The nascent entrepreneurship hub: goals, entrepreneurial
self-efﬁcacy and start-up outcomes
Diana M. Hechavarria •Maija Renko •
Charles H. Matthews
Accepted: 1 December 2010 / Published online: 14 August 2011
ÓSpringer Science+Business Media, LLC. 2011
Abstract Entrepreneurship involves human agency.
The entrepreneurial process occurs because people are
motivated to pursue and exploit perceived opportuni-
ties. It is rooted in the theory that action is the result of
motivation and cognition. Therefore, this paper applies
elements of goal theory and social cognitive theory to
develop a motivational model of nascent entrepreneur-
ial start-up outcomes. The objective of this model is to
renew attention on motivational constructs in entre-
preneurship research. Additionally, it provides predic-
tive value for the likelihood of new ﬁrm founding
among nascent entrepreneurs. Results suggest that
motivational antecedents among nascent entrepreneurs
signiﬁcantly inﬂuence the likelihood of quitting the
start-up process versus continuing nascent entrepre-
neurial start-up efforts.
Keywords Nascent entrepreneurship
Entrepreneurial self-efﬁcacy Business planning
JEL Classiﬁcations L25 L26 M13
The importance of human motivation in entrepre-
neurship is gathering increasing interest. However,
relatively little of this motivation research in entrepre-
neurship has considered the effects of motivation on
speciﬁc steps in the entrepreneurial process (Shane et al.
2003). This is a limitation in existing research since
starting up a new venture is a dynamic process, and is
likely to involve a variety of motivational factors at
various stages of the process. For example, an engineer
might be highly motivated to invent a new medical
device with a potential commercial application, but may
lack the motivation to assemble the required ﬁnancial
resources for a new venture. In this paper, we employ a
process approach to study motivation and new ﬁrm
formation among nascent entrepreneurs. A nascent
entrepreneur is a person who initiates actions that are
intended to culminate in a viable new ﬁrm (Reynolds
1994). Gartner (1988) has proposed that entrepreneur-
ship ends when organizational creation is over. Conse-
quently, we limit our focus to individuals in the
earliest stages of the start-up process to identify those
motivational factors that may differentiate nascent
D. M. Hechavarria (&)
Department of Management, University of Cincinnati,
Lindner Hall 516, Cincinnati, OH 45221-0020, USA
Department of Managerial Studies, University of Illinois
at Chicago, 2211 University Hall, 601 S Morgan Street,
Chicago, IL 60607, USA
C. H. Matthews
Center for Entrepreneurship Education & Research,
University of Cincinnati, 510 Carl H. Lindner Hall,
Cincinnati, OH 45221-0165, USA
Small Bus Econ (2012) 39:685–701
entrepreneurs who actually start new operative ﬁrms
from those who quit, and from those ‘‘hobbyists’’ who
continue in their start-up efforts for extended periods of
time. Our study is a response to a call for research that
would incorporate motivations into a dynamic, evolu-
tionary perspective on entrepreneurship by using moti-
vations to distinguish those individuals who continue to
pursue opportunities fromthose who abandon the effort
(Shane et al. 2003).
An examination of recent individual level entre-
preneurship research reveals a range of theoretical
and empirical approaches to explain new ﬁrm births,
often from a process approach (Gelderen et al. 2006;
Dimov 2007; Gruber 2007; Teece 2007; Brush et al.
2008; Harper 2008). This overall process approach,
however, does not discount the additional importance
of individual level entrepreneurial factors. There have
been a number of individual level factors that have
been studied in the entrepreneurship ﬁeld. Some of
the more common include the need for achievement
(McClelland 1965; Hansemark 2003), risk-taking
propensity (Brockhaus 1980; Brockhaus and Horwitz
1986; Corman et al. 1988), and internal locus of
control (Rotter 1966; Borland 1974; Hansemark
1998; Kaufman et al. 1995). However, there are
limited discussions of entrepreneurial motivation in
the process literature.
When motivation has been studied, it has often
been for the purpose of discussing why people enter
the start-up process from the labor force. Indeed,
Gatewood et al. (2002) provide evidence that moti-
vations prove most powerful in understanding the
determinants of organizational creation. Herron and
Sapienza (1992, p. 49) state, ‘‘motivation plays an
important part in the creation of new organizations,
theories of organization creation that fail to address
this notion are incomplete.’’ The concept of motiva-
tion is used to explain the direction, effort, and
persistence of action (Kanfer 1990). Focusing on the
direction of action, this study proposes that entrepre-
neurs are motivated to accomplish the goals that they
set for themselves (Naffziger et al. 1994). A goal is
what an individual is trying to accomplish; it is the
object or aim of an action (Locke et al. 1981). In the
case of nascent entrepreneurs, goals may vary from
cashing out quickly to pursuing one’s intrinsic goals.
The common denominator for these and other goals
of nascent entrepreneurs is that they all involve
establishing a new ﬁrm as a ﬁrst step.
The model developed in this paper suggests that
entrepreneurial start-up outcomes are, in part, driven
by an individual’s motivation. Theoretically, we base
our proposed model on concepts borrowed from goal
setting (Locke and Latham 1990) and social cognitive
theory (Bandura 2001). Goal setting theory is not
limited to but focuses primarily on motivation in
work settings, and the focus of goal setting theory is
on the fundamental properties of an effective goal.
Social cognitive theory and the research that under-
lies it are primarily focused on self-efﬁcacy. Both
theories agree about what is considered important in
performance motivation (Locke and Latham 2002).
As a result, we apply goal setting and social cognitive
theory as motivational mechanisms (although cogni-
tive elements are necessarily involved) to help
understand new ﬁrm emergence. We posit that the
coalescence of self-efﬁcacy and goal speciﬁcity
provide a robust individual level motivational model
for understanding the outcomes of the start-up
process. Such a model essentially answers the
research question of ‘‘Why do individuals start new
businesses?’’, and consequently has implications for
entrepreneurship policy and education, in addition to
The following two sections of this paper will
review the key theories on which our arguments are
based. Subsequently, we will explicate our operal-
ization of key constructs and overview our ﬁndings in
the ‘‘Analysis and results’’ section. Finally, we will
discuss the implications of out ﬁndings and summa-
rize our conclusions.
2 A starting point: the motivation hub
Our work draws from Locke’s (1991)motivation
sequence which attempts to understand human, and
especially work, motivation. In his motivation
sequence model, Locke ﬁrst proﬁles needs, which
directly feed into the motivation core (values and
motives). Secondly, the motivation core directly
impacts the motivation hub, which then leads to
rewards and, ﬁnally, satisfaction.
