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What Competition? Myopic Self-Focus in Market Entry Decisions


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This paper documents egocentric biases in market entry decisions. We demonstrate self-focused explanations for entry decisions made by three groups of participants: actual entrepreneurs (founders), working professionals who considered starting their own firms but did not (non-founders), and participants in a market entry experiment. Potential entrants based their decision to enter primarily on evaluations of their own competence (or incompetence) and paid relatively little attention to the strength of the competition. Our results suggest that excess entrepreneurial entry is more complicated than simple overconfidence, and can help explain notable patterns in entrepreneurial entry.
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Organization Science
Vol. 18, No. 3, May–June 2007, pp. 440–454
issn 1047-7039 eissn 1526-5455 07 1803 0440
doi 10.1287/orsc.1060.0243
© 2007 INFORMS
What Competition? Myopic Self-Focus in
Market-Entry Decisions
Don A. Moore
David Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue,
Pittsburgh, Pennsylvania 15213,
John M. Oesch
J. L. Rotman School of Management, University of Toronto, 105 St. George Street,
Toronto, Ontario M5S 3E6, Canada,
Charlene Zietsma
Richard Ivey School of Business, University of Western Ontario, 1151 Richmond Street North,
Ontario N6A 3K7, Canada,
his paper documents egocentric biases in market-entry decisions. We demonstrate self-focused explanations for entry
decisions made by three groups of participants: actual entrepreneurs (founders), working professionals who considered
starting their own firms but did not (nonfounders), and participants in a market-entry experiment. Potential entrants based
their decision to enter primarily on evaluations of their own competence (or incompetence) and paid relatively little attention
to the strength of the competition. Our results suggest that excess entrepreneurial entry is more complicated than simple
overconfidence, and can help explain notable patterns in entrepreneurial entry.
Key words: entrepreneurial entry; egocentrism; market entry; overconfidence; underconfidence
One of the key contributions of Cyert and March’s
(1963) behavioral theory of the firm is the notion that
organizations conduct a limited search for information.
Organizational decision makers conduct a local search
that Cyert and March characterized as “simple-minded”
because they “(1) search in the neighborhood of the
problem symptom and (2) search in the neighborhood of
the current alternative” (p. 121). They begin by examin-
ing the data most readily available for analyzing prob-
lems. While there are many domains in which this
simple heuristic may produce efficient solutions, it runs
the risk of leading to myopically biased choices when
important information lies further afield.
When decision makers consider entry into new mar-
kets, the most available and accessible information will
be about their own capabilities, strengths, and weak-
nesses. However, wise entry decisions must also con-
sider the capabilities of existing or potential competitors
as well as the market’s capacity. Do entrepreneurs give
sufficient weight to competitive assessment when they
consider starting businesses? Evidence suggests they
could do better. Some industries see perennially high
rates of entry, intense competition, and high rates of fail-
ure (Geroski 1996, U.S. Small Business Administration
2003). Many industries, especially emerging ones, go
through boom-bust cycles in which the rate of founding
is highest immediately before the highest rate of fail-
ure. New entrants into these markets are the most likely
to fail (Hannan and Carroll 1992). High rates of prior
entry should deter, rather than encourage, founding, yet
as the flurry of Internet business start-ups in the late
1990s (just prior to the bursting of the dot-com bubble)
demonstrates, new entrants did not appear to appreciate
Strategic management theory encourages decision
makers to attend to the intensity of competitive rivalry
and the threat of other entrants in an industry to as-
sess that industry’s attractiveness when making entry or
investment decisions (e.g., Porter 1980). A business op-
portunity will generate above-average returns when the
firm has a competitive advantage in the market, which
is valuable, rare, costly to imitate, and which the firm is
organized to exploit (Amit and Schoemaker 1993, Bar-
ney 1995). It is thus almost axiomatic in the field of
strategy that competitive factors and market capacity will
have a strong impact on new venture decision making
and success (Zahra et al. 2002).
Much of the strategy literature assumes that compet-
itive environments are objective and can be analyzed
formally, yet Cyert and March (1963) recognized that
managers’ perceptions of the competition are subjec-
tive and imperfect (see also Hodgkinson 1997). Prior
research with managers in established firms suggests
they have limited conceptions of their competitive
environment, viewing only a small fraction of objec-
tively discernible competitors as rivals (Gripsrud and
Gronhaug 1985, Porac et al. 1989). For instance, Porac
et al. (1989) found that local firms were seen as rivals,
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
Organization Science 18(3), pp. 440–454, © 2007 INFORMS 441
while the international competition was likely to be
ignored. Given the critical importance of accurate assess-
ment of the competitive landscape to a firm’s chances
for success, the firm’s ability to conduct this assessment
is an important issue (Baron and Ward 2004, Mitchell
et al. 2002).
By some estimates, half of all new manufacturers fail
within their first four years (Mata and Portugal 1994;
see also Dunne et al. 1988, Audretsch 1991, Wagner
1994). Grim statistics on entrepreneurial failure rates
are not reflected in entrepreneurs’ beliefs regarding their
own prospects. Cooper et al. (1988) found that one-
third of the 2,994 entrepreneurs in the industries they
surveyed rated their odds of success at 10 out of 10.
This finding, and others like it, has been cited as an
example of entrepreneurial overconfidence (Busenitz and
Barney 1997, Camerer and Lovallo 1999, Kahneman
and Lovallo 1993). Entrepreneurs’ decisions show biases
and imperfections, including the illusion of control, over-
confidence, faulty statistical intuitions, and reliance on
decision-making heuristics (Busenitz 1992, Simon et al.
2000, Alvarez and Busenitz 2001, Zacharakis and Shep-
herd 2001), and entrepreneurs may be more susceptible
to biased decision making than are other decision makers
(e.g., Busenitz and Barney 1997, Busenitz 1999, Palich
and Bagby 1995).
In this paper, we build on March and Cyert’s notion
of myopic information search to explain two market-
entry error types: Excess entry in some markets and
sparse entry in others. The entrepreneurship literature
(e.g., Busenitz 1992, Simon et al. 2000) has focused on
attempts to explain excess entry, yet insufficient entry,
or the failure to exploit profitable opportunities, is also
important. A focus on excess entry may exist because
the decision to enter is inherently more interesting to
entrepreneurship researchers, or it may simply be that
nonentrants are harder to find and more difficult to study.
In this paper, we include both entrants and nonentrants
in studies of entry decisions. We provide an integrative
theory that can help account for both excess and insuf-
ficient entry. Our research suggests that both of these
errors may be the result of entrepreneurs’ focus on them-
selves at the expense of understanding the competition.
To test our ideas, we conducted two studies.
In a qualitative field study, we examined the factors
that potential entrepreneurs considered when making
decisions about starting businesses. We note a surpris-
ing finding: Those who started businesses and those who
decided against starting businesses mentioned factors
predominantly internal to themselves or their ventures
when making their decisions. That is, they thought about
their personal abilities and their ventures, but they rarely
mentioned external factors such as the capacity of the
market they were entering or the strength of their com-
petitors. We sought explanations for this curious finding
in the literature on myopic biases in decision making
(Kruger 1999, Moore and Kim 2003). This line of the-
orizing depends heavily on Cyert and March’s origi-
nal ideas about the myopia of information search. We
designed an experiment to test three resulting hypotheses
about market-entry decisions.
Our findings are consistent with the hypothesis that
when making market-entry decisions, people focus on
personal considerations at the expense of competitive
considerations. As a result, potential entrants overenter
competitions that they see as easy, and underenter com-
petitive situations that they see as difficult. Because
others make the same judgments, we see systematic
overentry in some markets and underentry in others, rel-
ative to the expected returns available in those markets.
These findings are contrary to the prescriptions of strate-
gic management theory and rational decision making.
The paper proceeds as follows. We present the first
study, in which we take an inductive approach to identi-
fying what decision makers consider when making ven-
turing decisions. We then assess the findings of this
study in the context of the judgment and decision biases
literature. We develop hypotheses, which we then test in
the second study. After presenting the results of this sec-
ond study, we conclude by considering the implications
of both studies for research and practice.
Study 1
A number of prior studies have concluded that the de-
cision-making styles of entrepreneurs differ systemati-
cally from those of the rest of us (see, e.g., Busenitz and
Barney 1997, Busenitz 1999, Gaglio 1997, Kirzner 1979,
Palich and Bagby 1995). Business founders tend to be
more optimistic in their assessment of business oppor-
tunities (Cooper et al. 1988) and have greater confi-
dence in their own abilities (Krueger and Dickson 1994).
Palich and Bagby (1995) conducted a scenario study in
which they classified business people as entrepreneurs
or nonentrepreneurs based on their answers to a ques-
tionnaire about their growth and profitability aspirations,
innovation history, and their involvement in business
founding. While this method of sorting their sample
was somewhat unorthodox, these authors found that
those classed as entrepreneurs viewed equivocal infor-
mation more positively than did nonentrepreneurs and
that entrepreneurs saw fewer risks in uncertain situations
than did nonentrepreneurs.
