The role of social and human capital among
, Benson Honig
¨nkoping International Business School, Jo
University of Haifa and International Affiliate, Jo
¨nkoping International Business School,
Mount Carmel, 31905 Haifa, Israel
Received 1 January 2000; received in revised form 1 March 2002; accepted 1 March 2002
This study examines nascent entrepreneurship by comparing individuals engaged in nascent
activities (n= 380) with a control group (n= 608), after screening a sample from the general population
(n= 30,427). The study then follows the developmental process of nascent entrepreneurs for 18
months. Bridging and bonding social capital, consisting of both strong and weak ties, was a robust
predictor for nascent entrepreneurs, as well as for advancing through the start-up process. With regard
to outcomes like first sale or showing a profit, only one aspect of social capital, viz. being a member of
a business network, had a statistically significant positive effect. The study supports human capital in
predicting entry into nascent entrepreneurship, but only weakly for carrying the start-up process
towards successful completion.
D2002 Elsevier Science Inc. All rights reserved.
Keywords: Nascent entrepreneurship; Start-up process; Social capital; Human capital
1. Executive summary
Our knowledge about individuals who navigate various obstacles at the very earliest stages
of entrepreneurial activity remains limited. Many people who begin the process of starting a
0883-9026/02/$ – see front matter D2002 Elsevier Science Inc. All rights reserved.
* Corresponding author. Tel.: +972-4-8249582; fax: +972-4-8249194.
E-mail addresses: firstname.lastname@example.org (P. Davidsson), email@example.com (B. Honig).
Tel.: +46-36-15-64-30; fax: +46-36-16-10-69.
Both authors contributed equally to the writing of this paper and are listed alphabetically.
Journal of Business Venturing 18 (2003) 301 – 331
new business fail to achieve their goal, while others are quite successful. Do individuals who
attempt to start businesses begin with different levels of human or social capital? Do these
endowments affect their rate of success?
Previous research excludes many of the efforts that eventually result in termination before
the emergence of the firm. Therefore, the bulk of research, which comprises much of our
knowledge of entrepreneurship, suffers from selection bias, the result of sampling only
successful emergent entrepreneurs or enterprises. Further, efforts to examine start-up attempts
ex-post suffer from hindsight bias and memory decay.
This study examined nascent entrepreneurship by first comparing individuals engaged
in nascent activities (n= 380) with a control group of non-entrepreneurs (n= 608), both
drawn from a sample of the general population (n= 30,427) of Swedish adults. Within
the group of nascent entrepreneurs, we then sought to explain differences in the
frequency of gestation activities during an 18-month period, as well as two critical
outcomes of successful emergence: first sales and profitability. Our primary objective was
to help close a research gap regarding human capital and social capital influences on
nascent entrepreneurs. We examined the comparative importance of various contributions
and factors, such as personal networks, business networks, contact with designated
assistance agencies and taking business classes, on the likelihood of successful emergent
Our findings supported the role of formal education, as well as previous start-up
experience, in predicting who among a cross-section of the general population would
attempt to engage in any nascent activities. In contrast, formal education did not appear
to be a factor in determining success in the exploitation process, neither in terms of the
frequency of gestation activities over time nor in predicting those who succeeded with a
first sale or a profitable venture. Other human capital measures, such as previous start-up
experience and having taken business classes, were predictors of the frequency of
gestation activities over time. They were not found to be important in determining the
actual first sales or the profitability of the new enterprise, criteria we use to measure
Social capital variables were found to be very strong and consistent predictors in the
analysis. We used measures for both bonding and bridging social capital, based on strong and
weak ties. Overall, social capital was found to be higher in the nascent group than in the
control group. Bonding social capital based on strong ties, such as having parents who owned
businesses or close friends who owned businesses, was a good predictor in differentiating
those engaged in nascent entrepreneurship from the control population, as was active
encouragement from family and friends. Bridging social capital based on weak ties was
found to be a strong predictor of rapid and frequent gestation activities, i.e., for carrying the
start-up process further. Bridging social capital was also important in determining which of
the nascent entrepreneurs would report a first sale or a profit—both conceived of as critical
factors that determine successful firm emergence. Being a member of a business network
such as a member of the Chamber of Commerce, Rotary or Lions, was significant and strong
throughout the analysis. Those who were members of a start-up team were also more likely to
have a comparatively rapid pace of gestation activities.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331302
The findings from this study suggest that entrepreneurs would be well advised to develop and
promote networks of all sorts, particularly interfirm and intrafirm relations. Given the rapid
changes and advances in communication technologies, and the increasing feasibility of
entrepreneurs to work in autonomous, distantly separated environments, careful attention
toward the promotion and development of social, network and mentoring capabilities would
This research questions the value of many assistance programs provided to nascent
entrepreneurs. Contact with agencies may be promoting bureaucratic activities, but failed
to predict activities indicative of successful emergence, such as a first sale, profit or even
the speed with which the gestation activities occurred. Taking business classes was
associated with increased activities, but failed to predict who had a first sale or who
became profitable. Our research suggests that current efforts to promote entrepreneurial
development may be missing the mark. A plausible interpretation of our overall results is
that the further into the start-up process one gets, the more specific and idiosyncratic will
be the resources and information needed for further successful completion of the process.
National and regional governments considering intervention activities might be advised to
focus on structural relationships rather than on programs specifically targeted to promote
certain entrepreneurial activities, which may not be the most relevant in many individual
cases. For example, they might be advised to develop business centers that focus on the
facilitation of community and networking activities, thereby increasing each nascent
entrepreneur’s probability of finding the idiosyncratic inputs s/he needs.
Contemporary definitions of entrepreneurship or delineations of entrepreneurship research
focus on emergence (Gartner, 1988; Shane and Venkataraman, 2000). The suggestion is that
entrepreneurship research should deal with early stage phenomena, such as how opportunities
are detected and acted upon, or how new organizations come into being. Shane and
Venkataraman (2000) emphasize that entrepreneurship consists of two related processes,
discovery of entrepreneurial opportunities and exploitation
of such opportunities. We adopt
this perspective in the present research.
Given the suggested focus on emergence, it is somewhat ironic that published entrepren-
eurship research is dominated by studies based on samples of existing business firms
(Davidsson and Wiklund, 2001). Studies that include the earliest, prefirm stages are rare
(although not completely nonexistent, cf. Carter et al., 1996). There does exist a nonnegligible
literature on prefirm issues, but this line of research typically focuses on intentions rather than
behavior, and uses samples of individuals who have not yet entered into nascent entrepreneurial
activity (e.g., Bird and Jelinek, 1988; Davidsson, 1995; Krueger and Brazeal, 1994; Krueger
Note that, this as used here, the term ‘‘exploitation’’ should not be associated with the negative connotations
it might have in other contexts. ‘‘Exploitation’’ here refers to an opportunity being acted upon rather than merely
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 303
and Carsrud, 1993). The major limitation of this approach is the question of the intention–
behavior relationship, a relationship that has been shown to be weak in many cases (Foxall,
Therefore, despite the avowed importance of entrepreneurship to the economic system,
it can be argued that our empirically based knowledge about entrepreneurship understood
as emergence is still very limited (Ripsas, 1998; Wennekers and Thurik, 1999). For
example, we know little of the specific social processes that may enhance the ability to
recognize or exploit opportunities. Does formal education increase an entrepreneur’s
cognitive abilities to better evaluate opportunities, as asserted by Schultz (1959)? Are
memberships in social networks a potential source of scarce information leading to
opportunity recognition; are they facilitators of resource acquisition, or perhaps a location
of knowledge diffusion leading to increased competition? How do various forms of
educational and social resources differentially contribute to the dynamic processes of
opportunity recognition and exploitation?