The motivation hub is the core of action (Locke
1991). The motivation hub includes linkages between
goals, self-efﬁcacy and performance. As such, the
motivation hub is the central component of the model
(Locke 1991), and self-efﬁcacy is depicted as having
686 D. M. Hechavarria et al.
direct relationships to goals and performance.
According to Locke’s (1991) model, what people
do is powerfully (though not solely) inﬂuenced by
their goals or intents and by their perceived conﬁ-
dence in being able to take the actions in question.
Subsequent empirical ﬁndings in goal setting and task
motivation research have also found that self-efﬁcacy
operates as a moderator between goals and perfor-
mance (Bass 1985; Bandura 1997; White and Locke
2000; Locke and Latham 2002).
What Locke (1991) called the motivation hub,
meaning where the action is, consists of personal
goals and self-efﬁcacy. These variables are often,
though not invariably, the most immediate, con-
scious motivational determinants of action (Locke
and Latham 2002). Building and expanding on the
motivation hub, the nascent entrepreneurship hub is
aimed at understanding how goals, self-efﬁcacy, and
start-up outcomes (i.e. ‘performance’ in Locke’s
model) are linked in the context of new ﬁrm
2.1 The nascent entrepreneurship hub: goal
setting and social cognitive theory
as motivational mechanisms
A nascent entrepreneur is deﬁned as someone who
initiates activities that are intended to culminate in a
viable new ﬁrm (Reynolds 1994). Operationally,
nascent entrepreneurs (1) consider themselves as
starting a business, (2) have engaged in start-up
activities within the past year, (3) expect to own all or
part of the new business, and (4) have not experienced
more than three months positive cash ﬂow (Reynolds
2007). A beneﬁt of utilizing nascent entrepreneurs as
units of observation in entrepreneurial motivation
research is the explication of the cognitive sequence
for individuals who enter the start-up process. It is
advantageous because it provides an opportunity for
scholars to identify cognitive differences among
individuals who are subsequently (1) successful in
implementing a new ﬁrm, (2) unsuccessful in their
efforts, but keep on trying, and/or (3) unsuccessful and
quit (Reynolds 2007). Few studies to date have
investigated the role of entrepreneurial motivation
and its subsequent translation into action aimed at
organizational creation in general, and focused on
nascent entrepreneurial activity in particular. In this
manuscript, we introduce the elements of the ‘‘nascent
entrepreneurship hub,’’ based on Locke’s motivation
hub, and formulate hypotheses for empirical testing.
2.2 Goal speciﬁcity
Following Locke and Latham’s (1990) goal theory,
we know that goals direct attention and action to
goal-related activities. Second, goals have an ener-
gizing function. Harder goals lead to greater effort
than easier goals (Locke et al. 1981; Locke and
Latham 2002). Third, goals affect persistence. When
participants are allowed to control the time they
spend on a task, hard goals prolong effort (LaPorte
and Nath 1976). Fourth, goals affect action indirectly
by leading to the arousal, discovery, and/or use of
task-relevant knowledge and strategies (Wood et al.
1987). However, despite this obvious relevance for
entrepreneurship research, there is little extant
research that applies goal theory’s predictions to
entrepreneurial situations, with the exception of
Shane and Delmar’s (2004) study on business
In the context of nascent entrepreneurship, goal
setting theory suggests that undertaking business
planning before acting will enhance the start-up
performance of new ventures (Shane and Delmar
2004). Plans are particularly useful when tasks are
fuzzy or uncertain, and the decision maker cannot
rely on previous experience (Campbell 1988), which
is often the case in new business start-ups. Goal
setting theory also suggests that written planning
improves human action. Writing a plan clariﬁes goals
and permits people to set more speciﬁc objectives,
which facilitate the achievement of those goals
(Locke and Latham 1990; Shane and Delmar 2004).
In a retrospective empirical study of a limited sample
of entrepreneurs, van Gelder et al. (2007) found that
entrepreneurs running surviving businesses had set
more speciﬁc goals than entrepreneurs, whose busi-
nesses had ceased to exist.
In general, we posit that entrepreneurs have a set
of goals they seek to accomplish when they decide to
enter nascent entrepreneurship (e.g., Naffziger et al.
1994). In most goal setting studies, the term goal
generally refers to attaining a speciﬁc standard of
proﬁciency on a task, usually within a speciﬁed time
limit (Locke et al. 1981). These goals may vary for
each nascent entrepreneur: some may seek to rapidly
grow a ﬁrm and cash out while others may seek to
The nascent entrepreneurship hub 687
grow and build their venture over time. Despite
variability in goals, goal setting theory states that
speciﬁc and difﬁcult goals lead to higher performance
than vague or easy goals (Locke and Latham 2002).
For example, Wiese et al. (2002) found that individ-
uals who report setting difﬁcult work goals showed
stronger progress towards their goals than individuals
who perceived their goals as less difﬁcult. Indeed,
Shane and Delmar (2004) ﬁnd that this prediction of
goal setting theory holds with respect to the value of
business planning. Completing business plans before
undertaking marketing activities (i.e., specifying and
formalizing one’s goals) reduces the hazard of
termination of new ventures (Shane and Delmar
2004). While not speciﬁcally focused on goal setting
theory, other research suggests that business plan
is an antecedent to venture organizing
activity (Delmar and Shane 2003; Honig and Karls-
son 2004; Liao and Gartner 2006).
In sum, goals seem to regulate performance most
predictably when they are expressed in speciﬁc
quantitative terms (or as speciﬁc intentions to take a
certain action, such as quitting a job) rather than as
vague intentions to ‘‘try hard’’ or as subjective
estimates of task or goal difﬁculty. Given the ﬁndings
of prior research, as well as extant classroom and
community entrepreneurship pedagogy, which often
has an emphasis on planning, we hypothesize that
more speciﬁc goals (i.e., more formal plans) will
beneﬁt nascent entrepreneurs who aim to establish a
new ﬁrm. Since entrepreneurs self-select their goals,
a business plan would serve as a proxy to measure
how speciﬁcally they have formalized that self-
selected goal. Therefore, we hypothesize that:
H1 There is a positive relationship between goal
speciﬁcity and the new ﬁrm outcome status. Specif-
ically, nascent entrepreneurs with more formalized
and speciﬁc quantiﬁed goals are more likely to start
In Locke’s (1991) motivation sequence model, as
well as in the ﬁndings of Shane and Delmar (2004),
goals are directly related to performance (see also
Baum and Locke 2004). However, especially in the
context of nascent entrepreneurship, goals by them-
selves may not be sufﬁcient to lead to start-up
performance outcomes unless the goals are actually
being pursued by individuals who at a minimum feel
that they are capable of starting such a venture.
