The Study of Bias in
Stephen X. Zhang
Scholars use the theoretical lens of bias to research various behavioral phenomena in
entrepreneurship. We assess this body of research, focusing on deﬁnitional issues and
relationships. Furthermore, we discuss how the study of bias in entrepreneurship can be
advanced, given the new development in related ﬁelds such as cognitive sciences. The
assessments and discussions help reveal as well as address tensions in the literature,
identify numerous research opportunities that may not be obvious by looking at previous
work individually, and contribute to how the theory of bias can further help to understand
Most decisions that concern the minds and hearts of entrepreneurs are computation-
ally intractable (Mitchell et al., 2007). Consequently the research on bias, which refers to
the systematic deviation from rationality or norms in judgment and decision making (cf.
Baron, 2007; Haselton, Nettle, & Andrews, 2005; Tversky & Kahneman, 1974), becomes
relevant and interesting for entrepreneurship (Shepherd, Williams, & Patzelt, 2015). The
theory of biases provides a unique, practical, and empirically testable perspective on
decision making in entrepreneurship (Keh, Foo, & Lim, 2002; Zacharakis & Shepherd,
Research on biases in entrepreneurship (hereafter entrepreneurial bias) has increased
rapidly since its inception and has become an important area for entrepreneurship
(Krueger, 2005). Many individual papers on entrepreneurial bias have become founda-
tional to the development of the entrepreneurship ﬁeld to date. Two decades of research
have demonstrated bias as a widespread phenomenon in entrepreneurship. As studies on
entrepreneurial bias accumulate, a number of issues become critical, such as the consis-
tency in deﬁnitions, the analysis of discrepancies among studies, and the overall direction
of this stream of research. Scholars have pointed out the need to reveal, understand, and
resolve such issues, calling a review on entrepreneurial bias to advance this important
area of research (Shepherd et al., 2015).
To generate cumulative progress and point to future directions, we assess the deﬁni-
tional issues, the relationships examined using biases, as well as the situations of entrepre-
neurial bias research in a context of other closely related research streams.
Please send correspondence to: Stephen X. Zhang, tel.: (56 2) 2354-4825; e-mail: email@example.com
November, 2015 1
C2015 Baylor University
Entrepreneurial bias research has inherited from cognitive psychology a variety of
deﬁnitions of biases with variations in both conceptualization and operationalization. For
example, overconﬁdence has three distinct deﬁnitions (Moore & Healy, 2008), which
have been used interchangeably, even within a single article. The variations in deﬁnitions
impede our accumulation of knowledge on entrepreneurial bias.
Scholars have examined a wide range of relationships between bias and other key
constructs in entrepreneurship. This has led to a rich but somewhat disconnected body of
research. To synthesize existing studies, we organize them by a typology of biases
(Baron, 2007) as well as the consequences and antecedents of biases. Such organization
not only facilitates the comprehension and synthesis of existing literature, but it also
uncovers numerous tensions and equivocal ﬁndings. For instance, the empirical evidence
does not corroborate the numerous theorizing efforts on how experience could increase or
decrease certain biases.
Moreover, we situate entrepreneurial bias research in the context of the development
of entrepreneurial cognition and emotion that took place after the second millennium
(Cardon, Foo, Shepherd, & Wiklund, 2012; Mitchell et al., 2002) and ongoing debates on
bias in cognitive science (Stanovich, 2009; Tetlock & Mellers, 2002). Situating entrepre-
neurial bias research into its related streams of inquiry sheds light on how we interpret
bias in entrepreneurship and opens the door to further research. For example, how do we
scholars view entrepreneurial biases? To date, the explanations of entrepreneurial biases
often compete between the original theoretical deﬁnition of bias as errors (Tversky &
Kahneman, 1974) and the empirical ground that “if biases are bad, how could biased
entrepreneurs have created so many wonderful companies?” Drawing from “the great
rationality debate” (Stanovich; Tetlock & Mellers) and research on emotion and cogni-
tion, we posit that the interpretation of biases depends on the representations of individual
entrepreneurial decisions as well as the extent of the match between decision ecologies
and the evolutionarily adapted mechanisms that underlie the bias.
Bias and Entrepreneurship
Bias refers to the systematic deviation from rational choice theory when people
choose actions and estimate probabilities (Baron, 2007; Tversky & Kahneman, 1974).
The theory of bias has had enormous inﬂuence, resulting in the creation of new ﬁelds
such as behavioral economics (Kahneman, 2003) and behavioral law (Jolls, Sunstein, &
Thaler, 2000). The theory of bias is also transforming many ﬁelds—see reviews of biases
in medical decision making (Bornstein & Emler, 2001), auditing (Solomon & Trotman,
2003), accounting (Ashton & Ashton, 1995), and public policy (Rachlinski, 2004).
Biases permeate decisions in entrepreneurship, and entrepreneurs display higher lev-
els of bias than do managers in established organizations (Busenitz & Barney, 1997).
This can be due to various factors including, but not limited to, high uncertainty, informa-
tion overload and velocity, a lack of historical information and organizational routines,
and time pressure (Baron, 2004; Busenitz & Barney; Hayward, Shepherd, & Grifﬁn,
2006; Holcomb, Ireland, Holmes, & Hitt, 2009; Simon, Houghton, & Aquino, 2000;
Zacharakis & Shepherd, 2001). Meanwhile, more biased decision makers are more com-
fortable under ambiguous, uncertain, and complex decision contexts (Gigerenzer &
Gaissmaier, 2011); consequently, they have an easier time making entrepreneurial deci-
sions and are more likely to become entrepreneurs (Busenitz & Barney; Busenitz & Lau,
1996). Another inﬂuential group of decision makers in entrepreneurship, venture
2ENTREPRENEURSHIP THEORY and PRACTICE
capitalists (VCs), are similarly biased in their new venture evaluation and investment
decisions (Zacharakis & Meyer, 2000; Zacharakis & Shepherd).
Searching, Selecting, and Coding Research of Bias in Entrepreneurship
Before assessing entrepreneurial bias research, to set the stage, we ﬁrst lay out how
we search, select, and code the existing literature. Following the procedure of a systematic
review (Tranﬁeld, Denyer, & Smart, 2003), we searched literature from 1973 (the year
bias research started in psychology) to January 1, 2014 for articles and analyzed their con-
tents. The rest of this section documents this procedure.
Searching for Articles
To systematically locate the relevant articles, we integrated the approaches of
egoire, Corbett, and McMullen, (2011); Kiss, Danis, and Cavusgil, (2012); and Klotz,
Hmieleski, Bradley, and Busenitz (2014) in a two-stage search process (see more details
of the search process including the search algorithm in Table A1 in the Appendix).
First, we scanned the top entrepreneurship and management journals in the Financial
Times journal list: Academy of Management Journal, Academy of Management Review,
Academy of Management Perspectives, Entrepreneurship Theory and Practice, Journal
of Business Venturing, Journal of International Business Studies, Journal of Management
Studies, Management Science, Organization Science, Organization Studies, Organiza-
tional Behavior & Human Decision Processes, with the addition of Strategic Entrepre-
neurship Journal (added by the authors).
Second, we checked the articles discovered in the ﬁrst step to identify an inventory of
biases to create an enhanced list of keywords. The keywords include entrepreneur, entre-
preneurial, entrepreneurship, venture capital or VC and speciﬁc biases identiﬁed in the
previous step, such as overconﬁdence and illusion of control. The Appendix contains the
exact keywords used. Lastly, we searched the list of keywords in the Scopus database,
and found 286 published or in-press articles that contained the keywords.
It is possible that relevant articles may have escaped our sampling procedures despite
the use of a large database (Scopus). There are two possible types of omissions: articles
not written in English (because of the use of English keywords) and articles that either do
not mention or use a different nomenclature for a particular bias in its abstract, title, or
keyword list. One example is Sandri, Schade, Mußhoff, and Odening (2010), which stud-
ies status-quo bias but instead calls it “psychological inertia.” This article has been added
to our analysis thanks to a reviewer.
Selecting and Coding Articles
We further selected and coded the articles through a selection and coding process
egoire et al., 2011; Moroz & Hindle, 2012) with the following questions in mind:
1) Do the articles investigate decision making in entrepreneurship?
2) Do the articles study biases as part of their central inquires, containing biases as
representation, attributes, antecedents, or consequences in their theoretical models
egoire et al. 2011)?
November, 2015 3
3) Who possesses the biases (entrepreneurs or VCs)?
4) What is the level of analysis?
5) What is the research method?
6) What are the independent and dependent variables, if they are distinguishable?
7) What are the antecedents and consequences of entrepreneurial biases?
8) What are the ﬁndings and proposed future directions?
The ﬁrst two questions aim to select articles that study biases as entrepreneurial phe-
nomena instead of biases in general as in the ﬁeld of psychology. To examine whether the
articles study biases as part of their central inquires, we chose articles that developed spe-
ciﬁc propositions, hypotheses, and models using biases, regardless of their methodologi-
cal approaches. Articles that did not develop models using biases are not included, such
as those mentioning biases generally or using biases to discuss possible (non)ﬁndings.
