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International Journal of Entrepreneurial Behavior & Research
Differentiating the effects of entrepreneurs’ intelligence and educational
attainment on venture outcomes
Brian D. Blume,
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Brian D. Blume, (2018) "Differentiating the effects of entrepreneurs’ intelligence and educational
attainment on venture outcomes", International Journal of Entrepreneurial Behavior & Research,
https://doi.org/10.1108/IJEBR-12-2017-0507
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Differentiating the effects of
entrepreneurs’intelligence and
educational attainment on
venture outcomes
Brian D. Blume
University of Michigan, Flint, Michigan, USA
Abstract
Purpose –Intelligence or general mental ability (GMA) is a strong predictor of job performance across most
occupations, and educational attainment has been shown to be a predictor of entrepreneurial outcomes.
However, there has been little research examining the simultaneous effects of entrepreneurs’GMA and
educational attainment on their venture outcomes. The purpose of this paper is to examine the impact of these
human capital resources on venture performance and survival.
Design/methodology/approach –Using a sample of 234 self-employed entrepreneurs from a longitudinal
database, regression analysis was employed to examine the predictors of venture performance. A hazard
model was utilized to assess venture survival.
Findings –Entrepreneurs’intelligence influenced venture performance directly and indirectly via
educational attainment. Entrepreneurs with higher GMA were subsequently able to obtain more education,
and GMA had an indirect, positive influence on venture performance through this additional educational
attainment. Findings also demonstrated an inverted-U, curvilinear effect on venture survival for GMA and
educational attainment. This indicates that both intelligence and educational attainment should be considered
when examining how likely entrepreneurs are to persist or survive in their ventures.
Originality/value –Entrepreneurs with higher GMA had ventures that performed better and obtained more
education, which influenced venture survival. These findings suggest that entrepreneurs’intelligence is likely
to be an important predictor of venture outcomes, as well as a source of entrepreneurs’human capital
acquisition. Therefore, GMA should have a more central role in the human capital discussion within the
entrepreneurship literature.
Keywords Cognition, Human capital
Paper type Research paper
Introduction
Human capital has long been considered an important asset that can lead to personal and
organizational success (Becker, 1975; Pfeffer, 1994; Ployhart and Moliterno, 2011). Human
capital can be defined as the skills and knowledge that individuals acquire through
investments in schooling, on-the-job training and other types of experience (Becker, 1975).
Human capital has been shown to be an important resource for success in entrepreneurial
firms (e.g. Cooper et al., 1994; Florin et al., 2003; Sexton and Upton, 1985; Unger et al., 2011).
There are several reasons provided by the entrepreneurship literature regarding why higher
human capital leads to increased venture success (Unger et al., 2011), including an increased
capability to exploit opportunities and to acquire additional resources such as financial capital
(Brush et al., 2001; Chandler and Hanks, 1998; Parker and van Praag, 2006; Rauch et al., 2005;
Shane, 2000). Unger et al.’s (2011) recent meta-analytic review examined the relationship between
multiple measures of entrepreneurial human capital and different measures of venture success.
Their review noted that education (e.g. level, years), experience (e.g. start-up, industry specific
and managerial) and to a lesser degree, skills (e.g. management skills) were the most common
International Journal of
Entrepreneurial Behavior &
Research
© Emerald Publishing Limited
1355-2554
DOI 10.1108/IJEBR-12-2017-0507
Received 18 December 2017
Revised 15 May 2018
1 August 2018
Accepted 2 August 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1355-2554.htm
The author would like to thank to Dan Holland, Jie Li, Kirby Shedden, Mark Simon and two anonymous
reviewers for their insightful comments and assistance in developing the manuscript. Thanks also to the
Hagerman Center for Entrepreneurship and Innovation for financially supporting this project.
Venture
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measures of human capital used in the entrepreneurship literature. Overall, Unger et al. (2011)
found small, significant relationship between these human capital variables and venture success.
Becker (1975) recognized that individuals with higher intelligence or general mental
ability (GMA) would tend to invest more in human capital. GMA is the cognitive ability to
reason correctly with concepts, solve problems, comprehend surroundings and to learn from
experiences (Lubinski, 2004; Schmidt and Hunter, 2004). In discussing the effects of human
capital on the growth and failure of newly founded businesses, Rauch and Rijsdijk (2013,
p. 935) suggested, “Moreover, cognitive ability could be a variable that accounts for the
effects of general human capital because cognitive ability is related to performance […].”
Surprisingly, studies regarding the relationship between intelligence and entrepreneurial
activity are uncommon (Baum and Bird, 2010; Raffiee and Feng, 2014). In fact, although
GMA is an important predictor of how much human capital someone will acquire
( Judge et al., 2010; Ployhart and Moliterno, 2011), it has typically not been examined as a
predictor variable in studies of the educational attainment of entrepreneurs or the success of
their ventures (Dimov, 2017; Raffiee and Feng, 2014).
This has led to a gap in the literature in that there is not a good understanding of the
potentially distinct effects of GMA and education on entrepreneurship outcomes. While it is
generally accepted that entrepreneurs’education level is positively related to entrepreneurial
outcomes (Martin et al., 2013; Unger et al., 2011), the fact that GMA is highly correlated with
educational outcomes (Berry et al., 2006) makes it difficult to distinguish between the effects of
entrepreneurs’GMA vs their educational attainment on entrepreneurial outcomes. A primary
goal of this study is to differentiate between the influence of education and GMA on venture
outcomes (i.e. venture performance and survival). Research questions include: does the GMA
of entrepreneurs have a direct effect on venture performance, or does GMA only have an
indirect influence on performance due to the higher educational attainment that these
entrepreneurs might obtain? Also, when both GMA and educational attainment are examined
simultaneously, what influence does educational attainment have on venture outcomes?
Before discussing specific hypotheses to examine these questions, the theoretical background
regarding human capital and research in entrepreneurship area is reviewed, with a particular
focus on education and GMA.
Human capital and entrepreneurial outcomes
Several studies demonstrate that a founders’human capital, such as their past employment
experience, industry experience and educational attainment, positively influences the
ventures they form and success in their ventures (e.g. Bates, 1990; Cooper et al., 1994; Ding,
2011). In a representative study in this area, Bosma et al. (2004, p. 228) found support for
their hypothesis that, “Higher levels of human capital are associated with stronger
performance by business founders.”Ding (2011) proposed that the founder’s educational
background is important to the success of the venture for several reasons. Entrepreneurs
learn specialized knowledge and information-processing patterns which enable them to
better recognize opportunities in the surrounding environment (Baptista et al., 2014; Shane,
2000). They also could be influenced by the professional values they internalize during the
educational process (Ding, 2011). Finally, they could be influenced by shaping
the entrepreneur’s network of contacts (e.g. student or alumni networks), which could
serve as important sources of information and support (Ucbasaran et al., 2008).