Individuals must have the ability to attain or at least
approach their goals. Therefore, self-efﬁcacy
undoubtedly plays a critical role in directing behavior
aimed at goal attainment.
Self-efﬁcacy refers to the extent to which a person
believes that he/she can organize and effectively
execute actions to produce given attainments (Ban-
dura 2001). It is one of the single best predictors of an
individual’s performance in general (Locke and
Latham 2002). It is considered a state-like character-
istic that generally increases with experience and is
highly related to actual ability (Phillips and Gully
1997). This mechanism of personal agency is most
central to social cognitive theory, a theory of self-
regulation (Bandura 1989,2001).
Self-efﬁcacy beliefs inﬂuence an individual’s level
of motivation, as reﬂected in how much effort he/she
will exert in an endeavor, and how long he/she will
persevere in the face of obstacles (Bandura 1994).
For instance, Cervone and Peake (1986) found that
the higher was the instated perceived self-efﬁcacy,
the longer individuals persevered on difﬁcult and
unsolvable problems before they quit. Therefore,
individuals with a strong sense of self-efﬁcacy will
put forth a high degree of effort in order to meet their
commitments, and attribute failure to things which
are in their control, rather than blaming external
factors (Bandura 1994; Zacharakis 1999). Self-efﬁ-
cacious individuals also recover quickly from set-
backs, and ultimately are likely to achieve their
personal goals (Bandura 1997). Conversely, individ-
uals with low self-efﬁcacy believe they cannot be
successful, and thus are less likely to make a
concerted, extended effort and may consider chal-
lenging tasks as threats that are to be avoided
(Margolis and McCabe 2006).
Self-efﬁcacy has been linked theoretically and
empirically with managerial and entrepreneurial phe-
nomena (Krueger et al. 2000; Markman and Baronb
2003). In organizational research, separate meta-
analyses by both Stajkovic and Luthans (1998) and
Judge and Bono (2001) have demonstrated a robust
positive relationship between employee self-efﬁcacy
Business plan formalization is the degree of speciﬁcity for
the business plan.
688 D. M. Hechavarria et al.
and performance. Self-efﬁcacy, and particularly entre-
preneurial self-efﬁcacy (McGee et al. 2009), appears to
be an important antecedent to new venture intentions
and creation (Chen et al. 1988; Boyd and Vozikis 1994;
Zhao et al. 2005; Barbosa et al. 2007; Markman et al.
2005; Wilson et al. 2007; Townsend et al. 2010).
Entrepreneurial self-efﬁcacy is a context-speciﬁc
measure of self-efﬁcacy. This research focuses on the
belief of individuals in their ability to perform entre-
preneurship-related tasks. For example, Chen et al.
(1988) created a measure of entrepreneurial self-
efﬁcacy comprised of dimensions related to marketing,
innovation, management, risk-taking, and ﬁnancial
control. Using this measure, Chen et al. (1988) found
entrepreneurial self-efﬁcacy to signiﬁcantly differen-
tiate entrepreneurs from non-entrepreneurs. In addition
to its effect on entrepreneurial intent and immediate
venture creation, entrepreneurial self-efﬁcacy of the
founder has even been found to inﬂuence performance
outcomes on the ﬁrm level (e.g., Baum and Locke
2004; Hmieleski and Baron 2008).
Based on prior self-efﬁcacy research, we believe
that nascent entrepreneurs who have a strong belief in
their capabilities—that is, high entrepreneurial self-
efﬁcacy—exert greater effort when they face difﬁcult
or ambiguous challenges in the start-up process. Such
strong perseverance contributes to the outcomes of
the process. Therefore, we hypothesize that:
H2 There is a positive relationship between entre-
preneurial self-efﬁcacy and the new ﬁrm outcome
status. Speciﬁcally, nascent entrepreneurs with higher
degrees of entrepreneurial self-efﬁcacy are more
likely to start new ventures.
Both goal setting and social cognitive theory
literature suggests that the relationship between
self-efﬁcacy and goal attainment varies as a function
of goal speciﬁcity. This would suggest that the
nascent entrepreneurial motivation hub is incomplete
without more fully considering the moderating effect
that self-efﬁcacy has on goal-speciﬁcity to start-up
2.4 Goal speciﬁcity and entrepreneurial
Behavior is powerfully inﬂuenced by both goals and
by the perceived conﬁdence in being able to take
action (i.e., self-efﬁcacy) (Locke 1991; Latham and
Pinder 2005a,b). Previous research from the goal
setting perspective has found that speciﬁc, challeng-
ing (difﬁcult) goals led to higher output than vague
goals such as ‘‘do your best’’ (Locke 1968). As
described earlier, self-efﬁcacy has also been found to
be an antecedent of a variety of positive task
outcomes. Goal setting and social cognitive theories
in general, and self-efﬁcacy in particular, are consid-
ered to be the most direct and immediate motivational
determinants of work performance (Lathan and
Pinder 2005a,b), and thus particularly relevant to
understanding why nascent entrepreneurs engage in
the ﬁrm creation process.
Building on previous research that has investigated
the role of self-efﬁcacy as an intervening variable in
entrepreneurial models (Zhao et al. 2005; Hmieleski
and Corbett 2008) and between goals and perfor-
mance (Locke and Latham 2002), we believe that
perceived self-efﬁcacy will also moderate the rela-
tionship between speciﬁc goals and positive start-up
process outcomes. Those who have a strong belief in
their capabilities exert greater effort, and when
directed by speciﬁc goals, such efforts will result in
desired outcomes. However, individuals who have
doubts about their entrepreneurial skills and knowl-
edge—i.e. their self-efﬁcacy concerning entrepre-
neurial tasks is low—are not likely to fully beneﬁt
from the speciﬁc goals that they still may be able to
set for their start-up endeavors. As Bandura (2001)
implies, making a decision is not the same as
implementing the decision, and entrepreneurial self-
efﬁcacy is needed to carry out the implementation
stages of the nascent venture. This suggests that
critical insights can be gained from the interaction of
goals and self-efﬁcacy on start-up outcomes. There-
fore, we hypothesize that:
H3 Entrepreneurial self-efﬁcacy moderates the goal
speciﬁcity to new ﬁrm outcome status relationship,
such that a higher degree of entrepreneurial self-
efﬁcacy will strengthen the positive relationship
proposed in Hypothesis 1.
