an option is exercised
(McGrath et al., 2004). The additional uncertainty in markets with high network externality
effects will be only resolved after ventures have fully committed to a given product option
and fully invested into it. It means that a venture applying real options strategy will either
have to keep all the product options open and fully invest into them (which is costly), or
choose one or two product options having less information than in markets with low network
externalities. Because of the late uncertainty resolution in markets with high network
externalities, ventures will be less accurate in their predictions about which product options
will be more successful. This means that over the long run, ventures will show worse
performance in markets with high externalities as opposed to markets with low externalities.
Thus, we hypothesize:
Hypothesis 6. The higher the network externalities, the weaker the effect of the
real options strategy on new venture performance.
4.3
Methodology
4.3.1 Sample and data collection
Our sampling frame consists of 11,029 venture-backed young technology firms in the
VentureOne 2001 database and 982 new technology venture firms which were members of
the 1995-2000 Inc 500 (a listing of the fastest-growing private companies in the United
States, as selected by Inc magazine).
The names of the contact person and contact
information were obtained from VentureOne 2001 and the Dun & Bradstreet Market
Identifiers database.
Risk and uncertainty management strategies 91
2,000 new technology ventures were randomly selected for the survey. In
administering the survey, we followed the total design method for survey research (Dillman,
1978). The first mailing packet included a personalized letter, a project fact sheet, the survey,
a priority postage-paid envelope with an individually-typed return-address label, and a list of
research reports available to participants. The package was sent by priority mail to 2000
selected new ventures. 569 mailing packages were returned due to undeliverable addresses or
names. Thus, the adjusted sample was 1,431 new technology ventures.
To increase the response rate, we sent four follow-up mailings to the companies. One
week after the mailing, we sent a follow-up letter. Two weeks after the first follow-up, we
sent a second package with same content as the first package to all non-responding
companies. After two additional follow-up letters, we received completed questionnaires
from 420 firms, representing a response rate of 29% (420/1431).
Our sample represents industries from electronic and electrical equipment (25%),
pharmaceutical, drugs, & medicines (12%), industrial machinery & equipment (9%),
telecommunications equipment (9%), semiconductors & computer related products (28%),
instruments and related products (6%) and other industry segments (11%). The ventures in
our sample have the ratio of R&D/revenues of 20% and above, which means that these
ventures are highly R&D intensive.
4.3.2 Measurements
In our survey, we used existing scales from the literature if possible. When there were
no scales available we deduced items from the definitions, examples and ideas in the existing
literature. Because of these new scales we had to pre-test the survey.
We conducted a pre-test by extensively interviewing eleven entrepreneurs. In the
beginning of each interview entrepreneurs told us about the background of their ventures, how
they started, how they discovered the opportunity and how the business idea developed over
the time. This allowed us to break the ice and better interpret their answers on the
questionnaire. In the last part of the interview we used the protocol method and asked the
entrepreneurs to "think aloud" as they filled out the English questionnaire (Hunt, Sparkman,
Jr., and Wilcox, 1982). The interviews were recorded and two researchers made careful notes
of the verbalizations and the thinking process of the entrepreneurs. The analysis of interviews
led to changes in wording of instructions and items.
92 Risk and uncertainty management strategies
Appendix A4.1 provides the construct reliabilities, the response format employed in
the questionnaire, and the details of the measurement items used in this study.
Dependent variable. We use three objective measures of new venture performance.
First, we ask for return on investment (ROI) in the last fiscal year. Second, we ask for
customer retention rate in the primary served market, and third, we measure the rate of sales
growth in the last fiscal year. These measures are based on Lambert (1998) and McDougall et
al. (1994). We treat these measures as three separate dependent variables.
Independent variables. For the traditional strategic risk management strategies we
mainly base ourselves on Miller (1992). The 3-item scale of strategic avoidance (α=0.79) is
based on Shane (2003) and Miller (1992), while the 3-item scale of strategic imitation
(α=0.81) is taken from Miller (1992) and Gatignon and Xuereb (1997). Strategic control is
measured with a 3-item scale (α=0.86), taken from Miller (1992). Strategic cooperation is also
measured with a 3-item scale (α=0.65). This scale is based on Li and Atuahene-Gima (2001).
Real options strategy is measured with a 3-item scale (α=0.86), based on
Huchzermeier and Loch (2001), and McGrath et al. (2004). It measures three basic sources of
flexibility that are core for the concept of real options reasoning, following the approach of
Churchill, Jr (1979).
Moderating variables. The 1-item scale of technology standards is based on Warner
et al. (2006). It measures whether the technology standard in the venture's primary served
market is well-established or emerging. In our measurements we distinguish between direct
and indirect network externalities (Katz and Shapiro, 1985; 1986). Direct network
externalities is measured by 2 items (α=0.72), based on Katz and Shapiro (1985; 1986), while
indirect network externalities is measured by 1 item taken from Schilling (2002).
4.3.3 Analysis
Table 4.2 displays the descriptive statistics and correlations for the variables in our
conceptual model. In our sample, real options strategy is most extensively used, while
strategic avoidance is least extensively applied.
Risk and uncertainty management strategies 93
Table 4.2. Descriptive statistics and correlation matrix
ROI
Customer
Retention
Sales growth
Avoidance
Imitation
Control
Cooperation
Real Options
Tech. Standards
Direct Netw. Ext.
Indirect Netw. Ext.
A
Mean St.Dev.
76.85
78.94
B
47.55
24.17
C
D
E
F
G
H
I
G
K
126.79
3.57
4.89
4.42
4.75
5.10
0.58
4.36
4.75
66.09
1.79
1.15
1.36
1.16
1.33
0.49
1.74
1.97
A
B
C
D
0.54
0.49
-0.08
0.19
0.28
0.35
0.30
0.17
0.05
0.12
0.84
-0.20
0.42
0.52
0.33
0.53
0.39
0.27
0.41
-0.18
0.45
0.49
0.38
0.57
0.27
0.21
0.30
-0.23
-0.03
-0.03
0.00
-0.03
0.19
-0.03
E
F
0.37
0.15
0.47
0.01
0.23
0.29
0.17
0.39
0.07
0.07
0.18
Correlations above |0.08| are significant at p=0.05, above |0.12| at p=0.01, and above |0.16| at p=0.001
G
0.28
-0.14
0.10
0.17
H
I
G
0.04
0.42
0.42
0.04
0.03
0.66
94 Risk and uncertainty management strategies
We purified our measurement scales by performing a CFA using Maximum
Likelihood estimation in LISREL 8.54. The analysis was carried out for our independent
variables, the strategic risk management strategies. We reviewed each construct and deleted
items that loaded on multiple constructs or had low item-to-construct loadings. The
measurement model is presented in Table 4.3.
Table 4.3. Confirmatory factor analysis and Cronbach α's
Construct
Item
Avoidance
AV1
AV2
AV3
Imitation
IM1
IM2
IM3
Control
CTR1
CTR2
CTR3
Cooperation
CO1
CO2
CO3
Real options
RO1
RO2
RO3
Factor loadings
and
Cronbach's α
α=0.79
0.85
0.70
0.72
α=0.81
0.71
0.87
0.72
α=0.86
0.91
0.67
0.88
α=0.65
0.83
0.62
0.42
α=0.86
0.83
0.81
0.83
T-value
17.91
14.55
15.01
15.31
20.08
15.80
22.33
14.85
21.26
13.38
10.87
7.58
19.60
19.10
19.78
χ2=219.37, df=80; RMSEA=0.064; DELTA2=0.96; CFI=0.96; NFI=0.94; NNFI=0.94
In Table 4.3 we also report Cronbach α's. They range between 0.79 and 0.86 with an
exception for strategic cooperation (α is 0.65). These outcomes suggest good reliabilities
(Nunnally, 1978). Our measurement model has an acceptable fit with χ2/ df is 2.74, RMSEA
is 0.064, DELTA2 is 0.96, CFI is 0.69, NFI is 0.94 and NNFI is 0.94 (Hair, Anderson,
Tatham and Black, 1998). All loadings on the respective constructs are highly significant
(p<0.001), while standardized loadings of the items were in all cases but one, greater than 0.5.
Therefore, our scales demonstrate convergent validity (Fornell and Larcker, 1981). Since no
inter-factor correlations had a confidence interval containing a value of one (p<0.01) and the
multivariate Lagrange multiplier test indicated that all item-level correlations between
Risk and uncertainty management strategies 95
constructs were insignificant (Kim, Cavusgil and Calantone, 2006), we can also conclude that
our scales possess discriminant validity.
This study tested the hypotheses using ordinary least squares multiple regression. We
first examined the main effects of the strategic risk management strategies on the performance
variables ROI, customer retention rate and sales growth in models 1, 4 and 7 respectively.
Then we added the technology standard variable to our model and examined the moderating
effect of the availability of well-accepted technology standards on the strategy performance
relationships in models 2, 5 and 8 respectively. Next, we added the network externality
variables to our original model and examined the moderating effect of the high direct and
indirect network externalities in the primary served markets on the strategy performance
relationships in models 3, 6 and 9. As suggested by Kenny and Judd (1984), prior to the
regression analysis all variables were mean centered. A multicollinearity test revealed no
substantial multicollinearity between the variables in our study (Hair et al., 1998).
Finally, we performed a split group analysis to examine the mutual moderating impact
of technology standards, direct and indirect network externalities on the strategiesperformance relationships. This approach involved creating high and low levels of each
moderator variable by performing a mean split (Cohen and Cohen, 1983).