Similar arguments could be made for the importance of GMA as an important aspect of
human capital (Baum and Bird, 2010; Frese et al., 2007). Key components of intelligence or
GMA proposed by an official taskforce of the American Psychological Association include the
“ability to understand complex ideas, to adapt effectively to the environment, to learn from
experience, to engage in various forms of reasoning, to overcome obstaclesby taking thought”
(Neisser et al., 1996, p. 77). As implied by the taskforce, GMA would theoretically be expected
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to influence employee and entrepreneurial outcomes for a number of reasons. These include
being a precursor to effectively pursuing education, having stronger analytical skills and
enabling an entrepreneur to learn more from their experiences (Ding, 2011; Frese et al., 2007;
Jensen, 1998; Schmidt and Hunter, 2004).
Regarding entrepreneurship entry, advanced educational attainment could both encourage
and discourage entrepreneurship (De Clercq and Arenius, 2006; Lofstrom et al., 2014). Greater
education generally enhances one’s analytical abilities and communication skills, but
simultaneously leads to more options for salaried employment, thereby increasing the
opportunity costs of pursuing entrepreneurship (Gimeno et al., 1997). Whereas the net effects
of educational attainment on venture entry and survival may be less clear (Lofstrom et al.,
2014), there is some evidence that educational attainment has a positive effect on venture
survival (Gimmon and Levie, 2010). For GMA, there are very few studies that examine the
relationship of entrepreneurs’GMA with their venture entry or survival. A notable exception
is Raffiee and Feng (2014), who focused on hybrid entrepreneurs who initiate a business while
retaining their “day job.”With regard to GMA, for entrepreneurs with lower cognitive ability
they found that extended stays in hybrid entrepreneurship before committing full-time as an
entrepreneur was more likely to reduce the hazard of exiting a venture.
In addition to these studies examining the outcomes of human capital, researchers have
emphasized the difference between context-generic and context-specific knowledge, skills
and abilities (Ployhart and Moliterno, 2011; Ucbasaran et al., 2008), as well as emphasized
the importance of considering the situational relevance of human capital (Unger et al., 2011).
GMA could be considered to be one of the most context-generic abilities, as it is relatively
stable and endures across time and situations ( Jensen, 1998; Ployhart and Moliterno, 2011).
Based on the notion of context-specific skills, Brüderl et al. (1992) proposed that there is
entrepreneur-specific human capital, including variables such as prior self-employment
experience. Depending on the type of education and knowledge gained, educational
attainment could produce a range of context-generic (e.g. improved analytical skills) and
more context-specific skills (e.g. opportunity identification and assessment skills) for
entrepreneurs (Brüderl et al., 1992; Ployhart and Moliterno, 2011).
Unger et al. (2011, p. 353) noted that, “A process point of view on learning will also
acknowledge that, in the face of rapidly changing environments, any specific knowledge is
likely to have a decreasing shelf life (Reuber and Fisher, 1999). Some skills and knowledge will
even have to be unlearned, that is, replaced by other and better knowledge and skills.”This
perspective indicates that context-generic abilities like GMA will be important in the process
of gaining new context-specific skills (e.g. helping entrepreneurs learn new skills and learn
from their experience; Ployhart and Moliterno, 2011). In this way, GMA can be viewed as a
context-generic ability that enables entrepreneurs to gain other context-generic
(e.g. educational attainment such as a college degree) and context-specific knowledge and
skills (e.g. specific knowledge/skills needed to run a business) that enhance the success of their
venture (Frese et al., 2007). Despite the importance of GMA as a predictor of human capital
resource acquisition and success across nearly every job and occupation (Ployhart and
Moliterno, 2011; Schmidt and Hunter, 2004), it has not received sufficient attention within the
entrepreneurship literature. This study addresses this gap by differentiating between the
effects of entrepreneurs’GMA and educational attainment on entrepreneur outcomes.
Hypotheses
In this section, hypotheses are developed regarding how entrepreneurs’GMA and educational
attainment will affect venture performance (see Figure 1 for a summary of these relationships)
and venture survival. The assumption made is that the performance of a venture for someone
who is self-employed can be measured by the profits/earnings generated by the venture
(Rauch et al., 2000; Unger et al., 2011). These profits can be reinvested back into the business
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venture or can be distributed to the entrepreneur in two ways. The profits will either be taken
out of the business in the form of the salary/earnings of the entrepreneur or be reported as net
income of the venture at the end of the year (in which case the self-employed entrepreneur
could receive a financial payout from the company). If the entrepreneur chooses to invest
money back into the business venture, this would presumably be due to an expectation that
the venture will grow and become more profitable, leading to higher earnings in the future.
Therefore, a key performance outcome variable measured in this study is the total earnings
(in salary and profits) that the self-employed entrepreneur receives from the business venture,
which is a direct measure of the performance of the venture (Rauch et al., 2000; Unger et al.,
2011). A second important measure of an entrepreneur’s success discussed below is the
survival of the venture (Brinckmann et al., 2010; Headd and Kirchoff, 2009).
General mental ability (GMA) and educational attainment
While some have argued that education leads to increases in GMA (Ceci, 1991; Ng and Feldman,
2010) and there is some evidence that education may lead to small increases in ability as
measured by achievement test scores (Hansen et al., 2004), stronger evidence exists that higher
GMA enables individuals to attain higher educational levels (Berry et al., 2006; Judge et al., 2010).
For example, GMA measured when individuals were young adults is highly correlated at 0.63
with subsequent education attained over the next 21 years (Berry et al., 2006). In support of this
perspective, Ployhart and Moliterno (2011, p. 133) stated that GMA is, “stable throughout
adulthood and, hence, is not affected by advanced education or experience.”
Past research has demonstrated that GMA is one of the strongest predictors of
educational attainment (Berry et al., 2006; Jensen, 1998; Ployhart and Moliterno, 2011).
Judge et al. (2010) found that growth in human capital acquisition (e.g. education, training)
and extrinsic career success occurs more quickly for individuals with high GMA than for
individuals with low GMA. More specifically, they found that, on average, those with low
GMA obtained less than one additional year of education over the subsequent 27-year
period, while those with high GMA obtained 2.5 additional years of education over this
time peri od ( Judge et al., 2010). In summary, GMA is an important predictor of how much
an individual will increase their human capital, particularly their educational attainment:
H1. An entrepreneur’s GMA has a positive effect on his or her educational attainment.