Data for the empirical analysis come from the Panel
Study of Entrepreneurial Dynamics I (PSEDI). The
PSEDI is a longitudinal study that involves more than
The nascent entrepreneurship hub 689
100 entrepreneurship scholars who came together as
part of the Entrepreneurial Research Consortium
(ERC). Initially, random digit dialing calls was made
to 31,261 individuals in 1998–1999. The study
methodology allowed researchers to identify nascent
entrepreneurs—those individuals in the process of
starting up a new venture—from this pool of
individuals and to longitudinally follow their pro-
gression through data collection periods over time.
The breadth and quality of the PSED data provides a
unique opportunity to avoid the survival bias typical
for studies of young ﬁrms. Since the PSED focuses on
nascent entrepreneurs (individuals actively involved
in the start-up process who have yet to experience
three months of positive operating cash ﬂow) and is a
longitudinal study, it also avoids the recollection bias,
typical for cross-sectional surveys. The PSED dataset
and related codebooks are publicly available on the
A total of 830 nascent entre-
preneurs were identiﬁed for this longitudinal study.
These nascent entrepreneurs were then followed up at
about one-year intervals to enquire about the current
status of their start-up efforts. Three such follow-up
waves were completed. Additional detailed descrip-
tions of the methods and sampling used to generate
the PSED can be found in Reynolds and Curtin
3.1 Dependent variable
3.1.1 Outcome status
For this assessment, start-up outcome status has three
possibilities for nascent start-ups. A nascent entre-
preneur can quit the start-up process, reach new ﬁrm
status, or continue in the start-up process. For our
dependent variable (i.e., new ﬁrm, start-up continues,
quit initiative), we utilized a time-lagged measure
from the PSED sample which asks respondents to
categorize the status of their potential venture at each
follow-up wave. These results were cross-checked
with a related question where the respondents
reported the actual year and month the venture began
operation as well as respondent statements on the
cash ﬂow ﬁgures for the nascent venture. Conse-
quently, a nascent start-up is classiﬁed as a new ﬁrm
if the respondent answers that the ﬁrm is up and
running, and, furthermore, the venture has experi-
enced at least 3 months of positive cash ﬂow within
the 72-month investigation period. If the nascent
start-up has not experienced at least three months
consecutive positive cash ﬂow, it remains in the
continued start-up category. Finally, all respondents
who self-identiﬁed as disengaging from the process
were categorized as ‘‘quits’’.
3.2 Independent variables
3.2.1 Business plan formalization
In order to measure goal speciﬁcity, we use PSED
items q114-r/s/t571 to compute the ﬁnal form of
business planning as an ordinal variable with four
levels (none, unwritten in head, informal, and formal).
Business planning has engendered a lengthy discus-
sion by various scholars regarding its efﬁcacy to
facilitate goal attainment largely around business
planning helping ﬁrm founders to undertake guided
venture development activities (Cyert and March
1963; Simon 1964; Locke 1968; Latham and Yukl
1975; Bird 1988; Smith et al. 1990; Timmons 2000;
Baum et al. 2001; Shane and Delmar 2004; Baum and
Locke 2004). Therefore, guided by Shane and Delmar
(2004), we measure goal speciﬁcity of nascent
entrepreneurs by looking at their business planning
activities. A realized business plan is deﬁned as
having (formally or informally) identiﬁed the current
state and the presupposed future of the ﬂedgling
organization (Honig and Karlsson 2004). Writing a
plan typically clariﬁes goals while allowing entrepre-
neurs to set more speciﬁc goals (Locke and Latham
1990; Shane and Delmar 2004). In the PSED protocol,
the level of business plan formalization is assessed
through two questions. First, the nascent entrepre-
neurs were asked: ‘‘A business plan usually outlines
the markets to be served, the products or services to be
provided, the resources required—including money—
and the expected growth and proﬁt for the new
business. Has a business plan been prepared for this
start-up?’’. If the answer to this question was ‘‘No’’,
the value for business plan formalization is zero. For
those who answered ‘‘Yes’’ to the question above, the
level of formalization was then subsequently queried.
Individuals with unwritten, ‘‘in head’’ plans were
coded 1, individuals with informally written plans
690 D. M. Hechavarria et al.
were coded 2, and individuals with formally prepared
written plans were coded 3.
3.2.2 Entrepreneurial self-efﬁcacy
Bandura (1977) has argued that self-efﬁcacy should
be focused on a speciﬁc context and activity domain.
The more task-speciﬁc one can make the measure-
ment of self-efﬁcacy, the better the predictive role
efﬁcacy is likely to play in research on the task-
speciﬁc outcomes of interest (Bandura 1977; McGee
et al. 2009). To measure degree of entrepreneurial
self-efﬁcacy, ﬁrst interview (Wave Q) responses were
utilized in order to overcome post hoc rationalization
among respondents. A direct approach is taken in
measuring entrepreneurial self-efﬁcacy, based on
responses to Likert scale items from the mail
questionnaire. Cassar and Friedman (2009), validated
the following items as a measure of entrepreneurial
self-efﬁcacy: Qk1a, Qk1d, QK1e and QK1f (see
Table 1for variable descriptions).
tor analysis conﬁrmed the unidimensional factor
structure. Some examples of items included in the
entrepreneurial self-efﬁcacy scale are: ‘‘If I work
hard, I can successfully start a business,’’ and ‘‘I am
conﬁdent, I can put in the effort needed to start a
business,’’ where 1 indicated completely disagreed
and 5 indicated completely agreed. The items
selected were then averaged to create the entrepre-
neurial self-efﬁcacy scale (Cronbach a=.80).
3.3 Control variables
In the analysis, several control variables were iden-
tiﬁed for inclusion. These include educational attain-
ment, income and wealth measures, sex of the lead
respondent, total number of individuals on the team,
degree of ﬁrm innovativeness, prior industry experi-
ence, prior start-up experience and conception lag (in
months) for the start-up. Educational attainment is an
ordinal variable (grade school, no high school degree,
high school degree, post-high school no college
degree, college degree, post-college experience). This
measure was used since education is often a factor
reported to inﬂuence business planning (Autio et al.
1997; Krueger 1993; Honig and Karlsson 2004).
Income and wealth measures were self-reports of
respondent household income and assets (adjusted for
inﬂation in 2009). This measure was included because
availability of resources may positively inﬂuence new
ﬁrm emergence. Sex was self-report from the respon-
dent. Sex was included as a measure because men are
more likely than women to engage in entrepreneurial
activity (Robb and Coleman 2009). Team size was
also controlled for since more team members may
positively inﬂuence the availability of human, social,
ﬁscal and cultural resources. Degree of innovativeness
was controlled for, since degree of innovation may
impact the difﬁculty of new ﬁrm emergence as
routines and competencies are new and often foreign
to the market (Aldrich and Ruef 2006). Prior start-up
and industry experience were included because such
measures have been found to positively inﬂuence
operational ﬁrm status (Reynolds 2007). Finally,
conception lag was included to control for time, since
individuals who have been in the start-up process
longer may more likely reach some form of resolution
(Reynolds 2007; Reynolds and Curtin 2004).