The purpose of our research is to provide methodologically sound empirical longitudinal
observations leading to a better understanding of aspects of human and social capital that may
be influential during the emergent phases of the entrepreneurial process. We believe that this
study will further our theoretical understanding of the dynamic processes involved in
entrepreneurial opportunity recognition and exploitation, by comparing and testing theoretical
assumptions previously unmeasured for nascent entrepreneurs. We also expect that aspects of
our findings will assist entrepreneurs and those who counsel them in their ability to
successfully engage in entrepreneurial processes.
Our approach to studying nascent entrepreneurship is to overcome methodological
problems introduced by hindsight bias and memory decay resulting from retrospective
study. We identify a random sample of nascent entrepreneurs or start-up efforts from the
general population at a very early stage. By so doing, we can also include those efforts that
fail or are abandoned at early stages, as well as providing a control sample from the general
population of non-nascent entrepreneurs. Our design excludes mere intentions, where no
concrete steps towards starting a business have been taken, and it also excludes firms that
are already up and running. Hence, we focus on the sample that is in the process of
Our objective is to analyze and map various theoretical components of human and social
capital to both of the subprocesses suggested by Shane and Venkataraman (2000), discovery
and exploitation. To study issues related to discovery is one of the most important and at the
same time most difficult challenges for entrepreneurship research, especially if real time study
is required in order to avoid success bias. Entrepreneurial discovery (or opportunity
recognition) is likely to be infrequent and therefore difficult and costly to capture in real
time. Further, there is no way to know or sample from the universe of not-yet-discovered
entrepreneurial opportunities. We have chosen to investigate the influence of human and
social capital on discovery in an indirect manner. More specifically, we compare different
theoretical components of human and social capital of nascent entrepreneurs with those of a
control group, i.e., we compare a group of people who have made what they perceive to be
discoveries that are worthwhile to pursue with a group who currently has not done so. If the
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331304
groups differ on human and social capital factors that are theoretically claimed to assist with
entrepreneurial discovery we will infer that the group differences represent causal effects.
In order to study exploitation, we follow the sample of nascent entrepreneurs over time
and examine various theoretical human and social capital influences over time, on the
outcomes of the process. Our measurements include both the exploitation effort and the
exploitation outcome over time. We are aware of no other study that utilizes such a
The paper proceeds as follows: in Section 3, we review theory and previous research on
human and social capital. This leads to the generation of six hypotheses to be tested. We then
describe the methods we have used for data collection and analysis. Following that, we
present the results of our analysis. The paper concludes with Section 6, where we interpret
our results and state their implications.
3.1. Human capital and the entrepreneur
Human capital theory maintains that knowledge provides individuals with increases
in their cognitive abilities, leading to more productive and efficient potential activity
(Schultz, 1959; Becker, 1964; Mincer, 1974). Therefore, if profitable opportunities for
new economic activity exist, individuals with more or higher quality human capital
should be better at perceiving them. Once engaged in the entrepreneurial process, such
individuals should also have superior ability in successfully exploiting opportunities.
One weakness in the theory is that it essentially takes a black box view of educational
production and accumulation activities at equilibrium. Although the theory assumes that
more human capital is always better, social systems may bias individuals to either
over-invest or under utilize their investment. Further, the amount previously invested in
human capital may influence life career choices, including attitudes towards entrepren-
eurial activity, in various ways. For example, over-investment leading to high levels of
certification may discourage risk taking, while under-investment may encourage it. For
this reason, migrants are frequently engaged in entrepreneurial activities—they reside in
a new social structure that may not reward their formal human capital investments. In
our study, we are concerned with the implications of accumulated knowledge and how
it affects agents who might or might not be nascent entrepreneurs. Although we do
not attempt to ascertain levels of absolute knowledge or propensity towards risk taking,
we examine a range of formal and nonformal human capital activities that may lead to
knowledge promotion. We examine these factors to observe entrepreneurial outcomes for
both nascent entrepreneurs and the general population and, in terms of performance, for
nascent entrepreneurs alone. Because we utilize a longitudinal study, we can begin to
examine what types of human capital promote, or fail to facilitate the discovery and
exploitation processes for these two population groups. We believe this approach to
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 305
Previous knowledge plays a critical role in intellectual performance. It assists in the
integration and accumulation of new knowledge, as well as integrating and adapting to new
situations (Weick, 1996). Knowledge may be defined as being either tacit or explicit (Polanyi,
1967). Tacit knowledge refers to know-how, the often noncodified components of activity.
Know-what consists of the explicit type of information normally conveyed in procedures,
processes, formal written documents and educational institutions. Solving complex problems
and making entrepreneurial decisions utilizes an interaction of both tacit and explicit
knowledge, as well as social structures and belief systems. Thus, individuals may be able to
increase their knowledge as a result of formal education, such as university education, informal
education, such as work experience and nonformal education, such as adult education.
Formal education is one component of human capital that may assist in the accumulation
of explicit knowledge that may provide skills useful to entrepreneurs. Empirical research has
demonstrated a range of results regarding the relationship between education, entrepreneur-
ship and success, with education frequently producing nonlinear effects in supporting the
probability of becoming an entrepreneur, or in achieving success (Bellu et al., 1990;
Davidsson, 1995; Evans and Leighton, 1989; Gimeno et al., 1997; Honig, 1996; Reynolds,
1997). A number of studies have found that, for men, returns to education are conditional on
both the industry and higher levels of education, such as college or graduate studies (Bates,
1995; Honig, 1998). For female entrepreneurs, education seems to be particularly important
Human capital is not only the result of formal education, but includes experience and
practical learning that takes place on the job, as well as nonformal education, such as specific
training courses that are not a part of traditional formal educational structures. Thus, broad
labor market experience, as well as specific vocationally oriented experience, is theoretically
predicted to increase human capital (Becker, 1964). Although empirical results have been
mixed (cf. Davidsson, 1989, pp. 37– 38), there are studies showing labor market experience,
management experience, and previous entrepreneurial experience as significantly related to
entrepreneurial activity, particularly when controlling for factors such as industry and gender
(Bates, 1995; Gimeno et al., 1997; Robinson and Sexton, 1994).
In all, previous research tends to support the existence of a positive relationship between
human capital and entrepreneurial activity. However, studies examining this relationship have
not yielded consistently strong results. Conflicting findings are easily found. Research
suggests that the relationship between human capital and entrepreneurial activity may be
confounded by a number of factors. For example, it has been demonstrated that the
relationship between persistence and education is nonlinear, with human capital increasing
performance, but not persistence (Gimeno et al., 1997). In addition, different types of human
capital may be more important at different stages of the entrepreneurial process. Unfortu-
nately, much of the available research only examines the latter stages of entrepreneurial
development (Preisendorfer and Voss, 1990). A frequent further limitation is that few studies
have attempted to incorporate extensive measures of social structure, factors that may amplify
or mitigate human capital outcomes (see, e.g., Bates, 1995; Bruderl and Preisendorfer, 1998;
Preisendorfer and Voss, 1990; Robinson and Sexton, 1994). We discuss the implications of
social capital in Section 3.2.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331306
Thus, although we predict that knowledge is critical to both the discovery and exploitation of
entrepreneurial opportunities, previous research gives only very imprecise understanding of
what types of learning experiences will be helpful at what stages of entrepreneurial processes. In
particular, the lack of a control population constrains our understanding of how and what types
of knowledge are utilized. In this research, we examine both the control and the nascent
entrepreneur populations on a range of four different aspects of human capital, viz. years of
education, years of work experience, years experience as a manager and whether or not an
individual has previous start-up experience, as independent variables. We add having taken
business classes as an independent human capital variable for our nascent entrepreneurs,
longitudinally. We regard these measures as indicators of human capital representing both tacit
knowledge, gained through experience and explicit knowledge, gained through formal
education. We do not make a priori assumptions as to the relative influence of tacit and explicit
knowledge at various stages of the process, expecting to inductively infer if, how and when each
is most relevant. The following hypotheses regarding the role of human capital are proposed:
Hypothesis 1: Human capital, representing tacit and explicit knowledge, will be positively
associated with entrepreneurial discovery, as indicated by the probability of entering into
nascent entrepreneurial activities.