Venture performance
Schmidt and Hunter (2004, p. 3) stated, “Other things equal, higher intelligence leads to
better job performance on all jobs. Intelligence is a major determinant of job performance
[…]. This principle is very broad: it applies to all types of jobs at all job levels.”Past research
has demonstrated that intelligence or GMA is one of the strongest predictors of job
performance, promotion rates and salary ( Jensen, 1998; Schmidt and Hunter, 1998).
H6b
H6a
H2
H3
General Mental Ability
H1
Venture
Performance
Educational
Attainment
H4 and H5
Venture
Survival
Figure 1.
Hypothesized model
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GMA relates directly to the ability to learn, and a key reason that higher intelligence
leads to higher job performance is that people who are more intelligent learn more job
knowledge and learn it faster. Individuals with higher GMA also benefit more from their
past experiences and acquire knowledge more deeply and can better apply information to
new situations ( Jensen, 1998). Another reason is that more intelligent workers are better
able to solve problems that come up on the job (Schmidt and Hunter, 2004).
GMA is most important for performing well in complex jobs because these jobs require the
most learning and job knowledge (Schmidt and Hunter, 2004). Professionals and managers
typically have higher job complexity in their positions (Hunter, 1980; Schmidt and Hunter, 1998),
and entrepreneurs are likely to encounter at least as much complexity in their jobs as managers.
For instance, Schjoedt (2009) found that the entrepreneurs reported slightly higher task variety
in their jobs than non-founding top managers. Entrepreneurs engage in a number of different
activities, including developing products and services, conducting marketing activities,
developing customer relationships, managing employees and solving problems with operations,
suppliers and customers (Schjoedt, 2009). In addition, considering that entrepreneurs are often
exploring uncharted territory and regularly experience novel problems in starting a business,
GMA would be expected to be important to effectively handle these issues. Therefore, GMA
might be especially important for entrepreneurs to be more effective in their ventures.
Consistent with this reasoning, in a study of African business owners, Frese et al. (2007)
found that GMA was a predictor of business size and success. In addition, Hartog et al.
(2010) found that entrepreneurs with higher GMA made more money. For these reasons, an
entrepreneur’s GMA is expected to be an important predictor of the venture’s performance:
H2. An entrepreneur’s GMA has a positive, direct effect on venture performance.
Education is likely to have at least two potential benefits for venture performance. First,
as discussed in the prior section, there should be a direct effect since entrepreneurs with
more education should have gained more knowledge and problem-solving skills that are
likely to be beneficial when starting and running a business (Coleman, 2007; Cooper and
Gimeno-Gascon, 1992; Cooper et al., 1994). Van Praag et al. (2013) found that entrepreneurs
have higher financial returns to formal education than non-entrepreneurs. They suggested
that this benefit could stem from the fact that, when compared to employees, entrepreneurs
face fewer organizational constraints and have more control over how to utilize their
human capital.
Second, signaling theory proposes that educational attainment may enable the entrepreneur to
gain legitimacy with stakeholders (Gimmon and Levie, 2010; Parker and van Praag, 2006; Spence,
1973; Van der Sluis et al., 2008). Signaling theory was originally developed as an explanation of
how job seekers invest in human capital via educational attainment to serve as a signal of their
value to potential employers. In a similar way, educational attainment can serve as a signal to
stakeholders (e.g. bankers, customers, etc.) that an entrepreneur has higher legitimacy and is more
likely to be successful (Backes-Gellner and Werner, 2007; Hsu, 2007; Ulvenblad et al., 2013):
H3. An entrepreneur’s educational attainment has a positive effect on venture performance.
Given that those with higher GMA obtain more education (Berry et al., 2006) as proposed in H1,
educational attainment is expected to mediate the positive effect of GMA on venture
performance (see Figure 1). Judge et al. (2010) found that educational attainment partially
mediated the relationship between GMA and external career success (i.e. income and
occupational prestige) for the general labor force. Therefore, in addition to the positive, direct
effect that entrepreneurs’GMA has on venture performance; GMA is expected to also have a
positive, indirect effect on venture performance via entrepreneurs’educational attainment:
H4. An entrepreneur’s GMA has a positive, indirect effect on venture performance via
higher educational attainment.
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Venture survival
A second dimension of entrepreneurial success examined in this study is venture survival
(Brinckmann et al., 2010). This is an important dimension of entrepreneurial success given
that an entrepreneur would be more likely to persist in a venture that is profitable or shows
good potential (Brüderl et al., 1992). Conversely, an entrepreneur is more likely to terminate a
venture that is less profitable. It is frequently observed that many new ventures do not
survive more than a few years (Wiklund et al., 2010; Yang and Aldrich, 2012).
Human capital such as educational attainment has generally been found to be important
to venture survival (Brüderl et al., 1992; Cooper et al., 1994; Gimmon and Levie, 2010).
However, Gimeno et al. (1997) noted that while general human capital should improve the
performance of a venture, the relationship with survival depends on the relative payoff of
the human capital in the venture vs available options outside the venture (i.e. expected
income from alternative employment). Similarly, Lofstrom et al. (2014) noted that
educational credentials can impact the likelihood of entry into entrepreneurship in offsetting
ways. They suggested that greater education increases one’s options in salaried
employment (increasing the opportunity costs of entrepreneurship), but also enhances
one’s analytical abilities and skills needed to run certain types of ventures. Thus, although
entrepreneurs with higher GMA and educational attainment are likely to perform better in
their ventures, they may also have higher performance requirements to remain in their
venture than those with lower human capital (i.e. an entrepreneur with higher educational
attainment is likely to have a higher opportunity cost of remaining in the venture).
Regarding education, Gimmon and Levie (2010) reviewed eight studies that examine the
relationship between the founder’s education level and venture survival, noting that seven of
these eight studies found significant, positive effects. Based on these findings, it appears that
educational attainment could positively influence venture survival due to the additional
knowledge/skills gained by entrepreneurs as well as the signaling effect from their higher
education (Cooper et al., 1994; Gimmon and Levie, 2010). There also may be a positive effect of
GMA on venture survival given that entrepreneurs with higher GMA are likely to perform better
and have a lower probability of being “forced out”of their ventures (Raffiee and Feng, 2014).
In addition, there may be an interaction effect between GMA and educational attainment,
such that entrepreneurs with higher GMA and more education are more likely to survive
longer in their ventures. This could occur if higher GMA enables entrepreneurs with higher
educational attainment to make better use of their education (i.e. gain and retain more
knowledge/skills; Gottfredson, 1997). They will also be better equipped to apply their
educational experiences to the problems encountered in the venture ( Judge et al., 2010). For
example, if two entrepreneurs both obtain the same undergraduate degree from the same
university, but one has lower GMA and the other has high GMA, the entrepreneur with
higher GMA would likely make better use of his education (Gottfredson, 1997; Jensen, 1998;
Judge et al., 2010). This could translate into longer survival for these high-GMA
entrepreneurs in their ventures:
H5. There is an interaction between GMA and education when predicting venture
survival, such that entrepreneurs with both more education and higher GMA have
longer venture survival.