4.4
Results
The results are presented in Table 4.4. They partly support our hypotheses with a
number of intriguing exceptions. Hypothesis 1 suggested a positive relationship between
traditional risk management strategies and new venture performance. It is supported for
strategic control and strategic cooperation with β-coefficients ranging between 0.176 and
0.334 and between 0.173 and 0.279 respectively for the different performance variables; the
data partly support our hypothesis for strategic imitation (β-coefficients range between 0.006
and 0.123) and they do not support our hypothesis for strategic avoidance (β-coefficients
range between -0.063 and -0.165). Consistent with hypothesis 2 a positive relationship
between real options strategy and new venture performance is found for all performance
variables (β-coefficients range between 0.147 and 0.345).
Table 4.4. Results of regression analyses
Avoidance
Imitation
Control
Cooperation
Real Options
Existing Technology
Standards (TS)
Avoidance * TS
Imitation * TS
Control * TS
Cooperation * TS
Real Options * TS
Direct Network
Externalities (DNE)
Avoidance * DNE
Imitation * DNE
Control * DNE
Cooperation * DNE
Real Options * DNE
Indirect Network
Externalities (INE)
Avoidance * INE
Imitation * INE
Control * INE
Cooperation * INE
Real Options * INE
F
R2
∆R2
Adj. R2
†
H1 (+)
H1 (+)
H1 (+)
H1 (+)
H2 (+)
Model 1
-0.063
0.006
0.176***
0.279***
0.147**
ROI
Model 2
-0.057
0.006
0.167***
0.320***
0.117*
Model 3
-0.070
-0.006
0.124*
0.293***
0.242***
Customer retention
Model 4
Model 5
Model 6
-0.165***
-0.152***
-0.201***
0.082†
0.099**
0.054
***
0.334
0.305***
0.294***
***
***
0.173
0.229
0.154***
***
***
0.315
0.282
0.321***
0.193***
H3 (-)
H3 (-)
H3 (-)
H3 (-)
H4 (-)
0.387***
0.005
-0.066
0.066
0.049
-0.044
0.145
0.102*
0.123*
0.002
-0.141*
-0.021
20.42***
0.198
0.188
p<0.10; * p<0.05; ** p<0.01; *** p<0.001.
11.94***
0.244
0.046***
0.223
-0.167*
-0.130*
-0.034
-0.102
0.202**
8.02***
0.253
0.055***
0.222
0.024
**
0.262
0.152*
-0.020
0.132†
-0.213***
72.12***
0.466
0.459
62.31***
0.627
0.161***
0.617
Model 9
-0.142***
0.103*
0.247***
0.207***
0.386***
0.009
-0.067†
0.123***
0.042
-0.017
0.052
***
H5a (-)
H5a (-)
H5b (+)
H5b (+)
H6 (-)
Sales growth
Model 8
-0.130***
0.128**
0.252***
0.262***
0.310***
0.271***
-0.013
0.006
0.112**
0.021
0.022
-0.061
H5a (-)
H5a (-)
H5b (+)
H5b (+)
H6 (-)
Model 7
-0.138***
0.123**
0.267***
0.215***
0.345***
0.038
0.084
0.090†
0.039
-0.046
0.141**
0.019
-0.094†
-0.041
-0.081†
0.000
0.195***
26.89***
0.532
0.066***
0.512
-0.069
-0.051
-0.095†
-0.016
0.134*
24.62***
0.510
0.024***
0.489
78.30***
0.486
0.480
49.70***
0.573
0.087***
0.561
Risk and uncertainty management strategies 97
In general our results do not support hypotheses 3 and 4: we did not find any
significant moderating effects between existing technology standards and strategic risk
management strategies with the exception of strategic control and strategic imitation.
Consistent with hypothesis 3 strategic imitation has a negative interaction with technology
standards on sales growth. Contrary to this hypothesis, strategic control has a positive
interaction with technology standards on customer retention rate and sales growth. Existing
technology standards also have a strong direct positive effect on all performance variables.
Hypothesis 5a concerns the negative moderating effect of network externalities on the
strategic avoidance and strategic imitation performance relationship. Our findings only partly
support this hypothesis. For sales growth we did not find any moderating effects. For ROI and
customer retention rate the hypothesis is mainly supported with respect to indirect network
externalities. However, direct network externalities appear to be a positive moderator of the
strategic avoidance and strategic imitation performance relationship. Hypothesis 5b regards
the positive moderating effect of network externalities on the strategic control and strategic
cooperation performance relationship. Again this hypothesis is partly confirmed. We found a
positive moderating effect of direct network externalities on the strategic control customer
retention rate and strategic control sales growth relationship. We also found a positive
moderating effect of direct network externalities on the strategic cooperation ROI
relationship. However, we found a negative moderating effect of indirect network
externalities on the strategic control customer retention rate and strategic control sales growth
relationship. All other moderating effects were insignificant. Hypothesis 6, suggesting a
negative moderating effect of network externalities on the real options strategy performance
relationship, is mainly supported for direct network externalities. However, for indirect
network externalities we found a positive moderating effect on the real options strategy
performance relationship.
4.5
Discussion
In this study, we compared the performance consequences of different traditional risk
and uncertainty management strategies and the recently emerged real options strategy. These
strategies represent a scope of possible responses to risks and uncertainties arising from the
external environment of the venture, which are harder to influence than those arising from the
firm itself. Thus, it is important to study the strategies to mitigate these external risks and
98 Risk and uncertainty management strategies
uncertainties. We also investigated the moderating effects of important market characteristics:
established technology standards, direct network externality effects and indirect network
externality effects. While our main effects hypotheses are largely confirmed, the more
exploratory moderator hypotheses delivered a number of surprises.
In the total picture of the risk and uncertainty management strategies, only three
strategies have unambiguously positive effects on new venture performance: strategic control,
strategic cooperation and real options strategy. Among the other two strategies, strategic
imitation, does increase sales growth, but does not influence return on investment (ROI) and
customer retention rate. Strategic avoidance also has no effect on ROI, but has a negative
effect on customer retention rate and sales growth. This runs contrary to the notion of
“starting small, growing large”, which is a popular advice for risk management in new
ventures (Shane, 2003). Both imitation and avoidance are the most conservative risk
management strategies in the set we researched: they involve no or little additional
investments to realize the strategy itself and drastically decrease the costs that the venture
otherwise would have to incur. The disadvantage of imitation and avoidance is that they also
limit the potential revenues of the venture. While in case of strategic imitation the decrease in
costs seems to offset the (potential) decrease in revenues, it is not the case for strategic
avoidance. Our findings suggest that strategic avoidance – i.e. introducing products to low
uncertainty niches first and growing from the small scale – is the only strategy that new
technology ventures should avoid. The best overall strategy in terms of ROI is strategic
cooperation, in terms of customer retention rate – strategic control and in terms of sales
growth – real options strategy.
Markets with existing technology standards have a positive direct effect on new
venture performance. Their moderating effects arise in case of strategic control, which will
additionally improve customer retention and sales growth in such markets. Strategic control is
the risk management strategy that most directly influences the external environment of the
venture, and it seems also most sensitive for this external environment. Strategic control can
be more effectively implemented under existing technology standards probably because new
technology ventures can focus their efforts and their scarce resources on a limited group of
products that comply with the technology standards. Except in one other case with strategic
imitation, the risk and uncertainty management strategies obviously do not show any
interaction with existing technology standards. This implies that existence of technology
standards does not constitute a significant decrease in technology uncertainty for new
Risk and uncertainty management strategies 99
technology ventures. Thus, there are other important sources of technology uncertainty, more
important than technology standards in the market and industry. As a result, research into
sources of uncertainty becomes an interesting alley of scientific inquiry.
As opposed to technology standards, network externalities can be seen as moderators.
They do not have direct effect on new venture performance (except for the effect of indirect
network externalities on customer retention rate), but instead strengthen or weaken the effects
of the risk and uncertainty management strategies. Three main conclusions about network
externalities can be made based on our findings.
First, direct and indirect network externalities have opposite moderating effects on
new venture performance – i.e., when direct network externalities strengthen the effect of risk
and uncertainty management strategies, indirect network externalities weaken them, and vice
versa. Second, the traditional risk management strategies and real options strategy also have
opposite effects within each type of network externalities: direct network externalities
generally weaken the effect of real options and strengthen the effect of traditional risk
management strategies, while indirect network externalities generally strengthen the effect of
real options strategy and weaken the effect of traditional risk management strategies. Finally,
these effects are especially important for ROI and customer retention rate and to a smaller
extent for sales growth.
A possible explanation for these findings is that firms enact the direct network
externalities, when they exist, by increasing the number of users of the firm's products. In
order to enact the existing indirect network externalities, firms have to increase the number of
complementary products and services (Schilling, 2002). The traditional risk management
strategies are actively intervening in the venture's environment, while real options strategy is
relatively passive and aims to increase flexibility by preparing several product options as a
response to external uncertainty (Huchzermeier and Loch, 2001; McGrath, 2004; Miller,
1992). This means that it is easier to increase the number of the ventures' products users by
intervening in the market directly than by building flexibility by real options. Similarly, it is
easier to increase the availability of complementary products by pursuing several product
options (which may be complementary to each other) than by trying to influence other firms
to produce such products.
How should managers and entrepreneurs interpret these results? The expectations of
the performance potential are often influenced by vivid examples in the entrepreneurial
100 Risk and uncertainty management strategies
environment or by the capabilities that new ventures and their staff possess. Our findings
allow entrepreneurs to choose more rationally, thereby optimally distributing the scarce
resources of their ventures.
Strategic avoidance is the only strategy that is detrimental for new venture
performance – this kind of cautiousness does not work well for new technology ventures. In
order to facilitate the choice among the other four strategies, we present the best risk and
uncertainty management strategies under different market conditions in Table 4.5 and 4.6.
