The Relationship between Entrepreneurial Self-Efficacy and Firm Performance: A Meta-Analysis of Main and Moderator Effects

Abstract

Entrepreneurial self-efficacy (ESE) is an important construct in entrepreneurship research. It captures entrepreneurs’ specific self-efficacy in accomplishing entrepreneurial tasks. Because various empirical results exist in past studies of the ESE-firm performance relationship, we employed meta-analysis to review and synthesize the current literature concerning this relationship and to address moderators that influence it. We meta-analyzed 27 samples from 26 studies with a total sample size of 5,065 firms and found that the corrected ESE-firm performance correlation is 0.309. We found that the firm performance measurement is a significant moderator and we suggest scholars to further identify moderators.

Full text

The Relationship between Entrepreneurial Self-
Efficacy and Firm Performance: A Meta-Analysis of
Main and Moderator Effects*
by Chao Miao, Shanshan Qian, and Dalong Ma
Entrepreneurial self-efficacy (ESE) is an important construct in entrepreneurship research. It
captures entrepreneurs’ specific self-efficacy in accomplishing entrepreneurial tasks. Because vari-
ous empirical results exist in past studies of the ESE-firm performance relationship, we employed
meta-analysis to review and synthesize the current literature concerning this relationship and to
address moderators that influence it. We meta-analyzed 27 samples from 26 studies with a total
sample size of 5,065 firms and found that the corrected ESE-firm performance correlation is
0.309. We found that the firm performance measurement is a significant moderator and we sug-
gest scholars to further identify moderators.
Introduction
Self-efficacy is an important construct first
proposed by Bandura (1977a) in his social
learning theory and refers to an individual’s
belief in his or her personal capability to accom-
plish a job or a specific set of tasks. It is an indi-
vidual’s cognitive estimation of his or her
“capabilities to mobilize the motivation, cogni-
tive resources, and courses of action needed to
exercise control over task demands” (Bandura
1990, p. 316). Bandura (1977b, 1997) argued
that self-efficacy should focus on two dimen-
sions to achieve a high predictive power. One
dimension is a specific context, referring to
beliefs about one’s confidence in accomplishing
one specific task successfully (Cassar and Fried-
man 2009), and the other is an activity domain,
defined as an individual’s confidence in abilities
that apply to several related tasks within a
domain (Cassar and Friedman 2009). Following
Bandura’s recommendation, entrepreneurship
researchers proposed the construct of entrepre-
neurial self-efficacy (ESE), defined as an individ-
ual’s belief in his or her ability to achieve
various entrepreneurial tasks (e.g., Chen,
Greene, and Crick 1998; De Noble, Jung, and
Ehrlich 1999). In addition, empirical studies
have consistently found positive relationships
between ESE and entrepreneurial intentions,
and between ESE and new venture creation
(e.g., Barbosa, Gerhardt, and Kickul 2007;
Chen, Greene, and Crick 1998; Markman, Mad-
dox, and Baldwin 2005; Townsend, Busenitz,
and Arthurs 2010; Wilson, Kickul, and Marlino
2007; Zhao, Seibert, and Hills 2005).
However, there are some unsolved problems
when ESE is used in the entrepreneurship
research. First, we do not know the overall rela-
tionship between an entrepreneur’s ESE and his
*We thank Professor Alan Carsrud, the Associate Editor, and anonymous reviewers for their valuable feedback
to improve this paper. We are also thankful to Professor David Dubofsky for his helpful comments.
Chao Miao and Shanshan Qian are first authors and equally contributed to this paper.
Chao Miao is an Assistant Professor of Management at Wilkes University.
Shanshan Qian is an Assistant Professor of Entrepreneurship at Towson University.
Dalong Ma is an Assistant Professor of Entrepreneurship at Missouri Western State University.
Address correspondence to: Shanshan Qian, Department of Management, College of Business and Eco-
nomics, Towson University, Towson, MD 21252. E-mail: sqian@towson.edu.
MIAO, QIAN, AND MA 87
Journal of Small Business Management 2017 55(1), pp. 87–107
doi: 10.1111/jsbm.12240

or her venture’s post-start-up performance
(McGee et al. 2009). The results of recent empir-
ical studies regarding the role of ESE on firm
performance are not congruent (Hmieleski and
Baron 2008), showing both weak and strong
relations (e.g., Amatucci and Crawley 2011;
Chandler and Jansen 1997; Prajapati and Biswas
2011; Segal 1995). Second, explanations for the
variation of the effect sizes across studies are
largely missing from the literature. We lack
knowledge regarding what factors may vary the
ESE-firm performance relationship. Thus, this
study aims to address a contingency framework.
Given that ESE is context dependent (Bandura
1986; Soll 1996), Hmieleski and Baron (2008)
found that dispositional optimism and environ-
mental dynamism can moderate the ESE-firm
performance relationship. We argue that other
factors, such as entrepreneurs’ personal charac-
teristics (e.g., Chen, Greene, and Crick 1998;
McGee et al. 2009; Ucbasaran, Westhead, and
Wright 2008), may also moderate the ESE-firm
performance relationship. Third, although schol-
ars argued that using generalized self-efficacy
(GSE)
1
could not precisely predict entrepreneur-
ial outcomes (Bandura 1997; Gist 1987), there is
no review comparing the predictive power of
GSE with that of ESE. Thus, we will find out
whether ESE is methodologically better than
GSE to predict firm performance.
To tackle the aforementioned problems, this
paper seeks to answer the main research ques-
tion “to what extent is an entrepreneur’s ESE
related to his or her firm’s performance?” by
applying meta-analytic techniques. In this study,
firm performance refers to financial achieve-
ment, such as sales revenues after entrepreneurs
establish their firms. Meta-analysis is an appro-
priate evidence-based approach that can resolve
conflicting and ambiguous empirical findings in
literature (Rosenbusch, Brinckmann, and
Bausch 2011; Schmidt 1992). Because the fact
that ESE interacts with entrepreneurs’ other
characteristics and the environment (Chen,
Greene, and Crick 1998; McGee et al. 2009), we
also aim to investigate the moderating factors
associated with entrepreneurs and contexts.
This study contributes to the literature in sev-
eral aspects. First, we meta-analytically integrate
the literature on ESE and provide an overall esti-
mate of the ESE-firm performance relationship
(Frese et al. 2012; Rauch and Frese 2006). More
importantly, we show how important ESE is rel-
ative to other constructs in entrepreneurship
research. Second, we identify conditions that
moderate the ESE-firm performance relation-
ship, advancing research from Hmieleski and
Baron (2008), by offering clearer explanations
of how the ESE-firm performance relationship
varies under different contingencies. In other
words, our study can offer entrepreneurs infor-
mation about the conditions under which ESE is
beneficial. This can provide implications for
entrepreneurs and the public as to why some
entrepreneurs are more successful than others
at growing their ventures and how to cultivate
entrepreneurs’ ESE. Third, our meta-analytic
review compares ESE with GSE. We provide evi-
dence for scholars regarding which self-efficacy
measurement should be used and developed,
and we point out where future studies can head
in order to improve the examination of the ESE-
firm performance relationship.
Theory and Hypotheses
Development
ESE and Firm Performance
Self-efficacy has an extensive theoretical
foundation and empirical research support in
predicting future performance (e.g., Bandura
1977b, 1982, 1986; Bandura and Schunk 1981;
Luthans 2002; Wood and Bandura 1989). Based
on the rationales of social learning theory (Ban-
dura 1977a), ESE can lead to task-specific
effects. Specifically speaking, as entrepreneurs
have ESE, which means that they hold strong
beliefs in their own abilities to accomplish tasks
in entrepreneurial areas (Bandura 1986; Linds-
ley, Brass, and Thomas 1995), they establish
challenging goals, display persistence, invest
efforts regarding entrepreneurial tasks, and
recover rapidly from failure (Bandura 1997; Cer-
vone and Peake 1986; Trevelyan 2011). For
example, entrepreneurs with ESE are likely to
set challenging growth expectations for their
firms and persist in their efforts to accomplish
these goals. Furthermore, the effects of these
invested efforts are reflected in performance
(Wood and Bandura 1989). In other words, ESE
transforms entrepreneurs’ beliefs into efforts,
which, in turn, improve firm performance.
1
For a review see Rauch and Frese (2007). GSE refers to an individual’s confidence in any tasks and entrepreneur-
ial outcomes (e.g., Markman, Balkin, and Baron 2003; Poon, Ainuddin, and Junit 2006).
JOURNAL OF SMALL BUSINESS MANAGEMENT88