In a scenario study of 124 entrepreneurs’ and 95
managers’ decision making, Busenitz (1999) found that
entrepreneurs exhibited overconfidence biases and em-
ployed the representativeness heuristic more extensively
than did managers, and as a result, perceived less risk
(Busenitz and Barney 1997). To assess overconfidence,
decision makers were asked to rate their levels of confi-
dence in their answers to a standard trivia task. In a sepa-
rate scenario study, Simon et al. (2000) found that MBA
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
442 Organization Science 18(3), pp. 440–454, © 2007 INFORMS
students were more likely to make positive venturing
decisions when they exhibited stronger illusion of con-
trol biases and stronger beliefs in the “law of small num-
bers.” They reasoned that entrepreneurs are more likely
to be susceptible to these biases. Biases such as belief in
the law of small numbers, overconfidence, and illusion
of control are thus expected to increase the likelihood
of venturing (Busenitz 1999, Palich and Bagby 1995,
Simon et al. 2000). If potential entrepreneurs focus on
only a few key aspects of the problems (Krueger and
Dickson 1994) and use few decision rules (Kahneman
and Lovallo 1993), they may fail to fully acknowledge
the risks associated with venturing.
These studies share the characteristic that they are
scenario studies, not based in a specific venturing sit-
uation. In our first study, we examined the decisions
of both entrepreneurs (founders) and managers who
decided against venturing (nonfounders) through inter-
views in which they generated their own list of the
most important criteria they considered while making
their entry decisions. The founders had established high-
technology businesses, while the nonfounders were a
sample of high-technology professionals who had seri-
ously considered starting specific businesses, but decided
not to.
The sample for this study was drawn from a survey of
the 1,821 members of the British Columbia Technology
Industries Association (BCTIA) in 1996, which asked
respondents if they had considered founding a venture
and if they were employed. We received 630 responses.
We eliminated 482 respondents who either indicated
no recent venture-founding decisions (455), or whose
responses were incomplete (27), and added four addi-
tional founders identified from local media sources. We
further qualified the remaining participants over the tele-
phone based on the following criteria: (1) Venture deci-
sions must have been made within the previous decade;
(2) “founders” had to have been principals in the found-
ing of high-technology ventures who had equity stakes
in their businesses and were working full time within
them at the time of interview; and (3) “nonfounders”
had to have managed high-technology businesses or sig-
nificant projects and had to have seriously considered
founding a specific business, but chose not to. Thirty-
three nonfounders and 25 founders did not meet the cri-
teria or could not be contacted. Twenty-six nonfounders
and 14 founders declined to be interviewed. In total, 34
founders and 20 nonfounders met our criteria and agreed
to be interviewed. Most were male (88.9%) and univer-
sity graduates (92.5%). A minority (31.5%) had gradu-
ate degrees. The interviews were conducted 5.1 years on
average after the start of the venture for the founders,
and 3 years on average after the decision not to found for
the nonfounders. Table 1 summarizes the participants’
Table 1 Numbers of Founders and Nonfounders with Various
Types of Previous Work Experience
Founders Nonfounders
(n = 34) (n = 20)
Length of work experience
Less than 5 years of work 5 2
5–10 years work experience 4 4
10–15 years work experience 12 9
15 or more years work experience 13 5
Relatedness of work experience
Highly related experience 20 4
Moderately related experience 9 9
Unrelated experience 5 7
Prior entrepreneurial experience
Prior entrepreneurial experience 16 6
prior work experience. Founders tended to have slightly
more, and slightly more related, experience than non-
founders on average, and were somewhat more likely to
have had prior entrepreneurial experience. In all but two
cases, when founders had business partners we inter-
viewed the lead entrepreneur—that is, the entrepreneur
who generally held the role of president or CEO, and
who had been the strongest driver in founding the busi-
ness. In the two exceptions, we interviewed technology
experts who founded businesses with lead entrepreneurs.
Excluding these two interviewees changes our results by
less than 1% in each founder category, and our conclu-
sions remain the same.
The first section of each interview was semistructured,
and lasted 45 minutes on average: Participants described
their career paths to date and told the story of a spe-
cific entrepreneurial opportunity they had faced. Neutral
probing questions were used to uncover specific details,
clarify timelines, and bring feelings to the surface. Par-
ticipants reflected on their decision-making processes
and the factors they considered when deciding whether
to start the venture. We also asked participants about
their personal lives during the time the decision was
made to help jog their memory to the time and con-
text of their decision. The second and third parts of the
interview were not relevant to the present study.
Data and Analysis
Transcriptions of the interviews were analyzed using a
qualitative coding scheme, focusing on naturally occur-
ring events in natural settings (Miles and Huberman
1994) and our informants’ perceptions of the factors
influencing their decisions. The initial pass through the
transcripts produced an exhaustive list of factors that par-
ticipants considered when they were deciding whether
to start a specific venture. As each new factor was iden-
tified, it was added to the coding scheme until no new
factors were identified. This procedure identified 21 fac-
tors, some of which were phrased as concerns about a
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
Organization Science 18(3), pp. 440–454, © 2007 INFORMS 443
Table 2 Description of Coding Categories
Category Description Factors in this category
Ability Factors related to the Confidence in myself
abilities/experiences Confidence from my past experience
of the decision maker Confidence from personal optimism
Concern about my ability to manage
Internal Factors related Confidence in the
to the business itself product/opportunity/technology
Confidence that the task will be simple
Confidence based on another’s
assessment of the business
Concern that the business will fail
Concern about the capital intensity
of the business
Concern about cash flow
Concern about the potential
profitability of the business
Concern about vulnerability
to accounts receivable
Concern about vulnerability
to partners
Concern that the business
will be difficult to manage
Concern about managing/being
responsible for employees
Concern about the technology
External Factors outside the Confidence regarding the competition
business that may
affect the business
Concern about the competition
Concern about changes in the
economic climate
Concern about changes in
government regulations
Concern about the impact of
technological change
particular aspect of the decision, and others that indi-
cated confidence about an aspect of the decision. These
factors were then sorted into three general categories:
Ability factors, external factors, and factors internal to
the new venture, with confidence and concern subcate-
gories for each category. These are shown in Table 2.
Ability factors included those related to the individual
decision maker’s assessment of his or her ability to suc-
cessfully run the business, such as self-confidence in, or
concerns about, his or her skills or experience. Internal
factors were about the business itself. These included
concerns about conflicts between partners, the projected
costs and selling prices of products, the capital intensity
of the business, or the decision maker’s confidence or
concerns about the opportunity or technology. External
factors included those within the external environment of
the business, such as competitors, the broader economy,
or the government. A research assistant unaware of the
present hypotheses was asked to assign the 21 factors
into the three general categories, and achieved perfect
agreement with the coding author.
Once this more abstract coding scheme was devel-
oped, one of the authors made a second detailed pass
through each of the transcripts, carefully coding them
according to the new coding scheme. Each participant’s
statements received as many codes as issues they men-
tioned. A research assistant trained in the coding scheme
independently coded 15 of the transcripts (10 founders
and 5 nonfounders). Interrater reliability was 0.92, and
discrepancies were easily resolved through discussion.
Coding results are shown in Table 3. Both founders
and nonfounders focused much more on personal ability
(43% of 131 total factor mentions for founders and 24%
of 54 total factor mentions for nonfounders) and internal
factors (44% of 131 total factor mentions for founders
and 67% of 54 total factor mentions for nonfounders)
than they did on external factors (13% of 131 total factor
mentions for founders and 9% of 54 total factor men-
tions for nonfounders). This common tendency among
both founders and nonfounders to neglect external fac-
tors when making market-entry decisions is striking.
Founders and nonfounders did show some significant
differences in their descriptions of the factors on which
they based their venturing decisions. Founders were more
likely to speak confidently about ability factors (93%
of 56 ability factor mentions were viewed with confi-
dence) than nonfounders (46% of 13 ability factor men-
tions were viewed with confidence), and overall, spoke
more about ability factors than did nonfounders (43%
of all founder mentions versus 24% of all nonfounder
mentions). It is possible that the founders in our sam-
ple were more confident than nonfounders because they
were more experienced, rather than because they exhib-
ited more bias than the nonfounders. To address this con-
cern, we compared a subsample of 12 founders and 12
nonfounders, matched on the basis of experience.
found the same pattern of results. Founders confidently
mentioned ability factors 21 times and internal factors
20 times versus four and three confident mentions for
nonfounders, respectively. Nonfounders mentioned con-
cerns about ability factors four times and concerns about
internal factors 24 times, versus one and four concerns
for founders, respectively. Neither group made any con-
fident mentions about the external environment, and only
Table 3 Founders’ and Nonfounders’ Mentions of Decision
Founders’ Nonfounders Total
decision factors decision factors mentions
Ability 56 13 69
Confidence 52 6 58
Concern 4 7 11
Internal factors 58 36 94
Confidence 41 5 46
Concern 17 31 48
External factors 17 5 22
Confidence 11 0 11
Concern 6 5 11
Total mentions 131 54 185
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
444 Organization Science 18(3), pp. 440–454, © 2007 INFORMS
three concerned mentions were made about the exter-
nal environment—one by a founder, and two by non-
Social desirability and self-esteem issues could have
biased nonfounders away from speaking about them-
selves. Nonfounders may have chosen to explain a
decision not to found using factors other than their inad-
equacies as entrepreneurs. The converse effects are likely
for successful founders. For internal factors, the same
logic may apply. Nonfounders emphasized internal fac-
tors more than founders did (67% of nonfounders’ total
factor mentions versus 44% of founders’ total factor
mentions), and mentioned more concerns than confi-
dence factors (57% of 54 internal factor mentions were
concerns), while the converse was true for founders
(only 29% of 58 internal factor mentions were concerns).