Hypothesis 2: Human capital, representing tacit and explicit knowledge, will be positively
associated with successful exploitation in terms of being able to make the process move
forward, as indicated by the frequency and pace by which nascent entrepreneurial activities
Hypothesis 3: Human capital, representing tacit and explicit knowledge, will be positively
associated with successful exploitation in terms of creating a viable business entity, as
indicated by obtaining sales and achieving profitability.
3.2. Social capital and the entrepreneur
Social capital theory refers to the ability of actors to extract benefits from their social
structures, networks and memberships (Lin et al., 1981; Portes, 1998). Social networks
provided by extended family, community-based, or organizational relationships are theorized
to supplement the effects of education, experience and financial capital (Bourdieu, 1983;
Coleman, 1988, 1990; Loury, 1987). Social capital is multidimensional, and occurs at both
the individual and the organizational levels (Nahapiet and Ghoshal, 1998). Social capital is
broadly defined in the literature, such that a precise link between definition and operation-
alization is necessary in order to explain any aspect of the many network processes and
reciprocities characterized under this umbrella term (Baron and Hannan, 1994).
In this study, we broadly utilize social capital in terms of social exchange (Emerson, 1972),
to examine the effects of exchange ties on performance. Exchange effects may range from the
provision of concrete resources, such as a loan provided by a mother to her daughter, to
intangible resources, such as information about the location of a new potential client. We are
thus interested in factors related to social relations, as opposed to market or hierarchically
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 307
based relations (Adler and Kwon, 2002). These consist of the pattern of particular ties
between actors, where variation in the network in the existence or strength of ties is
meaningful and consequential (Burt, 2000; Cook and Whitmeyer, 1992, p. 118).
Social capital can be a useful resource both by enhancing internal organizational trust
through the bonding of actors, as well as by bridging external networks in order to provide
resources (Adler and Kwon, 2002; Putnam, 2000). A major factor enhancing the strength of
social capital consists of trust, often a result of obligations, threat of censure and exchange
(Coleman, 1988; Granovetter, 1985). This trust forms a bonding (or exclusive) glue that holds
closely knit organizations together. A second aspect of social capital consists of ties that
provide resources such as information, providing a bridging (inclusive) lubricant (Putnam,
2000). Ties that result in social capital can occur at both individual and organizational levels,
although they are frequently attributed primarily to the individual agents involved. These ties
may be either direct or indirect, their intensity may vary, and the outcomes (in terms of
bonding or bridging social capital) contingent on the type of network being analyzed. In
Granovetter’s (1973) classic work, he highlights the importance of maintaining an extended
network of weak ties in obtaining resources (information about potential jobs). Weak ties are
loose relationships between individuals, as opposed to the close ties that would be found in a
nuclear family. Weak ties are useful in obtaining information that would otherwise be
unavailable or costly to locate. They extend one’s network by linking individuals or
organizations together and providing an interface for exchanges to take place. Nascent firms
might, for example, rely upon weak ties such as membership in a trade organization in order
to learn about the latest technological innovations. In contrast, an example of strong ties
would be a sibling or parent helping out for free in some aspect of the start-up activities.
Thus, strong ties, such as those derived from family relationships, provide secure and
consistent access to resources. The more personal resources one has, the less likely one is to
rely on strong ties and the more attractive weak ties become (Cook and Whitmeyer, 1992).
We depict the various components of social and human capital relevant to the nascent
entrepreneurial process in Fig. 1.
Fig. 1. Social capital, human capital, and the nascent entrepreneur.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331308
Social capital is often operationalized through the identification of networks and network
relationships, sometimes defined by the strength of ties, repetitive group activity such as the
frequency of meetings and other formal interactions, as well as informal gatherings and other
social activities, and social and family relationships. From an entrepreneurial perspective,
social capital provides networks that facilitate the discovery of opportunities, as well as the
identification, collection and allocation of scarce resources (Birley, 1985; Greene and Brown,
1997; Uzzi, 1999). Social capital may also assist with the entrepreneurial exploitation
process, by providing and diffusing critical information and other essential resources.
During the discovery process, social capital assists nascent entrepreneurs as individuals by
exposing them to new and different ideas, world views, in effect, providing them with a wider
frame of reference both supportive and nurturing to the new potential idea or venture (Aldrich
and Zimmer, 1986; Aldrich et al., 1998). Entrepreneurs frequently make decisions as a result
of associations based on friendship or advice (Bruderl and Preisendorfer, 1998; Paxton,
1999), often consisting of social capital based on weak ties. Strong ties maintained by
entrepreneurs and other team members may also assist in the discovery process. Aldrich et al.
(1998) refer to the importance of family socialization by inspiring autonomy, as well as the
delivery of personal networks that provide valuable resources. Strong ties within the nascent
venture may also yield increased efficiency in resource utilization.
The discovery process is defined by asymmetrical information between entrepreneurs and
the owners of resources (Shane and Venkataraman, 2000). Because information is limited,
both bridging and bonding social capital may enhance the flow of information. Cooper and
Dunkelberg (1986) found that entrepreneurs often start businesses related to their former
occupations. Microbusinesses are particularly dependent upon the advice of friends and
relatives in order to retain confidentiality as well as personal control (Bennett and Robson,
1999). Ideas, innovations, opportunities, perspectives and normative world views are factors
that may yield benefits for those individuals who live in environments that may be considered
discovery enriched as a result of bridging social capital. Bonding social capital can also assist
in the discovery process. A family in banking, for example, may discuss new financial
activities and potential discoveries occurring in their business during the course of daily
routine activities, such as a family dinner conversation. A member of such a family may
recognize opportunities provided by this bonding strong tie social capital. Thus, we expect
that individuals who come from families who own businesses (bonding social capital), or
from community networks that own or encourage self-employment (bridging social capital),
will utilize their individual level social capital resulting in more successful discovery
activities than those who do not.
The exploitation process also provides individuals with an opportunity to leverage social
capital resources. Aldrich and Zimmer (1986) found social factors instrumental in obtaining
critical resources to exploit opportunities. Bonding social capital provides individuals with
networks that facilitate the evaluation, procurement and utilization of resources necessary for
exploitation. Bridging social capital, often based on weak ties at the individual level, utilizes
what an individual has developed within their own associations, and reflects their own value
structure, priorities and resource allocations. For example, we choose our own friends, and
these relationships may provide resources (Greene and Brown, 1997). These resources may
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 309
include conventional factors of production such as capital, where assistance may facilitate
relationships with an angel investor or a venture capitalist, as well as critical production or
marketing information diffused through appropriate efficient networks. Thus, social capital is
predicted to provide considerable resources when properly leveraged for the nascent
entrepreneur, and may be of particular importance in environments of incomplete information
and weak economic markets, such as new and nascent industries, products, markets and
technologies (Leff, 1979).
At the organizational level, as nascent firms emerge and exploit opportunities, they also
appropriate advantages provided by social capital. The importance of intra-organizational
trust as a factor enhancing the performance and efficiency of organizations has been noted in,
for example, the diamond market and ultra-orthodox Jews (Coleman, 1988) and among
members of rotating credit associations (Coleman, 1990). Coleman refers to these social
arrangements as having closure (Coleman, 1990). Such bonding social capital provides
additional information of within group activity (intra-organizational), and provides efficiency
gains through threats of censure or due to reciprocity. These gains translate into the
exploitation of new opportunities by providing lower opportunity costs (Shane and Ven-
Bridging social capital also assists new firms by linking different organizations through
weak ties. Informal networks may facilitate the establishment of new firms through the use of
multiple ownership and the ensuing relationships they bring (Teach et al., 1986). Network
holes provide advantages for organizations composed of individuals who span different
networks (Burt, 1980, 1992). Bridging social capital at the inter-organizational level consists
of collective relations such as organizational networks, engaging in interdependent activities
utilizing a web of overlapping structures based on loosely coupled open systems (Burt, 1980;
Galaskiewicz and Wasserman, 1993; Pfeffer and Salancik, 1978). These networks serve as
conduits of information about innovation, the availability and character of markets, products
In this study, we attempt to examine individual indicators of social capital that theoretically
result in both bridging and bonding relationships, and consist of both strong and weak ties.