Empirical evidence also supports this positive
ESE-firm performance relationship (e.g., Baum,
Locke, and Smith 2001; Hmieleski and Baron
2008; McGee et al. 2009). For example, Forbes
(2005) conducted a survey of new venture
founders and identified a positive relationship
between ESE and new ventures’ revenue per-
formance. Similarly, Baum and Locke (2004)
found that ESE has the strongest direct effect on
venture growth among the predictors they stud-
ied. Thus, given the theory and empirical
results, we predict that ESE is positively related
to entrepreneurs’ firm performance. Hence, we
offer the following hypothesis:
H1: ESE is positively related to firm performance.
ESE-Firm Performance versus GSE-Firm
Performance
We argue that ESE yields higher criterion-
related validity than GSE in predicting entrepre-
neurs’ firm performance. Morgeson et al. (2007a,
2007b) suggested that personality measures
should be contextualized or tailored for specific
jobs and organizations to achieve content valida-
tion. Via contextualizing measures, empirical
research yields better results (Schmit et al. 1995).
To support this argument, Gupta, Ganster, and
Kepes (2013) conducted empirical research on
sales self-efficacy in a retail context and found
that sales self-efficacy predicted sales perform-
ance better than non-contextualized Big 5 meas-
ures did.
Analogously, ESE should have a predictive
validity greater than GSE because the specificity
of ESE captures detailed entrepreneurial tasks
required for achieving high firm performance.
The ESE scale development exemplifies a
construct-oriented approach (Hough and Paullin
1994) to theoretically match the predictor with
the criterion to increase the potential explana-
tory power of it (Morgeson et al. 2007a, 2007b).
For example, Chen, Greene, and Crick (1998)
addressed the ESE construct in marketing, inno-
vation, management, risk-taking, and financial
control dimensions. De Noble, Jung, and S. Ehr-
lich (1999) designed ESE scales consisting of six
core dimensions, such as interpersonal and net-
working management skills, procurement and
allocation of critical resources. In contrast, GSE
describes individuals’ overall and general self-
efficacy, which does not precisely capture spe-
cific content in the criterion domain (i.e., entre-
preneurs’ firm performance). This lack of focus
caused by matching GSE with firm performance
may lead to the bandwidth problem suggested
by Schneider, Ehrhart, and Macey (2011, 2013),
which means that one will need an extremely
wide bandwidth to offset the lack of focus in
measure.
2
As a result, we argue that as com-
pared to GSE, a contextualized ESE measure can
produce higher criterion-related validity than a
non-contextualized GSE measure. We compare
our study with the one from Rauh and Frese
(2007), which investigated the GSE-firm per-
formance relationship, and we hypothesize that
the relationship regarding ESE and firm per-
formance is stronger than the GSE-firm perform-
ance relationship. Thus, we predict:
H2: The ESE-firm performance relationship is
stronger than the GSE-firm performance
relationship.
Moderator Variables
By examining a series of research findings, we
found that some studies show a weak ESE-firm
performance relationship or negative ESE-firm
performance relationship (e.g., Alsos, Isaksen,
and Ljunggren 2006; Amatucci and Crawley
2011; Trevelyan 2011; Vengrouskie 2010). Con-
tingency theory argues that the relationship
between two variables depends on the level of a
third variable (Lawrence and Lorsch 1967; Rauch
et al. 2009). Thus, we propose three groups of
moderators: entrepreneurs’ experience, contex-
tual moderators, and methodological moderators.
First, ESE interacts with entrepreneurs themselves
(Chen, Greene, and Crick 1998; McGee et al.
2009). Specifically, we argue that the ESE-firm
2
Schneider, Ehrhart, and Macey (2011, 2013) indicated that early climate research was characterized by little
focus and having a generic approach to climate. With respect to the bandwidth problem, they mentioned that
“without a focus for the outcomes of interest, you need a very wide bandwidth to compensate for the lack of
focus” (p. 379, Schneider, Ehrhart, and Macey 2011). They demonstrated that the “climate for what” or the
“focused climate” or the “strategic climate” approach has substantially improved validity of the climate construct.
Analogously, to improve the validity of self-efficacy in the field of entrepreneurship, the construct of self-efficacy
should be contextualized to capture “self-efficacy for what,” which can be entrepreneurial self-efficacy.
MIAO, QIAN, AND MA 89