Knowing in which market(s) a particular venture operates, entrepreneurs can use these results
to choose for the risk and uncertainty management strategy with the greatest performance
potential. Table 4.5 presents our findings for performance in terms of return on investment
(ROI) and Table 4.6 – for performance in terms of customer retention rate and sales growth.
Real options strategy dominates the total picture and appears to be the best in six
cases, strategic control – in four cases, strategic cooperation – in three cases and imitation – in
two cases. In one case, no strategy has performed well. In markets with existing technology
standards new technology ventures are better off with strategic control as opposed to real
options in markets with emerging technology standards. In markets with high direct network
externalities new technology ventures should choose for strategic control as opposed to real
options strategy under low direct network externalities. Finally, in markets with high indirect
network externalities new technology ventures should go for real options strategy as opposed
to strategic control under low indirect network externalities.
In most situations, the best risk and uncertainty management strategy remains the best
for all performance types. However, the best strategies differ in two cases: under high direct
and indirect network externalities and under low direct and indirect network externalities. In
these two cases, new technology ventures in markets with existing technology standards
should use strategic cooperation to have the best ROI and strategic control to have the best
customer retention rate and sales.
Risk and uncertainty management strategies 101
Table 4.5. The best risk and uncertainty management strategies under different market conditions (performance in terms of ROI)
Existing technology standards:
Technology standards are emerging:
Direct Externalities
Real Options
Control
Cooperation
Low
High
High
Cooperation
High
Real Options
Real Options
Low
Low
Indirect Externalities
High
Low
Indirect Externalities
Direct Externalities
Imitation
Cooperation
102 Risk and uncertainty management strategies
Table 4.6. The best risk and uncertainty management strategies under different market conditions (performance in terms of
customer retention and sales growth)
Existing technology standards:
Technology standards are emerging:
Direct Externalities
Real Options
Control
Control
Low
High
High
Control
High
Real Options
-
Low
Low
Indirect Externalities
High
Low
Indirect Externalities
Direct Externalities
Imitation
Real Options
Risk and uncertainty management strategies 103
There are three potential limitations in our study. Although they do not make our
results less valuable, it is important to name them as they simultaneously represent future
directions of research. First, we focused on strategies tackling risks and uncertainties arising
from the environment external to the new technology venture. Although some of our
strategies, like real options, can be also used to manage internal risks and uncertainties of the
ventures, external risks and uncertainties remain the greatest challenge for new ventures since
they are most difficult to influence and can be relatively easily overlooked.
Second, our operationalization of strategic cooperation corresponds to vertical
alliances (Kotabe and Swan, 1995) and supply-chain integration (Song et al., 2008)
constructs. At the same time, we know that other types of cooperation are important for new
technology ventures – such as R&D alliances or linkages with universities (Song et al., 2008).
Future research may consider the alternative types of cooperation in the examination of risk
and uncertainty management strategies.
Finally, since we were not able to identify a survey instrument measuring real options
strategy, we had to develop one ourselves. We followed Churchill, Jr. (1979) in developing
this new scale and used the items tackling the main dimensions of the real options strategy
(Huchzermeier and Loch, 2001; McGrath, 2004). Using this approach, we have
operationalized the real options strategy as a reflective construct. However, the real options
scale can be also conceived as a formative construct (Jarvis, Mackenzie and Podsakoff, 2003),
where each item represents a different kind of real option that a firm can use – i.e. expansion,
contraction, switching and so on (Huchzermeier and Loch, 2001). Such a construct would be
formative because theoretically firms do not have to use all the different kinds of real options
simultaneously. Future research should investigate these different conceptualizations of real
options strategy.
4.6
Conclusion
We contribute to the strategic management literature by explicitly comparing the
different ways to manage externally determined risk and uncertainty in the context of new
technology ventures. To the best of our knowledge, the traditional risk management strategies
have never been compared empirically. Although the general intuition would suggest that
managing risk and uncertainty is better than not doing that, our results suggest otherwise.
Strategic avoidance should not be generally used by new technology ventures – it improves
104 Risk and uncertainty management strategies
new venture performance only in a couple of very specific situations. Similarly, strategic
imitation has limited impact on performance. Overall, the best traditional strategies are
strategic control and strategic cooperation.
The need to integrate the findings on risk management strategies has become even
more evident with the emergence of the real options. For example, Miller and Arikan (2004)
raised doubts about the performance advantages of real options strategy: "[Real] options
reasoning may be justified as a way to manage risk, but it may or may not enhance firm
value". Our study shows that real options as a strategy to manage risk and uncertainty has
definite advantages for the new venture performance.
Our contribution to the real options theory is two-fold. First, although real options
theory has received considerable attention in the strategic management literature recently,
there have been relatively few empirical studies testing the theoretical claims. To the best of
our knowledge, our study provides the first empirical test of the real options as a strategy for
new technology ventures. We compare real options with more traditional strategies to manage
risk and uncertainty and find that although real options is indeed an important strategy, it is
not a panacea and the traditional risk management strategies may outperform the real options
strategy. The second contribution is related to one of the main assumptions of the real options
theory: real options work better under the conditions of uncertainty (Huchzermeier and Loch,
2001; McGrath, 1999). However, our results show that it is clearly not the case when
uncertainty arises from the absence of established technology standards or from presence of
direct network externality effects on the firm's market.
We also contribute to the literature on network externalities by showing the
importance of distinguishing between direct and indirect network externality effects. The
essence of network externalities is that the utility that a user derives from consumption of the
good increases with the number of other agents consuming the good. These effects can have a
number of sources including direct physical effect of the number of purchasers on the quality
of the product (direct network externality effects) and indirect effect of consumption
externalities via complementary products and services (Katz and Shapiro, 1985). Previous
studies investigating the effects of these two sources typically find that both of them have
positive effect on technology acceptance as part of the dominant design, products' prices and
firms' success – indicating that the effects of these two types of network externalities are in
the same direction (Brynjolfsson and Kemerer, 1996; Katz and Shapiro, 1985; Schilling,
Risk and uncertainty management strategies 105
2002). Our study contributes to the network externality literature by showing that the effects
of direct and indirect network externalities can be actually of opposite direction.
Although there may be different reasons to choose for a particular strategy our results
suggest that new technology ventures should prioritize the kind of performance they want to
optimize and choose the strategy rationally, based on the characteristics of their target market.
Both direct and indirect network externalities play an important role in this choice. Our results
highlight the importance of distinguishing between direct and indirect network externality
effects because they have opposite effects on risk and uncertainty management strategies.
Finally yet importantly, our results support the theoretical distinction between the traditional
risk management strategies and the real options strategy by showing that these two types of
strategies can have opposite effects in certain circumstances.
106 General discussion
Chapter 5
General discussion
The focus of this dissertation is on the entrepreneurial risk-taking. In the previous
three chapters, we answered the following main research questions: (1) What kinds of risks do
entrepreneurs take? (2) How do they take the risks? (3) What kinds of strategies do they use to
manage the risks?
Chapter 2, the meta-analysis, gives a comprehensive quantitative review of factors
important for the performance of new entrepreneurial firms. Due to the lack of studies
examining entrepreneurial risks directly, we decided to focus on the positive side and consider
risks as the opposite of success factors: i.e. if it is truly a success factor, then not possessing it
would mean a risk for an entrepreneurial firm. While Chapter 2 is focusing on factors that
objectively matter for new technology ventures, Chapter 3 presents research about how
intuitive and rational thinking of entrepreneurs influence their level of cognitive biases and
risk-taking propensity. Chapter 3 can be also seen as an investigation into the mechanism
distorting the objective picture of entrepreneurial risks. In Chapter 4, we focus once again on
new technology ventures and research the performance effects of five different risk and
uncertainty management strategies. We also look at how these effects change in markets with
existing vs. emerging technology standards, and markets with direct and indirect network
externality effects.
We summarize hereunder the most important conclusions for entrepreneurs, their
firms and stakeholders. We supplement the discussion by future directions of research per
General discussion 107
chapter. These directions of research are building further on the studies presented in Chapters
2, 3 and 4.
5.1
Discussion of Chapter 2: The meta-analysis of success factors
5.1.1 Conclusions
There are very few studies directly studying risks in new technology ventures. In order
to reveal the potential risks in this context, we defined a risk as absence of a success factor
(factor associated with high performance) and conducted a meta-analysis of the success
factors in new technology ventures. We can draw four main conclusions about risk factors
from our meta-analysis.
First, all studies agree that the following factors represent important risks for new
technology ventures: not having supply chain integration, limited scope and breadth of the
firm's markets, small age of the firm, small founding teams, low financial resources, low
marketing experience of the founders, low industry experience of the founders, and no patent
protection. Second, another range of factors emerged that has a strong potential for becoming
important risk factors: internationalization, low-cost strategy, market growth rate, marketing
intensity, R&D investments, firm size, non-governmental financial support, and university
partnerships. Although the importance of these factors varies for different sub-populations of
new technology ventures, on average they are also highly significant. Third, having radical
innovation strategy and engaging in R&D alliances are very strong risk factors for
independent technology ventures. Independent new technology ventures that are still pursuing
the radical innovation strategy or engaging in an R&D alliance should make sure that they
have the resources and support similar to those of the corporate ventures, for which the same
factors are in fact success as opposed to risk factors.
Finally, there is a number of factors that are clearly no risk factors. Counterintuitively, both prior start-up experience and R&D experience were insignificant, that may be
a further evidence of overestimation of the role of prior start-up experience. Competition
intensity, environmental dynamism and environmental heterogeneity are another three
insignificant factors, supporting their role as environmental moderators.
108 General discussion
5.1.2 Future directions of research
Since meta-analysis is a future-oriented research technique, this section is relatively
long. Our meta-analysis should not and must not preclude future research, but rather stimulate
and direct it. Based on our results and implications and current literature (e.g., Gartner, 1985;
Timmons and Spinelli, 2004), we suggest a theoretical framework for future research, as
depicted by Figure 5.1.