We do not attempt to examine social capital at the firm level. Examples of bonding social
capital based on strong ties may include having parents in business, being encouraged by
family or close friends, and being married. Examples of bridging social capital based on weak
ties may include membership in organizations, contacts with community agencies, business
networks and the development of friendships with other businesspersons. Although we were
unable to test social capital based on bridging weak ties with our control population, we tested
variables indicative of both bonding strong ties and bridging weak ties at the individual level
during various stages of our nascent entrepreneurial sample.
When studying organizational emergence over time, it is difficult to keep a clear
demarcation between individual and organizational social capital (Adler and Kwon, 2002).
We regard our analysis as a test primarily of individual rather than organizational social
capital, although theory suggests that the social capital the nascent entrepreneur brings to their
activities may promote inter- and intra-organizational relationships. We begin with the
individual because we are studying per-emergent activity of what may become organizations
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331310
at a later stage. At the current state of knowledge, and with the data available, we do not feel
we are in a position to predict which specific type(s) of individual social capital (bonding or
bridging) will be more important for what aspect (discovery or exploitation) of nascent
entrepreneurship. Instead, we predict a general positive effect of individual social capital.
This discussion leads to the following hypotheses.
Hypothesis 4: Individual social capital will be positively associated with entrepreneurial
discovery, as indicated by the probability of entering into nascent entrepreneurial
Hypothesis 5: Individual social capital will be positively associated with successful
exploitation in terms of being able to make the process move forward, as indicated by the
frequency and pace by which nascent entrepreneurial activities are completed.
Hypothesis 6: Individual social capital will be positively associated with successful
exploitation in terms of creating a viable business entity, as indicated by obtaining sales
and achieving profitability.
Studies based on samples of established firms sometimes deal with questions related to the
earliest stages of development, such as start-up motivations or how resources for the would-
be business were acquired. This approach has serious shortcomings. Firstly, it has been
estimated that only half of all aspiring business founders succeed in creating new organ-
izations that are ever recorded in public records (Aldrich, 1999). Therefore, even samples of
very young firms, where few have failed after once getting up and running, are subject to
success bias. That is, the results are based solely on those cases that successfully completed
and survived the creation process. We learn nothing about those that drop out early, and it
cannot be ruled out that what appears as success factors among the survivors was equally
characteristic for those efforts that were terminated at an early stage. Because abandoned
efforts are censored, any factor that increases the dispersion of outcomes rather than their
average level would falsely be interpreted as success factors. Secondly, research that
investigates established cases and asks about their history suffers from potential bias due
to memory decay and hindsight bias, or rationalization after the fact. This means that there is
risk that outcomes are attributed to factors that were not truly present at the relevant time.
Another limitation of cross-sectional research is that it cannot determine at what stages of
the entrepreneurial process different aspects of human and social capital are influential. Our
design thus aims to overcome several methodological weaknesses of most earlier entrepren-
eurship research on human and social capital. We start by identifying a random sample of
nascent entrepreneurs or start-up efforts at a very early stage, and then we follow that sample
over time. By so doing, we can also include those efforts that fail or are abandoned at early
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 311
stages, thus, avoiding success bias and biases due to hindsight or memory decay. Further, we
explicitly examine within the same study the influence of human and social capital on
discovery as well as on exploitation. As regards discovery, we compare the human and social
capital characteristics of nascent entrepreneurs to a control group from the general population.
We then compare process progression and outcomes within the group of nascent entrepre-
neurs in order to assess exploitation success. This allows at least tentative conclusions as to
whether the influences of human and social capital characteristics appear to be different at
different stages of the entrepreneurial process.
Data are based on a two-part sample of randomly selected individuals living in Sweden.
The first part of the sample consists of individuals aged between 16 and 70 years and the
second part consists of individuals aged between 25 and 44 years. The purpose of the first
was to get a representative sample of the adult population in Sweden, while the second was
designed to increase the yield of nascent entrepreneurs. Previous research indicated that this
age group has the highest rate of business founders (Reynolds, 1997).
Because nascent entrepreneurs constitute a relatively small group in society, we had to start
with a very large sample of adult individuals. Every respondent went through a screening
interview with the objective of selecting out the nascent entrepreneurs and a control group (a
random 4% of the original sample). The wording associated with the nascent entrepreneur
question was (in translation) as follows: Are you, alone or with others, now trying to start a
new independent firm? Of the 49,979 individuals randomly selected, it was possible to obtain
a telephone number for 35,971 (71.9%). The remaining 28.1% were not listed (n= 13,338),
had severe disabilities (n= 381) or had moved abroad (n= 289). Of those contacted by
telephone, 30,427 individuals (84.6%) agreed to participate. Out of these, 961 respondents
qualified for the longer interview by answering in the screening interview that they were
starting a business either independently (nascent entrepreneur) or as part of a current job
assignment (nascent intrapreneur). We will focus here on nascent entrepreneurs only. The
longer interview started immediately if possible, although in many cases the interviewer had
to finish the screening and call back later. Failure to establish renewed contact lead to the loss
of 147 cases. Another 132 individuals were dropped from the active case file after detecting,
in the longer interview, that they did not qualify. As a result, 623 individuals completed the
longer interview, as did a randomly selected control group of 608, from among the screened
set that did not qualify as nascent entrepreneurs. From those who qualified for the longer
interview, a final sample of 380 verified and accessible nascent entrepreneurs were identified
(see Appendix A).
The initial interviews were conducted during the period of May– September 1998. The
qualified nascent entrepreneurs were contacted for follow-up interviews as long as they were
still active. The follow-ups were conducted after 6, 12 and 18 months. Sixty-one firms
reported abandoning their activity following the initial screening: 44 in month 6, 8 in month
12 and 9 in month 18. Because all of these start-up efforts had done some gestation activities
and some had a first sale, we elected to keep them in our analyses.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331312
4.2.1. Nascent entrepreneur
An individual was considered a nascent entrepreneur if he or she initiated at least one
gestation activity for a current, independent start-up by the time of the interview. Gestation
activities were determined as any of 20 different behaviors that were considered demon-
strative of actively beginning the business creation process (see Appendix B). A business was
regarded as already started if 6 or more months before the study (a) money was invested, (b)
income exceeded expenses and (c) the firm was already a legal entity (Carter et al., 1996).
This left 380 nascent entrepreneurs that were compared with the control group. Note that
while the nascent entrepreneur is always ‘nascent’ with regard to the current start-up effort, he
or she may previously and/or concurrently (have) run other businesses. That is, not all nascent
entrepreneurs are novices.