Based on the literature, there is also likely to be non-linear effects (i.e. an inverted-U
curvilinear effect) of GMA and education on venture survival (Davidsson and Honig, 2003).
When examining the relationship between educational attainment and whether someone
was trying to start a new business, Kim et al. (2006) found a curvilinear impact of education
in that both too little and too much education discouraged attempted entrepreneurship.
Within the context of entrepreneurs who have already begun new ventures, this could occur
if entrepreneurs with the lowest and highest levels of GMA and educational attainment are
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less likely to survive or persist in their ventures. As discussed below, the mechanisms that
could cause these entrepreneurs’(i.e. those with the least and most education and the lowest
and highest GMA) ventures to be less likely to survive would be different.
In the case of educational attainment, entrepreneurs who have lower educational
attainment (e.g. high school degree or less) may not have enough education to provide the
necessary skills to survive in their venture (Cooper et al., 1994; Davidsson and Honig, 2003)
or to signal to stakeholders that their business is legitimate (Gimmon and Levie, 2010;
Spence, 1973). In contrast, those who have very high educational attainment (e.g. masters or
professional degrees) may have good alternative employment opportunities and
subsequently leave their ventures (Becker, 1975; Gimeno et al., 1997; van Praag, 2003).
Signaling theory suggests that this could occur if their more distinguished degrees provide
information to employers in the external job market, leading these entrepreneurs to be more
likely to be recruited away from their ventures (Spence, 1973). Those with moderate
educational attainment (e.g. associate degrees) might not feel the effects of these forces as
strongly since they have enough education to provide them with requisite skills and to
signal legitimacy, but not so much as to provide highly valuable alternative opportunities
of employment in the labor market. Therefore, those entrepreneurs with moderate amounts
of education may be more likely to persist in their ventures.
For GMA, these forces are similar to those described in the employee turnover literature as
push and pull forces ( Jackofsky, 1984; Maltarich et al., 2010). For example, entrepreneurs with
lower GMA may be forced to exit their venture due to its poor performance (i.e. a push force), or
alternatively entrepreneurs with higher GMA maybemoreattractiveinthegenerallabormarket
and therefore might also be more likely to leave their venture for a better opportunity (i.e. a pull
force). Jackofsky (1984) described how lower performing employees are pushed out of the
organization as a result of actual or perceived threat of administrative action. In contrast, higher
performers are more likely to experience pull forces, primarily based on market forces that
increase workers’opportunities outside of the organization. Given the robust relationship
between GMA and performance, Maltarich et al. (2010) proposed that push and pull forces will
vary across different levels of employee GMA. They found a curvilinear (inverted-U) relationship
between GMA and voluntary turnover in jobs with high cognitive demands, such that turnover
was highest for those with the lower GMA, but also increased for employees with higher GMA.
Given that entrepreneurs are likely to experience high cognitive demands (Schjoedt, 2009),
they may experience similar forces as those reported by Maltarich et al. (2010). In the case of an
entrepreneur’s venture, turnover would translate into exiting the venture or not surviving for as
long in the venture. In other words, an inverted-U curvilinear relationship may also exist
between entrepreneurs’GMA and venture survival. This would occur if those with lower GMA
are most likely to be pushed out of their ventures due to the actual or perceived threat of failure
because of their inability to perform well in the venture, while those with the highest ability are
pulled or recruited out of their ventures by market-based forces (e.g. opportunities in the
traditional labor market). Based on the above discussion, the following hypotheses are proposed:
H6a. There is an inverted-U curvilinear relationship for entrepreneurs’educational
attainment when predicting venture survival.
H6b. There is an inverted-U curvilinear relationship for entrepreneurs’GMA when
predicting venture survival.
Method
Sample
The sample consists of a subset of individuals enlisted in the National Longitudinal Survey
of Youth (NLSY79), a nationally representative sample of 12,686 individuals (Center for
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Human Resource Research, 2004). The NLSY79 is administered by the Bureau of Labor
Statistics, a branch of the US Department of Labor. NLSY79 participants were interviewed
annually from 1979 until 1994, when a biennial interview schedule was adopted.
This study tracks a specific venture of self-employed entrepreneurs. In order to track the
performance and survival of the venture over time, it focuses on individuals that began a
new business between the years of 1993 and 1997 (as indicated in the NLSY79 survey years
1994 and 1998). This time period was selected to give a large enough sample of
entrepreneurs to test the hypotheses and also to enable the tracking of an entrepreneur’s
venture for at least 10 years through the end of the data collection in 2008. Examining this
time period also provided the advantage of sampling individuals in the database who were
more mature (between the ages of 29 and 37 in 1994), rather than limiting the sample to only
individuals who chose to become entrepreneurs early in their careers (e.g. data from the
1980s in the NLSY79). Using later survey data allowed NLSY79 participants plenty of time
to complete their initial educational pursuits (e.g. even those pursuing professional degrees)
and for most participants to be in the workplace for about one decade. For the analyses, data
from 1993 to 2008 was used, representing a 15-year sample window.
Survey questions in the NLSY79 include a wide range of topics, including questions
about employment issues. One NLSY79 survey question asks respondents whether they are
self-employed (vs working for a private company, the government, a non-profit organization
or a family business). All respondents who began their business between 1993 and 1997 were
included in the data set for this study (with the exception of respondents who indicated that
they expected their self-employment in the venture to be temporary rather than permanent).
In total, there were 234 entrepreneurs that met these criteria and had available data for the
control, independent, and at least one of the dependent variables described below. The sample
was 53 percent male, 65 percent white and on average was 32.3 years old in 1994.
Independent variables
Educational attainment. Education was coded using the following five levels: less than high
school diploma (12 percent); high school diploma (58 percent); associate degree (8 percent);
bachelor’s degree (16 percent); and master’s or professional degree (6 percent). This was
measured around the time the entrepreneurs began their business (e.g. 1994).
General mental ability (GMA). The Armed Forces Qualification Test (AFQT) was used as
a measure of GMA. The AFQT is a composite score created from selected sections of the
Armed Services Vocational Aptitude Battery (ASVAB). The ASVAB was administered to
NLSY79 participants (16–23 years of age at the time) in 1980 for purposes of developing
national norms for the test. The AFQT is a composite of word knowledge, paragraph
comprehension, math knowledge and arithmetic reasoning and is highly correlated with
other ability measures (Center for Human Resource Research, 2004).