Entrepreneurial
Team:
Members’ characteristics
Experience, knowledge and
skills
Values and beliefs
Behaviors and leadership
styles
Entrepreneurial
Resources:
Strategic and
Organizational Fit:
Competitive strategy
Structure
Processes
Systems
Financial means and
investments
Intellectual property
Partnerships and networks
Institutional
characteristics
Performance
Entrepreneurial
Opportunity:
Opportunity dimensions
Environmental characteristics
Market characteristics
Figure 5.1. Integrated framework for studying new entrepreneurial firm performance
The theoretical framework consists of five elements, Entrepreneurial Opportunities,
Entrepreneurial Team, Entrepreneurial Resources, Strategic and Organizational Fit, and
Performance. The dotted lines represent the fit. In general, we suggest to take this framework
as a basis for future research and to examine its factors and, in particular, the linkages into
more detail in future research. Below, we will define and describe the categories of the
framework, list examples of factors in these categories and give future research directions
following from our meta-analysis.
General discussion 109
Entrepreneurial Team
Entrepreneurial Team is defined as the management team of the new venture
(Timmons and Spinelli, 2004). Entrepreneurial Team is a core element of the
entrepreneurship
phenomenon.
Shane
and
Venkataraman
(2000)
characterize
entrepreneurship as the nexus between the individual and the opportunity. Researchers
identified the following factors in this category:
•
•
•
•
Members' characteristics (age, attributes, biases, thinking styles, etc.)
Experience, knowledge, and skills
Values and beliefs
Behaviors and leadership styles.
According to our meta-analysis, future research should include cognitive biases and
thinking styles, the quality, variety and complementarity of team member experiences, as well
as the mediating and moderating influences of the team factors on other antecedentperformance relationships. In this research, industry and marketing experience may be
included as control variables, because the literature on new technology ventures performance
agrees on the magnitude of the effect of these factors.
Entrepreneurial Opportunity
Entrepreneurial Opportunities are those situations in which new goods, services, raw
materials, and organizing methods may be introduced and sold at higher price than their cost
of production (Shane and Venkataraman, 2000). The contemporary definitions of
entrepreneurship emphasize that it is opportunity-driven. Therefore, Entrepreneurial
Opportunity is an essential part of the entrepreneurship framework (Eckhardt and Shane,
2003; Shane and Venkataraman, 2000; Timmons and Spinelli, 2004). Researchers distinguish
the following factors in this category:
• Opportunity dimensions (type of opportunity, form of opportunity, source of
opportunity, etc.)
• Environmental characteristics (environmental dynamism, environmental
heterogeneity, internationalization, etc.)
• Market characteristics (market growth rate, competition intensity, entry
barriers, buyer and supplier power, etc.).
110 General discussion
Based on our meta-analysis, future research may include the direct examination of
opportunity dimensions, as well as the search for moderators of the internationalization
performance and the market growth rate – performance relationship. The results of our metaanalysis also suggest that the role of market scope is clear. However, due to its importance
this factor may be included as a control variable in further studies.
Entrepreneurial Resources
Entrepreneurial Resources include all tangible and intangible assets that a firm may
possess and control (Chrisman et al., 1998; Timmons and Spinelli, 2004). Gartner (1985) has
identified the resources accumulation process as an essential part of the entrepreneurial
functions, while Timmons and Spinelli (2004) consider Entrepreneurial Resources as an
important building block of their venture creation framework. Important factors within this
category are:
• Financial means and investments (financial resources, non-governmental
financial support, R&D investments, etc.)
• Intellectual property (patent protection, licensing, etc.)
• Partnerships and networks (R&D alliances, supply chain integration, university
partnerships, etc.)
• Institutional characteristics (firm age, firm size, firm type, size of the founding
team, etc.).
From our meta-analysis we suggest to include more qualitative measures of resources
into future research, like the value, rareness, non-imitability, and non-substitutability of
resources (Barney, 1991). Moreover, we advice more moderator research on the nongovernmental financial support performance and the R&D investment performance
relationship, as well as the relationships between university partnerships and performance,
and firm size and performance. The results of our meta-analysis also suggest that the role of
financial resources, patent protection, supply chain integration, firm age and size of the
founding team for venture performance is clear. However, these factors may be considered as
control variables in future research.
General discussion 111
Strategic and Organizational Fit
Strategic and Organizational Fit is defined as the congruence between strategy and
organization of the new venture and the driving forces Entrepreneurial Team, Entrepreneurial
Opportunity, and Entrepreneurial Resources (Chrisman et al., 1998; Timmons and Spinelli,
2004). Fit regards an important uniting aspect of the various elements of the framework.
Gartner (1985) refers to a new venture as a gestalt of individuals, environment, organization,
and process dimensions, indicating that all elements in a new venture must be balanced. We
consider the following factors in this category:
• Competitive strategy (low cost strategy, market scope, marketing intensity,
product innovation, etc.)
• Structure
• Processes
• Systems.
Our meta-analysis suggests more interaction research between competitive strategies
and environmental characteristics, such as environmental dynamism and competition
intensity. In particular, other competitive strategies than product innovation may be examined.
Performance
Our framework suggests that the better the fit between the driving forces and the
strategy and organization of the venture, the better the performance. In our meta-analysis we
found a broad scope of performance measures, which can also moderate the relationship
between different factors and venture performance. In particular, we found this moderating
effect for the type of the firm (i.e. independent vs. corporate). Therefore, we suggest to have
several performance measures in future new venture research and to experiment with different
subsets of performance measures.
5.2
Discussion of Chapter 3: The mechanism of entrepreneurial risk-
taking
5.2.1 Conclusions
Chapter 3 provides five main insights into entrepreneurial risk-taking. First,
entrepreneurial cognitive biases derive from intuition with the exception of base-rate fallacy.
112 General discussion
Second, all the seven biases we researched influence entrepreneurial risk-taking in a stable
way, by changing the level of the general tendency to take or avoid risks. This implies that
although some of these biases can be evoked by the nature of the situation (Simon and
Houghton, 2003), taking biased decisions is a persisting characteristic of entrepreneurs. Third,
although most biases increase risk-taking, the hindsight bias represents a clear exception.
Scoring high on hindsight bias means that entrepreneurs do not learn properly from their past
misjudgments and mistakes. The higher entrepreneurs score on hindsight bias, the fewer risks
they take. On the long run, this may lead to making the same errors over and over again,
failing the venture and not restarting due to lower propensity to take risks. Fourth, more then
the half of cognitive biases can be corrected purely by more rational thinking, which
represents a universal de-biasing method. Last, but not least, it is not universally clear how
much additional rational thinking should there be, because the effects of its influence on
biases differ in magnitude. Similarly, the effects of different cognitive biases on risk-taking
also vary. Thus, the whole set of biases should be taken into account in order to make a
personal recommendation for a particular entrepreneur.
5.2.2 Future directions of research
Building upon the discussion within Chapter 3, we can outline another three future
research directions.
First, we would like to focus on where heuristics and biases derive from: intuition
(Kahneman, 2003). How do entrepreneurs build their intuition? Theoretically, there are two
main sources of intuitive thinking: prior knowledge and emotions (Sadler-Smith and Shefy,
2004). Emotions have a special place because they can both inform the judgment themselves
and prime further decision-making (Forgas, 1995). In the first role, emotions serve as
heuristics themselves. The particular kind of emotions – happiness, fear, anger, etc – is
directly transformed into the judgment, such as judgment of riskiness (Loewenstein, Weber,
Hsee, and Welch, 2001). In their second role, emotions serve as a mechanism determining
which part of human knowledge will be further used in a particular judgment via selective
attention, and selective encoding and selective retrieval of knowledge (Forgas, 1995). They
may similarly determine whether rational or intuitive processes will be activated. In case of
intuitive processes, emotions may determine which particular heuristics, such as
General discussion 113
representativeness and availability (Tversky and Kahneman, 1974) will be used in these
judgments.
While the knowledge part of intuition seems to be at least acknowledged in the
entrepreneurship literature (e.g. Shane, 2000), the entrepreneurial emotions and the role they
play are largely missing. This gives rise to a whole new set of research questions: When
entrepreneurs make decisions intuitively, which part of their intuition do they use the most:
knowledge or emotions? In which situations are entrepreneurs particularly prone to using
emotions as heuristic for their judgments and what are the consequences of doing that? What
are the results of the emotional priming on entrepreneurial decision-making? Future research
should clarify these issues.
Second, there should be theoretically two heuristics underlying the seven cognitive
biases we studied: availability and representativeness (Tversky and Kahneman, 1974). An
attempt to reveal a structure of heuristics underlying these biases is a promising avenue of
research. Until now scholars in the heuristics and biases stream of research focused on the
biases part, while heuristics remained a largely philosophical and theoretical matter.
Heuristics – and not the biases – are likely to substantiate a different, sustainable way of
thinking, which may be a driver of (sustainable) competitive advantage for entrepreneurial
firms (Busenits and Barney, 1997). Biases, which are per definition errors in judgments
involving such heuristics (Kahneman, 2003), are rather an indication of how well the rational
system of a particular entrepreneur is developed and how much it is capable of detecting and
eliminating such biases. Therefore, further research into heuristics and their nature is a
promising line of inquiry. A number of questions deserving further attention emerge: Which
heuristics cause the entrepreneurial biases? If the biases arise due to the use of heuristics, is it
also possible that heuristics instead of biases are a more influential predictor of decision
making efficiency and errors? How do the relative advantages of heuristics compare to their
relative disadvantages (i.e. biases)? Which heuristics have more advantages than
disadvantages and vice versa? Direct measures of heuristics should be developed and used for
these questions.