This selection procedure resulted in 41 articles that study biases as part of their central
inquiry in entrepreneurial decision making. The selection used the two selection questions
indicated previously and involved three raters, with a reliability rating of 95% based on
intraclass correlation (McGraw & Wong, 1996). Table 1 lists all of these articles chrono-
logically. A total of 32 are empirical papers, of which 18 (56%) performed surveys, 8
(25%) conducted (quasi) experiments (including conjoint analysis), 3 (9%) held inter-
views, 3 (9%) used a scenario technique in which respondents read hypothetical situations
and stated their presumed behaviors or attitudes, 2 (6%) used case studies, and 8 (25%)
analyzed secondary sources. As we did not limit our search based on methodology, we
included also 9 theoretical papers that developed speciﬁc propositions using biases (in
italic in Table 1). As these theoretical papers do develop speciﬁc propositions, they are
similar to the “front end” of empirical papers simply without empirical testing. The spe-
ciﬁc propositions, together with empirical papers, allow us to synthesize what the ﬁeld
has done on entrepreneurial bias and identify future opportunities. Last, we have not
found any review articles on entrepreneurial bias to date. None of the conceptual or
empirical papers to date has examined the body of entrepreneurial bias research in its
Entrepreneurship literature has introduced 11 biases to explain entrepreneurship phe-
nomena (Table 2). While most of these biases were investigated in just a single study, sev-
eral biases have been studied repeatedly in a few articles. An examination of such studies
reveals discrepancies in the conceptualization and operationalization of some of the most
researched biases in entrepreneurship. Our assessment aims to examine the most promi-
nent deﬁnitional issues in entrepreneurial bias research.
The Issue With Overconfidence
Although overconﬁdence appears to be a clear and precisely deﬁned concept on its
surface, an analysis of 365 overconﬁdence papers by Moore and Healy (2008) uncovered
three routinely muddled deﬁnitions of overconﬁdence: (1) overestimation of one’s actual
performance, (2) overplacement of one’s performance relative to others (better-than-aver-
age effect), and (3) overprecision of one’s beliefs in an analysis.
Studies of entrepreneurial biases have incorporated all three deﬁnitions to conceptual-
ize and measure overconﬁdence. A single paper may use one deﬁnition to conceptualize
4ENTREPRENEURSHIP THEORY and PRACTICE
Selected Articles Studying Entrepreneurial Biases (Italics Denote Conceptual Papers)
Author Purpose Method Sample Bias
McCarthy, Schoorman, and Cooper
Examine the presence of escalation com-
mitment in reinvestment decisions by
Survey 1112 ﬁrms in the U.S. Overconﬁdence, escalation of
Busenitz and Barney (1997) Examine differences in the decision-
making processes used by entrepre-
neurs and managers in large
Survey and scenario technique 124 entrepreneurs and 95
managers in the U.S.
Cable and Shane (1997) Study the decision to cooperate based on
implicit similarities in the
Conceptual – Similarity
Busenitz (1999) Examine entrepreneurial risk through the
lens of cognitive psychology and deci-
Survey 176 entrepreneurs and 95
managers in the U.S.
Coval and Moskowitz (1999) Study the local equity preference in
Secondary data 10 fund managers in the U.S. Local bias (similarity)
Simon et al. (2000) Explore how individuals cope with the
risks inherent in their decisions
Survey and scenario technique 191 MBA students in the U.S. Overconﬁdence, illusion of
control, law of small numbers
Bernardo and Welch (2001) Analyze how overconﬁdent behavior
Simulation – Overconﬁdence
Zacharakis and Shepherd (2001) Investigate if VCs are overconﬁdent in
their decision-making process
Conjoint analysis 51 VCs in the U.S. Overconﬁdence
Keh et al. (2002) Examine opportunity evaluation under
Survey and scenario technique 77 owners of SMEs in Singapore Illusion of control, law of
small numbers, overconﬁdence,
Simon and Houghton (2002) Analyze the relationships among biases,
misperceptions, and the introduction
of pioneering products
Conceptual – Illusion of control, law
of small numbers
Simon and Houghton (2003) Examine the effects of overconﬁdence on
Survey and interview 55 managers of small computer
companies in the U.S.
Wickham (2003) Demonstrate the impact of representa-
tiveness on decision quality
Experiment 155 entrepreneurship students in the
November, 2015 5
Author Purpose Method Sample Bias
Rogoff, Lee, and Sub (2004) Analyze the existence of a self-serving
attribution bias when entrepreneurs
enumerate the factors that contribute
to or impede their business success
Survey 425 owners of small business and experts
in the U.S.
Forbes (2005) Examine differences in the degree to
which entrepreneurs exhibit the over-
Survey 108 managers of new ventures in the
Wu and Knott (2006) Analyze entrepreneurial risk propensity
and market entry
Simulation and secondary data Banking sector in the U.S. Overconﬁdence
De Carolis and Saparito (2006) Advance a model suggesting that entre-
preneurial behavior is a result of the
interplay of environments (social net-
works) and certain cognitive biases
Conceptual – Overconﬁdence, illusion of control
Franke et al. (2006) Analyze biases arising from similarities
between a VC and the members of a
Conjoint analysis 51 VCs in Munich, Berlin, and Vienna Similarity
Hayward et al. (2006) Develop a hubris theory of entrepreneur-
ship to explain why so many new ven-
tures are created under high risk
Conceptual – Overconﬁdence
Lowe and Ziedonis (2006) Analyze the impact of overoptimism on
Secondary data 734 inventions from the University of
Bryant (2007)Explore the role of self-regulation in
Conceptual – Representativeness
Burmeister and Schade (2007) Examine whether the empirical ﬁnding
that entrepreneurs are more biased
than other individuals, is generally
Experiment 427 students, 135 bankers,
and 240 entrepreneurs in Germany
Koellinger, Minniti, and Schade
Study the antecedents of the decision to
start a business
Survey and secondary data 40,000 entrepreneurs in 18 countries (Over)conﬁdence
Moore and Cain (2007) Aim to understand when and why people
underestimate (and overestimate) the
Experiment 91 university students in the U.S. Overconﬁdence
6ENTREPRENEURSHIP THEORY and PRACTICE
Author Purpose Method Sample Bias
Grichnik (2008) Develop a model of entrepreneurial risk-
taking behavior in different cultural
Experiment and survey 252 entrepreneurship students and
entrepreneurs in Germany and the
Parwada (2008) Analyze the determinants of the decision
of ﬁrm location and stock selection of
Secondary data 358 executives at 207 ﬁrms in the U.S. Local bias (similarity)
Cassar and Craig (2009) Analyze how previous failures affect
hindsight bias concerning the proba-
bility of venture formation
Survey 198 nascent entrepreneurs in the U.S. Hindsight bias (representativeness)
De Carolis, Litzky, and Eddleston
Analyze the inﬂuence of social capital
and cognition in the progress of new
Survey 269 students entrepreneurs in the U.S. Illusion of control
Parker (2009)Analyze how overoptimism and self-
serving attributions explain homophily
in start-up teams
Simulation – Self-serving attribution
Barbosa and Fayolle (2010) Examine the effect of new information in
risk perceptions and the decision to
start a venture
Survey Entrepreneurs and students (number not
Availability and anchoring
Carr and Blettner (2010) Examine the effects of illusions of con-
trol on decision quality
Survey 163 small ﬁrm founders in the U.S. Illusion of control
Cassar (2010) Examine the rationality of the expecta-
tions and overoptimism of nascent
Secondary data and interviews 386 entrepreneurs from “Panel Study of
Cumming and Dai (2010) Examine local bias in VC investments Secondary data Investments from 1008 VCs in the U.S. Local bias (similarity)
Hayward , Foster, Sarasvathy, and
Explain why more conﬁdent founders of
failed new ventures are better posi-
tioned to start subsequent ventures
Conceptual – Overconﬁdence
Sandri et al. (2010) Investigate the disinvestment behaviors
of entrepreneurs when choices are
Experiment 39 entrepreneurial students and 37 non-
Psychological inertia (status quo)
November, 2015 7
Author Purpose Method Sample Bias
Murnieks, Haynie, Wiltbank, and
Investigate the extent to which similarity
in decision-making process might bias
Survey and conjoint analysis 60 VCs in the U.S. Similarity
Simon and Shrader (2012) Identify which entrepreneurial actions are
associated with an entrepreneur’s
Interview and survey 55 managers of small computer compa-
nies in the U.S.