performance relationship varies because entre-
preneurs have different amounts of prior self-
employment experience (Bates 1990; Bosma
et al. 2004; Gimeno et al. 1997). Previous
research suggests that ESE and entrepreneurs’
prior experience can interactively impact firm
performance (Bosma et al. 2004; Dimov 2010).
Thus, we argue that the positive relationship
between ESE and firm performance will change
if entrepreneurs differ in experiences. Second,
ESE is largely context dependent (Soll 1996) and
theeffectsofESEarecontingentoncontextual
variables (Bandura 1986). Thus, we use firm age
as a moderator because age brings different situa-
tions and challenges to entrepreneurs. That is to
say, firm age is a unique context because it
exposes entrepreneurs to different levels of envi-
ronment competition and resources. In addition,
entrepreneurs have different cultural back-
grounds. Thus, we investigate the moderating
role of cultural context that may explain why
some entrepreneurs in a particular country have
a higher ESE-firm performance relationship than
those in a different country. Third, we identify
the methodological moderator, which may influ-
ence the magnitude of the ESE-firm performance
relationship.
Entrepreneurial Experience: Habitual versus
Nascent. In this research, we focus on entre-
preneurial experience (i.e., previous experience
in establishing ventures). Habitual entrepre-
neurs and nascent entrepreneurs are different
based on their different amount of entrepreneur-
ial experience. Habitual entrepreneurs are indi-
viduals who have established, inherited and/or
purchased more than one business (Westhead
and Wright 1998), while nascent entrepreneurs
are those who have no prior business owner-
ship experience and are in the process of estab-
lishing a business venture (Reynolds and White
1997).
We argue that habitual entrepreneurs and
nascent entrepreneurs will exhibit a different
ESE-firm performance relationship. The positive
relationship between ESE and firm performance
exists because ESE influences entrepreneurs’
level of effort, persistence, etc. (Markman, Bal-
kin, and Baron 2002; Wood and Bandura 1989).
However, we argue that this positive ESE-firm
performance relationship will become weak if
an entrepreneur lacks prior business ownership
experience. Business ownership experience
includes managerial experience, enhanced repu-
tation, and wider social and business networks
(Shane and Khurana 2003). We argue that habit-
ual entrepreneurs use their experience and abil-
ities to help achieve challenging goals. It is
noted that entrepreneurs with this experience
can guide their efforts toward venture perform-
ance (Dimov 2010). In other words, ESE helps
entrepreneurs understand how much effort
should be invested; entrepreneurs with prior
business ownership experience can understand
where to exert effort to achieve firm perform-
ance. Thus, the experience strengthens the role
of ESE on firm performance. However, nascent
entrepreneurs who have high ESE but little or
no prior experience are unable to leverage
experience and knowledge into superior per-
formance (Tversky and Kahneman 1974; Ucba-
saran, Westhead, and Wright 2008). Thus, the
ESE-firm performance relationship will become
weak for nascent entrepreneurs. Therefore,
because of the differences regarding habitual
entrepreneurs and nascent entrepreneurs, we
hypothesize:
H3: The relationship between ESE and firm per-
formance is stronger for habitual entrepre-
neurs than for nascent entrepreneurs.
Contextual Moderators: (1) Firm Age.Young
firms and old firms differ regarding their year of
establishment in the industries. We used the def-
inition from Hmieleski and Baron (2009), who
argued that firms with ages from one to six years
are young ventures and those with ages longer
than six years are defined as old firms. As com-
pared to young firms that face the liability of
newness (Stinchcombe 1965), old firms possess
a particular resource base that assists entrepre-
neurs in efficiently operating in a given condi-
tion (Amit and Schoemaker 1993; Thornhill and
Amit 2003). Although it seems that age favors
entrepreneurs by providing many benefits, we
argue that the ESE-firm performance relationship
is stronger in young firms than in old firms. Ban-
dura (1986) asserted that individuals with high
self-efficacy tend to persist when facing difficul-
ties and attempt to execute new behaviors.
These individuals are more willing to undertake
the challenge of introducing new products, act
on their environment, and take on risky projects.
Applying attributes of self-efficacy in an entre-
preneurship context, Chen, Greene, and Crick
(1998) argued that the relationship between self-
efficacy and behavior is best demonstrated in
high risk and uncertain contexts. According to
their argument, entrepreneurs are more likely to
JOURNAL OF SMALL BUSINESS MANAGEMENT90

expend their effort and show persistence under
uncertain and risky circumstances. Accordingly,
as entrepreneurs start their businesses, the par-
ticularchallengesanduncertainty they face can
motivate them to be persistent and hardworking,
thus helping them to achieve goals. Therefore,
the ESE-firm performance relationship becomes
salient in young firms. In contrast, old firms
have greater certainty and fewer challenges, and
hence entrepreneurs may not be as persistent as
entrepreneurs in young firms. As a result, the
role of ESE on firm performance is not as promi-
nent in old firms as it is in young firms. We
predict:
H4: The relationship between ESE and firm per-
formance is stronger in young firms than in
old firms.
(2) Cultural Context.Thedifferentcontext
can lead to various magnitude of the relation-
ship between ESE and performance. Cultural
context specific to a group or society can moti-
vate individuals to behave in certain ways (Hof-
stede 1998). Hofstede (2003) proposed four
dimensions of a cultural framework: individual-
ism, uncertainty avoidance, power distance, and
masculinity. Hayton, George, and Zahra (2002)
argued that Hofstede’s four cultural dimensions
influenced entrepreneurial characteristics, such
as motives, values, and beliefs in different cul-
tures. Hofstede (1991) identified that individual-
istic and collectivistic cultures are key
characteristics of cultural orientation. Thus, in
the current study, we use the individualism-
collectivism cultures as a moderator.
An individualistic culture is “a situation in
which people are supposed to look after them-
selves and their immediate family only” (Hof-
stede and Bond 1984, p. 419). In an
individualistic culture, people tend to be risk
takers and to value individual accomplishments
(Hofstede 2003). They are taught by personal
actions and potential. A collectivistic culture is
“a situation in which people belong to in-
groups or collectivities which are supposed to
look after them in exchange for loyalty” (Hof-
stede and Bond 1984, p. 419). A collectivistic
culture influences people to pursue security and
behave in a group. Likewise, entrepreneurs who
reside in a collectivistic culture will pay atten-
tion to goals relevant to group and society (Tay-
lor and Wilson 2012).
Individualistic and collectivistic cultures are
actual environments that can moderate the rela-
tionship between self-efficacy and performance
(Bandura 1986). When individuals strive to
accomplish a task, they may face performance
constraints (Bandura 1986, 1997; Gist and
Mitchell 1992). For instance, individuals will
evaluate different amount of available resources
and staff that are necessary to complete the
task, and the interdependence of the certain
task with other functions in the organizations.
The collectivistic culture encourages group
work and thus entrepreneurs can get support
from coworkers and social networking, decreas-
ing performance constraints that entrepreneurs
perceive. However, the individualistic culture
lacks the environment that can provide interde-
pendence and resources. Thus, under the collec-
tivistic culture, the ESE-firm performance
relationship will be strengthened due to fewer
constraints.
As such, we argue that a collectivistic culture
strengthens the relationship between ESE and
firm performance. Therefore, based on the dif-
ferences between individualistic and collectivis-
tic cultures, we predict that:
H5: The relationship between ESE and firm per-
formance is stronger in collectivistic coun-
tries than in individualistic countries.
Methodological Moderator: Firm
Performance Measurement
The most common way of measuring firm
performance is via financial measures from
archival data, such as profit, return on assets
(ROA), and sales (Richard et al. 2009). Archival
data is objective (e.g., Gomez-Mejia 1992).
3
When it is not convenient to obtain archival
data, researchers employ subjective perform-
ance measures by asking respondents their per-
ceptions concerning firm performance (e.g.,
Gilley and Rasheed 2000). However, entrepre-
neurs have psychological and cognitive biases,
3
We acknowledge that objective performance measure, such as archival data, might be biased because some
firms may alter their financial statements to avoid taxation. However, primary studies did not mention this possi-
ble problem in their data, and we treat archival data as objective as previous research did.
MIAO, QIAN, AND MA 91