The fact remains that external factors played a remark-
ably minor role in both sets of participants’ explanations
for their venturing decisions. Only 12% of total fac-
tor mentions focused on the external environment (13%
for founders and 9% for nonfounders), and of those,
only 10, or 5% of total factor mentions, dealt with com-
petition. Only 10 respondents mentioned competition at
all. In half of those cases, competition was mentioned
as a factor that was not a problem, as suggested by the
following quotations from the interview transcripts:
We didn’t have any competition.
What we had was something that was unique, something
that wasn’t available. We could just about charge any-
thing we wanted.
Well, we knew we could compete with the best. I’d
worked with the best team at Microsoft. I knew I could
play at that level. We knew we could do it technically.
We cannot determine whether this perceived lack of
competition is because decision makers had truly in-
novative technologies or that they were naïve about
the competition. One founder indicated he “stumbled
across” a competitor after he had been in business for
more than a year.
Other quotations from the interviews suggest that in
many cases, our participants had not bothered to do
much research on the external environment or the com-
I did this without any market research. It was a pas-
sion, not a deep analytical exercise.
It was all through intuition. I didn’t go down and say,
“who are the competing vendors?”
We used standard marketing surveys, but I used them to
produce business plans that were credible to investors,
not in any way to influence my own thinking. The data
was fluff and boilerplate used to substantiate opinions
that I held in here (pointing to his head).
Having no idea whatsoever of what we were getting into,
we borrowed money.
Only one of our respondents mentioned market capac-
ity as a consideration.
In contrast, our participants looked inward to deter-
mine their aptitude and appetite for entrepreneurship. In
several cases, the decision to found a business was made
based on personal factors, without a viable venture plan
prior to the launch of the business. Decisions against
venturing were also disproportionately made based on
personal factors.
You realize that you are either an inventor or a starter or
you’re not I wasn’t.
I have always stepped into something—not had every-
thing on the line. As I become confident with experience,
I may be more of a risk taker, but the real opportuni-
ties I looked at, at the end of the day, I didn’t have the
courage or the conviction to go through with them.
I am too conservative to launch into something entrepre-
I had never any idea as far as how you start your own
business. It was always like a big black cloud for me, I
mean not black as onerous but black as in not knowing
what’s inside—like a black box.
Factors internal to the venture were also important,
and reflected a consideration of how confident a partic-
ipant felt about a business or how easy it would be to
succeed in a market.
But for me this one was, this one was pretty easy, okay.
The opportunity is there. If I can get the money, I can
get the people, put a team together. Let’s go for it.
We had already decided that, you know, there were tons
of product opportunities around. This is the early 90s,
late 80s. Everybody was getting rich.
This is a pretty easy business you know. You get an order
before you developed it, it is easily bankable, and you
deliver this product on time. What’s the big deal?
I could see that I could make it work, so that was the
evaluation process, and it was mostly proving to myself
that this approach and this technology could be used in
the ways that we were trying to apply it.
I spent a year trying to figure out how to do it, and all of
a sudden you realize, and once you come against some
of the design problems, I got bogged down.
Discussion of Study 1
The results suggest that decision makers focused on per-
sonal ability factors followed by factors internal to the
venture. Among founders and nonfounders alike, few
identified factors in the external environment as influenc-
ing their decisions, despite the fact that strategic man-
agement theories would suggest an analysis of the envi-
ronment should be a starting point for entry decisions
(Porter 1980, Hitt et al. 2003). These myopic decision
processes, based on little or at best local information
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
Organization Science 18(3), pp. 440–454, © 2007 INFORMS 445
searches, are noteworthy given that our participants were
experienced and well-educated professionals. That the
decision processes for both those who founded a venture
and those who did not was similar is also noteworthy,
and is a new contribution to the study of market-entry
It may make intuitive sense to enter a market only if
one’s own abilities are very strong in that area. How-
ever, this approach could fail to exploit opportunities in
which the competition is particularly weak, such that
even a mediocre entrant would be successful. Further-
more, if decision makers fail to consider external fac-
tors, many of them are likely to enter markets in which
many feel competent, leading to intense competition and
higher rates of failure in those markets. Rational entry
decisions depend on the comparison between one’s own
strengths and those of the competition, relative to the
carrying capacity of the market. If market-entry deci-
sion makers truly disregard competitive factors, focusing
only on their own abilities and internal characteristics of
their business opportunities, we are likely to see overen-
try in markets in which many potential entrants believe
in their own abilities to offer a good or service, and
underentry in markets that most decision makers believe
will challenge their abilities to make such an offering.
Some industries—such as restaurants, bars, liquor stores,
and retail clothing—see perennially high rates of entry,
low profits, and high rates of failure (Dun & Bradstreet
1997a, b; U.S. Small Business Administration 2003).
Differences in rates of entry between industries are not
well accounted for by the size of an industry, the prof-
itability of its firms, or barriers to entry (Geroski 1996).
Our interview data cannot rule out some alterna-
tive explanations for participants’ tendency to focus on
themselves. First, it is possible that participants retro-
spectively felt the need to take personal credit for the
decision, regardless of its outcome. They may have con-
sidered the competition when making the decision, but
did not talk about it because they thought it might appear
that they lacked a compelling vision for themselves.
Second, our participants may simply have been better
at remembering or explaining their own roles in their
decisions. While our interview data do not allow us
to address these concerns, the results of the laboratory
study that follow are inconsistent with this alternative
Third, some of the internal factors, particularly is-
sues of profitability and confidence about the technol-
ogy/product, may have included an implicit focus on
competition. While our decision makers did not explic-
itly mention competition, it may be that their confidence
or concerns about having a good product or opportunity
were related to their assessment of the competitors for
that product. We cannot disentangle these issues with the
interview data.
Fourth, we cannot know whether the entry we ob-
served in the field was rational. It is possible that our
nonfounders did face worse venturing opportunities, and
thus made rational entry decisions. Also, we cannot
say that our participants’ degree of internal focus was
in error because we cannot specify what considera-
tions were important for their future success. We also
do not distinguish between successful and unsuccess-
ful entrepreneurs. Fifth, it is possible that those indi-
viduals who agreed to take part in our interviews did
so because they enjoyed talking about themselves, and
that our results do not broadly represent all potential
Because it is difficult to rule out these alternative ex-
planations for the results of Study 1, we turn to the
literature on decision biases to further investigate what
might be happening. We then present a laboratory exper-
iment in which we simulated the market-entry choice
and asked participants to describe their entry decisions.
In the laboratory, we were also able to exogenously
manipulate the difficulty of the competition, rather than
simply measure participants’ perceptions of difficulty.
Study 2
Prescriptive theories of strategic management and com-
mon sense both dictate that potential entrepreneurs
should compare their own capabilities to those of
existing and potential competitors, and also consider
the market’s carrying capacity relative to competitors’
offerings when making market-entry decisions (Porter
1980, Barney 1985). Comparative judgments are often
move strongly correlated with an individual’s own per-
formance than with the individuals true relative perfor-
mance. We base these predictions on the results of the
first study and on prior findings from research on myopic
biases in comparative judgment.
Theoretical Framework
When people compare themselves to others, their judg-
ments tend to be myopically biased (Kruger 1999,
Moore and Kim 2003). Comparative judgments more
closely represent people’s abilities with respect to a task,
rather than these abilities in relation to those of oth-
ers. When the task is easy or all competitors are strong,
each individual competitor tends to believe that he or she
will be above average. For example, when a professor
decides to make the exam open-book, students’ expec-
tations for getting an A on the exam go up dramatically,
even when it is common knowledge that the exam is
graded on a forced curve (Windschitl et al. 2003).
Evidence from a wide variety of domains suggests that
people routinely tend to overestimate their abilities rel-
ative to others. On average, people believe themselves
to be above-average drivers, investors, and negotiators
(Svenson 1981, Kramer et al. 1993, Moore et al. 1999).
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
446 Organization Science 18(3), pp. 440–454, © 2007 INFORMS
These overly optimistic estimates of ability can have
profound consequences in many domains: People over-
estimate their own ability to pick stocks, and then trade
stocks too often; they take inappropriate risks in product
development; they overestimate their chances of winning
in court and are therefore too willing to take their law-
suits to trial; and they take excessive risks in founding
firms (Neale and Bazerman 1983; Daniel et al. 1998;
Odean 1998, 1999; Simon and Houghton 2003).