Ebbers and Wijnberg (2012) Analyze if the individual reputations of
founders of nascent ventures can func-
tion as important signals to investors
Case study 141 ﬁlms’ ventures from Netherlands Similarity
Hogarth and Karelaia (2012)Analyze if overconﬁdence causes excess
entry and the high failure rates of
market entry decisions
Simulation – Overconﬁdence
Gudmundsson and Lechner (2013) Build a multilevel model explaining the
interplay of cognitive biases and cog-
nitive make-up and its performance
Survey 115 founders of small ﬁrms in Iceland Overconﬁdence
Khanin and Mahto (2013) Analyze if VC have a continuation bias Survey 51 VCs in the U.S. Continuation bias
and Kim (2014)
Analyze the experience–performance
relationship and the impact of contex-
Secondary data Swedish founder and managers Similarity
8ENTREPRENEURSHIP THEORY and PRACTICE
and another to operationalize. For instance, eight empirical papers conceptualized over-
conﬁdence as overestimation, yet only three out of the eight papers measured overestima-
tion accordingly (Simon & Houghton, 2003; Simon & Shrader, 2012; Zacharakis &
Shepherd, 2001). Instead, four of the eight papers measured overconﬁdence as over-
precision (Busenitz, 1999; Busenitz & Barney, 1997; Forbes, 2005; Simon et al., 2000),
and one paper measured it as overplacement (Grichnik, 2008).
Additionally, two concepts that may appear similar to overconﬁdence, conﬁdence and
entrepreneurial self-efﬁcacy, often appear alongside overconﬁdence. Conﬁdence denotes
one’s subjective certainty in his or her judgments, whereas overconﬁdence is the differ-
ence between one’s subjective certainty and his or her objective accuracy (Busenitz, 1999;
Gudmundsson & Lechner, 2013). Thus, while distinct, the two concepts are clearly related
to each other, and their relatedness draws the attention of entrepreneurship scholars.
Koellinger et al. (2007), empirically observing an excessive amount of conﬁdence in
entrepreneurs, reasoned that entrepreneurs must experience overconﬁdence and hence
theoretically developed their study on the notion of overconﬁdence. In a paper about the
conﬁdence of entrepreneurs, Hayward et al. (2010) argued that more conﬁdent entrepre-
neurs are better able to cope emotionally, cognitively, socially, and ﬁnancially, and that
these “second order” beneﬁts can potentially outweigh the negative consequences of
overconﬁdence in entrepreneurs.
Hayward et al. (2010) theorized using task-speciﬁc conﬁdence, which resembles self-
efﬁcacy. The construct of self-efﬁcacy differs from the colloquial term conﬁdence. Conﬁ-
dence refers to strength of belief; nonetheless, it does not necessarily specify what the cer-
tainty is about. Self-efﬁcacy denotes a belief in one’s speciﬁc capabilities to generate
speciﬁc attainment, and the concept of entrepreneurial self-efﬁcacy is concerned with the
self-efﬁcacy of individuals in performing entrepreneurial decisions (Chen, Greene, &
Crick, 1998; Zhao, Seibert, & Hills, 2005). Forbes (2005) proposed that entrepreneurs
Biases Studied in Entrepreneurship
Bias Behaviors of People in Decision Making
Overconﬁdence Perceive a subjective certainty higher than the objective accuracy (Busenitz, 1999;
Gudmundsson & Lechner, 2013).
Overoptimism Overestimate the likelihood of positive events and underestimate the likelihood of
negative events (Sharot, 2011).
Self-serving attribution Take credit for success while deny responsibility for failure (Rogoff et al., 2004).
Illusion of control Overemphasize how much skills, instead of chance, improve performance (Langer,
The law of small numbers Reach conclusions about a larger population using a limited sample (Haley &
Similarity Tend to evaluate more positively those who are more similar to themselves (Byrne
& Grifﬁtt, 1973).
Availability Make judgments about the probability of events based on how easy it is to think
of examples (Tversky & Kahneman, 1974).
Representativeness Use a familiar situation as a cognitive shortcut for making decisions (Wadeson,
Status quo Repeat a previous choice overly often (Samuelson & Zeckhauser, 1988).
Planning fallacy Underestimate the time needed for future tasks (Kahneman & Lovallo, 1993).
Escalation of commitment Persist unduly with unsuccessful initiatives or courses of action (Staw, 1977).
November, 2015 9
with higher levels of entrepreneurial self-efﬁcacy would be more overconﬁdent; however,
his test did not ﬁnd the relationship to be signiﬁcant. To capture task-speciﬁc overconﬁ-
dence in entrepreneurs, Simon and Shrader (2012) developed a context-speciﬁc measure
of overconﬁdence in entrepreneurship, or “over self-efﬁcacy.”
The Issue With Overoptimism
The concept of overoptimism differs from but overlaps with overconﬁdence. Over-
optimism (or overoptimism bias) refers to the notion that people overestimate the likeli-
hood of positive events and underestimate the likelihood of negative events (Sharot,
2011). It overlaps with the overestimation form of overconﬁdence in the case of positive
events only, but not in negative events. Another subtle but important difference is that
while overconﬁdence is related to an individual’s own capabilities and performance, and
thus, at least partially under the control of the individual, overoptimism can be com-
pletely detached from individual’s own inﬂuence (e.g., I can be overoptimistic that my
favorite sports team will beat the reigning world champion even if the odds are very low
and I cannot inﬂuence this odd).
Due to the overlap between overoptimism and overconﬁdence, many studies on
entrepreneurial overoptimism unsurprisingly drew on the overconﬁdence literature
(Cassar, 2010; Lowe & Ziedonis, 2006). Occasionally, the conceptualization of overop-
timism can be very broad, where it does not speciﬁcally refer to positive events, thus
blurring the distinction between overconﬁdence and overoptimism. For example, over-
optimism was conceptualized as an overplacement of entrepreneurs’ performance rela-
tive to that of others (Lowe & Ziedonis), rendering it indistinguishable from the
overplacement form of overconﬁdence.
In this section, we illustrate the most prominent issues in the conceptualization and
operationalization of well-studied biases in entrepreneurship: overconﬁdence and overop-
timism. The in-depth analysis of deﬁnitional issues is limited to these two particular
biases, because (1) their deﬁnitions are close to and can be confused with several other
important concepts in entrepreneurship, and (2) entrepreneurship literature has studied
the two biases recurrently, but with varying conceptualization and operationalization.
Such issues of inheriting conceptually and empirically distinct deﬁnitions and measures
from cognitive sciences may not be limited to these two biases. Even though other biases
have not been studied recurrently in entrepreneurship to show evidence of such issues,
they may be prone to similar problems, which we scholars need to be cautious about. The
good news is that mechanisms to distinguish and clean-up such issues in the ﬁeld of psy-
chology are underway, and have already begun to resolve inconsistent ﬁndings and long-
standing theoretical arguments such as on overconﬁdence (Kwan, John, Kenny, Bond, &
Robins, 2004). We believe future entrepreneurial bias research could beneﬁt from doing
the same. Otherwise, the variability in the deﬁnitions of closely related concepts and the
multitude of conceptualization and operationalization possibilities would greatly perplex
and hinder the analysis, comparison, and synthesis of ﬁndings to accumulate and advance
knowledge. At the very least, we entrepreneurship scholars need to be aware of the key
deﬁnitional issues, which exist in the studies of biases in entrepreneurship and psychol-
ogy, or ﬁelds with new concepts in general, in our effort to theorize and test relationships.
Key Relationships Studied Using Bias
Entrepreneurship literature has examined the relationships between biases and a
diverse range of constructs including perception of risk, decision to start a venture,
10 ENTREPRENEURSHIP THEORY and PRACTICE
evaluation of opportunities, and evaluation of start-up teams (Franke et al., 2006; Keh
et al., 2002; Simon et al., 2000). Overall, the relationships cluster around two themes:
what factors do biases affect and what factors affect these biases? This pattern is compara-
ble to the inputs-mediators-outcomes framework in reviews on many topics, for example,
new venture teams (Klotz et al., 2014), multimarket competition (Yu & Cannella, 2012)
and corporate social responsibility (Aguinis & Glavas, 2012).
For the purpose of theoretical identiﬁcation, we introduce a typology of biases
(Baron, 2007). The typology organizes biases theoretically into three types based upon
the mechanisms by which they depart from normative models. To reﬂect these underlying
mechanisms, we will name the three main types as: make-happy,sketchy-attribute, and
psycho-physics and explain them one by one.
First, the “make-happy” type includes biases that result from the effects of goals or
desires or beliefs (Baron, 2007). People often adopt beliefs that make them happy or com-
fortable. For example, people selectively expose themselves to evidence and assimilate
positive evidence, happily neglecting neutral or negative evidence, at least before they
suffer the consequences of acting on these beliefs. The mechanism of this type of bias
invokes not just cognition but also emotion, coinciding with the current surge of interest
in entrepreneurial emotion research (Baron & Tang, 2011; Baron, 2008; Cardon et al.,
2012). Three biases of this type: overconﬁdence, overoptimism, and self-attribution, have
appeared in entrepreneurship literature.
Second, the “sketchy-attribute” type of bias describes the behaviors of attending to
one attribute when other attributes are more relevant (Baron, 2007). The attribute in ques-
tion captures our attention because it is the result of recent or memorable events, it is a
good indicator for another attribute in another context, or it is mistaken as a salient or use-
ful indicator due to humans’ limited capacity for information processing (Bless, Fiedler,
& Strack, 2004). These biases largely arise from cognitive mechanisms, and many biases
of this type (such as availability, representativeness, the illusion of control, similarity,
local bias, the law of small numbers, status quo, and hindsight bias) are found in the entre-
Third, the “psycho-physics” type of bias refers to the distortion in our perception of
quantitative attributes (Baron, 2007). Our sensitivity usually diminishes as intensity
increases. The archetypal biases in this type include overweighting low probabilities
(Kahneman & Tversky, 1984) and framing effects for gains/losses (Levin, Gaeth,
Schreiber, & Lauriola, 2002). This type of bias is highly relevant to entrepreneurship, as
will be discussed in subsequent sections. Nevertheless, our systematic search has not
yielded studies on this type of bias in entrepreneurship literature to date, and there are
many fascinating avenues of research that may be pursued.