which can cause highly skewed and unrepresen-
tative subjective perceptions of performance
(Venkatraman and Ramanujam 1986). In their
study on the relationship between business
planning and firm performance, Brinckmann,
Grichnik, and Kapsa (2010) argued that because
of respondents’ bias, subjective performance
measures might not provide an assessment of
performance effects as accurate as objective per-
formance measures.
From a methodological perspective, Morge-
son et al. (2007b) recommended limiting the
criterion measures to objective measures,
because using self-reports to measure both pre-
dictor and criterion may bring forth the risk of
inflating estimates of validity. For instance,
common method variance (CMV), which refers
to “variance that is attributable to the measure-
ment method rather than to the constructs the
measures represent” (Podsakoff et al. 2003, p.
879), may plague the estimate of validity. It is
noted that variables measured with the same
method may inflate the correlations among
them due to CMV (Campbell and Fiske 1959;
Podsakoff, MacKenzie, and Podsakoff 2012).
When the same person responds to the meas-
ures of both predictor and criterion variables,
CMV may create bias to effect size due to the
consistency motif, implicit theories and illusory
correlations, social desirability, and leniency
biases (Podsakoff et al. 2003). Given that the
same entrepreneur self-reports both ESE and
firm performance, this common rater effect may
inflate the correlation between ESE and firm
performance.
As such, when entrepreneurs are asked to
report their subjective view of firm perform-
ance, the ESE-firm performance relationship
may be inflated due to aforementioned reasons.
Thus:
H6: The relationship between ESE and firm per-
formance is stronger when firm performance
is subjectively measured than when firm per-
formance is objectively measured.
Methods
Literature Location
To guarantee that our search is exhaustive and
precisely captures all relevant empirical articles,
we performed a comprehensive and systematic
literature search using several approaches. First,
we searched the literature by using electronic
databases including Web of Science (1972–2012),
Social Science Index Citation (1972–2012), Sci-
ence Direct (1980–2012), EBSCO Host Research
Databases (including Academic Search Complete,
Business Source Complete, Psychology and
Behavioral Sciences Collection, Regional Busi-
ness News) (1985–2012), Psychinfo Databases
(1987–2012), JSTOR Databases (1980–2012),
ABI/INFORM Database (1971–2012), ProQuest
Dissertations and Theses (2000–2012), and Goo-
gle Scholar (1980–2012). Second, we searched
major journals in management and entrepreneur-
ship: Administrative Science Quarterly (1999–
2012), Academy of Management Journal (1963–
2012), Journal of Management (1980–2012),
Organization Science (1990–2012), Management
Science (1954–2012), Strategic Management
Journal (1980–2012), Journal of Business Ven-
turing (1995–2012), Entrepreneurship Theory
and Practice (1988–2012), Journal of Small Busi-
ness Management (1971–2012), and Journal of
Applied Psychology (1980–2012). Third, we
searched presentations made at major manage-
ment and entrepreneurship conferences, such as
Frontiers of Entrepreneurship Research (1981–
2012), Academy of Management Proceedings
(1984–2012), Southern Management Association
(SMA) (2003–2012), and United States Associa-
tion for Small Business and Entrepreneurship
(USASBE) (1997–2012). Fourth, we employed
both Google and Google Scholar to identify
unpublished papers and working papers. Fifth,
we snowballed several critical articles developing
the ESE construct (e.g., Chen, Greene, and Crick
1998; De Noble, Jung, and Ehrlich 1999; Forbes
2005) to ensure that our search captured all of
the relevant studies related to ESE and firm per-
formance. The snowball stands for the approach
that we used to review the reference list of
selected critical articles as well as the papers or
books that cited selected critical articles in order
to identify additional studies. We found that all
relevant articles derived from the snowball search
were captured by our primary search outlined in
the first four steps. After we finished the search
by following this procedure, we also identified
twoarticlesthatwecouldnotfindonwebsitesor
in libraries, and contacted the authors via email
to obtain the articles. In summary, we are confi-
dent that our search to locate primary studies was
exhaustive.
Because the selection and exclusion of stud-
ies will influence the research outcomes of
meta-analysis, it is necessary to specify inclusion
criteria (Hunter and Schmidt 2004). We deemed
JOURNAL OF SMALL BUSINESS MANAGEMENT92

an empirical article as relevant if it met the fol-
lowing criteria:
(1) We only targeted articles focused on
understanding an entrepreneur’s ESE.
Therefore, samples in primary studies
must have been entrepreneurs, founders
or owners who established their ventures
and owned or partly owned their ven-
tures, and who provided their ESE score.
(2) We searched words such as organization
performance, venture performance, and
business growth. Firm performance is a
multifaceted construct and thus no single
indicator can fully capture its definition
(Brush and Wanderwerf 1992). Hence, in
order to measure performance, we
focused on financial performance meas-
ures, such as sales growth, revenue
growth and profitability (Zhao, Seibert,
and Lumpkin 2010).
(3) ESE is one of the major research interests
in our paper. We excluded any studies
that did not measure self-efficacy in spe-
cific entrepreneurial tasks. Our key words
to search ESE were entrepreneurial self-
efficacy or self-efficacy in specific entre-
preneurial domains/tasks, such as venture
self-efficacy, and investor relationship self-
efficacy.
We identified about 7,000 articles written or
published before June 2012. After discarding
articles that failed our selection criteria concern-
ing entrepreneurs, ESE, performance, non-
empiricalstudies,casestudies,qualitative
studies, and studies with unusable statistical
information, we were left with 33 studies. Three
of these did not make clear whether venture clo-
sure meant venture failure or closing current
ventures and opening a new venture and were
excluded from our analysis. In addition, we
could not include the articles of two authors
because we did not hear from them, and we
excluded two studies because their statistics
could not be precisely transformed into effect
size. Thus, we obtained 26 studies with 27 sam-
ples consisting of 16 papers published in jour-
nals and 10 unpublished papers (including
conference proceeding papers and dissertations).
To ensure the independence of effect sizes,
we used one effect size per study. If a study
used multiple measures of the same criterion
(e.g., different indictors of firm performance) in
the same sample, we subsequently averaged
across measures to form a single effect size data
point, which is consistent with the suggestions
by Martin, McNally, and Kay (2013), Park and
Shaw (2013), and Zhao, Seibert, and Lumpkin
(2010). Further, we utilized the method of
detection heuristics developed by Wood (2008)
to identify the studies that were based on identi-
cal or overlapping samples, and found that two
papers used overlapping samples. In order to
incorporate all possible information without vio-
lating sample independence, we averaged effect
sizes across studies that were based on overlap-
ping samples and included them only once in
our meta-analysis based on averaged effect
sizes, which is consistent with the approaches
chosen by Rosenbusch, Brinckmann, and
Bausch (2011) and Unger et al. (2011). Last, we
found one paper that used two independent
samples and thus we used them independently.
Thus, we have 27 independent samples, and
they are presented in Table 1.
Variable Coding Procedures
We used subgroup analysis to test moderator
effects. Subgroup analysis refers to the test for
the between-group significance of the differen-
ces in effect sizes (Borenstein et al. 2009;
O’Boyle et al. 2012). Using subgroup analysis
for testing moderator effects requires a sample
to be divided into sub-samples, so that we can
test for moderator effects by examining the sig-
nificance of differences in effect sizes between
sub-samples (Rosenbusch, Brinckmann, and
Bausch 2011). We chose to dichotomize our
continuous moderator variables by following
the approaches used in previous relevant stud-
ies (e.g., Rosenbusch, Brinckmann, and Bausch
2011; Unger et al. 2011). If no such studies
could be located to guide us regarding how to
dichotomize continuous moderator variables,
we opted to follow the median splits approach
(e.g., O’Boyle et al. 2012).
To identify the correlation between ESE and
firm performance, we chose the effect sizes (i.e.,
correlation coefficient) based on the correlation
tables displayed in the primary studies. If the
studies did not provide the correlations, we con-
verted the statistics into effect size by following
the procedures developed by Lipsey and Wilson
(2001). We explain the coding of five modera-
tors as follows: (1) Entrepreneurial experience:
habitual entrepreneurs versus nascent entrepre-
neurs. Prior research has already clearly stated
or indicated the type of entrepreneurs that they
used for the data analysis. We used only those
MIAO, QIAN, AND MA 93