Finally, since both intuitive and rational part of entrepreneurs are important, future
research may consider the rational heuristics, which derive primarily from the rational system
as opposed to the experiential (Frederick, 2002; Gigerenzer, Czerlinski, and Martignon,
2002).
114 General discussion
5.3
Discussion of Chapter 4: Risk and uncertainty management strategies
5.3.1 Conclusions
Chapter 4 improves our understanding of entrepreneurial strategies to manage risks
and uncertainties arising from the environment of the new technology ventures. First, the
results strongly indicate that new technology ventures should avoid being excessively
cautious when entering new markets: strategic avoidance (i.e. growing from small scale and
choosing for low uncertainty market segments) does not impact return on investment (ROI)
and is detrimental for both customer retention and sales growth. New technology ventures
should also think twice before imitating other firms. Strategies that do generally improve
performance of new technology ventures are strategic control, strategic cooperation and real
options strategy.
Performance consequences of all strategies may change under certain market
circumstances. Existing technology standards appear to have predominantly direct positive
effect on performance of new technology ventures and only change the effects of strategic
control and strategic imitation. However, risk and uncertainty management strategies perform
differently in markets with direct and indirect network externality effects. In particular, direct
and indirect network externalities have opposite moderating effects on new venture
performance. Similarly, traditional risk management strategies and real options behave
differently under a particular kind of network externalities, having opposite moderating
effects within both direct and indirect network externalities.
5.3.2 Future directions of research
We see this thesis as a step towards a more explicit link between theories of cognition
and entrepreneurial behavior and strategic management. In particular, we examined
mechanisms of how individual entrepreneurs build their risk-taking propensity and we studied
the performance consequences of the risk management strategies these entrepreneurs select on
the venture level. This is only the first step to link the theories of cognition and strategic
management.
Previous research has shown that entrepreneurs take decisions differently compared to
other populations; and that there is a certain variation within the population of entrepreneurs
General discussion 115
(Busenitz and Barney, 1997; Forbes, 2005; Stewart and Roth, 2001, 2004). To the extent that
entrepreneurs make decisions in a fundamentally different way, which may be a source of the
strategic advantage of the firms. This strategic advantage will be sustainable to the extent that
these differences are persistent (Busenitz and Barney, 1997). How can future research
approach this intriguing gap?
The main problem is of methodological nature: while cognition theories are mainly on
the individual and group levels, strategic management theories are on the strategic business
unit and firm levels. The current solutions to this problem are disaggregating the higher level
variable to a lower level, aggregating the lower level variables to the higher level or using the
hierarchical linear modeling (HLM). All three approaches have considerable disadvantages.
Disaggregation ignores the assumption of observations independence and assesses the impact
of higher level units based on the number of lower level units. Aggregation discards potential
meaningful lower-level variance (Hofmann, 1997). HLM maintains the appropriate levels of
analysis, but this technique is designed to investigate the influence of higher level units on
lower level outcomes. In case of research on the intersection of cognition and strategic
management research, the lower level variables are those from cognition and higher-level
from strategic management. This means that the general conceptual model in such research
would have strategic business unit or firm-level variable (e.g. performance) as its dependent
variable. This makes HLM unsuitable for this application.
The current feasible solution allowing to approach the cognition – strategic
management gap is to take a level of analysis where one person is still fully responsible for
decisions and takes most of the decisions that have strategic implications for the business unit
or the firm. An example of such unit of analysis is the project/product level (Simon and
Houghton, 2003). In this case, both the independent variables (cognitive processes of the
manager or entrepreneur) and dependent variable (strategic decisions and performance of the
product) can be on the same level of analysis. However, this solution discards a considerable
part of the strategic management aspects, since the majority of the strategic management
decisions are on the products portfolio of the strategic business unit and firm levels. Future
research should further explore this promising area of research.
116 General discussion
5.4
Final remarks
The title of this thesis is "Bringing entrepreneurial risk-taking beyond bounded
rationality: Risk factors, real options and traditional risk management strategies in new
technology ventures". In the main three chapters (2, 3 and 4) we answer the following
research questions: (1) What kinds of risks do entrepreneurs take? (2) How do they take the
risks? (3) What kinds of strategies do they use to manage the risks?
Each chapter presents a different, but complementary answer on how entrepreneurs
can improve their risk-taking. Chapter 2 focuses on the particular risks that new technology
ventures have. These are objective risks that entrepreneurs have to take care of. Chapter 3
highlights the individual-level mechanism of taking risks. It shows the importance of being
aware of the cognitive biases distorting the entrepreneurial vision. By balancing the use of
rational and intuitive thinking entrepreneurs can decrease the influence of cognitive biases
and improve their risk-taking. Chapter 4 is a firm-level investigation into the risk and
uncertainty management strategies that new technology ventures can apply to manage the
external risks and uncertainties. We show that it is important to make a distinction between
the traditional risk management strategies and real options strategy. We also show how
performance consequences of these strategies change under different market conditions.
Taken together, our findings can hopefully help bringing entrepreneurial risk-taking
beyond bounded rationality.
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Journal of Management 32(2): 279-298.
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Wärneryd, K. (1990). Legal restrictions and monetary evolution. Journal of Economic
Behavior & Organization 13(1): 117-124.
Zahra, S.A., Bogner, W.C. (2000). Technology strategy and software new ventures'
performance: Exploring the moderating effect of the competitive environment.
Journal of Business Venturing 15(2): 135-173.
Zahra, S.A., Covin, J. (1993). Business Strategy, Technology Policy and Company
Performance. Strategic Management Journal 14(6): 451–478.
Zahra, S.A., Ireland, R.D., Hitt, M.A. (2000). International expansion by new venture
firms: International diversity, mode of market entry, technological learning, and
performance. Academy of Management Journal 43(5): 925-950.
Zahra, S.A., Matherne, B.P., Carleton, J.M. (2003). Technological Resource Leveraging
and the Internationalisation of New Ventures. Journal of International
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Zahra, S.A., Neubaum D.O., Huse M. (1997). The effect of the environment on export
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Zuckerman, M., Knee, C.R., Kieffer, S.C., Rawsthorne, L., Bruce, L.M. (1996). Beliefs in
realistic and unrealistic control: Assessment and implications. Journal of
Personality 64(2): 435-464.
Appendix A: Measures
A2.1. Scales of the most important meta-factors from Chapter 2
Meta-factor
Study
MARKET and OPPORTUNITY
Market scope
10,11
Product innovation
15,16
17
Original
Construct
Sources
-
-
Product
Innovation
Strategy
Covin and Slevin
(1989); Zahra and
Covin (1993)
18
Eisenhardt and
Explorativeness
Schoonhoven
of the entry
(1994); Stuart and
strategy
Abetti (1986); etc.
28
Cooper (1984);
Lefebvre et al
Product upgrades
(1992); Zahra and
Covin (1993)
Scales
Number of products in market
Rate your firm relative to your competitors over the last three years on the
extent to which it has:
- Placed emphasis on developing new products through allocation of
substantial financial resources
- Developed a large variety of new product lines
- Increased the rate of new product introductions to the market
- Increased it overall commitment to develop and market new products
Rate on 4 (for technology) and 5-point scales:
- Newness of the core technology of the firm
- Newness of the target markets served by the firm
- Newness of the competition faced by the firm
- Newness of the users of the offering
Rate on the 5-point scale if the statement is true or not true:
- Introduces more new products than the competition.
- Introduces products to the market faster than competitors.
- Has reduced the time between the development and market introductions
of new products.
- Introduces many new products to the market.
α
-
0.83
0.72
0.71
130 Appendices
ENTREPRENEURIAL TEAM
Industry experience
22
Prior
industry
experience
of
management
team
RESOURCES
Financial resources
11
8, 14,
Firm age
22, 23,
29, 31
Firm type
10, 29
-
Patent protection
R&D alliances
28
Copyrights
10,11
-
28
Size
team
of
Supply
integration
founding
chain
9
5
10,11
-
Combined number of years that the members of the founding management
team spent in previous positions that were in similar industries or markets
-
-
Venture assets
-
-
Year of incorporation / # of years since establishment
-
-
If the venture was independent or corporate
Rate on the 5-point scale if the statement is true or not true:
Cooper (1984);
- Holds important patent rights.
Lefebvre et al
- Has more patents than its key competitors.
(1992); Zahra and
- Uses licensing agreements extensively to sell its products.
Covin (1993)
- Has increased its patenting efforts over the past three years.
-
Number of joint R&D, patent swaps, technology transfers and joint ventures
Rate on the 5-point scale if the statement is true or not true:
Cooper (1984);
- Uses joint ventures for R&D
Lefebvre et al
External sources
- Is heavily engaged in strategic alliances
(1992); Zahra and
- Collaborates with universities and research centers in R&D
Covin (1993)
- Contracts out a major portion of its R&D activities
Team size
-
Number of founders
Rate on the 5-point scale:
- Importance of suppliers as discussion partners
Gemunden, Ritter
Cooperation with
- Importance of suppliers for generating new product ideas
and Heydebreck
suppliers
- Importance of suppliers for conventionalizing new products
(1996)
- Importance of suppliers for developing new products
- Importance of suppliers for testing new products
Number of outsourcing and distribution links
-
0.71
-
0.73
-
0.92
-
Appendices 131
A3.1. Constructs, measurement items, and construct reliabilities for
Chapter 3
Dependent variable
Risk-taking propensity (α=0.87)
We present different scenarios below. Assuming that given probabilities are accurate,
what would you choose if you have to make a decision now without additional information?