We choose Baron’s typology over other categorizations, such as Tversky and Kahne-
man (1974) and its updated version in Kahneman and Fredekerik (2002), because Baron’s
typology is based on how biases arise, different from other categorizations, which classify
based on how biases are discovered, which is important for psychologists (Baron, 2007).
In addition, the other categorizations cannot properly account for overconﬁdence bias,
putting it into more than one category (Russo & Schoemaker, 1992; S
anchez, Carballo, &
errez, 2011). The use of the typology of biases as well as their consequences and ante-
cedents as a framework helps to identify the skeleton of the existing literature and to
reveal possible tensions. Figure 1 (a and b) gives an overview of the existing relationships
between bias and many other constructs in entrepreneurship. As the ﬁgure illustrates, the
entrepreneurship literature includes a wide range of these relationships. Our goal is not to
catalog the merit of all the individual relationships exhaustively, but instead to interweave
November, 2015 11
and highlight where the literature gravitates, where theoretical and empirical tensions sur-
face, and where interesting future research opportunities are high.
First, we will present the key issues in the consequences of each type of biases and
then the issues in their antecedents. The presentation of consequences precedes that of
antecedents because in general entrepreneurship literature ﬁrst is concerned with whether
and how biases would matter in entrepreneurship, before pursuing the antecedents of the
biases. Similarly to the deﬁnition issues we discussed, we will analyze key relationships
in which tensions exist or possibilities to surface future research opportunities are high.
Thus, not all relationships or biases will be analyzed in the following section. When nec-
essary, we also provide short in-section remarks to summarize the nuanced tensions or to
point to speciﬁc future opportunities.
(a) Overview of the Antecedents and Consequences of Make-Happy Biases
(b) Overview of the Antecedents and Consequences of Sketchy-Attribute Biases
12 ENTREPRENEURSHIP THEORY and PRACTICE
“Make-Happy” Type of Bias
Consequences of Bias. On Risk-Taking. Entrepreneurship requires a signiﬁcant
amount of risk-taking, and entrepreneurs display a greater amount of overconﬁdence and
overoptimism than nonentrepreneurs (Busenitz & Barney, 1997; Grichnik, 2008; S
et al., 2011); therefore, scholars have attempted to use biases to explain entrepreneurial
risk-taking. Scholars theorized that the biases of overconﬁdence and overoptimism make
entrepreneurs overlook uncertainty and potential negative outcomes, thereby decreasing
risk-perception and increasing risk-taking behaviors in new ventures (Cooper, Woo, &
Dunkelberg, 1989; Simon et al., 2000). However, the various attempts to test such reason-
ing have produced equivocal results to date.
Earlier studies uncovered that overconﬁdence empirically increased the likelihood of
risky decisions, such as the decision to expand ventures (McCarthy et al., 1993). Follow-
up studies proposed that overconﬁdence would decrease risk perception (a mediator),
thereby increasing new venture decisions (Simon et al., 2000) and boosted the evaluation
of opportunities (Keh et al., 2002); however, they were not empirically conﬁrmed. Like-
wise, overoptimism failed to signiﬁcantly explain entry decisions, a key risky entrepre-
neurial decision (Lowe & Ziedonis, 2006). Nevertheless, evidence from international
entrepreneurship research using data from Germany and the United States conﬁrmed that
overconﬁdence decreases risk perception and consequentially induces riskier behaviors
and decisions of entrepreneurs (Grichnik, 2008). In addition, research conﬁrmed the rela-
tionships between overconﬁdence and project-level risk-taking behaviors, such as intro-
ducing riskier products (Simon & Houghton, 2003).
In a quasiexperiment, Wu and Knott (2006) separated the rational component of risk-
taking behavior (to capture the real options values of risk-taking) and the irrational com-
ponent of overconﬁdence. They found overconﬁdence to contribute to new business
entry. However, in contrast, a later analytical study by Hogarth and Karelaia (2012) attrib-
ute the entry not to overconﬁdence or to any systematic bias, but instead to imperfect
Meanwhile, conceptually scholars continued to use overconﬁdence to develop theo-
ries of entrepreneurial risk-taking. For instance, Hayward et al. (2006) developed a well-
recognized hubris theory proposing that more overconﬁdent entrepreneurs display risky
behaviors, such as starting ventures with fewer resources but committing greater resour-
ces of their own, underestimating the need for key resources, but overestimating their
In summary, despite strong theorizing effort, research exploring the impact of bias on
risk-taking has not yet amassed a consistent empirical foundation on which to build strong
conclusions. The inconclusive relationships could be due to situational factors, as sug-
gested by Grichnik (2008). Future research could dive deeper into person-situation inter-
actionist models to identify speciﬁc situational factors that could interact with
overconﬁdence to trigger risk-taking.
On Performance Measures. Overconﬁdence and overoptimism carry both positive
and negative effects on performance; however, their positive effects and negative effect
are due to distinct theoretical reasons.
The negative effects of overconﬁdence and overoptimism gain their theoretical foun-
dation directly from the classical heuristics and bias research program, which originally
deemed biases as systematic errors in decision making (Kahneman & Tversky, 1996). If
biases are errors, researchers in entrepreneurship consequently become interested in
whether biases in entrepreneurial decisions impact new venture performance. Theoretical
November, 2015 13
studies have argued that overconﬁdence leads to underestimation of competitive response
or overestimation of demand. The inappropriate estimations in turn generate riskier and
less successful outcomes (Simon & Houghton, 2002). For instance, Hayward et al. (2006)
reasoned that overconﬁdent founders maintain low liquidity, which increases the likeli-
hood of failure. This line of reasoning is also in line with evidence in many industries that
involve high risk-taking, such as banking and market entry, where greater overconﬁdence
causes failures (Camerer & Lovallo, 1999; Wu & Knott, 2006).
Empirical evidence in entrepreneurship conﬁrmed that overoptimism prolongs entre-
preneurs’ unsuccessful development efforts, resulting in wasted resources, lower levels of
employment, and reduced revenues (Lowe & Ziedonis, 2006). In addition, overconﬁdent
entrepreneurs tend to underestimate competition, under-resource their ventures, rely less
on external networks for relational resources, and introduce riskier products. All of these
behaviors lower the likelihood of their ventures’ survival (Gudmundsson & Lechner,
2013; Koellinger et al., 2007).
The positive effects of biases get their support primarily from theories on fast-and-
frugal decision making as well as theories on emotions. Motivational theories reason that
overconﬁdence and overoptimism increase the motivation to initiate entrepreneurial
action (Cassar, 2010; Simon & Shrader, 2012), heighten resilience and work effort, and
help to cope with setbacks and failures during the entrepreneurial processes (Hayward
et al., 2010). Overconﬁdence and overoptimism induce higher ability and outcome
expectations, thereby enhancing performance (Van Eerde & Thierry, 1996). The interpre-
tations of these biases will be further discussed later.
Antecedents of Bias. The Role of Experience. Past experiences in part inﬂuence
human behaviors. More experienced decision makers rely more on intuition, thereby
developing a sense of security and conﬁdence that could potentially be unfounded (Mac-
millan, Zemann, & Subbanarasimha, 1987). Following these theories, entrepreneurship
scholars proposed various relationships between experience and biases but yielded incon-
clusive results. First, Zacharakis and Shepherd (2001) proposed a positive correlation
between experience and overconﬁdence in VCs, but did not ﬁnd it to be empirically sig-
niﬁcant. In another study using conjoint analysis, Shepherd, Zacharakis, and Baron
(2003) discovered that as the experience of VCs increased, their decision accuracy at ﬁrst
grew and then decreased after an optimal level of 14 years; their explanation is that VCs
become more overconﬁdent as they age. In a complementary vein, Hayward et al. (2006)
proposed that entrepreneurs with prior experience in founding successful ventures
become more overconﬁdent, despite the fact that their new ventures differ from their pre-
Such reasoning is rejected by empirical results that younger entrepreneurs were more
overconﬁdent than older entrepreneurs (Forbes, 2005) and nascent entrepreneurs were
more conﬁdent in their skills, knowledge, and experience than serial entrepreneurs
(Koellinger et al., 2007). Forbes explained that older managers were less overconﬁdent
because they sought more information and took longer to make a decision than did
younger managers (Taylor, 1975).