Table 1
Overview of Meta-Analysis Studies
Author Name (Year) Status of
Publication
r
xx
Firm
Performance
Measurement
r
yy
Sample
Size
Country Culture
(Individualism)
Effect
Size
1 Alsos, Isaksen, and Ljunggren (2006) Published 0.912 Objective 0.800 310 Norway Yes 0.104
2 Amatucci and Crawley (2011) Published 0.815 Objective 0.800 51 United States Yes 20.067
3 Anna et al. (1999) Published 0.790 Objective 0.800 103 United States Yes 0.098
4 Baum and Bird (2010) Published 0.840 Subjective 0.920 143 United States Yes 0.310
5 Baum and Locke (2004) Published 0.890 Subjective 0.940 229 United States Yes 0.300
6 Chandler and Jansen (1997) Unpublished 0.735 Subjective 0.780 66 United States Yes 20.035
7 Forbes (2005) Published 0.850 Subjective 0.836 95 United States Yes 0.190
8 Frese et al. (2007) Published 0.840 Subjective 0.810 215 Zimbabwe Yes 0.150
9 Hallak, Lindsay, and Brown (2011) Published 0.820 Subjective 0.928 298 Australia Yes 0.529
10 Hmieleski and Baron (2008) Published 0.920 Objective 0.800 159 United States Yes 0.120
Hmieleski and Corbett (2008) 0.160
11 Indarti and Langenberg (2004) Unpublished 0.770 Subjective 0.836 100 Indonesia No 0.119
12 Isaksen (2006) Unpublished 0.890 Subjective 0.840 285 Norway Yes 0.075
13 Kirkul, Gundry, and Iakovleva (2007) Unpublished 0.830 Subjective 0.950 524 Russia No 0.411
14 Kropp et al. (2009) Unpublished 0.701 Subjective 0.784 204 Australia Yes 0.466
15 Li (2008) Unpublished 0.815 Subjective 0.836 148 China No 0.253
16 Lindsay and Balan (2005) Published 0.815 Subjective 0.836 334 United States Yes 0.229
17 Luthans and Ibrayeva (2006) Published 0.880 Subjective 0.836 133 Kazakh and
Kyrgyz
No 0.263
18 Ma and Dong (2009) Unpublished 0.713 Subjective 0.786 193 China No 0.192
19 Moesel and Santiago (2008) Unpublished 0.880 Subjective 0.710 68 United States Yes 0.410
20 Murnieks, Mosakowski, and
Cardon (2012)
Published 0.780 Subjective 0.835 221 United States Yes 0.190
21 Prajapati and Biswas (2011) Published 0.670 Subjective 0.750 148 India No 0.703
22 Segal (1995) Unpublished 0.815 Objective 0.800 63 United States Yes 0.776
23 Slavec and Prodan (2012) Published 0.815 Objective 0.800 497 Slovenia No 0.103
24 Trevelyan (2011) Published 0.844 Subjective 0.835 44 Australia Yes 0.019
25 Vengrouskie (2010) Unpublished 0.740 Objective 0.800 41 United States Yes 0.080
26 Zhang, Yang, and Lv (2010): sample one Published 0.815 Subjective 0.836 113 China No 0.287
27 Zhang, Yang, and Lv (2010): sample two Published 0.815 Subjective 0.836 280 China No 0.300
r
xx
5predictor reliability; r
yy
5criterion reliability.
JOURNAL OF SMALL BUSINESS MANAGEMENT94

articles that either exclusively used samples of
habitual entrepreneurs or nascent entrepre-
neurs. In other words, we did not use any
articles that included samples from both types
of entrepreneurs. We divided entrepreneurs into
nascent or habitual entrepreneur groups. (2)
Firm age. We dichotomized this continuous
moderator variable based on Hmieleski and
Baron (2009), who argued that the firms with
ages from one to six years are categorized as
young ventures and those longer than six years
are defined as old firms. (3) Cultural context.
We reviewed Hofstede’s culture website, where
Hofstede clearly explains whether a country
belongs to individualistic cultures or collectivis-
tic cultures. Based on this, we coded countries
as either individualistic or collectivistic culture.
(4) Firm performance measurement. Some stud-
ies measured entrepreneurs’ firm performance
using archival data or by asking entrepreneurs
to report their actual firm performance figures,
while other studies asked entrepreneurs to
assess their perception of their firm’s perform-
ance on a Likert scale. According to this mea-
surement difference, we coded those studies
using archival data as objective firm perform-
ance, and those studies using the entrepreneur’s
perception to measure firm performance as sub-
jective firm performance. (5) We used publica-
tion status to investigate whether the ESE-firm
performance relationship differs between pub-
lished studies and unpublished studies (McDa-
niel, Rothstein, and Whetzel 2006; Rothstein,
Sutton, and Borenstein 2006). Generally, the dif-
ference between published and unpublished
studies may be due to publication bias, insuffi-
cient methodological rigor, or a lack of theoreti-
cal contribution. We coded those published in
journals as the published group, and conference
papers and dissertations as the unpublished
group.
Meta-Analytic Procedures
Primary Analysis. We followed the psycho-
metric meta-analysis guidelines developed by
Hunter and Schmidt (2004) to synthesize previ-
ous findings. Most of the studies in our meta-
analysis treated both ESE and firm performance
as continuous variables. Therefore, we opted to
provide the correlation coefficient (r)asour
effect size (Harrison 2011). We calculated and
reported the uncorrected sample-size-weighted
mean correlation (
ro). We utilized a random-
effects model to correct for the sampling error
by weighting each study’s effect size based on
its sample size (Hunter and Schmidt 2004). We
also corrected for measurement errors in our
independent and dependent variables for each
primary correlation. We imputed the missing
reliabilities for all independent and dependent
variables by using the average value of the reli-
abilities reported in other studies that were
included in our meta-analysis, which is consist-
ent with the approach used by Zhao, Seibert,
and Lumpkin (2010). Because some primary
studies measure firm performance with archival
data (e.g., revenue growth, sales, and profitabil-
ity), we chose a conservative .80 reliability
estimate, which is consistent with previous
meta-analytic reviews (e.g., Dalton et al. 1998,
1999, 2003). We reported the corrected sample-
size-weighted mean correlation (^
q)asanesti-
mate of population effect size. To check the
robustness of our results, we also used a reliabil-
ity of 1.00 for archival data for comparison pur-
poses and the results remained almost
unchanged.
We computed the corrected 95 percent confi-
dence interval and corrected 80 percent credibil-
ity interval. The confidence interval and the
credibility interval offer different information
with respect to the variability in the meta-
analytic estimates (Ilies, Nahrgang, and
Morgeson 2007; Judge, Piccolo, and Ilies 2004;
Whitener 1990). The confidence interval gives
the estimate of the variability around the mean
corrected correlation, whereas the creditability
interval offers the estimate of the variability in
individual correlations across studies (Ilies,
Nahrgang, and Morgeson 2007; Judge, Piccolo,
and Ilies 2004). A 95 percent confidence interval
that excludes zero indicates that the estimated
corrected mean correlation is statistically signifi-
cant. An 80 percent credibility interval that
excludes zero demonstrates that at least 90 per-
cent of individual correlations in the meta-
analysis are larger than zero (with regard to
positive correlations, less than 10 percent are
either zero or negative, and another maximum
10 percent locate at or beyond the upper bound
of the interval) (Ilies, Nahrgang, and Morgeson
2007; Judge, Piccolo, and Ilies 2004). We also
computed Var
art
% to assess the potential exis-
tence of moderators. Var
art
% denotes the per-
centage of variance in ^
qexplained by statistical
artifacts. Hunter and Schmidt (2004) recom-
mended that the moderators may exist if statisti-
cal artifacts account for less than 75 percent of
the variance in the meta-analytic correlations.
MIAO, QIAN, AND MA 95