RP1 (Taken from Mullins et al., 1999):
1. I would always choose the following scenario (please check one answer only):
□ A: an 80% chance of winning $400 and 20% chance of winning nothing, or
□ B: receiving $320 for sure
2. I would always choose the following scenario (please check one answer only):
□ A: receiving $300 for sure, or
□ B: a 20% chance of winning $1,500 and 80% of winning nothing
3. I would always choose the following scenario (please check one answer only):
□ A: a 90% chance of winning $200 and 10% chance of winning nothing, or
□ B: receiving $180 for sure
4. I would always choose the following scenario (please check one answer only):
□ A: receiving $160 for sure, or
□ B: a 10% chance of winning $1,600 and 90% chance of winning nothing
5. I would always choose the following scenario (please check one answer only):
□ A: a 50% chance of winning $500 and 50% chance of winning nothing, or
□ B: receiving $250 for sure
Coding algorithm: IF score=risky option THEN coding=1; ELSE coding=0;
Final coding=sum of all 5 sub-codings.
RP2 (New, based on Schneider and Lopes, 1986):
6. I would always choose the following scenario (please check one answer only):
□ receiving $900 for sure,
□ a 90% chance of winning $1,000 and 10% chance of winning nothing,
□ a 80% chance of winning $1,125 and 20% chance of winning nothing,
□ a 70% chance of winning $1,286 and 30% chance of winning nothing,
□ a 60% chance of winning $1,500 and 40% chance of winning nothing,
□ a 50% chance of winning $1,800 and 50% chance of winning nothing,
□ a 40% chance of winning $2,250 and 60% chance of winning nothing,
□ a 30% chance of winning $3,000 and 70% chance of winning nothing,
□ a 20% chance of winning $4,500 and 80% chance of winning nothing, or
□ a 10% chance of winning $9,000 and 90% chance of winning nothing
132 Appendices
RP3 (New, based on Schneider and Lopes, 1986) (R):
7. I would always choose the following scenario (please check one answer only):
□ a 10% chance of winning $100,000 and 90% chance of winning nothing,
□ a 20% chance of winning $50,000 and 80% chance of winning nothing,
□ a 30% chance of winning $33,333 and 70% chance of winning nothing,
□ a 40% chance of winning $25,000 and 60% chance of winning nothing,
□ a 50% chance of winning $20,000 and 50% chance of winning nothing,
□ a 60% chance of winning $16,667 and 40% chance of winning nothing,
□ a 70% chance of winning $14,286 and 30% chance of winning nothing,
□ a 80% chance of winning $12,500 and 20% chance of winning nothing,
□ a 90% chance of winning $11,111 and 10% chance of winning nothing, or
□ receiving $10,000 for sure
Mediating variables
Hindsight bias (α=0.75)
(New items, following Bukszar and Connolly, 1988; and Slovic and Fischhoff, 1977)
(On the last page of the survey)
Below are three questions which assesses your confidence on your answer to earlier
questions (please do not turn the pages back and change any answers).
HIN1. Earlier we asked you which country (Canada or New Zealand) has a higher percentage
of entrepreneurs. Let’s assume that the correct answer is New Zealand. Knowing this
new information, please answer the following question (score: 0-100%):
•
If your answer was New Zealand, how confident were you that New Zealand
would be the correct answer?
•
If your answer was Canada, how confident were you that Canada would be the
correct answer?
HIN2. Earlier we asked you whether London or Beijing is farther away from Seattle. Let’s
assume that the correct answer is Beijing. Knowing this new information, please answer
the following question (score: 0-100%):
•
If your answer was Beijing, how confident were you that Beijing would be the
correct answer?
•
If your answer was London, how confident were you that London would be the
correct answer?
HIN3. Earlier we asked you whether Nissan or Toyota was founded earlier. Let’s assume
that the correct answer is Nissan. Knowing this new information, please answer the
following question (score: 0-100%):
•
If your answer was Nissan, how confident were you that Nissan would be the
correct answer?
•
If your answer was Toyota, how confident were you that Toyota would be the
correct answer?
Coding algorithm: IF answer on the question from overconfidence measure was correct
THEN coding=0; ELSE coding = (score from overconfidence – score from hindsight).
Illusory correlations (α=0.67)
Appendices 133
(New items based on existing data from different fields; following Tversky and
Kahneman, 1974)
Below we list some personality characteristics. Please circle the number next to each
statement that best represents your degree of disagreement or agreement (where 1=Strongly
Disagree; 4=Neutral; 7=Strongly Agree; and numbers between 1 and 7 represent the varying
degrees).
COR1. Big businesses will often ruin the small ones.
COR2. Universities are more likely to license to big companies.
COR3. Cats that are spayed or neutered automatically gain weight.
Overconfidence bias (α=0.82)
(New items, following Forbes, 2005; and Brenner et al., 1996)
(On the first page of the survey)
Below are some challenging questions. One of the two possible answers is correct.
Please
work
through
the
questions quickly and check the box with response that best represents your answer.
OV1. Which country has a higher percentage of entrepreneurs: Canada or New Zealand?
How sure are you? (score: 50% … 100%)
OV2. What is farther away from Seattle: London or Beijing? How sure are you with your
answer to this question? (score: 50% … 100%)
OV3. Which company was founded earlier: Nissan or Toyota? How sure are you with your
answer to this question? (score: 50% … 100%)
Coding algorithm: IF answer is correct THEN coding=score-5; ELSE coding=score.
Base-rate fallacy (α=0.73)
We present some hypothetical scenarios below. Please give us your opinion for each of
the scenarios.
BAS1. (New case)
National statistics show that around 60% of funded high-tech startups failed in the first
five years. John has just received funding from a VC to start up a high-tech firm. During
your lunch today, one of John’s friends told you that he and John like to watch NFL
games together and that John is well liked by his friends. What is your estimated
probability that John's company will fail within the first five years? (0-100%)
Coding algorithm: IF score>=6 THEN coding=score-6; ELSE coding=6-score.
BAS2. (Case taken from Lynch and Ofir, 1989; and adjusted based on pre-test)
Your friend Tom is looking for a 5 year old used car and asks for your help. "Consumer
Reports" suggests that 50% of this model will require some major repairs during the 6th
year. Tom just called you to let you know that he went to look at the car. He really likes
the exterior color and the leather seats. What is your estimated probability that Tom will
need some major repairs next year? (0-100%)
Coding algorithm: IF score>=5 THEN coding=score-5; ELSE coding=5-score.
Illusion of control (α=0.88)
(Taken from Simon et al., 2000; and Zuckerman et al., 1996)
134 Appendices
Below we list some personality characteristics. Please circle the number next to each
statement that best represents your degree of disagreement or agreement (where 1=Strongly
Disagree; 4=Neutral; 7=Strongly Agree; and numbers between 1 and 7 represent the varying
degrees).
IC1. I can accurately predict total market demand for my venture's product and services for
the next 3 years.
IC2. I can accurately predict when larger competitors would enter the market.
IC3. I can succeed at making this venture a success, even though many others would fail.
IC4. There is no such thing as misfortune; everything that happens to us is the result of our
own doing.
IC5. In each and every task, not finishing successfully reflects a lack of motivation.
Law of small numbers (α=0.85)
(Modified from Simon et al., 2000; and Mohan-Neill, 1995)
Below we list some personality characteristics. Please circle the number next to each
statement that best represents your degree of disagreement or agreement (where 1=Strongly
Disagree; 4=Neutral; 7=Strongly Agree; and numbers between 1 and 7 represent the varying
degrees).
SN1. When making strategic decisions, it is sufficient to ask the opinion of a few of my
closest friends and colleagues.
SN2. When making strategic decisions, I always use more than one source of information.
(R)
SN3. I do not make decisions until I have results of large scale market research. (R)
Regression fallacy
(New, following the example from Kahneman and Tversky, 1973)
We present some hypothetical scenarios below. Please give us your opinion for each of the
scenarios.
Assuming that your firm operates in a stable economic environment. Two years ago, the sales
of your products increased by 15%. You made a decision to increase your advertising budget
by 25% last year. However, you just got a report showing that the sales decreased by 5% last
year. How likely would you conclude that the advertising was not effective? (0-100%)
Independent variables
Experiential system (α=0.98)
(Taken from Epstein et al., 1996; Pacini and Epstein, 1999)
Below we list some personality characteristics. Please circle the number next to each
statement that best represents your degree of disagreement or agreement (where 1=Strongly
Disagree; 4=Neutral; 7=Strongly Agree; and numbers between 1 and 7 represent the varying
degrees).
ES1. I like to rely on my intuitive impressions.
ES2. Using my gut feelings usually works well for me in figuring out problems in my life.
ES3. I believe in trusting my hunches.
ES4. Intuition can be a very useful way to solve problems.
ES5. I often go by my instincts when deciding on a course of action.
Appendices 135
Rational system (α=0.94)
(Taken from Epstein et al., 1996; Pacini and Epstein, 1999)
Below we list some personality characteristics. Please circle the number next to each
statement that best represents your degree of disagreement or agreement (where 1=Strongly
Disagree; 4=Neutral; 7=Strongly Agree; and numbers between 1 and 7 represent the varying
degrees).
RS1. I try to avoid situations that require thinking in depth about something. (R)
RS2. I enjoy solving problems that require hard thinking.
RS3. I am much better at figuring things out logically than most people.
RS4. I have a logical mind.