4.3.1. Dependent variables
Four different dependent variables were utilized in this study. The first dependent
variable is our indicator of discovery. This is a dichotomous variable with the value 1 for
nascent entrepreneur and 0 for the control group. This allows for the examination of
human capital, social capital and control variables regarding the probability of engaging in
The remaining three dependent variables constitute our indicators of successful exploita-
tion. The second and third dependent variables assess the progression of the exploitation
process in terms of the number of gestation activities being undertaken. For this, we utilize a
maximum number of 46 steps or sequences reflecting 20 gestation activities. For example, in
Appendix B, we show two sequences towards obtaining a patent or copyright. Application is
counted as one sequence, the granting or completion of the activity counted as a second
sequence. Preparing a business plan, however informal, was coded 1, a written informal plan
coded again as 1, and a formal written plan for external use was also identified. Thus, each
nascent might receive anywhere from zero to three sequences under the business plan
gestation behavior, with similar multiple sequence operations accounting for most of the
gestation behaviors. Sequences were totalled at the time of initial screening (dependent
variable 3) and over the course of three 6-month sampling points for the total at the end of the
18-month study (dependent variable 4). Note that, during the initial screening, there were
only 38 sequences surveyed—eight additional sequences were added in all the subsequent
waves. Gestation activities, which otherwise might have been considered a gestation
sequence behavior, such as organizing a team or contact with an assistance agency, were
omitted from the dependent variable because they were used instead as indicators of human or
The third dependent variable is a dummy variable indicating if any sales occurred at
each successive interview wave. Although it does happen that sales occur early in the
process (Bhave, 1994; Carter et al., 1996), a first sale often represents an evident
instrumental indication of a nascent firm’s eventual emergence. The fourth dependent
variable identified those firms whose owners indicated that they were profitable at the time
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 313
of survey, at either the 6-, 12- or 18-month follow-ups. As profitability is both nominally
essential and a primary goal of SMEs, we consider this to be a particularly good indicator
of successful exploitation.
4.3.2. Human capital
Human capital of the nascent firm owners was determined by a number of indicators.
Respondents were asked to indicate the highest level of education—representing explicit
knowledge—they had completed. This variable, ranging from primary to doctorate, was
coded into number of years. Much attention has focused on the specific training needs of
nascent entrepreneurs. Classes are typically available providing a wide range of information,
including legal, procedural, marketing and strategic aspects of starting a new business. We
asked respondents if they had ever attended any classes or workshops on starting a business.
Because we had no way of evaluating or comparing the different quality or range of course
content, a dummy variable was created to indicate if they had ever attended a business course.
One additional count was added for each of two classes, three classes and four or more
classes. Individuals who had previously attempted a start-up were also noted, indicated by a
To examine tacit knowledge, respondents were also asked their total years of full time paid
work experience in any field, to provide the experience variable. Supervisory or managerial
experience was also assessed in terms of number of years. Years of experience and years of
management experience were also squared and added to the equations to examine nonlinear
effects. Since none were found, they were subsequently left out of the analysis.
4.3.3. Social capital
Social capital was determined utilizing a number of variables that, by varying degrees,
capture the bonding– bridging or strong ties –weak ties dimensions, respectively. Several
dummy variables were used as indicators of each type. For example, one indicator of
bonding strong ties consisted of a dummy if either parent had ever owned a business
before. As discussed in the theory section, this variable has been shown to be influential
in a number of studies of entrepreneurship, and represents relationships characterized by
high levels of relational reciprocity and trust. We use it to indicate evidence of personal
business networks and relationships facilitated with the assistance of close family and
friends. Variables were also constructed for those individuals who indicated that close
friends or neighbors run their own businesses, and separately for those who agreed that
their family, relatives and close friends were encouraging of their starting a business. As
previously discussed, the family is a primary source of social organization, and has been
shown to influence the probability of self-employment (Sanders and Nee, 1996).We
include living with a spouse or partner as an additional indicator of strong ties that may
be indicative of bonding social capital. Two questions examined factors typically
consisting of bridging weak ties provided by individuals in the business community.
These were available only for the nascent entrepreneurs and therefore were not used in
the comparison with the control group. The first asked if the respondents had gotten
involved in any business networks, such as trade associations, chambers of commerce or
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331314
service clubs such as the Lions or Rotary. Affirmative responses were coded 1 in a
dummy variable. The second dummy was computed from a set of questions that explored
their specific contacts with organizations that dispense business advice assistance in
Sweden. Those who have sought assistance from any such organizations were coded one
on this variable. An additional social capital indicative of bridging factors was to note if
the nascent entrepreneur indicated s/he was a member of a start-up team as opposed to
pursuing a solo start-up effort.
4.3.4. Control variables
In most countries, gender has been found to be a significant factor in the probability of
establishing a business. Age has also been an associated factor—as individuals approach
retirement age, they are less likely to invest in the activities necessary to start a new
enterprise. We include these two variables as controls.
A correlation matrix for the entire data set, including the control and the nascent samples,
is provided in Appendix C.
4.4. Model specification
The first model constructed was a binomial logistic regression, analyzing the probability of
being a nascent entrepreneur as the dependent variable. The logistic regression tests the
probability of a dichotomous event happening, in this case engaging in a nascent activity. The
predicted proportion of activities follows the logistic model of ln P/(1 P
, where P
the probability of being a nascent entrepreneur (Hosmer and Lemeshow, 1989).The
logarithmic odds of these events are held to be linearly affected by a vector of covariates
with coefficient vector b. A one-unit change in covariate jalters the probability that an
individual will engage in one of the dependent variables by b
). Logistic probabilities
are given by maximum likelihood estimators and are provided for each group, those who
engaged in the activity and those who did not. Each cell of the matrix of covariates and
dependent variables is assigned a logistic probability. The null hypothesis is that the
difference between observed and predicted outcomes (maximum likelihood estimates) in
each cell of the logit table has occurred by chance. The maximum likelihood estimators
calculate the logit (log odds) of an event occurring. Computing from log odds to probability,
more commonly referred to as odds, is simply a matter of taking the coefficient to the e
these probabilities are calculated and discussed for the reader’s benefit in Section 5 (Hosmer
and Lemeshow, 1989).
The analysis of gestation activity utilized multiple linear regression analysis, with the
total number of gestation sequences as a dependent variable. The model was run using the
number of completed gestation sequences at the time the survey began, as well as for
total number of gestation sequences at the end of the 18-month period studied.
Unstandardized regression coefficients and their significance levels were reported. For
the analyses using first sales and probability as the dependent variables, we again
employed logistic regression as described above. We used the SPSS statistical package
for all statistical analyses.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 315
5.1. Results concerning human capital
Table 1 presents logistic regressions for the combined samples of control versus nascent
Eq. (1) examines the probability of being a nascent entrepreneur for the entire sample of
971 individuals, control group and nascents. Thus, it tests group differences regarding
discovery. The goodness of fit Chi-square of 977 tests the null hypothesis that the coefficients
for all of the terms in this model, except the constant, are zero (like an ftest in regression).
Chi-squared and log likelihood improvements show that the model is a statistically significant
improvement ( P< .001) over that with the constant alone, explaining the probability of an
individual ever beginning gestation activities.
Logistic regression, control group with nascent entrepreneurs
Eq. (1)—all cases
Dependent variable Nascent entrepreneur status
Years education 0.167*** (0.033)
Years experience as manager 0.022 (0.014)
Years work experience 0.077*** (0.016)
Previous start-up experience 0.779*** (0.172)
Parents in business 0.327* (0.151)
Encouraged by friends or family 0.642*** (0.164)
Close friends or neighbors in business 0.707*** (0.180)
Married 0.042 (0.174)
Age 0.102*** (0.016)
Gender ( f=1) 0.756*** (0.156)
2 log likelihood 1085.6
Overall hit rate 72.1%
Cases with missing data 25
Standard errors are in parentheses.