On the surface, the results of Forbes (2005) and Koellinger et al. (2007) seem to
largely refute prior theoretical developments (Hayward et al., 2006; Shepherd et al.,
2003). However, experience is a complex concept (Shepherd et al.) that could mean many
different things such as the number of past ventures, the number of successful ventures,
years of business experience, or even age. In addition, one needs to consider the quality of
the experience, for example, whether positive or negative. The roles of the decision mak-
ers (entrepreneurs, VCs, students in entrepreneurship programs, and so forth) and the
14 ENTREPRENEURSHIP THEORY and PRACTICE
context of each experience could also matter. Therefore, the relationships between
experiences and biases are inconclusive at best and offer an interesting avenue for fur-
ther research at both between-person and within-person levels. An alternative direction
is to investigate not only experience as an antecedent of overconﬁdence but also the
interaction effect of experience and overconﬁdence on decision quality or
The Easiness/Difﬁculty of Decision Tasks. The difﬁculty of a decision affects over-
conﬁdence in an interesting manner. When a task is easy, people choose to enter markets
overconﬁdently because they believe that they are better than average (overplacement);
when a task is difﬁcult, they become underconﬁdent about entering because they believe
they are worse than average (underplacement) (Moore & Cain, 2007). On the contrary, in
the case of another form of overconﬁdence, overestimation, people overestimate their
performance when tasks are difﬁcult and underestimate their performance when tasks are
easy (Moore & Healy, 2008). Therefore, the difﬁculty of decision task inﬂuences over-
placement and overestimation in opposite ways.
Interestingly, many factors that could increase the difﬁculty of decision tasks have
been proposed to positively correlate with overconﬁdence, such as environmental com-
plexity and environmental dynamism (Hayward et al., 2006; Simon & Shrader, 2012),
the riskiness of the contexts (Simon & Houghton, 2003), unfamiliar contexts (Zacharakis
& Shepherd, 2001), pioneering of product introduction, and the hostility of the environ-
ment (Simon & Shrader). All these proposed relationships were supported by empirical
evidence, except for environmental dynamism, which was negatively correlated with
overconﬁdence in a sample of 55 owners of small computer companies (Simon &
We need to note that all aforementioned relationships between contextual factors and
overconﬁdence in entrepreneurship treat overconﬁdence exclusively as “overestimation”
(Hayward et al., 2006; Simon & Houghton, 2003; Simon & Shrader, 2012; Zacharakis &
Shepherd, 2001). These contextual factors complicate decisions and increase the difﬁ-
culty of decision making; thus, they should increase overestimation and decrease over-
placement. It can be interesting for management scholars to study overplacement in situa-
tions of varying difﬁculty of decision tasks. It can also be important to formally examine
how task difﬁculty might mediate the relationships between contextual factors and
The Role of (Dis)trust. Trust creates conﬁdent expectations, rendering the trusting
party more comfortable about ambiguous or unclear situations (Rousseau, Sitkin, Burt, &
Camerer, 1998). Along this line of logic, entrepreneurship research has proposed that the
trust the entrepreneurs have in their networks increases overconﬁdence (De Carolis &
Subsequently, Gudmundsson and Lechner (2013) revealed empirically that distrust
(negative expectations in others) was positively associated with overconﬁdence. They
reasoned that a distrusting entrepreneur would be reluctant to delegate tasks to or seek
assistance from others (Gino & Moore, 2007), behaviors that could intensify miscalibra-
tion and lead to overconﬁdence. The contrasting relationships between (dis)trust and
overconﬁdence in De Carolis and Saparito (2006) and Gudmundsson and Lechner (2013)
appear to be a paradox. Still, while many studies equate distrust with lack of trust, treating
them as opposites (Gans et al., 2001; Omodei & McLennan, 2000; Ziegler & Lausen,
2005), neuroscience evidence considers trust and distrust distinct phenomena (Dimoka,
2010): trust deals with positive expectations about the trustee’s beneﬁcial conduct, and
November, 2015 15
distrust deals with negative expectations about the trustee’s harmful conduct (Cho, 2006;
Xiao & Benbasat, 2003). Trust and distrust should bear different relationships with their
antecedents and effects (Lee & Huynh, 2005). Thus, future research awaits to disentangle
the relationships among trust, distrust, and bias. An additional research opportunity is to
study this relationship in the reverse direction, that is, how overconﬁdence and overop-
timism might inﬂuence trust or distrust, because more biased entrepreneurs might be
more prone to trust or distrust others.
“Sketchy-Attribute” Type of Biases
Consequences of Bias. On Risk-Taking. Similarly to the “make-happy” biases,
scholars pay signiﬁcant efforts to investigate how “sketchy-attribute” biases inﬂuence
risk-taking (see Figure 1b), theorizing that biases decrease the perception of uncertainty
and thus increase risk-taking behaviors. Empirically, the various attempts to test such rea-
soning have, thus, far yielded inconclusive results. Simon et al. (2000) found that the
biases of illusion of control and the law of small numbers decreased individuals’ percep-
tions of the riskiness of new ventures and hence increased new venture decisions. Simon
et al. also proposed risk perception to fully mediate the relationships, but empirically the
mediation turned out to be partial.
Building on research by Simon et al. (2000), Keh et al. (2002) studied the evaluation
of opportunities instead of new venture decisions as the outcome variable in their models,
and empirically found that risk perception fully moderates the relationship between the
illusion of control and the evaluation of opportunities. Furthermore, Keh et al. also found
that the law of small numbers had a direct effect on opportunity evaluation without the
mediation of risk perception.
Building upon these ﬁndings, De Carolis and Saparito (2006) developed a conceptual
model in which the illusion of control and representativeness decrease risk perception,
thereby leading to the exploitation of entrepreneurial opportunities. Part of the model was
later tested and conﬁrmed; illusion of control and risk propensity were found to positively
correlate with the progress of a new venture (De Carolis et al., 2009).
Barbosa and Fayolle (2010) further extended the model to include an availability
bias. The availability of new information expressed in negative (positive) terms was
found to increase (decrease) the perceived risk associated with a new venture, thus, reduc-
ing (increasing) individuals’ willingness to start the venture.
On Performance. Biases of the sketchy-attribute type have important implications
on performance, since these biases originally denote errors in decision making (Tversky
& Kahneman, 1974). However, few researchers have examined these effects. To date,
only the illusion of control has been linked to performance-related measures. Carr and
Blettner (2010), citing evidence that bankers with greater illusions of control obtained
worse trading results, tested the hypothesis that illusion of control lowers the performance
of entrepreneurs in their decision making. Similarly, De Carolis et al. (2009) found illu-
sion of control to be positively correlated with new venture progress—a performance-
related measure. This stream of research is still incipient, with few articles largely discon-
nected from one another. Studies examining the performance implications of these biases
need further development by assessing multiple and more direct performance indicators
such as new venture survival and returns under uncertainty.
On New Venture Evaluation. Similarities between VCs and entrepreneurs, in demo-
graphic factors, work value congruence, and perceived power equality, were proposed to
16 ENTREPRENEURSHIP THEORY and PRACTICE
positively bias VCs’ willingness to invest (Cable & Shane, 1997). Empirically, VCs eval-
uate more positively new ventures founded by entrepreneurs who have similar types of
education and previous working experience (Franke et al., 2006) or similar process and
nature of decision-making (Murnieks et al., 2011).
Antecedents of Bias. Social Capital. The social networks of entrepreneurs matter
for biases. For instance, the structural holes in an entrepreneur’s network enable access to
various information sources, increasing the entrepreneur’s beliefs about his or her knowl-
edge base (Cohen & Levinthal, 1990). Adopting network theory on social capital, De Car-
olis and Saparito (2006) proposed that these structural holes could predict illusion of
control and that the strength of network ties could predict representativeness bias. Later,
De Carolis et al. (2009) theorized and empirically conﬁrmed that the extent of an entre-
preneur’s social network and personal capital would enhance shared attitudes and mental
models, which in turn would increase illusion of control. This line of study between net-
work positions and entrepreneurial behaviors has enormous potential, because virtual
entrepreneurship on social networks has been growing exponentially. Virtual entrepre-
neurs on a virtual social network “second life” in 2009 alone earned U.S.$55 million
(Rosenwald, 2010). Virtual social networks contain “big data,” opening unprecedented
new research opportunities in social, behavioral, and economic sciences (Bainbridge,
Looking through the literature, studies on the sketchy-attribute type of biases have
produced fewer discrepancies and inconclusive results than have studies on make-happy
type of biases, and this could be due to two reasons. First, the studies of sketchy-attribute
biases are highly fragmented and disconnected to one another, thus, having less opportu-
nity to yield contrasting results (see Figure 1b for six different antecedents studied in the
social capital theme alone). Second, biases in the sketchy-attribute do not carry as much
emotional and motivational implications as the biases in make-happy do, and therefore,
the relationships are less complex.
“Psycho-Physics” Type of Biases
Our search did not yield any research that studied biases of this type, and we believe
that this represents a key gap for future research. We will use two short examples to illus-
trate the importance of research on the psycho-physics type of biases.
The psycho-physics bias describes a distorted perception of probability in which one
underestimates medium and high probabilities and overestimates lower probabilities
(Kahneman & Tversky, 1979). Given the low probability of success of many entrepre-
neurial projects, especially those involving new and risky technology, future studies could
describe whether entrepreneurs’ perceptions of new venture success are biased differently
across ventures with different likelihoods of success (e.g., 0.001% vs. 0.1% vs. 10% vs.