4 Analysis and results
We apply univariate and bivariate techniques to
describe the sample. Moreover, multivariate statistics
such as multinomial logistic regression will be
utilized to test the hypotheses.
After executing listwise deletion of cases with
missing data, the sample size for this analysis totals
342 nascent entrepreneurs (n=342). Data for this
analysis are re-weighted to represent all nascent
entrepreneurs on which information on our study
variables are available at the end 72-month interview
window. Descriptive statistics for the sample can be
found in Table 2. The data show that, within the
sample, a third of all cases reach new ﬁrm status, a
third continue in their efforts, and a thirds quit after
the 72-month follow-up period. Men represent about
51% of the cases and women 49%. The mean for
education is post-high school or some college (about
32% of the cases) followed by college degree (25%
of the cases). The mean household income for the
Note that item Qk1a was originally developed as measure of
expectancy (Gatewood 2004) and is used as such by Renko
et al. in this journal issue. Since the theoretical constructs of
expectancy and self-efﬁcacy have similarities (Bandura 1977;
Steel and Konig 2006), item Qk1a has also been used in a
validated scale measuring entrepreneurial self-efﬁcacy by
Cassar and Friedman (2009).
The nascent entrepreneurship hub 691
sample is US$70,000, with a mean household net
worth of $177,000. On average, nascent entrepre-
neurs have about 8 years of industry experience and
have participated in about 1.2 other start-up teams.
However, it should be noted that the median for
previous start-up team experience is 0. On average,
most nascent start-ups score low on hi-tech emphasis.
Furthermore, the average team size for the sample is
two, and the time from conception, or ﬁrst initial
start-up action taken, to the ﬁrst wave of the PSED
interviews is 25.57 months, or a little over two years.
Furthermore, Table 2presents the bivariate corre-
lations among the variables, assuming compound
symmetry. Initial analysis of Table 2shows that
multicollinearity is not likely to affect our results.
Moreover, Table 2highlights the various relationships
between the control and independent variables. Par-
ticularly, business plan formalization shows a positive,
signiﬁcant correlation with education (r=.118;
p=.005), household income (r=.149; p=.001),
household net worth (r=.151; p=.001), start-up
experience (r=.140; p=.001), business hi-tech
index (r=.140; p=.001), and team size (r=.180;
p=.004). However, entrepreneurial self-efﬁcacy is
only signiﬁcantly correlated to the control variable
conception lag (r=-.149; p=.001). Therefore, as
conception lag increases, self-efﬁcacy decreases.
Moreover, business plan formalization and self-
efﬁcacy are signiﬁcantly correlated (r=.124; p=
.004). Therefore, as business plan formality increases,
self-efﬁcacy increases (see Table 2).
In order to investigate the effects of business plan
formalization and entrepreneurial self-efﬁcacy on new
ﬁrm emergence, multinomial logistic regression was
utilized. Multinomial logistic regression breaks the
regression up into a series of binary regressions
comparing each group to a baseline, or referent group.
The referent category for this analysis is continued
start-up status. In order to identify the most parsimo-
nious model, the independent variables were force
entered, and forward stepwise selection was simulta-
neously employed to identify the most signiﬁcant
control variables. The forward stepwise selection
criteria included control variables with a likelihood
ratio pprobability of pB.05 and removed the variable
if the likelihood ratio pprobability pC.10. This
procedure selected conception lag (in months) and sex
of the respondent as the only signiﬁcant controls
differentiating continuing start-up efforts from new
ﬁrms or quits. All other control variables were not
signiﬁcant (and therefore excluded from the model).
To assess the goodness of ﬁt for the model, the
deviance statistic was computed. Thus, we can
conclude that the model ﬁts the data relatively well
=705.53, df =772, p=.180) (Agresti 1996;
Tabachnick and Fidell 1996). Also, pseudo R square
Table 1 PSED I variables operalizations
Variable Description PSED Item Measurement
Educational attainment Educational attainment level Q343 Ordinal
Team size Total number of people on team TEAMSZ Ordinal
Industry experience Total years of same industry experience Q199 Continuous
Start-up experience Total number of other start-up initiatives engaged in Q200 Continuous
Household income Total household income from all sources Q386 Continuous
Household net worth Estimated current net worth of the household Q391 Continuous
Conception lag Months lag from conception to ﬁrst interview in
months. Computed from difference in ﬁrst start-up
activity reported to ﬁrst wave interview.
Degree of ﬁrm innovativeness Degree of innovativeness of the start-up Q299-Q301 Ordinal
Business plan formalization Degree of formality of business plan Q114-R/S/T571 Ordinal