RS5. I don't reason well under pressure. (R)
(R) Indicates a reversed item
136 Appendices
A4.1. Constructs, measurement items, and construct reliabilities for
Chapter 4
Dependent variables
Return on Investment (taken from and based on Lambert, 1998 and McDougall et al., 1994):
Please provide your best estimates for the following information about your firm:
The return on investment in the last fiscal year: ___________ %
Customer retention rate (taken from and based on Lambert, 1998 and McDougall et al.,
1994):
Please provide your best estimates for the following information about your firm:
Customer retention rate in your primary served market: ___________ %
Sales growth rate (taken from and based on Lambert, 1998 and McDougall et al., 1994):
Please provide your best estimates for the following information about your firm:
The rate of sales growth in the last fiscal year: ____________%
Independent variables
Strategic avoidance (based on Miller, 1992 and Shane, 2003) (α=0.79)
Please rate the following statements (7-point Likert scale: 0=strongly disagree to 7=strongly
agree):
1. We tend to introduce our products to market niches with low uncertainty first
2. We tend to postpone a market entry if the market is too uncertain
3. We prefer to grow from small scale to large scale when entering a new market
Strategic imitation (taken from and based on Gatignon and Xuereb, 1997 and Miller, 1992)
(α=0.81)
Please rate the following statements (7-point Likert scale: 0=strongly disagree to 7=strongly
agree):
1. Overall, our products are similar to our main competitors' products
2. For our products, we imitate certain manufacturing techniques of other firms
3. We follow our competitors in moving into new markets
Strategic control (based on Miller, 1992) (α=0.86)
Please rate the following statements (7-point Likert scale: 0=strongly disagree to 7=strongly
agree):
1. We try to increase entry barriers for new competitors to enter our primary served
market
2. We try to influence consumers through advertising
3. We try to use contractual agreements with suppliers for all of our products
Strategic cooperation (taken from Li and Atuahene-Gima, 2001) (α=0.65)
Please rate the following statements (7-point Likert scale: 0=strongly disagree to 7=strongly
agree):
Appendices 137
1. We have cooperative agreements with other firms to manufacture our products
2. We collaborate with other firms to promote our products
3. We jointly distribute our products with other firms
Real options strategy (based on Huchzermeier and Loch, 2001; McGrath et al., 2004):
(α=0.86)
Please rate the following statements (7-point Likert scale: 0=strongly disagree to 7=strongly
agree):
1. We invest in new products in stages to allow management to decide whether or not to
proceed with the projects based on newest information available
2. When developing a new product, we always make sure that we can expand the scale of
this project if market conditions turn out to be more favorable than expected.
3. When developing new products, we try to keep our technological design options open
until we have enough information to make a choice.
Moderating variables
Markets with technology standards (based on Warner, Fairbank and Steensma, 2006)
The technology standards in our primary served markets are (please check one only):
□ Well-established □ Emerging
Markets with high direct network externalities (based on Katz and Shapiro, 1985,1986)
(α=0.72)
Please rate the following characteristics of your firm and its market (7-point Likert scale: 1=
strongly disagree; 7= strongly agree):
1. In our primary served markets, the values of our product to customers depends not
only on the features of the products themselves, but also on the number of people who
are using these products
2. In our primary served markets, the price customers are willing to pay for a product
increases as more people adopt the product
Markets with high indirect network externalities (based on Schilling, 2002)
Please rate the following characteristics of your firm and its market:
In your primary served markets, the importance of the availability of complementary products
and/or services is: (7-point Likert scale: 1= very low; 7= very high)
Appendix B: Additional tables
B2.1. Methodological characteristics of the articles included in the meta-analysis
#
Article
Country
1
Bamberger, Bacharach and
Dyer 1989
Israel
2
Bantel 1997
3
4
Bloodgood, Sapienza and
Almeida 1996
Carpenter, Pollock and Leary
2003
US
US
US
Industry
Electronics, computer,
biotechnology and related
Semiconductors, magnetic media,
measuring and controlling
devices, optical instruments, etc
Medical products, commercial
research, computers, etc
Electrical and electronic
equipment
IT, electronics, mechanical
engineering, biotechnology, hightech consultancy
Performance
Measure
Sample type
Financial
VC-backed and other
DBs
General
General DBs
Financial
Venture
Origin
Not
indicated
Min
Age
Max
Age
N
0
10
35
Not
indicated
5
12
166
IPO and VC-backed
Not
indicated
0
5
61
Financial
IPO
Independent
0
10
97
Financial
General DBs
Not
indicated
0
10
55
5
Chamanski and Waagø 2001
Norway
6
Doutriaux 1991
Canada
Electronics, telecom and related
Financial
Government support
programs
Both
0
8
73
7
Doutriaux 1992
Canada
Electronics, telecom and related
Financial
Government support
programs
Both
0
8
73
8
Dowling and McGee 1994
US
Telecom equipment
Financial
IPO
Independent
0
**
52
9
Eisenhardt and Schoonhoven
1990
US
Semiconductors
Financial
General DBs
Independent
4
4***
66
10
George, Zahra, Wheatley and
Khan 2001
US
Biotechnology
Financial
IPO
Both
0
**
143
Appendices 139
#
Article
Country
Industry
Performance
Measure
Sample type
Venture
Origin
Min
Age
Max
Age
N
11
George, Zahra and Wood
2002
US
Biotechnology
Financial
IPO
Both
0
**
147
12 Kazanjian and Drazin 1990
US
Electronics, computer and related
Financial
VC-backed
Independent
0
15
105
13 Kazanjian and Rao 1999
US
Computer hardware and related
equipment
Financial
VC-backed
Independent
0
15
71
Korea
Electrical and electronic products,
biotechnology, software
Financial
General DBs
Independent
0
**
137
General DBs
Both
0
8
184
General DBs
Both
0
8
184
General DBs
Both
0
8
184
General DBs
Independent
0
5
88
IPO
Not
indicated
1
16
28
General DBs
Both
0
8
62
14 Lee, Lee and Pennings 2001
15 Li 2001
China
16 Li and Atuahene-Gima 2002
China
17 Li and Atuahene-Gima 2001
China
18 Lumme 1998
Finland
19 Marino and De Noble 1997
US
20 McDougall and Oviatt 1996
US
21
McDougall, Covin, Robinson
and Herron 1994
22 McGee and Dowling 1994
IT, telecom, computing,
electronics, optic-mechanic and Financial and
electric products, new energy and
market
materials, biotechnology, etc
IT, telecom, computing,
electronics, optic-mechanic and
General
electric products, new energy and
materials, biotechnology, etc
IT, telecom, computing,
electronics, optic-mechanic and Financial and
electric products, new energy and
market
materials, biotechnology, etc
Telecom, electronic and industrial
Financial
equipment, chemicals, etc
Medical and navigation
Financial
equipment and instruments
Financial and
Computer and telecom equipment
market
US
Computer and telecom equipment
Financial
General DBs
Independent
0
8
123
US
Electronics, computing, telecom
and related
Financial
IPO
Independent
0
8
210
140 Appendices
#
23
Article
McGee,
Dowling
Megginson 1995
Country
and
US
Industry
Telecom and computing
equipment, prof. and scientific
instruments
Electrical and electronic products,
software
Biotechnology
Electronics, computer,
biotechnology and related
Performance
Measure
Sample type
Venture
Origin
Min
Age
Max
Age
N
Financial
IPO
Independent
0
8
210
Not
indicated
Independent
0
**
112
Financial
Government support
programs
General DBs
5
**
67
Financial
IPO
Independent
0
6
115
24 Miles, Preece and Baetz 1999
Canada
25 Qian and Li 2003
Robinson and McDougall
26
2001
US
27 Seiders and Riley 1999
US
Internet
Financial
IPO
Not
indicated
0
**
38
28 Zahra and Bogner 2000
US
Software
Financial and
market
General DBs
Both
0
8
116
29 Zahra, Ireland and Hitt 2000
US
Medical products, software,
telecom, semiconductors, etc.
Financial
International
Both
0
6
321
Both
0
8
67
Not
indicated
0
8
121
US
General
Zahra, Matherne and Carleton
US
Software
Financial
International
2003
Zahra, Neubaum and Huse
31
US
Telecom equipment
Financial
General DBs
1997
* - interaction effect significant at 0.05 level
** - no information was reported in the study; study included on the basis of means and standard deviations
*** - correlation matrix was given for the companies in their 4th year
30
Appendices 141
B2.2. Publication sources of the studies included in this meta-analysis
Publication Source:
Academy of Management Journal
Administrative Science Quarterly
Doctoral dissertation
Entrepreneurship Theory and Practice
Frontiers of Entrepreneurship Research Conference
papers
Human Resource Management
IEEE Transactions on Engineering Management
Internal report/working paper
Journal of Business Venturing
Journal of High Technology Management Research
Journal of International Entrepreneurship
Journal of Small Business Management
Management Science
Organization Studies
Strategic Management Journal
Number of
studies in metaanalysis
2
1
1
2
1
1
1
1
6
4
1
1
1
1
7
142 Appendices
B3.1. LISREL results for the Systems-Biases-Risk-Taking mediation (standardized solution)
Independent
variables:
Risk-taking
propensity
Hindsight bias (H1a)
Illusory correlation (H1b)
Overconfidence (H1c)
Base-rate fallacy (H1d)
Illusion of control (H1e)
Law of small numbers
(H1f)
Regression fallacy (H1g)
Experiential system (H2a)
Rational system (H2b)
-0.73**
0.21**
0.68**
0.19**
0.36***
0.26***
0.25***
-0.03a
-0.17**b
Hindsight
bias
(H3a and
b)
Illusory
correlation
(H3a and b)
Overconfidence
(H3a and b)
Base-rate
fallacy
(H3a and b)
Illusion of
control
(H3a and b)
Law of
small
numbers
(H3a and b)
Regression
fallacy
(H3a and b)
0.25***
-0.31***
0.38***
0.01
0.29***
-0.26***
0.09
0.04
0.22***
-0.28***
0.38***
-0.02
0.13*
-0.12*
Significance levels are based on unstandardized coefficients
*
p<0.05; ** p<0.01; *** p<0.001
a
Total std. effect of the Experiential system on Risk-taking propensity is 0.28
b
Total std. effect of the Rational system on Risk-taking propensity is -0.25
Model fit: χ2=943.48, df=468, RMSEA=0.059, DELTA2=0.97, CFI=0.96, NFI=0.93, NNFI=0.96
Appendices 143
Appendix C: Formulas
C2.1. Formulas for variances calculations
Vartotal = Varreal + Varartif + Vars.e. ,
where :
Vartotal : variance of the observed correlations from the primary studies;
Varreal : real variance of the population correlation;
Varartif : variance due to artifacts (dichotomization and reliabilities);
Vars. e. : variance due to sampling error.