The AFQT composite score was statistically corrected for differences in age (Ing et al., 2014;
also see Berry et al., 2006). Each AFQT score was converted into a percentile score between 0 and
100. These age-adjusted AFQT percentile scores were used as this study’s measure of GMA.
Dependent variables
Venture performance. Similar to prior research that has examined the entrepreneur’searnings
or salary as a measure of performance (e.g. Rauch et al., 2000; Unger et al., 2011), a measure of
how well the venture was performing was calculated based on the average income obtained
from the venture by the entrepreneur. This income included any salary taken by the
entrepreneur from the business venture as well as any profits made by the venture. The
income related to the venture was reported by the individual in whatever way was easiest for
them (e.g. annual wage earnings and/or net income related to the venture), and then converted
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to an hourly rate based on the average number of hours worked in the venture per week.
An average hourly rate for the venture was calculated using each time this measure of
financial performance was reported (i.e. typically biennially)throughout the life of the venture.
For example, if an entrepreneur (who began her venture in 1996 and closed the venture in
2000) reported her income related to the venture in 1996, 1998 and 2000 as $20 per hour,
$30 per hour and $10 per hour, respectively, her venture performance average income would
be $20 per hour. In this way, an overall indicator of the performance of the venture throughout
the life of the venture was obtained. To adjust for inflation, all earnings were adjusted to 2008
dollars using Consumer Price Index data. Due to a positive skew in this variable, the log
transformation of venture performance was utilized in the regression equations.
Venture tenure and survival. For the purposes of this study, venture tenure is defined as
the length of time that the entrepreneur remains with the venture (van Praag, 2003). Venture
survival is a binary dummy variable indicating whether the venture was still in existence at
the end of the data collection period.
Control variables
The following were control variables since prior research indicates that they could impact
new venture performance or survival (e.g. van Praag, 2003).
Sex. Prior studies have demonstrated that female-owned businesses tend to
underperform male-owned businesses, although recent research has questioned this
finding (Robb and Watson, 2012).
Age. Although the age range was restricted in this database, it was included as a control
variable since older individuals had more time to obtain education, and education was
expected to be related to venture outcomes. The entrepreneur’s age in 1994 is reported.
Race. Race was examined as white vs minority (e.g. Hispanic and African-Americans)
since past research has found that non-minority-owned businesses tend to outperform
minority-owned ventures (Robb, 2002).
Self-employment entrepreneur experience. Past research has demonstrated a positive
effect of entrepreneurial experience on venture performance and survival (Unger et al., 2011).
For each year of the survey, respondents were asked if they were self-employed.
If respondents indicated that they were self-employed in any of the years prior to when
they began the venture of interest, they were coded as having self-employment experience
(0/1 dummy code). In total, 49 percent of the sample had prior self-employment
entrepreneurial experience before beginning the venture of interest.
Full-time vs part-time in venture. The average amount of time per week devoted to a
venture could influence venture outcomes (e.g. Cron et al., 2006; Raffiee and Feng, 2014).
Respondents reported the average number of hours per week that they spent working in their
venture. Not all of the self-employed entrepreneurs worked on their ventures on a full-time
basis. This could be because they were not working full-time (i.e. only worked 25 h per week)
or because they were working at other jobs in addition to their venture. The number of hours
that respondents reported working in their venture was reported. If the entrepreneur reported
working an average of 30 or more hours per week at any time during the tenure of the venture,
it is coded as full-time. Otherwise, the entrepreneur was coded as working part-time in the
venture. Based on this criterion, 85 percent of the sample worked full-time in the venture.
Industry. The industry that entrepreneurs began their business in was reported and
coded in the data set using 1980 Industrial Classification System (US Bureau of the Census,
1980). Based on these codes, six different dummy variables were utilized representing the
following industry groups: agriculture/manufacturing/transportation, construction, trade
(including retail stores), business and repair services (including data processing services
and automotive repair), personal services (e.g. cleaning services, beauty shops) and
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professional services (e.g. physician or legal services). The coding of these industries is very
similar to van Praag (2003), who noted the importance of controlling for industry-related
variables when examining venture outcomes.
Industry experience. Research has shown that experience of the business owner in the
same industry in which a new business is started improves a firm’s chances of survival and
firm performance (Bosma et al., 2004; van Praag, 2003). The respondents indicated which
industry they had begun their venture in. These historical employment data were utilized to
determine the extent to which they worked in the same industry as the one in which they
were beginning their venture. This experience in the industry was coded as: 0 –no
experience (23 percent of sample); 1 –one or two years of experience (27 percent of sample);
and 2 –three or more years’experience (50 percent of sample).
Analyses
In order to test the hypothesized model in Figure 1, Hayes’(2013) Process macro in SPSS
was utilized to examine the proposed direct and indirect effects on venture performance
(MacKinnon et al., 2012; Preacher and Kelley, 2011). The GMA and educational attainment
variables were mean-centered before entering them into the regression equations[1]. All the
industries were entered in the regressions except for the personal services industry, so this
industry provides the baseline and the other industries are compared to it.
Survival analysis is a collection of statistical procedures for which the outcome variable
of interest is the time until an event occurs (Harrell, 2001), in this case the time until the
entrepreneur exits the venture. A hazard model (i.e. Cox’s, 1972 regression) is utilized to
examine the predictors of venture exit or survival (Anavatan and Karaoz, 2013; van Praag,
2003). Cox’s regression is commonly used in the turnover literature (e.g. Lyness and
Judiesch, 2001; Sims et al., 2005). It is designed for use with dependent variables that are
dichotomous (i.e. venture survival) and also incorporates the venture tenure, measured
continuously (Sims et al., 2005). In addition to the control variables outlined above, venture
performance was also included as a control variable in the Cox regression ran in SPSS.
Results
The correlations among study variables are presented in Table I. Similar to prior research (Berry
et al., 2006; Judge et al., 2010), GMA and educational attainment were found to be highly
correlated (r¼0.63). Individuals with higher educational attainment were less likely to have self-
employment experience (r¼−0.17). Self-employed entrepreneurs in personal services industries
had the lowest venture performance (r¼−0.28) and had shorter tenure in their ventures
(r¼−0.19), while entrepreneurs in the professional services industries had the highest income
from their ventures (r¼0.23). In addition, entrepreneurs with higher GMA were more likely to
begin businesses in professional service industries (r¼0.32) and less likely to begin businesses
in personal service industries (r¼−0.12). Consistent with expectations, males were much more
likely than females to work in the construction industry (r¼0.43), whereas females were much
more likely than males to begin businesses in the personal services industry (r¼0.45).