Entrepreneurial self-efﬁcacy Degree of belief in skills and abilities for
Qk1a, Qk1d, Qk1e, Qk1f Ordinal
Outcome status 72-month outcome status based on respondent self-
reports and month and year revenue ﬁrst exceeded
R/S/T502, R/S/T622_my Nominal
Sex Sex of respondent NCGENDER Nominal
692 D. M. Hechavarria et al.
Table 2 Descriptive statistics and correlations
Variable Mean SD Mode 1 2 3 4 5 6 7 8 9 10
1. Educational attainment 4.210 1.100 4.000 1.000
2. Team size 1.740 0.970 1.000 .079* 1.000
3. Industry experience 8.720 9.980 0.000 .105** -0.051 1.000
4. No. of start-up experiences 1.190 3.240 0.000 .144** .107** .136** 1.000
5. Household income (1,000) 70.120 93.410 59.000 .125** .129** 0.054 0.040 1.000
6. Household net worth
176.920 391.480 234.000 .200** .155** .112** .192** .476** 1.000
7. Conception lag in months 25.570 35.880 10.280 -.077* -0.025 .257** -0.010 -0.030 -0.016 1.000
8. Degree of ﬁrm
0.970 0.900 1.000 0.047 .109** 0.029 0.056 0.012 0.013 -0.012 1.000
9. Business plan
1.410 1.150 0.000 .118** .180** 0.039 .140** .149** .151** -0.014 .140** 1.000
10. Entrepreneurial self-
4.230 0.590 4.000 0.024 -0.001 0.074 0.009 -0.007 0.019 -.149** -0.010 .124** 1.000
11. Outcome status 2.040 0.810 3.000
12. Sex 0.510 0.500 1.000
* Correlation is signiﬁcant at the 0.05 level (2-tailed)
** Correlation is signiﬁcant at the 0.01 level (2-tailed)
The nascent entrepreneurship hub 693
statistics show that about 12% to 6% of the variation
in start-up outcome status is explained by the model
in this analysis. Finally, the overall model ﬁtting
criteria (2LL for the overall model: v
df =10, p\.0005) is signiﬁcant, and therefore we
reject the null hypothesis that business plan formal-
ization, self-efﬁcacy and their interaction effect, as
well as control variables of sex and conception lag (in
months) make no difference in odds for continuing
start-up efforts versus new ﬁrms or quitting (see
Examination of Table 3in regards to continued
start-up status versus new ﬁrm status shows that none
of the independent variables or control variables
inﬂuence the odds of new ﬁrm emergence versus
continuing start-up efforts. Hence, we ﬁnd no support
for our H1–H3. However, comparing nascent entre-
preneurs who continue start-up efforts to those that
quit, signiﬁcant patterns emerge, different to what we
In this model, both concep-
tion lag (p=.0005) and sex (p=.012) are signif-
icant control variables. A one unit increase in
conception lag (months) decreases the odds of
quitting by 2% versus continuing start-up efforts.
Similarly, men are more likely to quit than women
compared to continuing start-up efforts. Moreover,
business plan formalization is signiﬁcant (p=.062),
such that a one unit increase in business plan
formalization decreases the odds of quitting by
81%, after controlling for other variables in the
model. Similarly, entrepreneurial self-efﬁcacy is also
signiﬁcant (p=.01), such that a one unit increase in
entrepreneurial self-efﬁcacy among nascents
decreases the odds of being in the quit category by
66%, controlling for other variables in the model.
Finally, the interaction term for business plan
formalization and self-efﬁcacy is signiﬁcant
(p=.052), such that a one unit increase in the
interaction coefﬁcient increases by 50% the chances
of being in the quit category. Although our hypoth-
eses were not supported, we did ﬁnd evidence that
coincides with goal theory and social cognitive
theory. The subsequent discussion further explores
the nature of our ﬁndings, particularly the interaction
effect of goal speciﬁcity and entrepreneurial self-
efﬁcacy found between nascent entrepreneurs who
quit compared to those who continue on the start-up.
4.1 Post hoc analysis
In order to further investigate the nature of the
relationship between the dependent and independent
variables, we will employ cross-classiﬁcation data
Table 3 Multinomial logistic regression results for the entrepreneurship hub model
Outcome status B SE Wald df p Exp (B) 95% CI of Exp (B)
Intercept -1.733 1.909 .824 1 .364
Entrepreneurial self-efﬁcacy .357 .438 .663 1 .415 1.429 .605 3.374
Business plan formalization .068 .927 .005 1 .941 1.071 .174 6.593
Business plan 9entrepreneurial self-efﬁcacy -.002 .214 .000 1 .993 .998 .656 1.519
Conception lag (months) -.003 .006 .250 1 .617 .997 .985 1.009
Sex .204 .273 .558 1 .455 1.226 .718 2.095
Intercept 4.603 1.738 7.017 1 .008
Entrepreneurial self-efﬁcacy -1.066 .415 6.584 1 .010 .344 .153 .777
Business plan formalization -1.668 .893 3.485 1 .062 .189 .033 1.087
Business plan 9entrepreneurial self-efﬁcacy .411 .211 3.771 1 .052 1.508 .996 2.282
Conception lag (months) -.038 .009 16.670 1 .000 .962 .945 .980
Sex .730 .289 6.355 1 .012 2.074 1.176 3.658
The reference category is start-up continues
We set our level of signiﬁcance at a=.10.
694 D. M. Hechavarria et al.
techniques. As a result, the independent variables
were categorized into dichotomous variables. Busi-
ness plan formalization was recoded to no planning
and yes planning (where unwritten, informal, and
formal were all grouped). And entrepreneurial self-
efﬁcacy was recoded into low and high, where all
cases over the median (MD =4.33) were coded as
high and cases under the median as low. Table 4
shows the frequencies for each classiﬁcation of self-
efﬁcacy by planning among the three outcome
categories (new ﬁrm, quits, and start-up continues).
Initial analysis shows that business planning and
entrepreneurial self-efﬁcacy are signiﬁcantly posi-
tively correlated (see Table 2). Subsequent chi-
square analysis also shows that business planning
and entrepreneurial self-efﬁcacy are dependent con-
=11.55, df =1, p=001). Therefore, the
column and row frequencies for entrepreneurial self-
efﬁcacy and business planning are not random.
Therefore, in order to more closely examine
differences in entrepreneurial self-efﬁcacy and busi-
ness planning among nascent outcome categories,
odds ratios were calculated. This procedure will aid
in identifying the nature of the relationship between
business planning and entrepreneurial self-efﬁcacy.
First, we examine the odds ratio, which is the ratio of
two conditional odds. If we look at Table 4, the
question can be raised whether the odds ratio is
homogenous across categories of nascent outcome
start-up status. To test this, we perform the Mantel–
Haenszel test. The Mantel–Haenszel chi-square coef-
ﬁcient tests whether the common odds ratio across the
various start-up outcome categories is 1.0, indicating
no effect of the stratiﬁcation variable (Agresti 1996).
This test of conditional independence shows that the
odds ratio does vary by category according to the
Mantel–Haenszel test (v
=6.70, df =1, p=.01).
Additionally, we see that in each outcome cate-
gory, the odds ratio is greater than 1 (see Table 4).