However, the tendency toward believing that one is
better than others is not universal. Average people rate
themselves below average on difficult tasks such as jug-
gling and unicycle riding (Kruger 1999). The character-
istic feature of such worse-than-average effects is that
they occur in domains where success (in objective terms)
is rare, whereas people tend to rate themselves as above
average in easy domains in which they generally feel
capable (Kruger 1999, Windschitl et al. 2003). The per-
ceived ease of a task derives from a combination of
personal ability and task attributes. It can be influenced
by variations in either perceived ability or task diffi-
culty. Moore and Kim (2003) manipulated difficulty by
varying the difficulty of the task itself. They found that
people who had completed a simple trivia quiz expected
to be above average relative to others who had com-
pleted the same quiz. Those who had completed a diffi-
cult trivia quiz, by contrast, expected to be below aver-
age relative to others who had completed the same quiz.
Camerer and Lovallo (1999) varied participants’ ability
and obtained a similar effect. When participants in their
study were self-selected trivia enthusiasts, they expected
to outperform the other participants, despite that partic-
ipants knew the others were also trivia enthusiasts.
In other words, judgments of relative performance
tend to be biased, discounting others’ abilities and over-
weighting personal factors. When people predict they
will perform well, such as on a simple task, they expect
that their performance will be above average, despite the
fact that simple tasks are simple for everybody and not
everybody can be above average. When people expect to
perform poorly, such as on a difficult task, they believe
that their performance will be below average, despite the
fact that difficult tasks are difficult for everybody and not
everybody can be below average. Researchers have con-
cluded that comparative judgments are often based on
myopic self-evaluations (Klar and Giladi 1997, 1999).
Entrepreneurship occurs at the nexus of individuals
and opportunities (Venkataraman 1997). As such, entry
decisions should consider estimations of entrepreneurial
competence as well as considerations of the potential
of an opportunity, yet human judgment is egocentrically
biased. People have more information about themselves
than about others. Psychological research documents
many of the difficulties of escaping one’s own point of
view when trying to understand others, including the
competition (Ross et al. 1977, Krueger and Clement
1994, Moore 2004a). This bias has profound implica-
tions for market-entry decisions. If people focus on their
personal abilities and fail to consider how they stack up
against the strength of the competition when predicting
their ability to compete, then their entry decisions will
be biased.
We conducted a laboratory experiment in which we
exogenously manipulated both the difficulty of the task
upon which competition is based and the capacity of the
competition to reward entrants. We observed the effects
of these manipulations on entry decisions and on expla-
nations for those decisions. In the experiment, partici-
pants made decisions about whether to enter each of four
rounds of a competition. The decision to enter resulted
in either a gain or a loss of money, depending on the par-
ticipant’s performance on a trivia quiz relative to seven
other potential competitors. The trivia quiz results pro-
vided a proxy for results in a competitive market. Thus,
each of the game’s four rounds involved a decision to
either stay out of a new market or to commit resources
to entering that market. Sometimes the task that deter-
mined market ranking was simple; in other rounds, it
was difficult. If participants focused too much on per-
sonal factors and too little on their competitors, they
would be too eager to enter the competition in simple
rounds and too reluctant to enter on difficult rounds.
Hypothesis 1. People will more frequently choose to
enter markets in which competition is based on simple
tasks than markets in which competition is based on dif-
ficult tasks.
Our theory predicts that self-focus should moderate
the effect of task difficulty on entry rates:
Hypothesis 2. Greater self-focus will lead individu-
als to be more likely to enter simple markets and less
likely to enter difficult markets.
Likewise, changes in a market’s capacity would not
influence a participant’s trivia quiz scores; so although
changes in market capacity should affect entry rates if
people made their entry decisions rationally, we pre-
dicted that they will not.
Hypothesis 3. Market-entry rates will be insuffi-
ciently affected by market capacity.
The strictest test of self-focus would come from a
comparison of participants’ entry behavior with their
own estimates of competitors’ market entrance frequen-
cies. We expect that participants will correctly anticipate
that others will enter simple markets more frequently
than difficult markets. However, we expect that they will
then neglect the implications of these predictions for
competitive intensity.
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
Organization Science 18(3), pp. 440–454, © 2007 INFORMS 447
Participants were 96 undergraduates at the University of
Toronto who were paid for participating. At the start
of the experiment, each participant received a $10 base
payment. Participants could also win or lose additional
money depending on their performance in each of four
rounds of a market-entry game. Each round of the game
offered a maximum $15 payoff and a maximum $10
loss. If players stayed out of a market, their payoffs for
that round of the game would be zero; they neither won
nor lost any money. Participants’ payoffs were averaged
over the four rounds. A player who lost $10 in each
of the four rounds would leave the experiment empty-
handed: The $10 loss would wipe out his or her base
payment. A player who ranked first in all four rounds
would leave the experiment with $25.
Experimental Design
The experiment had a 2 (quiz difficulty: simple versus
difficult) × 2 (market capacity: three versus four) within-
subjects design. The quiz difficulty manipulation varied
the degree of difficulty of the trivia quizzes used to rank
entrants in each round. Each participant saw two simple-
rank and two difficult-rank markets. The market capacity
manipulation varied the number of entrants the market
could sustain profitably. In a capacity 3 market, the top
three entrants would earn $15, $10, and $5, respectively.
Entrants ranked fourth through eighth would each lose
$10 (see Table 4 for payoffs).
To rule out idiosyncratic effects of order not rele-
vant to the present hypotheses, the sequence in which
both the quiz difficulty and market capacity manipula-
tions were presented was counterbalanced, as shown in
Table 5. For each round, experimental instructions em-
phasized that all eight competitors received the identical
quiz in the same market. All participants decided inde-
pendently and simultaneously whether to enter before
they took the trivia quiz. Instructions explained that any
tied scores would be resolved by a final tiebreaker ques-
tion on each quiz, and that therefore ties were unlikely.
After Round 2, participants received performance feed-
back about the amount of money they had won, but
did not receive any feedback about the number of other
entrants or others’ scores on the first two rounds of
Table 4 Payoffs as a Function of Rank Within Market for
Markets of Capacity 3 and 4
Rank Capacity 3 ($) Capacity 4 ($)
1st 15 15
2nd 10 10
3rd 5 5
4th 10 0
5th 10 10
6th 10 10
7th 10 10
8th 10 10
Table 5 The Four Different Sequences of Quiz Difficulty
(Simple vs. Difficult) and Market Capacity (3 vs. 4)
over the Four Rounds
Sequence Round 1 Round 2 Round 3 Round 4
1 Simple, Cap. 3 Difficult, Cap. 4 Difficult, Cap. 3 Simple, Cap. 4
2 Simple, Cap. 4 Difficult, Cap. 3 Difficult, Cap. 4 Simple, Cap. 3
3 Difficult, Cap. 3 Simple, Cap. 4 Simple, Cap. 3 Difficult, Cap. 4
4 Difficult, Cap. 4 Simple, Cap. 3 Simple, Cap. 4 Difficult, Cap. 3
the trivia quiz. This arrangement was designed to paral-
lel the early-stage entry choice. Potential entrepreneurs
must often decide whether to commit some amount of
nonsalvageable fixed costs to enter a new industry with
only imperfect information about their performance rel-
ative to other competitors.
The experiment had one process variable and two
dependent variables. Verbal protocols served as a pro-
cess variable. Each participant provided a description of
what they thought about during the process of deciding
whether to enter. Of the participants, 48 recorded ver-
bal protocols into digital recording devices. Participants
responded to these instructions: “Please talk through
every part of your thought/decision process that you
can. The other 48 participants recorded written proto-
cols, responding to “Please jot down every part of your
thought/decision process that you can.” Each participant
took part in the experiment in a different room, to reduce
mutual influence on think-aloud protocols. Three trained
research assistants unaware of the present hypotheses
rated each of the protocols. Each protocol was rated on
a three-point scale according to the degree of self-focus
represented in the stated thoughts. Higher scores indi-
cated a greater degree of self-focus. In addition, the use
of two types of pronouns in each protocol was counted:
Pronouns referring to the self (I, me), and pronouns
referring to others (them, they).
The first dependent variable was market entry. Partic-
ipants indicated whether they would enter the market in
each round of the game. The other dependent variable
was an estimate of the number of other competitors who
would enter the market in each round. Four participants
were excluded because they appeared to misunderstand
the rules of the game: Their estimates of the number of
competitors who would enter the market in each round
exceeded the maximum of eight in more than one round.
Tests for the effects of ordering quiz difficulty and mar-
ket capacity revealed no significant main or interaction
effects of order, F ’s < 1. Therefore, subsequent analyses
collapse across order conditions.