Table II contains the regression results to test H1–H4. With educational attainment as
the dependent variable, the results indicate that those who have self-employment experience
obtained less education (Β¼−0.38, po0.01). In addition, those who work in the
professional services (Β¼0.65, po0.01) and business repair and services (Β¼0.44,
po0.05) industries also have more education than those working in personal services. GMA
has a significant, positive relationship (Β¼0.20, po0.01) with educational attainment,
supporting H1. The variance explained for educational attainment was 0.48.
With venture performance as the dependent variable, the variance explained was 0.29.
All of the variance inflation factors for the variables in the model were lower than 2.5,
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
1. GMA
2. Educational attainment
a
0.63
3. Self-employment experience
b
−0.04 −0.17
4. Full-time venture −0.06 −0.03 0.10
5. Male 0.03 −0.07 0.11 0.33
6. Race –white 0.39 0.18 −0.03 −0.08 0.07
7. Age in 1994 0.11 0.16 −0.04 0.03 −0.11 0.03
8. Industry experience
c
0.04 0.06 0.24 −0.05 −0.05 0.05 −0.09
9. Personal services −0.12 −0.12 −0.08 0.05 −0.45 −0.13 0.07 −0.03
10. Agriculture, manufacturing and
transportation −0.13 −0.11 −0.04 0.05 0.21 −0.02 −0.02 −0.13 −0.19
11. Construction −0.12 −0.20 0.08 0.16 0.43 0.03 −0.06 0.04 −0.25 −0.20
12. Trade −0.05 −0.07 −0.02 −0.17 −0.11 −0.05 −0.07 −0.01 −0.16 −0.13 −0.18
13. Business and repair services 0.06 0.09 0.12 −0.08 0.01 0.09 −0.02 0.09 −0.21 −0.17 −0.23 −0.15
14. professional Services 0.32 0.38 −0.05 −0.05 −0.12 0.06 0.09 0.02 −0.24 −0.20 −0.27 −0.17 −0.22
15. Venture tenure
d
0.08 0.06 0.23 0.15 0.33 0.16 0.00 0.01 −0.19 0.06 0.14 −0.04 0.03 0.01
16. Venture survival
e
0.12 0.11 0.13 0.17 0.33 0.09 0.01 −0.02 −0.20 0.05 0.10 0.01 −0.01 0.07 0.79
17. Venture performance 0.32 0.34 0.03 0.02 0.15 0.17 0.03 0.09 −0.28 0.01 −0.05 −0.04 0.12 0.23 0.16 0.14
Mean 48 1.48 0.49 0.85 0.53 0.65 32.3 1.27 0.19 0.13 0.21 0.10 0.16 0.21 6.62 0.28 26.14
SD 30.4 1.09 0.50 0.36 0.50 0.48 2.24 0.81 0.39 0.34 0.41 0.30 0.37 0.40 4.71 0.45 28.79
Notes: n¼234, except for correlations involving venture tenure and venture survival (n¼224), venture performance (n¼229) and the correlation between venture
performance and both venture tenure and venture survival (n¼219).
a
Educational attainment (coded 0 –no high school diploma; 1 –high school diploma; 2 –associate
degree; 3 –bachelor’s degree; 4 –master’s degree or higher);
b
Self-employment experience (0 –No; 1 –yes);
c
industry experience (0 –no experience; 1 –one or two years of
experience; 2 –three or more years’experience);
d
tenure in years of venture;
e
survival of venture to the end of the data collection period (0 –no; 1 –yes). Correlations |0.13|
to |0.17|are significant at po0.05; correlations |0.18|and higher are significant at po0.01
Table I.
Means, standard
deviations, and
intercorrelations
among variables
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indicating that multicollinearity was not a concern (Hair et al., 2009). The results from this
model indicate that, when compared to entrepreneurs who begin businesses in the personal
services industry, entrepreneurs in all five other industries (i.e. agriculture/manufacturing/
transportation; construction; trade; business and repair services; and professional services)
have ventures that perform significantly better. None of the other control variables had a
significant effect on venture performance, including self-employed entrepreneurial
experience or experience in the industry in which the venture was started in (although
“male”had a marginally significant effect of B¼0.12, p¼0.07). GMA has a positive, direct
effect (Β¼0.03, po0.05) on venture performance, supporting H2. Educational attainment
had only a marginally significant direct effect on venture performance (Β¼0.06, p¼0.06),
so H3 is not supported. The indirect effect of GMA on venture performance via educational
attainment was significant (B¼0.01, po0.05), so H4 is supported.
The Cox regression results to examine the hypotheses with venture survival are reported in
Table III. The log likelihood for the model was 1,465.59 and the overall χ
2
was 64.10 (significant
at po0.01). For ventures in the data set, the average life expectancy of a venture was 6.61 years,
which is consistent with prior research on venture survival (Yang and Aldrich, 2012). While the
Estimate 95% CI SE t
Direct effects (DV: education attainment)
Constant −1.37* (−3.01, 0.26) 0.83 −1.66
Agriculture, manufacturing and transport →education 0.18 (−0.24, 0.61) 0.22 0.85
Construction →education 0.01 (−0.040, 0.41) 0.21 0.03
Trade →education 0.09 (−0.34, 0.52) 0.22 0.41
Business and repair services →education 0.44** (0.05, 0.83) 0.20 2.25
Professional services →education 0.65*** (0.29, 1.01) 0.18 3.59
Age →education 0.04 (−0.01, 0.08) 0.02 1.49
Industry experience →education 0.12* (−0.02, 0.26) 0.07 1.68
Self-employment experience →education −0.38*** (−0.60, −0.16) 0.11 −3.36
Full-time venture →education 0.19 (−0.14, 0.52) 0.17 1.15
Male →education −0.14 (−0.42, 0.15) 0.14 −0.95
Race (white) →education −0.16 (−0.40, 0.08) 0.12 −1.29
H1: GMA →education 0.20*** (0.16, 0.24) 0.02 9.72
Direct effects (DV: venture performance)
Constant 0.99*** (0.25, 1.73) 0.37 2.64
Agriculture, manufacturing and transport →performance 0.32*** (0.13, 0.51) 0.10 3.32
Construction →performance 0.34*** (0.16, 0.52) 0.09 3.68
Trade →performance 0.31*** (0.12, 0.51) 0.10 3.21
Business and repair services →performance 0.36*** (0.18, 0.53) 0.09 3.99
Professional services →performance 0.34*** (0.17, 0.50) 0.08 4.06
Age →performance −0.00 (−0.02, 0.02) 0.01 −0.21
Industry experience →performance 0.02 (−0.05, 0.08) 0.03 0.51
Self-employment experience →performance −0.01 (−0.11, 0.09) 0.05 −0.19
Full-time venture →performance −0.03 (−0.17, 0.12) 0.07 −0.38
Male →performance 0.12* (−0.01, 0.24) 0.06 1.80
Race (white) →performance −0.01 (−0.12, 0.10) 0.06 −0.15
H2: GMA →performance 0.03** (0.01, 0.05) 0.01 2.51
H3: Education →performance 0.06* (−0.00, 0.12) 0.03 1.92
Indirect effect
H4: GMA →education →performance 0.012** (0.00, 0.02) 0.006 1.95
Notes: n¼229. The log transformation of venture performance was utilized. Unstandardized coefficients are
reported.
a
For educational attainment, R
2
¼0.48 and adjusted R
2
¼0.45; for performance, R
2
¼0.29 and
adjusted R
2
¼0.25. *po0.10; **po0.05; ***po0.01
Table II.