An odds ratio of 1.0 indicates that there is no
association for the two variables. Moreover, the
further the odds ratio is away from 1.0, the more
different are the conditional odds. Among those who
reach new ﬁrm status, the conditional odds of having
a plan are 1.52 as high among respondents with high
entrepreneurial self-efﬁcacy than low entrepreneurial
self-efﬁcacy (see Table 4). Moreover, for those who
continue in their start-up efforts, the conditional odds
of having a plan is 1.29 times as high among those
with high self-efﬁcacy as among those with low
Table 4 Relationship between business plan formalization and entrepreneurial self-efﬁcacy by outcome status
New ﬁrm Quit Start-up continues
Total Low self-
Total Low self-
No plan 16 21 37 37 15 52 27 26 53
43.20% 56.80% 100% 71.20% 28.80% 100% 50.90% 49.10% 100%
Yes plan 38 76 114 50 55 157 50 62 112
33.30% 66.70% 100% 47.60% 52.40% 100% 44.60% 55.40% 100%
Total 54 97 151 87 70 157 77 88 165
35.80% 64.20% 100% 55.40% 44.60% 100% 46.70% 53.30% 100%
Odds Odds Odds
High self-efﬁcacy and no plan/
low self-efﬁcacy and no plan
High self-efﬁcacy and no plan/
low self-efﬁcacy and no plan
High self-efﬁcacy and no plan/
low self-efﬁcacy and no plan
1.31 0.41 0.96
High self-efﬁcacy and yes plan/
low self-efﬁcacy and yes plan
High self-efﬁcacy and yes plan/
low self-efﬁcacy and yes plan
High self-efﬁcacy and yes plan/
low self-efﬁcacy and yes plan
2.00 1.10 1.24
Odds ratio Odds ratio Odds ratio
1.52 2.71 1.29
The nascent entrepreneurship hub 695
entrepreneurial self-efﬁcacy. Finally, we can con-
clude that there is a strong positive association
between entrepreneurial self-efﬁcacy and business
planning, particularly among the quit group, whose
odds ratio =2.71. So among nascents who plan and
quit, the odds of high entrepreneurial self-efﬁcacy are
about 2.71 times greater than those who do not plan.
This implies that, in the quit group, highly efﬁcacious
nascents plan more readily. Therefore, planning and
high entrepreneurial may lead to quitting the start-up
process more readily than continuing in start-up
This assessment has contributed to the understanding
of how motivation inﬂuences the outcomes of the
nascent entrepreneurship process from a goal setting
perspective. Goal setting theory purports that more
speciﬁc goals, self-efﬁcacy, as well as their interaction
increase task performance. A critical outcome for
nascent entrepreneurs is the establishment of a viable,
new business. Hence, our hypotheses predicted that
having speciﬁc goals (H1) and higher self-efﬁcacy
(H2) would be related to the establishment of a new
business among nascent entrepreneurs. Surprisingly,
we found no hypothesized effects for ‘‘new ﬁrm status’’
as an outcome category. What we did ﬁnd, however,
was having a more formalized business plan and higher
self-efﬁcacy contributed to maintaining in a start-up
effort versus quitting among nascent entrepreneurs.
Therefore, the value of planning and entrepreneurial
self-efﬁcacy is that it facilitates the determination that
a given initiative is not economically viable (Reynolds
2007). Moreover, our ﬁndings conﬁrm prior ﬁndings
by Cassar and Friedman (2009). Although Cassar and
Friedman (2009) found entrepreneurial self-efﬁcacy
positively inﬂuenced operational status among nascent
entrepreneurs, they did not examine differences
between quits and continuing start-ups. Therefore,
our study advances our understanding of entrepreneur-
ial self-efﬁcacy, from a goal setting perspective, on
task performance. Particularly, demonstrating that
high entrepreneurial self-efﬁcacy and speciﬁc goals
positively inﬂuence the likelihood of continuing start-
up efforts versus quitting.
Our study operationalized goal speciﬁcity through
formality of business planning which is recognized
precedent in the entrepreneurial literature (Locke
1968; Latham and Yukl 1975; Bird 1988; Smith et al.
1990; Timmons 2000; Baum et al. 2001; Shane and
Delmar 2004; Baum and Locke 2004). Our analysis
provides evidence that entrepreneurial self-efﬁcacy
and business plan formality are dependent constructs.
Moreover, there is also evidence that goal speciﬁcity,
operationalized as business plan formality, varies as a
function of entrepreneurial self-efﬁcacy. Particularly,
among quits, there is compelling evidence that
individuals with low self-efﬁcacy are less likely to
plan formally. Although our hypotheses were not
supported, we did ﬁnd evidence that coincides with
goal theory and social cognitive theory.
These ﬁndings suggest that, in the context of nascent
entrepreneurship, goal speciﬁcity and entrepreneurial
self-efﬁcacy operate together to cue nascent entrepre-
neurs regarding the feasibility of their prospective
opportunity, thus increasing the likelihood of persist-
ing in continuing start-up efforts versus quitting.
Moreover, the higher levels of entrepreneurial self-
efﬁcacy with more formalized goals (or business
planning) increases the likelihood of quitting start-up
efforts versus persisting. Therefore, it could be inferred
that goal setting in the context of nascent entrepre-
neurship inﬂuences start-up outcomes, such that
nascent entrepreneurs who have high entrepreneurial
self-efﬁcacy and formalize goals via business planning
are more likely to identify unworthwile opportunities
more rapidly, and subsequently more likely to exit
efforts than individuals with formalized goals and low
entrepreneurial self-efﬁcacy and individuals with un-
formalized goal and high entrepreneurial self-efﬁcacy.
Thus, when people fail to fulﬁll a challenging standard,
they lower or change their goals, but others remain
conﬁdent and persist in the face of failure and even
raise their goals (Baum and Locke 2004).
Moreover the ﬁnding that individuals with high
entrepreneurial self-efﬁcacy who plan are more likely
to quit before those with low entrepreneurial self-
efﬁcacy and no plan is no surprise. Bandura and
Jourden (1991), as well as Stone (1994), found that
high self-efﬁcacy led to overconﬁdence in one’s
abilities. Instead of high self-efﬁcacy individuals
contributing more of their resources toward the task,
they contributed less. These participants were both
less attentive and effortful than were their low self-
efﬁcacy counterparts. One might conclude that,
although high self-efﬁcacy can motivate individuals
696 D. M. Hechavarria et al.
to adopt high level goals, it may reduce motivation
within a goal level. Hence, high self-efﬁcacy along
with highly formal goals likely lead to predictions of
higher states (i.e., reaching the goals sooner) than
predictions made when self-efﬁcacy is low (Vancou-
ver et al. 2002). The result is that self-efﬁcacy can
lower performance, and in turn explain the interaction
effect found in our analysis. Another reason why
individuals with higher degrees of entrepreneurial
self-efﬁcacy who plan to quit more readily maybe
because they ﬁnd and use better task strategies to
attain the goal of establishing a new ﬁrm based on the
negative feedback they may have obtained from that
initial opportunity. As a result, nascents may shift
their efforts to identify another opportunity to exploit
(Locke and Latham 1990; Seijts and Latham 2001).
Additionally, ﬁndings show that the time lag since
conception, the ﬁrst initial action taken towards
implementing the prospective new ﬁrm, is also
signiﬁcant in predicting the odds of quitting versus
continuing with start-up efforts. The longer nascent
entrepreneurs are engaged in their start-up initiative,
the lower their odds of quitting the start-up process.