Market Entry. As predicted, rates of entry differed
dramatically for the difficult and simple quizzes. Partici-
pants entered 69% of the time (or 5.5 out of 8) on simple
rounds, but only 39% of the time (or 3.1 out of 8) on
difficult rounds. This pattern deviates significantly from
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
448 Organization Science 18(3), pp. 440–454, © 2007 INFORMS
Table 6 Entry Rates over the Four Rounds in Simple and
Difficult Markets
Entry rate Entry rate value
in difficult in simple of diff. ×
rounds (%) rounds (%) Chi-square self-focus int.
Round 1 39 74 1132
Round 2 41 70 744
Round 3 43 65 438
Round 4 33 67 1113
Notes. Chi-square values indicate the degree to which entry rates
deviate from the null hypothesis of equal rates of entry into simple
and difficult markets. Beta values indicate the results of regression
analyses using the difficulty × self-focus interaction term to predict
the null hypothesis of equal entry rates in difficult and
simple rounds,
1 = 3310, p<0001. As Table 6
shows, this same pattern held across all four rounds.
These results are consistent with Hypothesis 1, which
predicted greater entry in simple-rank markets than in
difficult-rank markets, and with our theory, which pre-
dicted that entry decisions would be self-focused at the
expense of considering the competition.
By contrast, the manipulation of market capacity had
no significant effect on rates of entry. Participants were
as likely to enter markets with a capacity of 3 (56% entry
rate) as they were to enter markets with a capacity of 4
(52% entry rate),
1 = 085, and this pattern does not
deviate significantly from the null hypothesis of equal
rates of entry in rounds of capacity 3 and 4. This find-
ing is consistent with Hypothesis 3 and our expectation
that participants’ entry decisions would be self-focused.
Manipulations of market capacity did not influence the
absolute performance of the individual competitor on the
quiz, and so its effect on market-entry decisions was
similarly weak. Although we should be cautious about
attributing too much to a null effect, the (nonsignificant)
difference between the two means actually goes in the
opposite direction than one would predict, given per-
fectly rational entry decisions.
Estimates of Numbers of Competitors. A difficulty ×
capacity × round mixed ANOVA reveals that partici-
pants accurately forecast more entrants into simple- than
difficult-rank markets. In simple-rank markets, people
predicted that there would be an average of 6.05 (SD =
157) entrants, while in difficult-rank rounds, people pre-
dicted that there would be an average of 3.94 (SD =
162) entrants, F3 228 = 5656, p<0001. Table 7
displays a summary of means and standard deviations
for these estimates across all four rounds. No other main
effects or interactions are significant.
Participants who faced a simple trivia quiz market
anticipated correctly that more people would enter that
market than would enter a difficult trivia quiz market.
Implicit in this prediction is the belief that more money
Table 7 Mean Estimates of How Many Competitors Would
Enter a Market (Maximum 8)
Quiz difficulty Market capacity
Simple Difficult Three Four
Round 1 6.46 (1.37) 4.09 (1.63) 5.43 (2.04) 5.56 (1.71)
Round 2 6.33 (1.49) 4.06 (1.73) 4.95 (1.92) 5.03 (2.06)
Round 3 5.48 (1.48) 3.64 (1.51) 4.39 (1.83) 4.41 (1.68)
Round 4 5.89 (1.76) 4.24 (1.79) 4.98 (2.06) 5.46 (1.80)
Means 6.03 (1.53) 4.01 (1.67) 4.91 (1.96) 5.12 (1.81)
was to be made in difficult markets, yet they nevertheless
chose to enter simple markets more often. A comparison
of two means from Round 1 illustrates this point. Par-
ticipants who entered markets in Round 1 predicted that
an average of 6.15 (SD = 162) competitors would also
enter, while participants who did not enter estimated that
4.27 (SD = 173) competitors would enter, t = 496, p<
0001. The decision to enter despite the high number of
other competitors really only makes sense if participants
believed that they would be above average on the simple
quiz, but below average on the difficult quiz.
Protocols. Each participant provided statements about
his or her market-entry decision process in each of the
four rounds. The ratings of verbal protocols did not dif-
fer significantly from those of written protocols; mean
differences < 05, t’s < 1. We therefore combined two
conditions in our analyses. Statements were coded on a
one-to-three scale according to their degree of self-focus.
A code of 1 represented a judgment that the statements
included thoughts solely about factors external to the
participant and his or her ability, such as the market,
competitors, risks associated with entry, and probabil-
ities of success. Examples include statements such as:
“Everybody will do well on this one” and “Only three
people are going to not lose money.” A code of 3 repre-
sented a judgment that the statements included thoughts
solely about the participant and his or her success in the
market. Examples include statements such as: “It’s sup-
posed to be a simple quiz, so I should do OK” and “I am
going to stay out of this one because the last quiz was
impossible. Most sets of statements contained aspects
of both of these extremes. The following statement was
coded with a score of 2: “People will see ‘easy’ and go
in, less chance for me to place high, couldn’t answer
many easy ones anyway.
The mean rating for all protocols was 2.48 (SD =
062) with mean ratings for each of the three coders of
2.52 (SD = 068), 2.47 (SD = 070), and 2.44 (SD =
061). The three coders’ ratings held together with sat-
isfactory reliability, = 093. Where they disagreed,
the three ratings were averaged to form a single mea-
sure of self-focus for each participant in each round.
This measure of self-focus was modestly correlated with
use of the self-referential pronouns (r = 014), and was
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
Organization Science 18(3), pp. 440–454, © 2007 INFORMS 449
negatively correlated with references to others (r =
051). These ratings reflect a general tendency for peo-
ple to focus on themselves when describing their entry
decisions, consistent with our expectation that entry
decisions would focus on internal factors and neglect
consideration of the competition. The overall mean of
2.48 is significantly different from the midpoint of 2,
t91 = 1102, p<0001.
Hypothesis 2 predicts an interaction effect: Self-focus
would make people more likely to enter simple mar-
kets, but less likely to enter difficult ones. To test this
hypothesis, we created an interaction term that multi-
plied difficulty (difficult =−1, simple = 1) by self-focus
(1 to 3). We then used logistic regression to predict entry
with the following independent variables: difficulty, self-
focus, difficulty × self-focus, and fixed effects for both
round and for participant. The results reveal a significant
effect for the difficulty× self-focus interaction term, =
188, p<0001, and the overall R
= 058. These results
suggest that self-focus is associated with both more entry
into simple markets and less entry into difficult markets.
Indeed, in simple rounds, the degree of self-focus is pos-
itively correlated with the decision to enter (r = 028,
p<0001). However, in difficult markets, the degree of
self-focus is negatively correlated with the decision to
enter (r =−034, p<0001).
Discussion of Study 2
The results of the market-entry game reveal that the
manipulation that should not have influenced entry deci-
sions, namely the difficulty of the test, did influence
them. Test difficulty had no effect on the money-making
opportunities present, yet had a dramatic effect on entry
rates. By contrast, the manipulation that should have
influenced entry decisions, namely market capacity, did
not. Market capacity did affect the risk associated with
entry, but did not affect participants’ personal perfor-
mances (scores on the trivia quizzes), and it did not sig-
nificantly affect rates of entry. Perhaps most telling, our
participants ignored their own estimates of competitors’
entry rates and proceeded to enter markets that they per-
ceived to be simpler, even though they indicated believ-
ing that these markets would have more people entering.
This is a stringent test of the power of self-focus to affect
market-entry decisions.
Are rates of entry excessive? The typical simple round
had six entrants. Those ranked in the top three spots
all stood to make money. Those ranked lower all lost
money (with the exception of the fourth-ranked entrant
in capacity 3 rounds, who broke even). It may appear
that the decision to enter was a mistake for those who
lost money. However, the decision to enter was only an
error at the time if they knew they would be poorly
ranked when they decided to enter. Rational economic
agents with no idea at all how their quiz performances
would compare with those of others at the time they
decided to enter, should enter until the marginal value
of entry is zero. For example, in a capacity 3 round, if
four people have already entered but no one yet knows
how they will rank relative to each other, then it should
be tempting for a fifth person to enter. The top three
entrants will collectively make $30. The fourth-ranked
entrant will lose $10. The fifth entrant could rank any-
where, so the choice to enter has a positive expected
value ($2 to be precise, because $30 20/5 = $2.
The sixth entrant is likely to be ambivalent about entry,
because with six entrants, the amount lost by the losers
($30) is exactly equal to the amount gained by the win-
ners ($30). Because participants had not taken the trivia
quiz before they made their entry decisions, they had lit-
tle information about their performance relative to other
potential entrants.
Entry rates differed in the simple and difficult markets.
Typical simple-ranked rounds had six entrants, while
the typical difficult-rank round had three entrants. These
three entrants all made money, and the expected value
of additional entry was clearly positive. However, other
potential entrants stayed out. The best explanation for
this decision is that they expected to be below average
on the difficult tests. This reasoning is clearly myopic,
because not everyone can be below average, even on the
difficult test. However, it is entirely consistent with
the self-focused explanations offered by participants for
their entry decisions. It may be more noteworthy that the
rate of entry into simple-rank markets was not higher,
given other evidence demonstrating overconfidence in
entrepreneurial entry both in laboratory and in field data
(Cooper et al. 1988, Camerer and Lovallo 1999). The
obvious explanation for this finding is that people did
have some information about how they would perform
on the various trivia topics, and so those who knew they
would be worse than others were more likely to choose
to stay out.