Due to the fact that all moderators in our
meta-analysis are dichotomized, we followed
Hunter and Schmidt’s (1990) approaches (i.e.,
z-test) to test the statistical significance of mod-
erating effects (i.e., between-group differences
in effect sizes). The z-test is performed based on
uncorrected effect sizes. This is consistent with
the approach used by O’Boyle et al. (2012),
Rauch et al. (2009), and Riketta (2002).
Outlier Analysis. Outliers may influence the
validity and robustness of the conclusion drawn
from meta-analysis (Viechtbauera and Cheung
2010); thus, researchers generally advocate an
examination for potential outliers and influential
cases when applying meta-analysis (Hedges and
Olkin 1985; Light and Pillemer 1984; Lipsey and
Wilson 2001; Rosenthal 1995; Viechtbauera and
Cheung 2010). Therefore, we conducted outlier
analysis by employing Huffcutt and Arthur’s
(1995) sample adjusted meta-analytic deviancy
(SAMD) statistic with corrections recommended
in Beal, Corey, and Dunlap (2002). We used the
critical values at the 0.001 level. The result
showed that studies by Hallak, Lindsay, and
Brown (2011), Prajapati and Biswas (2011), and
Segal (1995) may be outliers. We reexamined
these studies and did not find any coding and
transcriptional errors. Thus, we investigated
whether these outliers resulted in any significant
changes in our meta-analytic results by conduct-
ing a series of z-tests. Our results demonstrated
that none of the outliers significantly impacted
our meta-analytic results. Results based on the
data with the exclusion of outliers can be
obtained from the authors.
Results
Primary Analyses
Overall Analyses. Table 2 presents the meta-
analytic results based on 26 studies, 27 inde-
pendent samples, and a total sample size of
5,065 firms.
4
The results show that all corrected
95 percent confidence intervals in Table 2 do
not include zero, therefore suggesting that all
corrected effect sizes (i.e.,^
q) in Table 2 are stat-
istically significant. The corrected correlation
between ESE and firm performance is moderate
and equals 0.309. Therefore, H1 is supported.
ESE is positively related to firm performance.
The Var
art
% for overall relationship is 15.9 per-
cent, which means statistical artifacts only
account for 15.9 percent of the variance in the
meta-analytic correlations. This indicates the
heterogeneous effect size distribution and justi-
fies our analysis for moderators that can influ-
ence the ESE-firm performance relationship.
H2 predicts that the ESE-firm performance
relationship is stronger than the GSE-firm per-
formance relationship. It is noted that Rauch
and Frese’s (2007) operationalization of success
is identical to our operationalization of firm per-
formance. Therefore, we extracted the statistics
(i.e., number of studies, sample size weighted
mean correlation, and observed variance) con-
cerning the relationship between GSE and suc-
cess from Rauch and Frese (2007) and
performed a z-test to determine the statistical
significance of effect size difference in terms of
the ESE-firm performance relationship as well as
GSE and firm performance (0.309 versus 0.247,
D^
q50.062). The z-test demonstrates that the
difference in uncorrected effect sizes is not stat-
istically significant. H2 is not supported.
Moderator Analyses for Entrepreneurial Expe-
rience: Habitual Entrepreneurs versus Nascent
Entrepreneurs. H3 predicts that the ESE-firm
performance relationship becomes stronger for
habitual entrepreneurs than for nascent entre-
preneurs. Meta-analytic results show that there
is a difference with respect to ^
qfor the habitual
entrepreneur subgroup and the nascent entre-
preneur subgroup (0.360 versus 0.301,
D^
q50.059). The z-test is applied and the result
demonstrates that the between-group difference
for uncorrected effect sizes is not statistically sig-
nificant. Thus, H2 is not supported.
Moderator Analyses for Contextual Modera-
tors. We suspected that firm age may be a
variable moderating the ESE-firm performance
relationship. The corrected correlations are
0.285 for the young firm subgroup and 0.269
for the old firm subgroup. However, between-
group difference is not statistically significant.
Therefore, we conclude that H4 is not sup-
ported. In terms of cultural context, we found
4
Schmidt and Hunter (1999) mentioned that “failure to control for biases induced by measurement error has
retarded the development of cumulative research knowledge” (p. 183). Thus, our interpretation of results was
based on corrected effect sizes (i.e.,^
q), corrected 95 percent confidence intervals, and corrected 80 percent credit-
ability intervals rather than uncorrected counterparts.
JOURNAL OF SMALL BUSINESS MANAGEMENT96

Table 2
Psychometric Meta-Analysis Results
KN 
roObserved
Variance
^
qVariance
of ^
q
Var
art
% Corrected
95 percent CI
Corrected
80 percent CR
z-test
Overall Relationship 27 5,065 0.257 0.029 0.309 0.036 15.90% 0.231 to 0.386 0.066 to 0.551
Entrepreneurial Experience
Habitual 8 1,511 0.296 0.053 0.360 0.079 8.00% 0.157 to 0.562 0.001 to 0.719 z 50.45
Nascent 8 1,581 0.253 0.019 0.301 0.016 27.40% 0.198 to 0.404 0.138 to 0.464
Firm Age
Old 5 1,498 0.221 0.020 0.269 0.020 17.60% 0.132 to 0.405 0.088 to 0.450 z 50.27
Young 8 1,175 0.240 0.006 0.285 0.000 100.00% 0.223 to 0.347 0.285 to 0.285
Cultural Value
Individualistic 18 2,929 0.238 0.031 0.283 0.036 18.00% 0.186 to 0.380 0.040 to 0.526 z 50.70
Collectivistic 9 2,136 0.285 0.026 0.345 0.033 13.70% 0.217 to 0.473 0.112 to 0.579
Firm Performance Measurement
Subjective 20 3,841 0.297 0.025 0.354 0.030 17.30% 0.270 to 0.437 0.132 to 0.576 z 52.39**
Objective 7 1,224 0.134 0.024 0.163 0.027 23.40% 0.024 to 0.302 20.047 to 0.373
Publication Status
Published 17 3,373 0.240 0.026 0.286 0.032 17.10% 0.193 to 0.378 0.059 to 0.513 z 50.77
Unpublished 10 1,692 0.293 0.034 0.355 0.041 15.00% 0.218 to 0.492 0.095 to 0.615
Note: K 5number of independent samples; N5sample size; 
ro5uncorrected sample-size-weighted mean correlation; Observed
Variance 5sample-size-weighted observed variance of correlations; ^
q5corrected sample-size-weighted mean correlation; Variance of
^
q5variance of true score correlations; Var
art
%5percent of variance in ^
qexplained by statistical artifacts; Corrected 95 percent CI 5corrected
95 percent confidence interval; Corrected 80 percent CR 5corrected 80 percent credibility interval; z-test 5z-test for the statistical significance
of moderating effect (i.e., between-group difference for uncorrected effect sizes). **p<.01.
MIAO, QIAN, AND MA 97