According to Reynolds (2007), it takes half a year or
longer to quit the start-up process than it does to
create an operating ﬁrm. Previous entrepreneurship
research has shown that entrepreneurs persist with
under-performing ﬁrms (DeTienne et al. 2008). On
the ﬁrm level, such a phenomenon has been explained
based on threshold theory (Gimeno et al. 1997) and
the escalation of commitment (Staw 1976). However,
the reasons for this increasing commitment to a start-
up effort among nascent entrepreneurs provide an
interesting topic for future research.
Future research should investigate other factors to
improve model ﬁt and to provide a more compre-
hensive test of Locke’s (1991)motivation sequence.
Our research has focused on the motivation hub of
Locke (1991), but future research would beneﬁt from
an analysis of other parts of the motivation sequence.
How, for example, do values and motives inﬂuence
nascent entrepreneurs’ goals? Or what are the
perceived rewards and satisfaction that entrepreneurs
achieve after establishing the start-up? Also, within
the motivation hub, additional research could look at
the effects of goal difﬁculty, goal commitment, and
goal acceptance in addition to goal speciﬁcity studied
here. Atkinson (1958) showed that task difﬁculty,
measured as probability of task success, was related
to performance in a curvilinear, inverse function. The
highest level of effort occurred when the task was
moderately difﬁcult, and the lowest levels occurred
when the task was either very easy or very hard
(Locke and Latham 2002). Although our analysis
included a control measure for degree of innovative-
ness of the start-up, it was a non-signiﬁcant covariate
in the analysis. We suggest that future studies attempt
to identify ways to operationalize goal difﬁculty
beyond industry classiﬁcation.
A question raised by our selected outcome vari-
ables concerns the desirability of these very out-
comes. We have found that having a formalized
business plan combined with high self-efﬁcacy of a
nascent entrepreneur is a recipe for an increased
likelihood of exiting the start-up process. One may
argue that this, after all, may not be such a negative
outcome as one might ﬁrst think. It is possible that the
mere engagement in a business gestation process
allows the individual to learn for their future career
and possible future start-up efforts. Along the same
lines, continued start-up effort may sometimes be a
signal of wasting resources and unwillingness to face
the market and competitive realities of the economy.
When considering the kind of ‘‘task performance’’
that goal setting theory might predict among nascent
entrepreneurs, one should keep in mind that a variety
of positive outcomes are possible, and some of them,
like quitting, may initially come in disguise.
Business planning is widely encouraged across
various entrepreneurship education programs. Our
results show that, compared to non-planners, nascent
entrepreneurs with formalized business plans perse-
vere longer in the process. Those who do not plan are
more likely to quit trying. As long as discouraging
exits from the ﬁrm gestation process is an outcome
sought by various business planning programs, our
results should be welcome news for entrepreneurship
educators. Also, previous research has suggested that
educators can inﬂuence students’ entrepreneurial
intentions by improving their entrepreneurial self-
efﬁcacy (e.g. Wilson et al. 2007). Our results suggest
that this heightened self-efﬁcacy with formalized
business planning may improve an individuals’
capabilities to promptly identify those business
opportunities that are not worthy further pursuit are
in line with this ﬁnding of previous research.
The nascent entrepreneurship hub 697
Using PSED data limited our research design in a few
ways. First, due to the large amount of questions
included in the survey, survey designers elected to
shorten many of the scales used to measure certain
cognitive variables (in some cases) to single
responses (Shaver 2004). We are conﬁdent that the
measures we use are consistent with the core theory
utilized here; however, we do recommend that future
research seek to re-conﬁrm our results with more
complete scales to further strengthen the evidence we
present in this paper. We also recommend that the
PSED data should be used to model changes in self-
efﬁcacy and goal commitment instead of stock
measures, as has been analyzed here. It may be that
over subsequent waves of data, the changes in these
measures are more inﬂuential than the actual level at
the onset of the processes itself.
Also, we do recognize that there is a long-running
debate in the entrepreneurship literature regarding the
differences between small business owners and high
growth entrepreneurial business ventures (Carland
et al. 1984; Shane and Venkataraman 2001; Mahoney
and Michael 2005). The majority of respondents in
the PSED sample are actually classiﬁed as reproducer
small business owners, and not as innovating entre-
preneurs (Aldrich and Ruef 2006). This, although a
true reﬂection of the kinds of new businesses that the
American population is starting, may disappoint
those who are more interested in understanding the
dynamics of high-growth innovator ﬁrms.
Finally, it should be noted that our analysis does
not causally link planning and self-efﬁcacy to
outcome status. Our ﬁndings highlight how the odds
of transitioning from start-up status to either new ﬁrm
or quitting the process are inﬂuenced by goal
formality and self-efﬁcacy. Therefore, we acknowl-
edge this may represent a study limitation.
Entrepreneurship involves human agency. People start
businesses, they are not started by macro-economic
conditions, presence of opportunities, availability of
ﬁnance, social networks, positive entrepreneurial cli-
mate, regional/geographic attributes, or market char-
acteristics. Although such factors are inﬂuential, the
entrepreneurial process occurs because people are
motivated to act and pursue perceived opportunities.
All action is the result of motivational factors. There-
fore, it is imperative for scholars to incorporate theories
of motivation into entrepreneurial research to better
comprehend the entrepreneur and how he/she operates.
Accordingly, we have developed and tested a frame-
work that looks at interconnections between goals,
self-efﬁcacy, and start-up process outcomes to under-
stand how individuals navigate the nascent entrepre-
neurial process. Goal-setting theory is not limited to
but focuses primarily on motivation in work settings.
Social cognitive theory and the research that underlies
it are primarily focused on self-efﬁcacy, its measure-
ment, its causes, and its consequences. The nascent
entrepreneurship hub framework presented here has
integrated the key constructs from both theories. Our
empirical results have shown that signiﬁcant relation-
ships between self-efﬁcacy, goal speciﬁcity, and start-
up process outcomes exist. As a result, we believe that
the nascent entrepreneurship hub truly provides a
starting point to understand how motivation impacts
outcome status among would-be entrepreneurs.
Acknowledgements The authors would like to acknowledge
the College of Business at the University of Cincinnati, which
awarded our project funds from Title VI Learning Grant to
facilitate presentation of this research at the 2008 International
Council for Small Business World Conference in Halifax,
Nova Scotia Canada. We would also like to thank participants
at the 2008 Symposium on the Panel Study of Entrepreneurial
Dynamics: ‘‘Research on Business Creation’’ in Greenville,
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