Another instance of bias occurs when people perceive the difference between a prize
of $10 and $20 subjectively to be larger than the difference between $1,010 and $1,020
(Baron, 2007). In entrepreneurship, we could ask “is there a difference in perception
between a VC investing $1 million or $1.1 million in a start-up versus 0.1 million or 0.2
million? What is the impact of this difference?”
Entrepreneurship deals with numbers often in a manner of nested real options that are
nonintuitive and often exceed the bounded rationality of decision makers (McGrath &
Desai, 2010; Zhang & Babovic, 2011), thus, the psycho-physics type of biases, that is, the
November, 2015 17
study of the distortion in entrepreneurs’ perception of quantitative attributes, can be
highly pertinent and a potentially rich source of future study.
Future Research Recommendations on Using Biases to Study Relationships
In assessing the biases and the relationships studied, we realized the paramount need
to pay attention to even highly nuanced differences in deﬁning biases. The inconsistent
ﬁndings on relationships to date, as reviewed above, could be in part due to high variation
in the conceptualization and operationalization of biases. Prior research has often attrib-
uted empirical nonﬁndings to measurement issues (e.g., Keh et al., 2002). Adopting pre-
cise and consistent deﬁnitions and measures may not only help resolve the outstanding
controversies, but can also facilitate possible future meta-analysis and the inclusion of
moderators to push forward ﬁner models of the entrepreneurial phenomena using bias
theory. As yet a vast majority of studies have limited themselves to the examination of
direct effects. Few have studied the interactions between biases and other factors, such as
risk perception (Grichnik, 2008; Keh et al; Simon et al., 2000) and prior experience (Carr
& Blettner, 2010). Of additional interest is the possibility of nonlinear effects (e.g., Shep-
herd et al., 2003) to gain a more nuanced understanding.
To yield ﬁner models, multilevel analysis in entrepreneurial decision making presents
a promising opportunity for future research (Shepherd, 2010). Multilevel studies can
potentially reveal the biases of teams and biases of entrepreneurs in teams to reﬂect on
how recursively biases operate within a team and feedback on those biases. This topic is
very pertinent because teams, rather than individuals, make many entrepreneurial deci-
sions, yet to date almost all studies on entrepreneurial biases are at the individual level
(see Gudmundsson and Lechner  for a rare exception). Future research may explore
if and how team-based decision making is biased and may also address how individual
biases impact team decision making. Future research should also examine how making
decisions in a team may alter the biases of individuals; for example, individuals may
exhibit different biases or different degrees of bias when making decisions in a team, ver-
sus making them alone.
Such multilevel research can also untangle the impact of cultural contexts. Existing
research has examined several country settings outside of the United States, including
Singapore (Keh et al., 2002), Australia (Shepherd et al., 2003), Austria (Franke et al.,
2006), Germany (Burmeister & Schade, 2007; Franke et al.; Grichnik, 2008), the Nether-
lands (Ebbers & Wijnberg, 2012), and Iceland (Gudmundsson & Lechner, 2013). These
studies provide insights into the generalizability of ﬁndings across cultural and national
borders. Future research could include international samples in their designs, as in Koel-
linger et al. (2007), who found that while biased perceptions had a crucial impact on new
business creation across 18 countries, people of certain cultures have a more natural tend-
ency toward overconﬁdence than others. Similarly, studies in cognitive sciences have
reported persistent cross-cultural variations in overconﬁdence: For instance, people of
Chinese culture on average are more overconﬁdent (Yates, Lee, & Bush, 1997; Yates,
Lee, Shinotsuka, Patalano, & Sieck, 1998). Cultural and institutional differences could
moderate the relationships between biases and other entrepreneurial constructs.
Lastly, future research design on entrepreneurial biases may consider theorizing and
measuring uncertainty, not just risk. When confronting risk, decision makers know the
probabilities of all outcomes for all alternatives; however, when confronting uncertainty,
probabilities are unknown or unknowable (Knight, 1921), which more appropriately
reﬂect the decisions in entrepreneurship (Baron, 1998; Busenitz & Barney, 1997). On a
18 ENTREPRENEURSHIP THEORY and PRACTICE
neurological level, decision making under risk differs from decision making under uncer-
tainty (Volz & Gigerenzer, 2012). Thus, entrepreneurial bias literature should adopt
uncertainty in addition to risk.
Situating Entrepreneurial Bias Research
Thirty years after the publication of the ﬁrst article on bias (Tversky & Kahneman,
1974), the bias research program has progressed greatly in cognitive sciences; moreover,
entrepreneurship as a ﬁeld meanwhile has prospered and advanced on many fronts. In this
section, we attempt to situate entrepreneurial bias research in the context of the research
streams developed subsequent to the original publication of bias, such as entrepreneurial
cognition (Mitchell et al., 2002), entrepreneurial emotions (Cardon et al., 2012), the
“great rationality debate” (Stanovich, 2009; Tetlock & Mellers, 2002), and studies of
biases in other ﬁelds. These streams of research have either taken off lately or have made
substantial new development, holding fundamental implications for entrepreneurial bias
The Tie to Entrepreneurial Cognition Research
Entrepreneurial bias research started as one of the ﬁrst works on entrepreneurial cog-
nition in the mid-1990s (Bird, 1992; Busenitz & Lau, 1996; Mitchell et al., 2002). Entre-
preneurial bias research much exempliﬁes entrepreneurial cognition research in general,
which aims to understand how entrepreneurs consciously or subconsciously reject elabo-
rate and complex decision-making procedures (Mitchell et al., 2007).
Bias, along with heuristics, intelligence, and knowledge, are some of the most studied
themes in cognitive psychology that can lend themselves easily to the studies of entrepre-
neurship (Frese, 2009; Frese & Gielnik, 2014). Heuristics, intelligence, and knowledge
all have inherent connections with bias.
Heuristics refer to simplifying shortcuts or principles that people use for problem
solving and information processing (Baron, 2007; Kahneman & Tversky, 1982; Wilcox,
2011). Thus, heuristics are fast and frugal, freeing people from making a complete and
systematic processing of information, which can often be impossible in entrepreneurship
or management in general (Bingham & Eisenhardt, 2011; Manimala, 1992). Because heu-
ristics simplify information processing, they are associated with biases: systematic depar-
tures from the normative rational theory (Gilovich, Grifﬁn, & Kahneman, 2002;
Kahneman & Tversky). However, the implications of many heuristics in entrepreneur-
ship, such as those discovered early on (Manimala), on biases are unknown. Moreover,
effectuation, a new theory in entrepreneurship, suggests that entrepreneurs use a set of
heuristics to make decisions (Sarasvathy, 2001), and the relationships between these
effectual heuristics and biases warrant theoretical discrimination and empirical identiﬁca-
tion. For example, intelligence correlates with the tendency to avoid some biases but not
some others (Stanovich & West, 2008), yet to date, intelligence has been missing either as
an antecedent or as a moderator in studies of entrepreneurial biases. Knowledge, espe-
cially “highly developed, sequentially ordered knowledge” known as entrepreneurial
expert script, can bias entrepreneurs toward commitment engagement (Mitchell, Smith,
Seawright, & Morse, 2000; Smith, Matthews, & Schenkel, 2009) and hence have impor-
tant implications for entrepreneurial bias research (Mitchell, Mitchell, & Smith, 2008).
November, 2015 19
In short, the study of biases has evolved to be a pillar of entrepreneurial cognition
research, yet biases are not just cognitive phenomena; they also have roots in emotions.
Affect Matters, Especially for Make-Happy Type of Biases
In folk decision analysis, emotion appears antithetical to rationality (Haidt, 2001);
thus, unbiased thinking necessitates the eradication of the inﬂuences of emotion. In scien-
tiﬁc studies on affect, which includes emotion, moods, and feelings, the absence of criti-
cal biases such as overoptimism leads to depression and anxiety, and the presence of
overoptimism beneﬁts physical health and is linked to greater activation (Sharot, 2011).
Overconﬁdence produces a crucial byproduct, positive affect (Armor & Taylor, 2002;
Lyubomirsky, King, & Diener, 2005).
The role of affect in bias is particularly relevant to the make-happy biases. These
make-happy biases arise not because people take inappropriate attributes (sketchy-attrib-
ute type), or distort large or negative numbers (psycho-physics type), but precisely
because they produce positive affective beneﬁts. Make-happy type of biases reduce anxi-
ety and depression and increase action (Sharot, 2011). The beneﬁts of positive affect due
to the bias may compensate for short-term loss in certain cases. Shepherd, Wiklund, and
Haynie (2009), in a similar vein, reasoned that many entrepreneurs do not immediately
drop failed projects despite ﬁnancial costs so as to better adjust their emotions for subse-
quent entrepreneurial actions.
Due to the mechanisms underlying the make-happy type of biases, affect has critical
implications. While biases of this type could result in less optimal short-term decisions,
the affective beneﬁts could lead to better well-being and performance outcomes (Puri &
Robinson, 2007). Future studies of the make-happy biases should examine these biases
not only as cognitive phenomena but also as affective ones.