Regression results
related to
venture performance
a
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majority (i.e. 161/224 or 72 percent) of entrepreneurs in the sample exited their ventures,
63 ventures (28 percent) were still in business at the end of the data collection period.
Anegativeβfrom the Cox regression results indicates that the independent variable
reduces the likelihood of the event (exiting the venture), which indicates that the variable
leads to longer venture survival. As seen in Table III, entrepreneurs who were male
(Β¼−0.90, po0.01) and those who had self-employment experience (Β¼−0.46, po0.01)
were more likely to survive longer in their ventures. In support of H5,therewasa
significant interaction effect between education and GMA (Β¼−0.11, po0.05), indicating
that those with more education and higher GMA were less likely to exit their ventures.
The Cox regression results in Table III were utilized to plot three separate curves for low,
mean and high educational levels across a range of cognitive ability (i.e. ±1standard
deviation). Figure 2 demonstrates these predicted curvilinear effects as indicated by the
Predictor b
Hazard ratio
Exp(b) 95% CI of hazard ratio
Agriculture, manufacturing and transportation −0.22 0.80 0.43–1.51
Construction 0.04 0.90 0.55–1.97
Trade −0.09 0.78 0.48–1.73
Business and repair services −0.08 0.92 0.52–1.63
Professional services −0.19 0.83 0.47–1.46
Age −0.03 0.97 0.90–1.05
Industry experience −0.05 0.95 0.77–1.18
Self-employment Experience −0.46*** 0.63 0.45–0.89
Full-time venture −0.40 0.67 0.42–1.06
Male −0.90*** 0.41 0.26–0.64
Race –white −0.33 0.72 0.49–1.05
Venture performance −0.00 1.00 0.99–1.01
General mental ability (GMA) −0.02 0.98 0.91–1.06
Education −0.28** 0.76 0.60–0.96
H5: GMA ×education −0.11** 0.89 0.81–0.99
H6a: education
2
0.31*** 1.36 1.11–1.66
H6b: GMA
2
0.04** 1.04 1.01–1.07
Notes: n¼224. Log likelihood: 1,465.59; overall χ
2
: 64.10. **po0.05; ***po0.01
Table III.
Cox regression results
predicting venture exit
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
–3 –1.5 0 1.5 3
Low Education (–1 SD)
Mean Education
High Education (+1 SD)
General Metal Ability (Standardized GMA)
Hazard (Probability to Exit Venture)
Figure 2.
Influence of general
mental ability
and educational
attainment on
venture survival
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significant squared terms for educational attainment (Β¼0.31, po0.01) and GMA
(Β¼0.04, po0.05), providing support for H6a and H6b. The curves in Figure 2 show a
U-shaped, curvilinear effect on venture exit, which translates to an inverted-U, curvilinear
effect of educational attainment and GMA on venture survival (i.e. a lower probability to
exit translates to longer venture survival).
Discussion
For this sample of self-employed entrepreneurs, when both GMA and educational
attainment were examined simultaneously, GMA influenced venture performance directly
and indirectly via educational attainment. This indicates the importance of GMA as a
context-generic human capital variable for entrepreneurs and supports the view that
higher GMA should be viewed as an important resource to enable entrepreneurs to
succeed in their ventures (Frese et al., 2007; Hartog et al., 2010). In addition, this study also
demonstrates that entrepreneurs with higher GMA were able to obtain more education in
subsequent years, and GMA also had an indirect, positive influence on venture
performance through this additional educational attainment. This provides evidence that
GMA is likely a primary antecedent or source of entrepreneurs’human capital acquisition
(Ployhart and Moliterno, 2011; Rauch and Rijsdijk, 2013). Therefore, GMA should be more
central to the human capital discussion in the entrepreneurship literature and theoretical
models that include GMA should be developed.
Some prior research has demonstrated that context- or entrepreneurship-specific human
capital (e.g. industry-specific experience or entrepreneurship experience) has a stronger,
direct influence on firm performance than more context-generic investments such as
education (Bosma et al., 2004). While this could be true in some instances, this perspective
also could underestimate the indirect influence that context-generic human capital resources
such as education and GMA (Ployhart and Moliterno, 2011) may have on entrepreneurship
outcomes. In other words, this study suggests that research on venture outcomes should
consider both the direct and indirect effects that a context-generic human capital variable
such as an entrepreneur’s GMA has on venture outcomes.
With regard to venture survival, Figure 2 illustrates entrepreneurs’probability to exit
the venture given differing levels of educational attainment and GMA. Those with high
levels of education and high GMA were the least likely to exit (or most likely to survive).
This could be due to the additional knowledge and skills gained via the education process
and/or the added legitimacy that the higher degree afforded the entrepreneur (Cooper et al.,
1994; Gimmon and Levie, 2010). These entrepreneurs with higher GMA are also likely to
effectively use their education to benefit their ventures (Gottfredson, 1997).
The curves in Figure 2 generally illustrate an inverted-U, curvilinear effect of
educational attainment and GMA on venture survival. For example, consider
entrepreneurs with average levels of education (i.e. high school diploma or an associate
degree). This curve for mean-education level demonstrates that those with the lowest and
highest GMA are less likely to continue with the venture (i.e. less likely to survive) when
compared to those with average GMA. This is likely due to entrepreneurs with the lowest
GMA being “pushed out”of their ventures because of a limited ability to handle the
complex demands of operating a new business (Cooper et al., 1994; Davidsson and Honig,
2003). Conversely, those with average education and higher GMA may be more likely to be
“pulled-out”of their ventures due to better opportunities in the general labor market. This
finding supports the theoretical reasoning that the relationship with survival depends on
therelativepayoffoftheentrepreneur’s human capital while operating within the venture
vs the options available outside the venture (Gimeno et al., 1997; Rauch and Rijsdijk, 2013).
In addition, while caution should be exercised in comparing the lines in Figure 2 to each
other since these contrast effects were not hypothesized or statistically examined for
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significance, those with low levels of education and high GMA seem to be very likely to
exit their venture (i.e. least likely to survive). This outcome was not necessarily
anticipated, and future research is necessary to examine possible explanations. Overall,
these findings indicate that studies examining venture survival should consider
curvilinear effects of context-generic human capital resources.