that collectivistic culture facilitates the ESE-firm
performance relationship, which conforms to
our hypothesized direction. The corrected corre-
lations between ESE and firm performance
under individualistic and collectivistic culture
are 0.283 and 0.345 respectively. However, this
difference is not statistically significant. Thus,
H5 is not supported.
Moderator Analyses for Methodological Moder-
ator. We argued that the ESE-firm perform-
ance relationship is more positive when firm
performance is measured subjectively. Our
results show that the corrected correlations
between ESE and firm performance are 0.354
and 0.163 for the subgroups of subjective and
objective measures of firm performance respec-
tively. The z-test further confirms that between-
group difference for uncorrected effect sizes is
statistically significant (p<.01). Therefore, H6 is
supported.
We also tested whether publication status
moderates the relationship between ESE and
firm performance. Our results indicate that cor-
rected correlations between ESE and firm per-
formance are 0.286 for the published subgroup
and 0.355 for the unpublished subgroup. The
results of the z-test suggest that uncorrected
effect sizes for these two subgroups do not sig-
nificantly differ. Hence, we conclude that publi-
cation status does not significantly moderate the
relationship between ESE and firm performance.
Discussion
We investigated the ESE-firm performance
relationship by integrating 27 independent sam-
ples with an overall sample size of 5,065. With
respect to the main effect, we identified that the
corrected correlation between ESE and firm per-
formance was 0.309, which is considered to be
moderate (Cohen 1977). Thus, our finding is
consistent with social learning theory that an
entrepreneur’s ESE is positively associated with
firm performance. In addition, regarding
whether increased self-efficacy specificity can be
abetterdriveroffirmperformance,wecom-
pared our study with the study from Rauch and
Frese (2007). Although the effect size of the
ESE-firm performance relationship (^
q5.309)
was larger than that of the GSE-firm perform-
ance relationship (^
q5.247), we found that the
difference is not statistically significant.
We also examined five moderators that may
influence the ESE-firm performance relation-
ship. These moderators are divided into three
groups: entrepreneurial experience, contextual
moderators, and methodological moderator.
First, our results do not support hypothesis pre-
dicting that the ESE-firm performance relation-
ship will be stronger for habitual entrepreneurs
than for nascent entrepreneurs. Second, regard-
ing contextual moderators, we tested firm age
and cultural value. Firm age does not show a
statistically significant result. In addition, culture
did not statistically moderate the relationship
between ESE and firm performance. Third, we
tested one methodological moderator. Firm per-
formance measurement significantly moderates
the ESE-firm performance relationship. It indi-
cates that as entrepreneurs self-reported their
firm performance, the ESE-firm performance
relationship is inflated due to CMV. Last, we
identify that publication status does not moder-
ate the ESE-firm performance relationship.
Implication
Our research provides researchers with theo-
retical implications in three perspectives: the
relationship between ESE and firm performance,
moderating effects on the focal relationship, and
the differences between ESE and GSE.
ESE-Firm Performance Relationship
We examined and confirmed a positive ESE-
firm performance relationship via meta-analysis.
ESE is an important construct in entrepreneur-
ship because it focuses specifically on entrepre-
neurship (Cassar and Friedman 2009). Providing
athoroughreviewofESEcaninformusofthe
importance of employing ESE in predicting
entrepreneurial outcomes. In addition, scholars
are identifying factors that can lead to successful
outcomes for entrepreneurs. For example, Zhao,
Seibert, and Lumpkin (2010) found that consci-
entiousness (^
q50.19) and openness to experi-
ence (^
q50.21) are most strongly and positively
associated with entrepreneurial performance
compared to the rest of the Big Five personality
traits. Unger et al. (2011) showed that the corre-
lation between human capital and success, such
as sales, growth, and so forth, is 0.098. Our
study demonstrates that as compared to these
successful factors, ESE plays a much more
important role in enhancing firm financial per-
formance. Thus, our study suggests that future
research should continue addressing social
learning theory and identifying the role of ESE
in a broad set of entrepreneurial outcomes.
JOURNAL OF SMALL BUSINESS MANAGEMENT98

Moderating Effects on the ESE-Firm
Performance Relationship
First, our results show that the positive correla-
tion between ESE and firm performance does not
statistically significantlyvarybyentrepreneurs’
prior self-employment experience, although the
results show the hypothesized direction. This can
be due to the subgroup analysis, which is a low
power test (Steel and Kammeyer-Mueller 2002).
After the dichotomization of continuous modera-
tor variables, it leaves us smaller number of sam-
ples in each distribution so that we fail to find
significant moderator effects. However, the results
still suggest the rationality of theories we used,
and the heterogeneous effect size distribution we
found calls for more research on moderators that
can influence the ESE-firm performance relation-
ship. Regarding entrepreneurs’ experience, we
suggest that future primary study can identify it at
a broad level. For example, entrepreneurs may
have industrial experience, failure experience,
entrepreneurial role models or parents who are
involved in family business, which can influence
the ESE-firm performance relationship.
With regards to firm age, we acknowledge the
ESE-firm performance relationship will become
stronger in highly uncertain and risky environ-
ments. However, we argue that firm age may not
be a sufficient proxy to measure environment.
Thus, we recommend future research to specifi-
cally examine the environment dimensions or
differences in some industries. For example, the
ESE-firm performance relies on the ambiguity of
task demands (Stajkovic and Luthans 1998).
Entrepreneurs are likely to face different situa-
tions at hands, causing various ESE-firm
performance relationship. In their meta-analysis
on self-efficacy and work-related performance,
Stajkovic and Luthans (1998) argued that the
changes in self-referent thought derived from task
complexity can change the relationship between
self-efficacy and performance. The self-referent
thought refers to faulty assessment of perform-
ance and self-efficacy and mismatch between self-
efficacy and performance domains (Stajkovic and
Luthans 1998). To illustrate, entrepreneurs whose
venturesareinanearlystageneedmorerequired
knowledge and cognitive abilities to establish the
ventures than entrepreneurs whose ventures are
in a later stage. However, complex tasks cause
the problem that they may not correctly assess
the self-efficacy referent thoughts regarding how
much effort needed to be further invested, and
when to make correct actions (Bandura 1986).
Thus, because the influence of environment, we
suggestthatmorespecific examination of envi-
ronment is needed.
Second, we provide implications with respect
to cultural value. As compared to entrepreneurs
in individualistic countries, entrepreneurs in col-
lectivistic countries may enjoy some beneficial
factors, such as personal contacts with key in-
group members who facilitate venture start-ups
(Erez and Earley 1993; Pinillos and Reyes 2011),
which can enhance the ESE-firm performance
relationship. In addition, we argue that an indi-
vidualistic culture may enhance the ESE-firm
performance relationship as well, but depending
on some situations. For example, Pinillos and
Reyes (2011) identified that individualistic cul-
tures can be beneficial to a country’s entrepre-
neurship rate when GDP per capita is high, but
it negatively influences a country’s entrepre-
neurship rate when GDP is medium or low.
Similarly, individualistic countries with high
GDP may motivate entrepreneurs to achieve
personal wealth and the ESE-firm performance
relationship can become stronger in this com-
petitive context. Hence, we suggest that future
study further identify different dimensions of
cultures and roles of cultural factors (McGee
et al. 2009) to better understand how they influ-
ence the ESE-firm performance relationship.
Third, our analysis suggests that the ESE-firm
performance correlation varies by the approach
to measure firm performance. It is consistent
with Stajkovic and Luthans’ (1998) argument
that when there is insufficient performance
information, individuals tend to be more inaccu-
rate at observing personal efficacy. As ESE and
firm performance are both self-reported by
entrepreneurs, the CMV issue inflates the ESE-
firm performance relationship. However, as
researchers employ objective data to measure
firm performance, the ESE-firm performance
effect size largely decreases from moderate to
small. It indicates that a firm’s performance can
be explained by other factors, such as entrepre-
neurs’ human capital (Unger et al. 2011), social
capital (Stam, Arzlanian, and Elfring 2013), and
personality (Zhao, Seibert, and Lumpkin 2010).
In addition, considering the methodological
aspect, this decreasing in effect size suggests
that researchers objectively measure firm per-
formance to avoid the CMV problem.
The Differences between ESE and GSE
We did not find the statistically significant
differences between ESE and GSE. Admittedly, it
MIAO, QIAN, AND MA 99