For sketchy-attribute type of biases, their mechanisms are primarily cognitive, and
thus, their relationships with affect are less direct and obvious. Even so, they still have
indirect but fundamental connections with affect. First, emotions have adaptive regulatory
effects on cognition that can facilitate or impede rationality (Stanovich, 2009) through the
appraisal dimensions of affect (Foo, 2011). Furthermore, valence and activation theories
posit that affect carries directive properties that inﬂuence cognition. For example, positive
affect (such as joy) relates to the broadening of psychological processes, such as divergent
thinking (Fredrickson, 2001). Negative affect (such as sadness) in contrast leads to the nar-
rowing of attention and to activities that promote self-preservation (Clore, Schwarz, &
Conway, 1994). The activation function of emotions also impact cognition, as high acti-
vating emotions (such as excitement or anger) also correlate with the narrowing of psycho-
logical processes (Harmon-Jones & Gable, 2008) whereas low activating emotions (such
as relaxation or despondency) broaden psychological processes, leading to diffuse atten-
tion (such as detachment) (Gable & Harmon-Jones, 2010).
In the reverse direction, biases could inﬂuence the appraisal and therefore alter emo-
tion. For instance, entrepreneurs can be overconﬁdent in their appraisal of venture pro-
gress, judging a setback in the eyes of a rational decision maker as a normal pace of
progress, and as a consequence the entrepreneurs will feel less negative emotions.
Since research has shown that emotions and biases both inﬂuence risk perception,
entrepreneurial behaviors, and opportunity evaluation (Foo, 2011; Hahn, Frese, Bin-
newies, & Schmitt, 2012; Podoynitsyna, Van der Bij, & Song, 2012), the interactions
between cognitive biases and affect on entrepreneurial actions offer potentially interest-
ing new lines of inquiry (Foo).
20 ENTREPRENEURSHIP THEORY and PRACTICE
In conclusion, various theories agree that affect inﬂuences heuristics and biases (e.g.,
Baron, 2007; Mackie & Worth, 1989; Park & Banaji, 2000), and thus, research on entre-
preneurial behavior and decision making should not separate cognition from affect,
another emerging stream of research in entrepreneurship (Baron, 2008; Cardon et al.,
2012; Foo, 2011; Foo, Uy, & Baron 2009; Shepherd et al., 2009).
Do We Interpret Biases as Bad or Good?—The “Great Rationality
As entrepreneurship scholars follow the path of cognitive scientists in documenting
numerous biases and analyzing them, it is worthwhile to note that cognitive scientists
have since started a huge debate about the interpretation of biases as decision errors.
Some scholars in cognitive sciences lament the pessimistic view of biases as errors
and instead advocate biased decision making as fast and frugal and well performing
(Goldstein & Gigerenzer, 2002). New evidence suggests that biased decision making,
which relies on few cues and ignores most accessible information (e.g., recognition heu-
ristic and “take-the-best”), leads to accurate judgments (Br€
oder & Eichler, 2006;
Goldstein & Gigerenzer; Rieskamp & Otto, 2006; Todd & Gigerenzer, 2003, 2007).
Scholars reason that biased decision making is a product of evolution: While it does
not work well in artiﬁcial settings (such as working with probabilities), it is well-adapted
to tackle naturalistic decisions under constraints of time, knowledge, and computational
capacity (Rieskamp & Hoffrage, 2008). As a consequence, cognitive biases have a beneﬁ-
cial evolutionary explanation and are not simply errors (see chapter 4 of Brase, Cosmides,
& Tooby, 1998; Cosmides, 1996; Rode, Cosmides, Hell, & Tooby, 1999).
The academic debate on how much irrationality to attribute to human cognition has
been so intensive and fundamental that cognitive scientists named it “the great rationality
debate” (Cohen, 1981; Gigerenzer, 1996; Kahneman & Tversky, 1996; Stanovich, 1999;
Stein, 1996; Winterfeldt & Edwards, 1986).
To date, the “great rationality debate” has largely not propagated to entrepreneurship
research. Other than the notable exception of Bryant (2007) that takes a positive stance
toward bias using the ecological approaches to decision making, the majority of entrepre-
neurial bias studies explicitly or implicitly adopt the classical view of bias by Tversky
and Kahneman (1973). Studies adopting this view have examined the negative connota-
tions of biases, such as on inadequate estimation of demand and competition (Simon &
Houghton, 2002), and poor decision quality of entrepreneurs (Carr & Blettner, 2010) and
VCs (Zacharakis & Shepherd, 2001). Nevertheless, many papers on entrepreneurial bias
have also discussed the possible beneﬁts of biases (c.f., Cable & Shane, 1997; Coval &
Moskowitz, 1999; Cumming & Dai, 2010; Sandri et al., 2010).
What Does the “Great Rationality Debate” Mean to Entrepreneurship
To examine better the pros and cons of bias in entrepreneurial decision making, we
think that it is timely to bring the “great rationality debate” to the entrepreneurship ﬁeld.
The two camps of the “great rationality debate” differ on two fundamental issues: deci-
sion ecology and decision representation.
Decision ecology matters—whether biased decision making serves us well depends on
the ecology of the decision. In situations where the decision ecology and ancient evolution-
ary ecology overlap, biased decision making tends to work. For example, the general public
November, 2015 21
performed equally as well as experts in predicting the winners of 2003 men’s Wimbledon
tennis tournament, even though the public adopted just one simple recognition heuristic: pre-
dict the player whose name you recognize more than the others. The same recognition heu-
ristic, however, when used in situations where the decision ecology is different, such as to
choose ﬁnancial services, overwhelmingly underperforms (Bazerman, 2001).
While our brain has evolved to make fast and frugal decisions for survival across the
Pleistocene environment, at times our brain may be maladaptive in the modern world deci-
sions. The key challenge is to identify the relevant ecology for each particular decision, in
our case, each decision that entrepreneurs make in the contemporary world. For decisions
that require entrepreneurs to work effectively with technological acceleration, network
externality, virtual environments, a failure-tolerating culture, or optionality in venture
growth, for instance, the ability to override our natural fast-and-frugal responses takes on
great importance (Einhorn & Hogarth, 1981). In situations that require entrepreneurs to
perform tasks that humans have been doing relatively consistently since the Pleistocene
era, such as building relationships, leading teams, or understanding customers, some of the
biased fast-and-frugal decision making could still serve us exceptionally well.
Decision representations matter too. Many experiments in cognitive science show
that if the decisions are represented “in a format that meshes with the way people natu-
rally think about probability, they can be remarkably accurate” (Pinker, 1997, p. 351).
We need to be cautious about the extent to which the representation of our current
world meshes with our evolutionarily designed cognitive mechanisms. For example,
while psychologists show that representing probability as frequency can eliminate bias,
probability still abounds because it allows better algorithmic and statistical operations.
The representations of information in many new venture decisions do not correspond
with how homo sapiens’ brains naturally respond. This discord between information rep-
resentation and one’s natural response may happen when entrepreneurs try to compare
deals from VCs and banks, decide whether to develop or buy a technology, or securitize
the footnote of legal documents of foreign suppliers. The modern world presents many
abstract and non-naturalistic decision environments, which require us to override the nat-
ural representations that ﬁrst take place.
However, the natural representations still function well enough for us to accurately dis-
cern faces, infer the intentions of others, and carry out many other computationally heavy
tasks without expending much cognitive effort. For countless tasks, our naturalistic represen-
tations perform at a level that the best artiﬁcial intelligence software today can only envy.
In summary, the performance of biased decision making depends on decision ecolo-
gies and representations. Thus, the extent of the match between evolutionarily adapted
mechanisms and the representations called for in entrepreneurial decision situations,
become essentially the puzzle which entrepreneurship research should seek to untangle.
In Light of the Study of Bias in Other Fields
Many other ﬁelds have similarly reviewed biases in decision making in their respec-
tive ﬂagship journals, such as medical decision making (Bornstein & Emler, 2001;
Elstein, Schwartz, & Schwarz, 2002), jurisdiction (Langevoort, 1998), behavioral audit-
ing (Shanteau, 1989), behavioral economics (Kahneman, 2003), and public governance
(Rachlinski, 2004). Comparing the studies of bias in those ﬁelds with ours in entrepre-
neurship, we identiﬁed the issue of debiasing (Pronin, Lin, & Ross, 2002) to be notably
missing in entrepreneurship research as well as management research in general (Milk-
man, Chugh, & Bazerman, 2009).