General Discussion
Taken together, the results of the field interviews and the
laboratory experiment are consistent with our hypothe-
ses. Potential market entrants focused on themselves at
the expense of considering potential competitors, and
this self-focus affected their entry decisions. Laboratory
participants and working professionals (firm founders
and nonfounders) displayed a strikingly myopic self-
focus in their explanations for their entry decisions. They
talked most about their own personal abilities, and spoke
little about the capabilities of potential competitors. We
show that explanations are related to the actual entry
decisions people make. Self-focus increased entry in
simple-rank markets, but decreased it in difficult-rank
These results suggest that overconfidence and excess
entry are not universal—they are restricted to markets
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
450 Organization Science 18(3), pp. 440–454, © 2007 INFORMS
in which potential entrants feel confident of their per-
sonal performance, even if that confidence ignores the
performance of competitors. This research contributes to
the body of knowledge concerning entrepreneurial deci-
sion making by providing evidence for a psychological
mechanism that can account for both entry and entry-
avoidance. The literature to date describes a series of
potential decision faults, all of which lead to entry.
Myopic self-focus not only accounts for entrepreneurial
overconfidence, but also for underestimations of ability
that result in avoidance of difficult markets.
A large number of people and institutional processes
are also at work making it possible for entrepreneurs to
succeed. Will venture capitalists fall victim to the same
myopic errors as the entrepreneurs? Many venture cap-
italists focus primarily on the abilities of the founder
when making funding decisions (Gupta 2000). Such a
narrowly focused information search is likely to repli-
cate the error of betting more on an entrant winning
in a simple than a difficult competition. Other labora-
tory evidence offers little hope that experience or feed-
back will eliminate the biases we document. Moore and
Cain (in press) conducted a market-entry game like our
experiment, except that their participants engaged in 12
rounds with full feedback after each round about all par-
ticipants’ scores, entry decisions, and rankings. Their
results show persistent excess entry into simple-rank
rounds and insufficient entry into difficult-rank rounds,
and no evidence that participants learned to avoid these
mistakes over 12 rounds.
What Makes an Industry Seem Easy?
There are numerous reasons why some entrepreneurial
entry decisions may be seen as easy. Success in some
industries (such as coffee shops, restaurants, or cloth-
ing retail) is based in part on knowledge or abilities
that most people believe they possess. Entry into these
highly visible industries tends to be excessive (Dun &
Bradstreet 1997a, b; U.S. Small Business Administration
2003). Furthermore, individuals with prior experience
in an industry—whether as an entrepreneur, employee,
or customer—may be more likely to see entry in the
same industry as easy. Both practice and vicarious learn-
ing help to build self-efficacy in a domain (Bandura
1977, Wood and Bandura 1989). As industries mature,
people gain familiarity with them and they gain legiti-
macy (Hannan and Freeman 1989). This legitimacy no
doubt contributes to an increase in the perceived ease of
starting a business in the industry. Local successes also
tend to increase founding rates in particular industries
(Haveman 1993, Stuart and Sorenson 2003). Seeing oth-
ers succeed may build the self-confidence of prospective
entrepreneurs (Sorenson and Audia 2000). By contrast,
unsuccessful businesses disappear from view quickly,
and only rarely do they attract a great deal of media
It may seem implausibly short-sighted of potential
entrepreneurs to be encouraged by salient successes, yet
fail to be discouraged by the competition implied by
those same incumbent firms. It is exactly this sort of
myopia that can account for the evidence we present.
People predict that they will be better than others on
easy tasks and are eager to bet on beating others, even
when it should be obvious to them that the task will
be just as easy for their competitors (Moore and Kim
2003). People correctly see that the easy test will mean
that they will perform well, and even correctly predict
that others will find the easy competition more attractive,
yet they fail to take the next logical step to realize the
competition will make it harder for them to win. Nego-
tiators can display similarly myopic judgments in the
presence of a deadline. Despite the fact that the deadline
means the end of negotiations for both buyers and sell-
ers, both sides predict that the deadline will hurt them
and help their opponents (Moore 2004a, b, 2005). It is
a short logical step from the observation of others’ suc-
cess to the inference that one might also be successful.
The inference that, because they will be competing with
you, their success decreases your chances of success
requires an additional logical step that we argue is less
frequently made.
Consequences of Myopic Entry Decisions
When perceived ease drives rates of founding, some
odd patterns in the spatial and temporal distribution of
organizations are likely to result. For example, starting
an Internet-based business seemed easy in 1998, when
rumors of venture capitalists backing entrepreneurs with
wild ideas were common. Excess entry was likely facil-
itated by the self-confidence that entrepreneurs’ gained
by observing others’ success (Sorenson and Audia
2000)—achieving success seemed easy. Ironically, like
the subjects in our experiment who entered simple mar-
kets too frequently, such confidence increases entry rates
in exactly those areas where strong competitors reside.
Some entrepreneurs are no doubt skilled at thinking
in sophisticated ways about the potential profits of entry
and the threats posed by existing and potential com-
petitors. However, our results suggest that at least some
potential entrants make their entry decisions based on a
myopic judgment of their own abilities. All else being
equal, these myopic entrepreneurs would be most likely
to enter when there are many examples of successful
entrants, such as when an industry is undergoing a boom.
This is, of course, not likely to be an opportune time
to enter, given that major industry expansions are often
followed by shakeouts that narrow the set of incum-
bent firms (Hannan and Carroll 1992, Singh 1993). The
result will be that firms founded at the peak of organi-
zational density are most vulnerable to failure. Carroll
and Hannan (1989) have documented this phenomenon,
which they labeled “density delay. Naturally, the peak
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
Organization Science 18(3), pp. 440–454, © 2007 INFORMS 451
of density is likely to coincide with the presence of
numerous examples of other successful entrants—and
therefore with high levels of perceived ease—but it is not
likely to be the point at which success is actually easiest
to achieve. Once the market niche reaches its carrying
capacity, competition will escalate.
Practical Implications
Our results provide some practical advice to entrepre-
neurs. Teaching entrepreneurs to think about, analyze,
and understand the competition may help them counter-
act self-focused and myopic tendencies. Entrepreneurs
are likely to be more receptive to suggestions that they
consider their specific competitive threats than they will
be to suggestions to consider base rates of entrepre-
neurial success and failure (as suggested by work on
the belief in the “law of small numbers, Tversky and
Kahneman 1971, Kahneman and Lovallo 1993, Busenitz
and Barney 1997) or the effect of chance (as suggested
by work on the illusion of control, Langer 1975, Simon
et al. 2000). Furthermore, rather than telling entrepre-
neurs that they do not have enough information to con-
fidently make their decisions (as suggested by work on
overconfidence), our research indicates specifically what
information they need. Entrepreneurs need to examine
the competition. Empirical research suggests that indus-
try effects (encompassing purely external factors such
as competition, industry structure, etc.) explain approx-
imately 17%–20% of the variance in business success
(Rumelt 1991, Schmalensee 1985, Wernerfelt and Mont-
gomery 1988), while organizational factors (such as
a firm’s strategy) have been found to explain more—
between 38% and 46% of performance variance (Hansen
and Wernerfelt 1989, Rumelt 1991). However, even to
set a firm’s strategy, managers must examine exter-
nal factors, especially the competition. At the time of
making a market-entry decision, an entrepreneur should
therefore have an understanding of the competition for
two reasons: (a) to determine if the industry is an attrac-
tive one to enter, and (b) to select a competitive posi-
tion within an industry. The field of strategy offers tools
that can help entrepreneurs assess competitive factors
(e.g., Porter 1980). If entrepreneurs are trained to avoid
myopic self-focus, they may make better entry decisions.
The professionals recruited for the first study were a self-
selected group, who may have agreed to be interviewed
simply because they enjoyed talking about themselves.
Such self-selection was not a factor in our laboratory
experiment. However, participants in the experiment
were younger, less experienced, and perhaps more naïve
than the average entrepreneur. They were undergradu-
ate students, and their youth or general lack of business
sophistication could have reduced their insight into the
strategic dynamics of the entrepreneurial entry game.
Three features of our results offer suggestions that our
results are likely to be robust to generalization beyond
these imperfect samples.
First, we demonstrate self-focused entry decisions not
only among students in the laboratory, but also among
older, experienced entrepreneurs. The parallels in the
results of the two studies should provide some assurance
regarding the generality of the effects we document.