*** P< .001.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331316
We will concentrate initially on the human capital effects (Hypothesis 1) and return to
the effects of the social capital indicators in Section 5.2. Explicit human capital as
measured by years of schooling has a small significant and positive effect. Each additional
year of education increases the probability of being a nascent by a factor of 1.18 (0.167e
Tacit human capital as measured by work experience has a very small positive effect on
nascent activity. However, having previous management experience failed to demonstrate a
significant effect. The strongest human capital variable appeared to be tacit knowledge
acquired from previous start-up experience, where the effects provide the strongest
coefficients in the equation. The logit probability (log odds) of people who report having
previous start-up experience as being a nascent entrepreneur is 0.779 (increased probability
by a factor of 2.17) and is statistically significant, indicating that generally, individuals
with previous start-up experience are more likely to be nascent entrepreneurs than those
who are not, controlling for the remaining variables in the equation. Note that this strong
effect occurs in spite of the fact that many of those with previous experience may
concurrently be occupied with running existing businesses in parallel to the novel start-up
effort. Our supplemental analysis showed that the Wald statistic (coefficient/standard error,
squared) was quite strong (Hosmer and Lemeshow, 1989). Thus, Hypothesis 1 is
supported. Certain aspects of human capital, representing both tacit and explicit know-
ledge, do increase the probability of entrepreneurial discovery, i.e., of entering into nascent
Having established support for the notion that human capital has a positive effect on
entrepreneurial discovery we now turn to the issue of successful exploitation of such
discoveries. Table 2 presents OLS regressions for the nascent entrepreneurs, comparing the
overall number of gestation sequences during the course of the study, with the summative
sequences of both the entire 18-month period and the count at the very start. This analysis
tests our Hypothesis 2, suggesting that human capital is positively associated with the ability
to make the process move forward. Table 3 presents logistic regressions for the nascent
entrepreneurs in terms of two critical outcomes: reporting any sales or being profitable during
the 18-month study. This is our test of Hypothesis 3, that human capital is associated with the
creation of a viable business entity. Because the logistic model computes each probability
independently, we include all the relevant variables for consideration. Again, we will
concentrate on the human capital effects in this subsection and return to the social capital
effects in Section 5.2.
Eq. (2) in Table 2 examines the number of total gestation sequences that we found at
the start of the initial screening activity. Both tacit and explicit human capital variables
were only weakly associated with the total number of gestation sequences at the onset of
the study. Significant effects appeared for years of experience as a manger and for having
taken one or more business classes. Note that three sequences of the potential 46 reflect
business class activity, thus, we anticipate a small measure of shared variance between
the business classes taken independent variable and the dependent variable in this
Eq. (3) takes an approach similar to Eq. (2), and counts the total number of gestation
sequences that occurred throughout the study, inclusive of those identified at the onset of the
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 317
study. The R
of .30 suggests considerable explanatory power for an analysis of this kind.
Two human capital variables appear significant and quite strong in this regression: having
taken business classes (explicit) and having previous start-up experience (tacit). The other
three human capital variables are statistically very weak and appear not to impact the pace of
activity. Overall, Hypothesis 2 is partly supported. Some aspects of human capital—in
particular previous start-up experience—are positively associated with successful exploitation
in terms of being able to make the process move forward.
Eq. (4) in Table 3 is a logistic regression that examines the probability of having a first
sale during the 18-month period of study. This constitutes the first part of our test of
Hypothesis 3. It turns out that neither tacit nor explicit human capital variables are
OLS regression, nascent entrepreneurs only
Dependent variable Eq. (2) Eq. (3)
Sum all gestations
at initial screening
Sum all gestations initial
through 18 months
Years education 0.006 (0.104) 0.006 (0.284)
Business classes taken 1.17* (0.546) 3.87** (1.49)
Years experience as manager 0.10* (0.05) 0.004 (0.135)
Years work experience 0.001 (0.053) 0.001 (0.146)
Previous start-up experience 0.705 (0.553) 4.841*** (1.45)
Parents in business 0.867 (0.494) 1.68 (1.34)
Encouraged by friends or family 0.948
(0.584) 4.23** (1.59)
Close friends or neighbors in business 0.835 (0.535) 3.17* (1.46)
Contact with assistance agency 1.08* (0.541) 1.94 (1.47)
Member of a start-up team 1.06*(0.511) 3.75** (1.39)
Member of a business network 2.69*** (0.574) 13.30*** (1.56)
Married 0.902 (0.572) 3.70* (1.56)
Age 0.003 (0.055) 0.006 (0.150)
Gender ( f=1) 0.354 (0.571) 2.70
Constant 5.66** (1.79) 7.35 (4.90)
df 14 14
Standard errors are in parentheses.
** P< .01.
*** P< .001.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331318
statistically significant in predicting the likelihood of having a first sale. Eq. (5) examines
the probability of reporting a profit during the course of our study, further testing the
hypothesis related to successful exploitation in terms of creating a viable business entity.
The human capital variables are all statistically weak in this analysis, although most have
positive coefficients. Thus, Hypothesis 3, stating that human capital is positively associated
with establishment of a viable firm, was not supported.
5.2. Results concerning social capital
For a test of Hypothesis 4, suggesting individual social capital is positively associated
with entrepreneurial discovery—we must return again to Table 1. The social capital
Logistic regression, nascent entrepreneurs only
Dependent variable Eq. (4) Eq. (5)
Any sales in 18 months Profitable in 18 months
Years education 0.029 (0.049) 0.017 (0.048)
Business classes taken 0.315 (0.256) 0.131 (0.248)
Years experience as manager 0.019 (0.024) 0.038 (0.023)
Years work experience 0.001 (0.025) 0.003 (0.024)
Previous start-up experience 0.321 (0.253) 0.472 (0.243)
Parents in business 0.082 (0.232) 0.059 (0.225)
Encouraged by friends or family 0.236 (0.272) 0.023 (0.267)
Close friends or neighbors in business 0.431* (0.256) 0.206 (0.244)
Contact with assistance agency 0.197 (0.255) 0.266 (0.247)
Member of a start-up team 0.167 (0.239) 0.207 (0.232)
Member of a business network 1.471*** (0.318) 1.443*** (0.282)
Married 0.444 (0.262) 0.391 (0.259)
Age 0.013 (0.026) 0.018 (0.025)
Gender ( f=1) 0.083 (0.272) 0.059 (0.261)
2 log likelihood 455.264 478.73
df 14 14
Overall hit rate 64.4% 66.2%
Standard errors are in parentheses.
** P< .01.
*** P< .001.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 319
variables in this equation appear even more influential in determining the probability of
nascent entrepreneurship than did the human capital indicators. This is in spite of the fact
that only a few social capital indicators were available for the control group and hence
could be used in this analysis. Married status was the only social capital indicator not to be
ascribed a statistically significant effect. Having parents in business, the most evident
bonding social capital variable, provided a coefficient of 0.327, increasing the odds of
being a nascent entrepreneur by a factor of 1.4. Interestingly, indicators outside of the
immediate family are ascribed the strongest effects. Being encouraged by friends produces
a coefficient of 0.642, effectively increasing the odds of being a nascent entrepreneur by a
factor of 1.9. Having close friends or neighbors in business is also strong and significant,
fully doubling the odds of someone being a nascent entrepreneur (0.707e
= 2.0). Hypo-
thesis 4 is thus strongly supported. Individual social capital is positively associated with
entrepreneurial discovery as indicated by the probability of entering into nascent entre-
For a test of Hypothesis 5—that individual social capital is positively associated with
successful exploitation as indicated by making the process move forward—we refer to
the analyses in Table 2. In Eq. (2), all variables get the expected positive coefficient and
three social capital variables reach statistical significance. Being a member of a business
network has the strongest coefficient in the equation (2.69). Contact with an assistance
agency and being a member of a start-up team also appeared to be associated with the
gestation sequence activity prior to the first interview. In Eq. (3), which may be regarded
a stronger test of the hypothesis, more indicators come out significant and the
explanatory power is greater. Some of the social capital variables appear quite strong
in the analysis. This is true of those indicators arguably more indicative of bonding
social capital, as well as those arguably more indicative of bridging social capital. The
variable membership in a business network again provided the strongest coefficient
(13.23) in the entire equation, and being a member of a start-up team also had a positive
and statistically significant influence. Leaning more toward the bonding domain, being
encouraged by family or friends, having close friends in business, and being married all
had statistically significant results in predicting gestation count activity. Overall, Hypo-
thesis 5 is strongly supported. Social capital is positively associated with successful
exploitation in terms of being able to make the process move forward.