22 ENTREPRENEURSHIP THEORY and PRACTICE
Simple cognitively effortful debiasing attempts can actually exacerbate bias, and
such alleviating of bias involves cognitive and emotional capabilities (Hodgkinson &
Healey, 2011). Often, biases can be altered instead by variation of setting, accessibility of
content, experiences (Sanna & Schwarz, 2003), and entrepreneurial approaches such as
effectuation and causation (Zhang, Cueto, & Vassolo, 2014). Decision aids, from simple
checklists to expert systems, are popular debiasing tools for a wide range of applications
such as health treatments, screening decisions (O’Connor et al., 1999; Stacey et al.,
2011), and risk communication (O’Connor, L
e, & Stacey, 2003). In fact, practitioners
of entrepreneurship do use decision aids in many forms. For example, VCs often use
spreadsheets or evaluation forms containing lists of criteria to facilitate the systematic
evaluation of new projects (Petty & Gruber, 2011). Many entrepreneurs use the Business
Model Canvas to avoid omitting important aspects of business models. Studying the
effects of decision aids on bias could be relevant and valuable for practitioning
Lastly, other ﬁelds, such as ﬁnance and marketing, have studied some biases that
have yet to appear in entrepreneurship literature but could heavily shape the future of
entrepreneurship research. For instance, the base rate fallacy, which describes the tend-
ency to ignore base rate information and instead focus on speciﬁc information (Baron,
1994), and irrational escalation, a phenomenon by which people justify increased invest-
ment in a decision based on the cumulative prior investment and despite new evidence
suggesting that the decision was probably wrong, could happen in new venture evalua-
tion. Table 3 lists a number of biases unstudied to date for future research considerations.
Unstudied Biases That Hold Potential Relevance to Entrepreneurship
Bias Deﬁnition Source
Illusory correlation Inaccurately perceive a relationship between two
Tversky and Kahneman (1974)
Irrational escalation Use prior investment to justify increased investment
Base rate fallacy Ignore base rate information and focus on speciﬁc
Ambiguity effect Avoid options whose probability seem “unknown”
due to missing information
Belief bias Evaluate the logical strength of an argument based on
the believability of the conclusion
Klauer, Musch, and Naumer (2000)
Conﬁrmation bias Search for, interpret, or recall information in a way
that conﬁrms one’s beliefs or hypotheses
Oswald and Grosjean (2004)
Backﬁre effect Either do not update existing beliefs or believe them
to be stronger, in the face of contradictory
Sanna, Schwarz, and Stocker (2002)
Consistency bias Incorrectly remember one’s past attitudes and behav-
ior as resembling present attitudes and behavior
Congruence bias Overly rely on directly testing a given hypothesis as
well as neglecting indirect testing
Iverson, Brooks, and Holdnack (2008)
Pseudo-certainty effect Make risk-averse choices if the expected outcome is
positive, but make risk-seeking choices to avoid
November, 2015 23
Discussion and Conclusion
Scholars have used the theory of bias to study various decision-making phenomena in
entrepreneurship. While this body of research has yielded many interesting insights, it
also is ridden with deﬁnitional disagreements, equivocal relationships, and overdue con-
nections to advancements in other relevant research streams. To generate cumulative pro-
gress, we have critically examined this body of research and unveiled a range of
interesting issues and future research opportunities.
Deﬁnitional disagreements are normally expected during the initial phase of new and
important theories (Gladwin, Kennelly, & Krause, 1995), but should be subsequently
resolved both conceptually and operationally. Equivocal results signal the formation of
certain initial common grounds of interest and suggest clear avenues for future research
to reach convergence or breakthroughs. Moreover, it is beneﬁcial to keep an eye on rele-
vant adjacent research ﬁelds and leverage their new development to further the study of
bias in entrepreneurship. In this regard, advances in cognitive sciences are particularly
important in furthering theoretical development. For instance, entrepreneurship scholars
Summary of Main Future Research Directions
Theme Research Directions
Deﬁnition Conceptualization and
To build future research on clear concepts (be especially cautious to
distinguish three distinct forms of overconﬁdence).
Relationship studies Risk-taking behavior Processual path analysis on how biases impact risk-taking behaviors
and performance via different paths based on different theories.
Role of experience The relationship between experience (a complex concept itself to be
disentangled) and biases at both between-person and within-person
levels, in consideration of the quality of experience (can be positive
Decision difﬁculty How task difﬁculty might mediate the relationships between contex-
tual factors (e.g., dynamism, complexity, hostility) and the three
forms of overconﬁdence.
Network The relationships between entrepreneurs’ network positions and biases.
Biases unexamined to date To study psycho-physics type of bias of the distortion in entrepre-
neurs’ perception of quantitative attributes (e.g., overweighing low
probabilities and perception of optionality). Also see Table 3 for a
list of potentially interesting biases for entrepreneurship research to
cover in future.
Research design Multilevel research on bias (e.g., national level and team level).
To distinguish, theorize, and test biases under uncertainty from biases
Broader situation Entrepreneurial cognition Relationships between bias and other well-studied factors in cognitive
psychology such as heuristics, intelligence, and knowledge.
Entrepreneurial emotion How does affect inﬂuence biases and how do biases inﬂuence affect?
The interactions between cognitive biases and affect on entrepreneur-
Great rationality debate The framing of entrepreneurial bias research based on recent advance
in cognitive psychology (esp. the great rationality debate).
The extent of the match between evolutionarily adapted mechanisms
and the representations in entrepreneurship.
Debiasing Effect of decision aids (venture evaluation form or business model
canvas) on biases.
24 ENTREPRENEURSHIP THEORY and PRACTICE
have accumulated signiﬁcant evidence both for and against biased decision making in
entrepreneurship. However, underlying such descriptive evidence, parsimonious and gen-
eral theoretical explanations have not been easy to construct. This is clearly an area that
can greatly beneﬁt from stronger ties to the research of biases in cognitive sciences. Since
the ﬁrst article on biases (Tversky & Kahneman, 1973), signiﬁcant progress has been
made with respect to the pros and cons of biases, that is, the “great rationality debate.”
And we have pinpointed the exact connections that entrepreneurial bias research can
make to beneﬁt from nearby ﬁelds.
This study has made a number of contributions. First, this research answers the call
by Shepherd et al. (2015) to provide the ﬁrst critical examination of the studies of biases
in entrepreneurship, carefully documenting discrepancies and dissonances in the litera-
ture to facilitate future cumulative progress. Second, to examine this body of literature, a
structure by a typology of bias and input-mediator-output framework has been devised.
This structure helps not only to unveil the tensions and opportunities for further research,
but also to organize this growing body of research going forward. Third, we argue that the
studies of bias in entrepreneurship could beneﬁt from adjacent ﬁelds, such as the study of
affect, as well as the rapid and exciting advances on biases in cognitive sciences. For
instance, the implications of “the great rationality debate” on entrepreneurship have been
speciﬁcally examined in this text. Lastly, this article can serve as a synthesized base for
future research theorizing using biases in entrepreneurship.
Bias has provided a captivating lens to study behavioral decision making in entrepre-
neurship for the more than 20 years. This article attempts to deliver an assessment and
reﬂection on these studies: on the deﬁnitions of biases, the relationships studied, and the
broader contexts. The 20 years of progress, as synthesized, generate even more funda-
mental and intriguing questions, both theoretical and empirical. We conclude by summa-
rizing the most important future research opportunities (Table 4).
These opportunities are only a partial list, as we continue realizing the potential of
using bias as a theoretical lens through which to study entrepreneurship. By now, not only
has bias occupied a central place in a complex net of entrepreneurial phenomena, but its
theoretical foundation in cognitive sciences is also being reshaped actively. Thus, the
study of bias in entrepreneurship is an intellectually fruitful endeavor that could help
shape the future of our scholarships of entrepreneurship.
Procedures and Criteria of the Article Search
1. We started the search with an initial set of
keywords of biases, entrepreneur (and its vari-
ous derived forms), and venture capital (VC)
in journals listed in Financial Times.
1. The top management journals in the Financial Times jour-
nal list are used to identify the most prominent publica-
tions relevant to the topic to start with. We read each of
articles to identify the biases studied.
2. We identiﬁed the relevant articles from step 1,
and locate the names of the biases studied in
those articles. The names of the biases were
2. The code we used in Scopus is the following:
(title-abs-key[entrepreneur] or title-abs-key[entrepreneurial]
or title-abs-key[entrepreneurship] or title-abs-key[venture
November, 2015 25
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limit-to(subjarea, “mult”) or limit-to(subjarea, “neur”) or
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thanks to a suggestion.
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Stephen X. Zhang is an assistant professor of entrepreneurship and innovation in Pontiﬁcia Universi-
olica de Chile, which ranks #1 in South America. His research focuses on entrepreneurial
decision making under uncertainties. Stephen is also the founding director of N
Research Center in Entrepreneurial Strategy Under Uncertainty. On the practitioning side, he acts as
a founding partner for Aukan, an innovation consulting ﬁrm in Chile.
Javier Cueto works as a technological broker and manager of the international acceleration program
at the Business Incubator from the Pontiﬁcia Universidad Cat
olica de Chile, IncubaUC. He received
his master and bachelor degrees from the Pontiﬁcia Universidad Cat
olica de Chile. Javier is also an
afﬁliated researcher at N
ucleo Milenio Research Center in Entrepreneurial Strategy Under Uncertainty,
in the most prestigious line of research centers in Chile.
The authors acknowledge the support from N
ucleo Milenio Research Center in Entrepreneurial Strategy
Under Uncertainty (NS130028) and Fondecyt 223845 (Chilean National Science and Technology
36 ENTREPRENEURSHIP THEORY and PRACTICE