Second, while students’ inexperience could account for
high rates of self-focus, it is not a good explanation
for the interaction between self-focus and market dif-
ficulty. Third, the research literature on judgment and
decision making repeatedly demonstrates the robustness
and generality of decision-making biases that result from
fundamental psychological processes (Lichtenstein and
Slovic 1973, Tversky and Kahneman 1974, Northcraft
and Neale 1987, Camerer 2000, Kahneman and Tversky
2000, Kahneman 2003). Gamblers in Las Vegas playing
for big stakes show the same preference inconsistencies
as do college students making hypothetical decisions in
the laboratory (Lichtenstein and Slovic 1973, Radzevick
and Moore 2005). Professional real estate agents fall vic-
tim to the same anchoring biases in their assessment of a
home’s value as has been demonstrated with college stu-
dents (Northcraft and Neale 1987). Professional statisti-
cians employ the same faulty statistical intuitions as do
the rest of us (Tversky and Kahneman 1974). While it
may tempting to assume that professionals will decide
differently than students, the evidence suggests instead
that we should assume the opposite: Basic psychological
processes in judgment and decision making tend to be
common across varying levels of age, experience, and
A limitation to our studies comes from our reliance
on self-reports. We cannot be sure that our participants
faithfully answered our questions. Participants may have
provided explanations that centered on their own abil-
ities to appear decisive and capable to the researcher,
although this alternative explanation accounts better for
self-focus leading to more entry in simple markets than
self-focus leading to less entry in difficult markets. Even
if our participants faithfully reported what they believed
to have caused their behavior, copious evidence suggests
that people are imperfectly aware of the reasons they
make the choices that they do (Wilson 2002). While our
data cannot prove that participants’ descriptions of their
thought processes are not epiphenomenal or that they
resulted from the decisions rather than caused them, par-
ticipants’ decisions were consistent with the self-focused
explanations they provided. They entered simple markets
too frequently and difficult markets too rarely.
Of course, our findings do not rule out the simulta-
neous operation of other decision biases. For example,
entrepreneurs may be more likely than others to see a
task as easy, or their own abilities as high. Our findings
do suggest, however, that we need to look again at such
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
452 Organization Science 18(3), pp. 440–454, © 2007 INFORMS
work to determine whether there are indeed cognitive
differences between entrepreneurs and nonentrepreneurs,
as some work claims (Gaglio 1997, Kirzner 1979, Palich
and Bagby 1995). Research into entrepreneurs’ expecta-
tions of success for the ventures they have already started
(e.g., Cooper et al. 1988) could be explained as easily by
self-focus as by illusion of control and overconfidence,
because such research samples on the dependent vari-
able of interest in our study. Only those decision mak-
ers who felt they had high expectations of success were
likely to enter in the first place. Research that com-
pares entrepreneurs with managers in making generic
entry decisions is likely to show differences because
entrepreneurs have already experienced entrepreneur-
ship, and therefore are likely to see repeat entrepreneur-
ship as an easier task. These effects are quite difficult
to disentangle, and it is likely that other decision biases
operate in entrepreneurial entry decisions in addition to
self-focus bias. Future research is required to make this
The studies presented here support our contention that
entrepreneurs tend to overweight personal factors and
underweight consideration of the competition when
making venturing decisions, suggesting that market-en-
try decisions are indeed driven by simple-minded logic
(Cyert and March 1963). These entry decisions result
from an information search that relies too heavily on
the most easily accessible data: information about one’s
own and one’s firm’s capabilities. This tendency can
lead to excess entry in some markets and insufficient
entry in others. Our findings suggest that decision mak-
ers do not give enough weight to competitive factors
when making entry decisions, and thus behave contrary
to the prescriptions of strategy theorists. And, contrary
to the conclusions of some entrepreneurship researchers,
our findings imply that entrepreneurs are not universally
overconfident. Our dual methodology, combining labora-
tory and field results, lends generality to our contribution
to research in entrepreneurial cognition. Our findings
also provide practical assistance to entrepreneurs by giv-
ing them more insight into the factors they need to con-
sider, but tend to ignore, when making entrepreneurial
entry decisions.
The authors would like to thank Tima Bansal, June Cotte, Eric
Morse, Patrick Saparito, Tal Simons, Dilip Soman, the partic-
ipants in the seminar series at the Ivey Business School and
the participants at the conference at Carnegie Mellon celebrat-
ing the 40th anniversary of Cyert and March’s A Behavioral
Theory of the Firm. for helpful comments on the paper. The
first author gratefully acknowledges the support of National
Science Foundation Grant SES-0451736.
To construct the matched sample, we categorized founders
and nonfounders into the categories shown in Table 1, and
assigned scores to each category: 1, 2, 3, or 4 for length
of experience; 1 or 2 for entrepreneurial experience; 1 for
unrelated experience, 2 for moderately related experience, and
3 for highly related experience. We then took a multiplicative
combination of length of experience, entrepreneurial experi-
ence, and relatedness of experience as the final score, and
constructed a sample that included one founder and one non-
founder for each matched score.
Of course, some amount of geographic agglomeration is at-
tributable to transportation costs or agglomeration externali-
ties. Also, greater entry in some industries, such as restaurants,
may be attributable to lower barriers to entry. These alternative
explanations, however, cannot account for the experimental
data from our second study.
Alvarez, S., L. Busenitz. 2001. The entrepreneurship of resource-
based theory. J. Management 27 755–775.
Amit, R., P. J. H. Schoemaker. 1993. Strategic assets and organiza-
tional rent. Strategic Management J. 14 33–46.
Audretsch, D. B. 1991. New-firm survival and the technological
regime. Rev. Econom. Statist. 73 441–450.
Bandura, A. 1977. Self-efficacy: Toward a unifying theory of behav-
ioral change. Psych. Rev. 84 191–215.
Barney, J. B. 1995. Gaining and Sustaining Competitive Advantage.
Addison-Wesley, Reading, MA.
Baron, R. A., T. B. Ward. 2004. Expanding entrepreneurial cogni-
tion’s toolbox: Potential contributions from the field of cognitive
science. Entrepreneurship Theory Practice 28 553–573.
Busenitz, L. W. 1992. Cognitive biases in strategic decision making:
Heuristics as a differentiator between managers in large orga-
nizations and entrepreneurs. Unpublished doctoral dissertation,
Texas A&M University, College Station, TX.
Busenitz, L. W. 1999. Entrepreneurial risk and strategic decision mak-
ing: It’s a matter of perspective. J. Appl. Behavioral Sci. 35
Busenitz, L., J. Barney. 1997. Differences between entrepreneurs and
managers in large organizations; biases and heuristics in strate-
gic decision-making. J. Bus. Venturing 12 9–31.
Camerer, C. F. 2000. Prospect theory in the wild: Evidence from
the field. D. Kahneman, A. Tversky, eds. Choices, Values, and
Frames. Russell Sage Foundation, New York, 288–300.
Camerer, C. F., D. Lovallo. 1999. Overconfidence and excess entry:
An experimental approach. Amer. Econom. Rev. 89 306–318.
Carroll, G. R., M. T. Hannan. 1989. Density delay in the evolution
of organizational populations: A model and five empirical tests.
Admin. Sci. Quart. 34 411.
Cooper, A. C., C. Y. Woo, W. C. Dunkelberg. 1988. Entrepreneurs’
perceived chances for success. J. Bus. Venturing 3 97–109.
Cyert, R., J. March. 1963. A Behavioral Theory of the Firm. Prentice
Hall, Upper Saddle River, NJ.
Daniel, K., D. Hirshleifer, A. Sabrahmanyam. 1998. Investor psychol-
ogy and security market under- and overreactions. J. Finance 53
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
Organization Science 18(3), pp. 440–454, © 2007 INFORMS 453
Dun & Bradstreet. 1997a. Business failure record: A comparative sta-
tistical analysis of geographic and industry trends in business
failures in the United States. Report, Dun & Bradstreet, New
Dun & Bradstreet. 1997b. Business starts record: A comparative sta-
tistical analysis of geographic and industry trends in business
failures in the United States. Report, Dun & Bradstreet, New
Dunne, T., M. J. Roberts, L. Samuelson. 1988. Patterns of firm entry
and exit in U.S. manufacturing industries. RAND J. Econom. 19
Fujita, M., P. Krugman, A. J. Venables. 2001. The Spatial Economy:
Cities, Regions, and International Trade. MIT Press, Cambridge,
Gaglio, C. M. 1997. Opportunity identification: Review, critique and
suggested research directions. J. A. Katz, ed. Advances in En-
trepreneurship, Firm Emergence, and Growth, Vol. 3. JAI Press,
Greenwich, CT, 119–138.
Geroski, P. A. 1996. What do we know about entry? Internat. J.
Indust. Organ. 13 421–441.
Gripsrud, G., K. Gronhaug. 1985. Structure and strategy in gro-
cery retailing: A sociometric approach. J. Indust. Econom. 33
Gupta, U., ed. 2000. Done Deals: Venture Capitalists Tell Their Sto-
ries. Harvard Business School Press, Cambridge, MA.
Hannan, M. T., G. R. Carroll. 1992. Dynamics of Organizational Pop-
ulations: Density, Legitimation, and Competition. Cambridge
University Press, New York.
Hannan, M., J. Freeman. 1989. Organizational Ecology. Harvard Uni-
versity Press, Cambridge, MA.