Hypothesis 6 predicts that individual social capital is positively associated with successful
exploitation in terms of achieving a first sale and profitability. For these analyses, we return to
Eqs. (4) and (5) (Table 3). As regards sales, it turns out that only two of the social capital
variables, having close friends or neighbors in business and being a member of a business
network have positive and statistically significant results. Membership in a business network
is particularly strong, increasing the odds of a nascent having a first sale by a factor of 4
= 4.34). With regard to profitability, membership in a business network, again,
demonstrates a very strong and positive relationship. Hypothesis 6 was clearly supported
only by one of the social capital variables, being a member of a business network. As no other
human or social capital indicators had a reliable influence on these outcomes, the overall
) of the predictive models in Table 3 was rather weak.
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331320
6.1. A summary and interpretation of the results
Our study empirically examined individual factors leading to both opportunity discovery
and exploitation. We examined, at the individual level, tacit and explicit human capital
factors, as well as bridging and bonding social capital, and we did so by including
comparisons with a control group of non-nascent entrepreneurs, as well as longitudinal
study of a population of nascent entrepreneurs.
Summarizing our human capital findings, we found effects of both tacit and explicit
knowledge primarily during entrepreneurial discovery, i.e., in differentiating the nascent
population from the general population. Swedish nascent entrepreneurs were better
educated reflecting more explicit knowledge. Those with greater levels of human capital
were more prone to discover opportunities perceived to be attractive enough to trigger
taking steps towards starting their own businesses (Shane and Venkataraman, 2000). This
finding concurs with previous studies examining new entrepreneurs (Bates, 1995;
Robinson and Sexton, 1994). This suggests that despite methodological shortcomings
such as success bias, previous research has not been off the mark on this issue.
We are still unsure as to what mechanisms govern this outcome. It may be that individuals
with more knowledge objectively discover more and/or better business opportunities, but this
is not the only possible interpretation. Perhaps individuals with higher amounts of human
capital have greater self-confidence, enabling them to make a choice toward independent
entrepreneurship. Alternatively, they may feel the risks are lower for them, in that they are
more easily re-absorbed by the labor market should their venture fail (Shane and Venkatara-
man, 2000). It remains unclear if our results are primarily due to cognitive or motivational
differences. We also found that nascent entrepreneurs had more work and start-up experience
reflecting greater tacit knowledge. Our study suggests that while both elements of human
capital are important for entrepreneurial discovery, tacit knowledge gained from previous
start-up experience is particularly influential.
When we examined successful exploitation within the nascent entrepreneur sample, the
effects of human capital indicators were weaker and much less consistent. With respect to
gestation activity, there were no measurable effects for general, formal education.
However, taking business classes did increase activity, as did previous start-up experi-
ence. It appeared that tacit knowledge was marginally more important during the
exploitation process. Alternatively, the explicit versus tacit distinction does not fully
account for the pattern that emerges from the results. The variables that are ascribed a
positive effect with regard to the number of gestation activities carried out through the 18
months of the study are business education and previous start-up experience. One
characteristic these have in common is that they are of more specific or immediate
relevance to the task of starting a business than are the nonsignificant variables years of
(any) education and years of (any) experience as manager.
None of the human capital variables were associated with obtaining a first sale or
being profitable during the study. Our findings suggest that while human capital increases
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 321
the probability of becoming a nascent entrepreneur, it may not reliably differentiate
successful from less successful entrepreneurial processes. Other factors, such as oppor-
tunity costs and propensity to accept risk may be more influential during exploitation
(Shane and Venkataraman, 2000). Even the most specific type of explicit human capital,
formal education as provided by business classes, only succeeded in increasing the pace
of gestation activities, not in affecting critical outcomes. This possible difference between
the roles of human capital for discovery versus for exploitation has not been highlighted
in previous studies. This may in part explain seemingly conflicting findings. A plausible
interpretation of our results is that both explicit and tacit human capital clearly facilitates
entrepreneurial discovery and to some extent the ability to get ahead with the exploitation
process, but also that human capital per se is not enough to ensure its successful
completion. As the process unfolds, more specific human capital appears to increase in
importance. We can only speculate about the precise reasons for this pattern. One
possibility is that as the nascent process moves from discovery to exploitation, increas-
ingly newer combinations of activities occur that are based on progressively more tacit
forms of human capital. An alternative explanation is that human capital facilitates
success only in conjunction with adequate levels of appropriate social capital, the effects
of which we will turn to next. There is also the possibility that individuals with higher
levels of human capital pursue higher potential opportunities that take longer time to
develop to evident market success (Shane and Venkataraman, 2000).
Our findings regarding social capital were particularly robust and noteworthy. With respect
to discovery, having parents and/or close friends or neighbors in business, as well as
encouragement from friends and family, was strongly associated with probability of entry.
We also found that social capital was important in predicting successful exploitation.
However, the results do not compellingly point to the importance of specific knowledge.
Encouragement also seems important. Encouragement by friends and family was quite
strongly associated with the pace of gestation activity during our 18 months of study. The
weaker tie (bridging) social capital variable member of a business network was consistently
important and significant in predicting gestation activity at the start of our screening, and the
pace during the following 18-month period. It was also a very strong predictor of having a
first sale or in being profitable, where most other variables failed to show an influence. Being
a member of a start-up team also demonstrated strong and significant results for gestation
Although we did not have elaborate specific measures of either, the results seemed to
indicate that bridging social capital becomes increasingly more important relative to bonding
social capital, as the process progresses. In terms of types of ties, it seems that weak ties
connecting to specific knowledge that the individual does not have which therefore is
unlikely to be available within the close network of strong ties, becomes increasingly
important as the process progresses.
We were somewhat surprised by the lack of effects from having contact with a
designated assistance agency. They appeared not to provide the kind of assistance or
access to resources expected of organizational networks. The agency contact variable
was found to be a predictor in only one model—increasing the number of gestation
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331322
activities prior to entering our sample set. As part of our exploratory analysis, we dis-
covered that individuals who had agency contact were more likely to produce a business
plan, but we were unable to associate producing such a plan with any of our measures
of success. Thus, there is some indication that the gestation activities advocated and
advanced through assistance agencies are displaced from the real requirements for
successful exploitation of entrepreneurial opportunities. Unlike a number of other social
capital variables, there was no indication that agency contact was positively associated
with the pace of gestation activity during the study, or with attaining a first sale or
Some overall patterns that emerge from our results are the following. Firstly, those
individuals in the population with higher levels of bonding social capital are more
disposed toward attempting to start a business enterprise. As we move from mere entry
into a start-up process towards its successful completion, bridging social capital comes
more to the fore, whereas the importance of human capital diminishes. This underlines
that successful entrepreneurship is a social game (cf. Schoonhoven and Romanelli,
2001). Apparently, while human capital factors can explain discovery and, to some
extent progression of the exploitation process, it is only when applied within the context
of a relevant social structure that such qualities can help achieving successful outcomes.