is argued that employing ESE produces the
sound predictive role that self-efficacy plays in a
task-specific outcome (Bandura 1997). We still
recommend future studies to explore the dimen-
sionality of ESE measurement, because current
studies largely use measurements from Chen,
Greene, and Crick (1998) or De Noble, Jung,
and Ehrlich (1999). Both of them concentrate
on search/creativity and planning/management
(Moberg 2011). Thus, they both develop ESE
scales at a moderate level of specificity. We call
for future studies to refine ESE scales. Similar to
McGee et al.’s (2009) argument, the current ESE
measures need to find out the most influential
areas of self-efficacy. Future studies can employ
McGee et al.’s ESE scale. In addition, we
addressed financial performance by examining
ESE, which however includes several facets.
Financial performance may depend on the pos-
ited influence of self-efficacy (Bandura 1997).
Thus, future study can investigate which aspects
of ESE mainly influence ventures’ financial
performance.
From a practical perspective, we provide the
following implications. As we demonstrate the
positive relationship between ESE and firm per-
formance, entrepreneurs can invest to increase
their ESE by taking entrepreneurial education
courses. This is consistent with previous schol-
ars’ findings (e.g., Florin, Karri, and Rossiter
2007; Wilson, Kickul, and Marlino 2007). For
example, Noel (2001) specifically addressed the
impact of entrepreneurship training on the
development of self-efficacy and found a posi-
tive relationship between them. Hence, we
argue that entrepreneurs can enhance their
judgment via specific training programs.
Limitations and Future
Research
Our study suffers from several limitations.
First, there are only a few studies included in
some subgroups. For instance, there are only
five and eight studies for subgroups of old and
young firms respectively. Thus, results based on
a small number of samples are susceptible to
second-order sampling error and require further
exploration. The other minor issue is that we
still lack studies on investigating other modera-
tors, such as environment variables, which pre-
vent us from meta-analyzing contextual
moderators. Except for the young firm sub-
group, all subgroups in Table 2 display the
value of Var
art
% below 75 percent. If statistical
artifacts fail to explain 75 percent of the var-
iance in the meta-analytic correlations, modera-
tors likely exist (Hunter and Schmidt 2004). Our
results table may provide us with a roadmap to
conduct further primary studies to search for
the potential moderators in the subgroups with
the Var
art
%lessthan75percent.
Because only 26 studies (27 samples) met
our criterions and were included in our study,
our abilities to test different theories are limited.
Although other factors may influence the ESE-
firm performance relationship, we cannot test
them all based on the limitation of our data. For
example, scholars argue that entrepreneurial
passion has a moderating effect on the relation-
ship between ESE and entrepreneurial persist-
ence and entrepreneurial performance (Cardon
and Kirk 2013; Shane, Locke, and Collins 2003).
Therefore, entrepreneurial passion can be one
important factor that scholars should consider
when investigating the ESE-firm performance
relationship. Unfortunately, we do not have
enough data to test it in this study. Other
remarkable and potential moderators can be
cognitive ability, personality (Big Five traits),
and optimism (e.g., Judge et al. 2007; Pipero-
poulos and Dimov 2015). We recommend that
future studies further investigate these modera-
tors that can influence the ESE-firm performance
relationship.
Second,therearesomelimitationsregarding
specific type of firm performance. For example,
we did not employ different indicators of firm
performance as a moderator due to the insuffi-
cient information from the primary studies. Simi-
lar to Unger et al.’s (2011) study that
investigated the moderating effect of type of suc-
cess measures on the relationship between
human capital and success, we expect that the
ESE-firm performance may depend on the differ-
ent indicators of firm performance. Thus, we rec-
ommend that future studies can take the specific
firm performance type to identify the ESE-firm
performance relationship. In addition, we were
unable to include firm closure as an indicator of
firm performance due to the unclear definition
of firm closure. We suggest future studies inves-
tigate the relationship between ESE and firm clo-
sure. We observed that most primary studies we
located employed cross-sectional design. There-
fore, we recommend that future studies use lon-
gitudinal design to examine whether ESE has an
enduring effect on firm performance.
The last limitation regards the correction of
effect size. Like many other meta-analysis
JOURNAL OF SMALL BUSINESS MANAGEMENT100

studies, we used coefficient alpha to disattenu-
ate each primary correlation coefficient due to
measurement errors. However, coefficient
alpha only captures item-specific factor error
and random response error but is unable to
capture transient error and scale-specific factor
error (Chiaburu et al. 2011; Le, Schmidt, and
Putka 2009; Schmidt, Le, and Ilies 2003). As a
result, coefficient alpha overestimates reliabil-
ity, and when it is used for correcting for mea-
surement errors, we are bound to
underestimate the corrected correlations (Chia-
buru et al. 2011). However, almost all of the
studies report only one coefficient alpha.
Therefore, we call for future primary studies to
provide a more precise indicator of reliability,
such as a coefficient of equivalence and stabil-
ity (CES; Cronbach 1947; Schmidt, Le, and Ilies
2003) or a generalized coefficient of equiva-
lence and stability (GCES; Le, Schmidt, and
Putka 2009).
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