Hansen, G., B. Wernerfelt. 1989. Determinants of firm performance:
The relative importance of economic and organizational factors.
Strategic Management J. 10 399–411.
Haveman, H. A. 1993. Follow the leader: Mimetic isomorphism and
entry into new markets. Admin. Sci. Quart. 38 593–628.
Hitt, M. A., R. D. Ireland, R. E. Hosskinson. 2003. Strategic Man-
agement and Competitive Globalization, 5th ed. South Western
Publishing, Mason, OH.
Hodgkinson, G. P. 1997. The cognitive analysis of competitive struc-
tures: A review and critique. Human Relations 50 625.
Hosmer, D. W., S. Lemeshow. 1989. Applied Logistic Regression.
Wiley Interscience, New York.
Kahneman, D. 2003. A perspective on judgment and choice: Mapping
bounded rationality. Amer. Psychologist 58 697–720.
Kahneman, D., D. Lovallo. 1993. Timid choices and bold forecasts:
A cognitive perspective on risk and risk taking. Management
Sci. 39 17–31.
Kahneman, D., A. Tversky. 2000. Choices, Values, and Frames. Rus-
sell Sage Foundation, New York.
Kirzner, I. M. 1979. Perception, Opportunity and Profit. University of
Chicago Press, Chicago, IL.
Klar, Y., E. E. Giladi. 1997. No one in my group can be below the
group’s average: A robust positivity bias in favor of anonymous
peers. J. Personality Soc. Psych. 73 885–901.
Klar, Y., E. E. Giladi. 1999. Are most people happier than their
peers, or are they just happy? Personality Soc. Psych. Bull. 25
Kramer, R. M., E. Newton, P. L. Pommerenke. 1993. Self-enhance-
ment biases and negotiator judgment: Effects of self-esteem and
mood. Organ. Behav. Human Decision Processes 56 110–133.
Krueger, F. N., P. Dickson. 1994. How believing in ourselves increases
risk taking: Self-efficacy and perceptions of opportunity and
threat. Decision Sci. 25 385–400.
Krueger, J., R. W. Clement. 1994. The truly false consensus effect:
An ineradicable and egocentric bias in social perception. J. Per-
sonality Soc. Psych. 67 596–610.
Kruger, J. 1999. Lake Wobegon be gone! The “below-average effect”
and the egocentric nature of comparative ability judgments.
J. Personality Soc. Psych. 77 221–232.
Langer, E. J. 1975. The illusion of control. J. Personality Soc. Psych.
32 311–328.
Lichtenstein, S., P. Slovic. 1973. Response-induced reversals of pref-
erence in gambling: An extended replication in Las Vegas.
J. Experiment. Psych. 101 16–20.
Mata, J., P. Portugal. 1994. Life duration of new firms. J. Indust.
Econom. 42 227–246.
Miles, M. B., A. M. Huberman. 1994. Qualitative Data Analysis: An
Expanded Sourcebook. Sage, Thousand Oaks, CA.
Mitchell, R. K., L. Busenitz, T. Lant, P. P. McDougall, E. A. Morse,
J. B. Smith. 2002. Toward a theory of entrepreneurial cognition:
Rethinking the people side of entrepreneurship. Entrepreneur-
ship Theory Practice 27 93–105.
Moore, D. A. 2004a. Myopic prediction, self-destructive secrecy, and
the unexpected benefits of revealing final deadlines in negotia-
tion. Organ. Behav. Human Decision Processes 94 125–139.
Moore, D. A. 2004b. The unexpected benefits of final deadlines in
negotiation. J. Experiment. Soc. Psych. 40 121–127.
Moore, D. A. 2005. Myopic biases in strategic social prediction: Why
deadlines put everyone under more pressure than everyone else.
Personality Soc. Psych. Bull. 31 668–679.
Moore, D. A., D. M. Cain. Overconfidence and underconfidence:
When and why people underestimate (and overestimate) the
competition. Organ. Behav. Human Decision Processes. Forth-
Moore, D. A., T. G. Kim. 2003. Myopic social prediction and the solo
comparison effect. J. Personality Soc. Psych. 85 1121–1135.
Moore, D. A., T. R. Kurtzberg, C. R. Fox, M. H. Bazerman. 1999.
Positive illusions and forecasting errors in mutual fund invest-
ment decisions. Organ. Behav. Human Decision Processes 79
Neale, M. A., M. H. Bazerman. 1983. The role of perspective-taking
ability in negotiating under different forms of arbitration. Indust.
Labor Relations Rev. 36 378–388.
Northcraft, G. B., M. A. Neale. 1987. Expert, amateurs, and real
estate: An anchoring-and-adjustment perspective on property
pricing decisions. Organ. Behav. Human Decision Processes 39
Odean, T. 1998. Volume, volatility, price, and profit when all traders
are above average. J. Finance 53 1887–1934.
Odean, T. 1999. Do investors trade too much? Amer. Econom. Rev.
89 1279–1298.
Palich, L. E., D. R. Bagby. 1995. Using cognitive theory to explain
entrepreneurial risk-taking: Challenging conventional wisdom.
J. Bus. Venturing 10 425–438.
Porac, J. F., H. Thomas, C. Baden-Fuller. 1989. Competitive groups
as cognitive communities: The case of Scottish knitwear manu-
facturers. J. Management Stud. 26 397–416.
Porter, M. E. 1980. Competitive Strategy: Techniques for Analyzing
Industries and Competitors. Free Press, New York.
Moore, Oesch, and Zietsma: Myopic Self-Focus in Market-Entry Decisions
454 Organization Science 18(3), pp. 440–454, © 2007 INFORMS
Radzevick, J. R., D. A. Moore. 2005. For the love of the game? Bet-
ting, prediction, and myopic bias in athletic competition. Unpub-
lished manuscript, Carnegie Mellon University, Pittsburgh, PA.
Ross, L., D. Greene, P. House. 1977. The false consensus effect: An
egocentric bias in social perception and attribution processes.
J. Experiment. Soc. Psych. 13 279–301.
Rumelt, R. P. 1991. How much does industry matter? Strategic Man-
agement J. 12 167–185.
Schmalensee, R. 1985. Do markets differ much? Amer. Econom. Rev.
75 341–351.
Simon, M., S. Houghton. 2003. The relationship between overconfi-
dence and the introduction of risky products: Evidence from a
field study. Acad. Management J. 46 139–150.
Simon, M., S. Houghton, K. Aquino. 2000. Cognitive biases, risk
perception, and venture formation: How individuals decide to
start companies. J. Bus. Venturing 15 113–134.
Singh, J. V. 1993. Review essay: Density dependence theory—Current
issues, future promise. Amer. J. Sociol. 99 464–473.
Sorenson, O., P. G. Audia. 2000. The social structure of entrepre-
neurial activity: Geographic concentration of footwear produc-
tion in the United States, 1940–1989. Amer. J. Sociol. 106
Stuart, T. E., O. Sorenson. 2003. Liquidity events and the geographic
distribution of entrepreneurial activity. Admin. Sci. Quart. 48
Svenson, O. 1981. Are we less risky and more skillful than our fellow
drivers? Acta Psychologica 47 143–151.
Tversky, A., D. Kahneman. 1971. Belief in the law of small numbers.
Psych. Bull. 76 105–110.
Tversky, A., D. Kahneman. 1974. Judgment under uncertainty:
Heuristics and biases. Science 185 1124–1131.
U.S. Small Business Administration. 2003. Longitudinal Establish-
ment and Enterprise Microdata. Office of Advocacy, Washing-
ton, D.C.
Venkataraman, S. 1997. The distinctive domain of entrepreneurship
research. J. Katz, ed. Advances in Entrepreneurship, Firm Emer-
gence, and Growth. JAI Press, Greenwich, CT, 119–138.
Wagner, J. 1994. The post-entry performance of new small firms
in German manufacturing industries. J. Indust. Econom. 42
Wernerfelt, B., C. A. Montgomery. 1988. Tobin’s q and the impor-
tance of focus in firm performance. Amer. Econom. Rev. 78
Wilson, T. D. 2002. Strangers to Ourselves: Discovering the Adaptive
Unconscious. Belknap, Cambridge, MA.
Windschitl, P. D., J. Kruger, E. Simms. 2003. The influence of ego-
centrism and focalism on people’s optimism in competitions:
When what affects us equally affects me more. J. Personality
Soc. Psych. 85 389–408.
Wood, R. E., A. Bandura. 1989. Social cognitive theory of organiza-
tional management. Acad. Management Rev. 14 361–384.
Zacharakis, A. L., D. A. Shepherd. 2001. The nature of informa-
tion and overconfidence on venture capitalists’ decision making.
J. Bus. Venturing 16 311–322.
Zahra, S. A., D. O. Neubaum, G. M. El-Hagrassey. 2002. Competi-
tive analysis and new venture performance: Understanding the
impact of strategic uncertainty and venture origin. Entrepreneur-
ship Theory Practice 26 1–28.
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