Varreal = Vartotal − Varartif − Vars.e.
95% confidence interval is 1.96 Varreal
Meta - factor is moderated if Varreal > 25% Vartotal
n
∑ [ N (r
i
Vartotal =
− roo ) 2 ]
oo i
i =1
,
n
∑N
i
i =1
where :
rooi : observed correlation of the primary study i;
roo : weighted average of the observed correlations of the primary studies,
n
∑N r
i oo i
so that roo =
i =1
.
n
∑N
i
i =1
2
2
2  Var ( Rxx )
Var ( Ryy ) 
Varartif = ρ 2 A V = ro V = ro 
+

Rxx
Ryy 

Vars. e.
2
2
2
2


1 − r 2 
1 − r 2   1 − r 2 
1 − r 2  
2
D 
D 
oo
oo
oo
oo


1
 +

 =
 +

 
=
   − 1

0.5625

Ndi − 1 
N −1
N −1
 ad 
 Ndi − 1 
di =1  
di =1 




∑
∑
Summary 145
Short summary
Entrepreneurial risk-taking beyond bounded rationality:
Risk factors, cognitive biases and strategies of new technology ventures
Entrepreneurship is inherently associated with the notion of risk. This dissertation
consists of three main parts approaching entrepreneurial risk from different perspectives. The
first part is about the risk and success factors that new technology ventures have to deal with.
The second part is about how experienced entrepreneurs take risks. Finally, the third part is
about strategies that should be followed to manage risks and uncertainties surrounding new
technology ventures.
The thesis starts with a systematic quantitative exploration of existing research on
success factors of new technology ventures. The absence of such factors represents risks that
new technology ventures should avoid. We use meta-analysis (Hunter and Schmidt, 1990;
2004) – a technique allowing to directly compare the results of extant studies by correcting for
sample size differences, measurement quality and eventual dichotomization of variables. We
found that scholars agree about the importance of only about half of the researched factors.
Among them, supply chain integration and broad market scope are the most crucial success
factors, while such factors as prior start-up and R&D experience are surprisingly not related to
performance of new technology ventures – at least not directly.
Potentially the strongest success and risk factors are among the factors that researchers
disagreed upon. Part of researchers' disagreement can be resolved by considering the
differences in the samples they used. For example, for independent new technology ventures
such factors as R&D alliances and product innovation turned out to be strong risk factors,
while in samples with corporate ventures those were strong success factors. Our review of
research on new technology ventures also indicated that there is little known about the more
precise mechanism of risk-taking applied by entrepreneurs: there is a lack of studies of
entrepreneurial decision-making in situations involving risk and of the strategies
entrepreneurs use to mitigate those risks.
146 Summary
The second part of the thesis digs deeper into how entrepreneurs take risks. Fast and
straight-forward intuitive decision-making is particularly useful for entrepreneurs, which are
often operating in complex and dynamic contexts with high time pressures. However,
intuition comes together with cognitive biases – errors in intuitive judgment arising due to the
heuristic nature of intuition. In this study we investigate a total of seven biases. For example,
overconfidence bias is the failure to know the limits of one's knowledge resulting in
unjustified confidence in one's own judgments. Simply put: it is not what you know, but
whether you know what you know and what you do not know. Another bias, base-rate fallacy,
occurs when only qualitative information is used to make a probability judgment, ignoring
available statistical information about prior probabilities (the base-rate frequency). It is
important since research shows that people tend to ignore statistics when at least some
qualitative information is available. What makes the bias really problematic is that base-rate
fallacy will still exist even if the qualitative information is irrelevant for the decision. These
and other cognitive biases can potentially distort the risks picture and result in a wrong
strategic decision.
We use dual process theory in order to explore whether rational and intuitive thinking
can help respectively avoid and strengthen the effects of the cognitive biases on the
entrepreneurial risk-taking. To answer this question, we use a dataset of 289 experienced
American entrepreneurs. The overall results show that while intuition strengthens
entrepreneurial risk-taking only indirectly via cognitive biases, rational thinking can both
diminish the influence of biases and decrease entrepreneurial risk-taking directly. Thus, the
more entrepreneurs think, the fewer risks they take. At the same time, intuition only infuses
risk-taking when entrepreneurs do not filter out the cognitive biases.
Focusing on biases in more detail, all the seven biases we investigated are
significantly related to the risk-taking propensity of entrepreneurs. However, six of the seven
biases increase risk-taking, while one of them – hindsight bias – actually leads to risk-averse
behavior. Rational thinking can correct the influence of four of the seven biases. Three biases
– illusory correlation, base-rate fallacy and law of small numbers – can not be corrected by
rational thinking. All the biases expect for base-rate fallacy derive from intuitive thinking.
Summary 147
As a result, this study shatters three myths about entrepreneurs. First, that all
entrepreneurial cognitive biases come from intuition; second, that all biases lead to greater
risk-taking; and third, that entrepreneurs only have to take their time and think to make the
biases disappear. Further, we found support for the statement that experienced entrepreneurs
are predominantly intuitive decision-makers.
In the third part of the thesis, the effectiveness of the major risk and uncertainty
management strategies of new technology ventures is compared under three conditions: when
they operate in markets with existing technology standards, in markets with high direct and in
markets with high indirect externalities. Direct network externalities cause an increase in
product value associated with having additional users in the network, such as in case of
telephone or MSN messenger. Indirect network externalities come into existence when
complementary products or services are of importance for the value of the product, such as in
case of DVD players and movies on DVD.
We evaluate four traditional risk management strategies (avoidance, imitation, control
and cooperation) and the more recent real options strategy, which is seen primarily as an
uncertainty management strategy. Avoidance strategy concerns being extremely cautious with
the ventures’ actions on the market – such as introducing products first to low-uncertainty
niches and growing from small scale. Imitation strategy includes imitating competitors’
products and entries into new markets as well as other firms’ manufacturing techniques.
Control strategy involves trying to increase entry barriers for competitors, influence
customers and use specific agreements with suppliers. Cooperation strategy covers
cooperation with other firms in terms of manufacturing, promoting and distributing the
venture’s products. Finally, real options strategy involves creating different new product
development options and targets stepwise, staged investment in one or more of these options.
The investments are made only when more information becomes available and uncertainty
surrounding these product options is resolved. As such real options represent a limited
commitment that creates future decision rights and improves managerial flexibility. Each risk
and uncertainty management strategy requires certain costs in order to be realized. Knowing
which strategy works best under which conditions should allow new technology ventures to
utilize their limited resources in a more efficient manner.
148 Summary
We test our hypotheses using a dataset of 420 new technology ventures in the USA.
The results indicate that in general new technology ventures should completely refrain from
the avoidance strategy since it does not affect their profits and is detrimental for their
customer retention rate and sales. New technology ventures should reconsider imitating other
firms, since it does not affect their profits and only slightly improves the sales growth.
Control, cooperation and real options strategies are generally very beneficial for the
performance of new technology ventures.
Well-established technology standards in the markets do not change the effectiveness
of risk and uncertainty management strategies, but do strongly improve the performance of
new technology ventures. Thus, new technology ventures should avoid participating in
markets with emerging technology standards.
As opposed to technology standards, both types of externalities do not influence
performance directly, but do change the effectiveness of the risk and uncertainty management
strategies. Not only do we find an opposite effect of direct and indirect network externalities
on the effectiveness of the risk and uncertainty management strategies, but we also find an
opposite effect for traditional risk management strategies and the real options strategy in
markets with network externalities. In particular, direct network externalities improve the
effectiveness of traditional risk management strategies and lower the effectiveness of the real
options strategy, while indirect network externalities decrease the effectiveness of traditional
risk management strategies and improve the effectiveness of the real options strategy.
This thesis aims to help entrepreneurs and organizations involved in their coaching
and financing distinguish the factors that are important for new technology ventures' success,
be aware of how different thinking processes influence entrepreneurial risks-taking decisions
and learn which risk and uncertainty management strategies are best to follow. By challenging
the implicit assumptions of both researchers and practitioners, the results of these three
studies may help bringing entrepreneurial risk-taking beyond bounded rationality and improve
the performance of entrepreneurial firms.
149
About the author
Ksenia Podoynitsyna was born in Moscow, Russia on December 14, 1980. In 2002 she
graduated with cum laude from Moscow Aviation Institute (Technical University). She did
her
graduation
project
at
Global
Risk
Management
Solutions
department
of
PricewaterhouseCoopers. Her Master’s thesis focused on Monte-Carlo simulations in risk
evaluation of software implementation projects. Soon after graduation she moved to the
Netherlands and did a couple of projects as IT systems administrator.
This dissertation is a result of the PhD research Ksenia conducted in the Organization
Science and Marketing Group within the Department of Technology Management at
Eindhoven University of Technology between 2003 and 2007. Her major research interests
include risk and uncertainty management, entrepreneurial cognition and creativity templates
in new product development and business models of new ventures.
151
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152
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30. Podoynitsyna, K.S. (11-06-2008), Entrepreneurial risk-taking beyond bounded
rationality: Risk factors, cognitive biases and strategies of new technology ventures.
Technische Universiteit Eindhoven, 152 pp.