Secondly, the further we move from discovery towards its successful exploitation, the
fewer human and social capital indicators are ascribed statistically significant positive
effects, and hence the weaker is the overall explanatory power of the models. This
suggests that relatively general and measurable characteristics like having or not having
self-employed parents and/or a certain level of education, and being married, can with
reasonable accuracy help predict who will and who will not enter into nascent
entrepreneurship. When it comes to successful continuation and completion of the
process, however, increasingly specialized knowledge, contacts or actions are required,
i.e., aspects of human and social capital that are less general and therefore not very
well captured by the type of measures we have used. Such an interpretation is sup-
ported when we look at which variables remain influential versus which drop out as we
move from nascent versus control towards having achieved profitability. The variables
that remain tend to be the more specific ones, whereas the more general variables drop
out. Hence, business education but not general education appears important in the
exploitation stage. The same is true for support from family and friends versus merely
having access to parents or friends in business, and for previous start-up experience
versus any work or management experience. This possibility of increased specificity of
success factors over time is something that should be considered in the design of future
Explicit human capital appears to be a good investment by increasing the probability
of someone in the population entering into the nascent process. For those who have
chosen to undertake nascent activities, formal business classes ought to provide more
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 323
directed assistance in creating profitable nascent enterprises. We found that while those
who attended business classes had demonstrably more gestation activity, they were no
more likely to be profitable or have a first sale than those who had not taken such
classes. A similar pattern was discovered with those who had contact with agencies
attempting to help small business establishments. Although they appeared to engage in
more gestation activities initially, those with agency contacts were no more likely to be
profitable or have a first sale. It appears as though more formal efforts to promote
entrepreneurship often fail in their intended objectives. When one considers the
complexity involved in both the discovery and the exploitation processes, it should
come as no surprise that trainers who are not themselves experienced in the particular
trajectories involved in exploitation fail to provide significant assistance. Our results seem
to indicate that highly specialized knowledge and actions are required for successful
exploitation. If so, the value of all forms of standard recipe is likely to be very limited,
and the real needs beyond the capacity of a generalist advisor. This particular finding
should come as a sharp warning to the business education establishment. The implica-
tions are that individuals are taught to engage in activities that are not necessarily
productively linked toward successful outcomes. Of course, this is a preliminary result,
limited to only one country. Subsequent cross-national longitudinal research focusing
specifically on assistance will be necessary to confirm or refute these findings.
An area where we found much greater opportunity for intervention was that of social
capital. We found several aspects of individual social capital to be very important predictors
of who would elect to become a nascent entrepreneur. We also found them to be important at
all stages of the nascent process, increasing the pace and—as regards membership in business
networks—the probability of sales or profitability. For entrepreneurs and nascent entrepre-
neurs, our findings suggest the importance of actively maintaining, pursing, and developing
social relations. In fact, our study suggests that these relations are more important than
maintaining contact with assistance agencies, or even in taking general business classes. In
particular, memberships in business networks appear to provide consistently strong results
over the life of a nascent activity.
Previous start-up experience had strong positive effects on discovery as well as on
exploitation. In contrast, managerial experience was not found to be a predictor during either
process. This finding should be of importance to firms seeking to promote an intrapreneurial
environment. Managerial activities may foster routines that do not facilitate opportunity
recognition and/or a resource acquisition and allocation procedures that are not suited for
successful entrepreneurial exploitation. Organizations seeking to promote new activities may
want to consider developing their bridging social capital, much as successful nascent
entrepreneurs appear to do. This may consist of building systems that promote and evaluate
new opportunities outside the normative boundaries of the organizational hierarchy, or
otherwise expanding or promoting linkages in order to widen potential sources of information
From a theoretical perspective, understanding the link between exploitation and social
capital represents an important area of future research. From a public policy perspective,
our research suggests that much of the activity related to training for the small business
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331324
sector, such as the format and production of business plans, may be missing the mark.
This study suggests that the facilitation and support of business networks and associations
may provide the most consistent and effective support for emerging businesses. For
example, many communities now provide business incubators that offer subsidized rent,
business advice, marketing assistance and encourage networking. With the growth of the
Internet many new firms no longer require geographical space in the form of subsidized
rent. They can conveniently work and grow their companies from their own homes,
obviating the need for expensive rent subsidies. On the other hand, the virtual nature of
many new businesses, as well as the rapid pace of technological change, highlights the
importance of maintaining social relations and networks. Our research clearly indicates
the value of effective networking activities, suggesting the importance in promoting and
facilitating social relations and mentoring activities for nascent entrepreneurs. In conclu-
sion, both entrepreneurs and public policy specialists may have cause to examine and
increase their efforts to build social capital. Furthering our understanding of these specific
nascent networks and learning how best to facilitate them represents an important activity
for future entrepreneurship research.
This research owes intellectually to The Entrepreneurial Research Consortium (ERC), a
temporary association of 30+ university centers and 100+ scholars who are carrying out
the US Panel Study of Entrepreneurial Dynamics (PSED). We have in large part
adopted the design developed by the ERC for its US study (in which we were also
involved). The Swedish study has been made possible through financing from the
Swedish Foundation for Small Business Research, the Knut and Alice Wallenberg’s
Foundation and the Swedish National Board for Industrial and Technical Development
Sample and response rates
Individuals randomly sampled 49,979
Individuals with identifiable phone number 35,971
Individuals screened 30,427
Percentage yes to NE, NI item 3.2%
No. of yes answer to nascent entrepreneur or
nascent intrapreneur item
Refused to volunteer 53
Not enough knowledge of Swedish 6
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 325
Twenty gestation behaviors and 46 gestation sequence questions
No contact, not clear if start-up 147
Started, but did not complete interview,
because they were no longer starting a business
(misunderstanding, changed situation)
No. of who accepted invitation to volunteer
and completed long interview
Did not meet the gestation criteria
(nascent intrapreneur; no gestation activities,
already up-and-running, etc.)
Missing data 10
Nascent entrepreneurs analyzed 380
Gestation activity Question
1. Business plan Have you prepared a business plan?
1. Business plan Is your plan written (includes informally for
1. Business plan Is your plan written formally for external use?
2. Development of
At what stage of development is the product or
service that will be provided to the customers?
3. Development of
Idea or concept
3. Development of
3. Development of
Tested on customers
3. Development of
Ready for sale or delivery
4. Marketing Have you started any marketing or promotional
4. Patent/copyright Have you applied for a patent, copyright or trademark?
4. Patent/copyright Has the patent, copyright or trademark been granted?
5. Raw material Have you purchased any raw materials, inventory,
supplies or components?
6. Equipment Have you purchased, leased or rented any major
items like equipment, facilities or property?
7. Gathering information Have you gathered any information to estimate
potential sales or revenues, such as sales forecasts
or information on competition, customers and pricing?
7. Gathering information Have you discussed the company’s product or
service with any potential customers yet?
8. Finance Have you asked others or financial institutions for
8. Finance Has this activity been completed (successfully or not)?
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331326
8. Finance Have you developed projected financial statements
such as income and cash flow statements,
9. Saved money Have you saved money in order to start this business?
10. Credit with supplier Have you established credit with a supplier?
11. Household help Have you arranged childcare or household help to
allow yourself time to work on the business?
12. Workforce Are you presently devoting full time to the business,
35 or more hours per week?
12. Workforce Do you have any part time employees working
for the new company?
12. Workforce How many employees are working full time for
the new company? One?
12. Workforce How many employees are working full time for
the new company? Two?
12. Workforce How many employees are working full time for
the new company? Three or more?
13. Nonowners hired Have you hired any employees or managers for
pay, those that would not share ownership?
14. Education Have you taken any classes or workshops on
starting a business?
14. Education How many classes or workshops have you taken
part in? One only
14. Education How many classes or workshops have you taken
part in? Two only
14. Education How many classes or workshops have you taken
part in? Three or more
15. Contact information Does the company have its own phone number?
15. Contact information Does the company have its own mail address?
15. Contact information Does anyone in the team have a mobile mainly
used for the business?
15. Contact information Does the company have its own visiting address?
15. Contact information Does the company have its own fax number?
15. Contact information Is there an e-mail or internet address for this new
15. Contact information Has a web page or homepage been established for
16. Gestation Marketing Have you started any marketing or promotional efforts?
17. Gestation income Do the monthly expenses include owner/manager
salary in the computation of monthly expenses?
18. Obtained licenses Has the new business obtained any business licenses
or operating permits from any local, county or state
19. Legal form Has the new business paid any federal social
19. Legal form Has the company received a company tax certificate?
20. National specific Have you applied for start-up benefits? (cf. U.K.
‘enterprise allowance scheme’)
20. National specific Has the application (the answer) regarding start-up
benefits been completed?
20. National specific Has the new business received a company tax certificate?
P. Davidsson, B. Honig / Journal of Business Venturing 18 (2003) 301–331 327
Means, standard deviations and correlation coefficients for nascent entrepreneurs
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1. Years of education 12.58 2.57
2. Business class taken 0.43 0.49 .037
3. Years of experience
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