Given that this study found GMA to have a prominent influence on entrepreneur
outcomes and that GMA has a high correlation with educational attainment, this suggests
that certain effects attributed to educational attainment could have been influenced by GMA
for studies that do not include or measure GMA (Rauch and Rijsdijk, 2013; Van der Sluis
et al., 2008). If this is the case, research that has demonstrated the positive relationship
between education level with both venture performance and survival (Cooper et al., 1994;
Davidsson and Honig, 2003; Unger et al., 2011) may be indicative that these entrepreneurs
are more successful because of their higher intelligence, rather than solely due to their
higher educational attainment. Therefore, researchers can gain a better understanding of
entrepreneurs’human capital and its influence on venture outcomes by assessing both
educational attainment and GMA.
Practical implications
These findings have implications for the screening process used by financial institutions
when determining in which entrepreneurial ventures to invest. While entrepreneur
characteristics such as prior entrepreneurial experience are often utilized in these decisions
(Hall and Hofer, 1993), the entrepreneur’s GMA is typically not assessed. Since the
entrepreneur’s GMA is likely to influence venture performance as well as the acquisition and
utilization of human capital throughout the lifetime of the venture, financial institutions
could consider screening entrepreneurs and/or the founding management team of the
proposed venture by assessing their GMA (Frese et al., 2007). This would follow the
relatively common practice in other industries of using intelligence as part of the selection
criteria for managerial and professional positions (Schmidt and Hunter, 1998, 2004). This
type of screening that includes an assessment of cognitive ability (along with other
entrepreneur characteristics such as personality) is already being utilized by financial
institutions in emerging markets (Klinger et al., 2013; De Mel et al., 2008).
In addition,anecdotal accounts often implythat entrepreneurs who do not attend college or
who drop-out of college to begin their own business (e.g. Bill Gates, Michael Dell and Steve
Jobs) can be as financially successful as those who receive a college degree (Greenberg, 2009).
Based on this study, this may especially be the case for those with higher GMA. However,
it should also be recognized that the data presented here indicate that, on average, increased
educational attainment leads to better venture outcomes among self-employed entrepreneurs.
The results from this study of general educational attainment should not be confused with
studies that focus on education and training directly related to entrepreneurship, which has
also been shown to have positive effects on entrepreneurship outcomes (Martin et al., 2013).
Limitations
As with all studies, this study has limitations. While the NLSY79 archival database enabled a
longitudinal study, it also prevented a determination of why entrepreneurs may have decided to
leave their ventures. In addition, there were some missing data and some variables were not able
to be measured as precisely as they could have been. For example, self-employment experience
wasonlyabletobeexaminedasadichotomousvariableratherthanobtainingamoredetailed
measure such as the number of years of experience or the quality of this experience (e.g. see
Gabrielsson and Politis, 2012). Therefore, the non-significant relationships between venture
performance and context-specific human capital variablessuchasself-employment experience
and industry experience could be due to these relatively “blunt”measures.
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However, self-employment entrepreneurial experience was predictive in the Cox
regression analysis (i.e. it had a positive effect on venture survival), and the study utilized
very precise measures of the human capital variables GMA and educational attainment,
which were the key variables in the study. In addition, the findings of the study may not
generalize to entrepreneurs beginning larger-scale or venture-capital backed businesses
since this database examined individuals who became self-employed entrepreneurs in
ventures that typically included just themselves or a few other employees.
Future research
When considering human capital theory, GMA is likely to influence one’seducation,
experiences, and skills (Rauch et al., 2005) as well as the ability to acquire general or specific
human capital (Becker, 1975). In other words, the examination of human capital within
entrepreneurship is incomplete without taking GMA into consideration since GMA is likely to
influence many of these areas. For example, similar to the hypothesized mediating effect of
GMA on education, GMA could have indirect and/or moderating effects on the value that
entrepreneurs would be able to gain from their entrepreneurial or industry experience (i.e. the
ability to effectively learn from this experience and utilize this experience to increase venture
performance) (Gottfredson, 1997; Judge et al., 2010). While it may be difficult to obtain a measure
of GMA from an IQ test from entrepreneurs, other assessments such as an entrepreneur’s
American College Testing or Scholastic Assessment Test score could be utilized (Frey and
Detterman, 2004; Schmitt et al., 2009). Overall, additional research is needed to better understand
the role that GMA plays in the human capital development of entrepreneurs.
In addition, a notable finding is that the industry that the entrepreneur began the venture
in had an impact on the venture performance (van Praag, 2003). As would be expected,
entrepreneurs in personal services industries had the lowest income, while entrepreneurs in
the professional services industries had the highest income from their ventures. Although
this finding is intuitive, the significant correlations of both GMA and educational attainment
with these industries indicate that some industries appear to have higher “cognitively
loaded”barriers to entry. These findings are consistent with Lofstrom et al. (2014), who
found that wealth and educational background of entrepreneurs predisposed them to make
different industry choices due to the different rewards available to them and the different
entry barriers they faced. Future research could examine whether those with lower levels of
GMA are less likely to participate in certain industries, and whether they are therefore more
likely to begin businesses in industries where entrepreneurs have lower earning potential.
This is also one of the first studies to demonstrate curvilinear effects for GMA and
education on venture survival. Future research examining context-generic human capital
variables and venture survival should consider curvilinear relationships. This research can
build on the push and pull forces described in the employee turnover literature ( Jackofsky,
1984; Maltarich et al., 2010). Whereas context-generic variables might be likely to
demonstrate these curvilinear relationships, context-specific human capital variables may
be less likely to have curvilinear effects since they may not be as valuable outside the
entrepreneurship context (e.g. prior entrepreneurship experience), which would reduce
the pull force on the entrepreneur to leave the venture.
Conclusion
This study demonstrates the importance of the GMA for entrepreneurs and their venture
performance and survival. While there is substantial and well-developed research on the
importance of GMA across most occupations and jobs (Hunter, 1980; Schmidt and Hunter,
1998), hopefully this study will encourage additional research examining this important
context-generic human capital resource, the interaction of GMA with other context-specific
human capital resources, as well as its impact on entrepreneurial outcomes.
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Note
1. In addition, to create a scale that is similar to the other variables and more appropriate for the
regression analyses, the GMA variable (i.e., percentile score) was divided by 10. Therefore, when
interpreting the regression analyses, a 1 percent change in the GMA variable corresponds to a
decile change in the dependent variable.
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Corresponding author
Brian D. Blume can be contacted at: blume@umflint.edu
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