Work experience and the generation of new business ideas among entrepreneurs: An integrated learning framework

Abstract

Purpose – This paper seeks to develop an integrated framework to examine how entrepreneurs' work experience is associated with the generation of new business ideas. The framework combines human capital theory with theory and research on entrepreneurial learning.

Design/methodology/approach – A statistical analysis on a sample of 291 Swedish entrepreneurs is conducted.

Findings – The paper finds that a learning mind-set that favors exploration is the strongest predictor of the generation of new business ideas. It also finds that breadth in functional work experience seems to favor the generation of new business ideas while deep industry work experience is negatively related to new business idea generation. In addition, the paper finds indications that a learning mind-set that favors exploration is required to more fully benefit from investments in human capital.

Research limitations/implications – The study's findings add to knowledge of how investments in human capital via work experience, and the employment of a learning mindset that favors exploration, influence performance outcomes in the early stages of the entrepreneurial process.

Practical implications – The study's findings suggest that entrepreneurs should develop and nurture a learning mind-set that favors exploration as this will increase their ability to generate more new business ideas. Moreover, movements across different functional work areas appear to have great potential as sources of ideas for new products and markets.

Originality/value – Prior empirical studies have not taken individual learning preferences among entrepreneurs into account. Nor have they explicitly tested the effect of depth versus breadth in work experience. The paper thus provides novel insights with respect to how these factors interact in the process of generating new business ideas.

Full text

Work experience and the
generation of new business ideas
among entrepreneurs
An integrated learning framework
Jonas Gabrielsson
CIRCLE, Lund University, Lund, Sweden and
School of Business and Engineering, Halmstad University, Halmstad,
Sweden, and
Diamanto Politis
School of Business and Engineering, Halmstad University, Halmstad, Sweden
Abstract
Purpose – This paper seeks to develop an integrated framework to examine how entrepreneurs’
work experience is associated with the generation of new business ideas. The framework combines
human capital theory with theory and research on entrepreneurial learning.
Design/methodology/approach – A statistical analysis on a sample of 291 Swedish entrepreneurs
is conducted.
Findings – The paper finds that a learning mind-set that favors exploration is the strongest predictor
of the generation of new business ideas. It also finds that breadth in functional work experience seems
to favor the generation of new business ideas while deep industry work experience is negatively
related to new business idea generation. In addition, the paper finds indications that a learning
mind-set that favors exploration is required to more fully benefit from investments in human capital.
Research limitations/implications – The study’s findings add to knowledge of how investments
in human capital via work experience, and the employment of a learning mindset that favors
exploration, influence performance outcomes in the early stages of the entrepreneurial process.
Practical implications – The study’s findings suggest that entrepreneurs should develop and
nurture a learning mind-set that favors exploration as this will increase their ability to generate more
new business ideas. Moreover, movements across different functional work areas appear to have great
potential as sources of ideas for new products and markets.
Originality/value – Prior empirical studies have not taken individual learning preferences among
entrepreneurs into account. Nor have they explicitly tested the effect of depth versus breadth in work
experience. The paper thus provides novel insights with respect to how these factors interact in the
process of generating new business ideas.
Keywords Business ideas, Ideas generation, Entrepreneurs, Experiential learning, Human capital,
Work experience
Paper type Research paper
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1355-2554.htm
This work has been conducted within the Linnaeus research program “Innovation,
entrepreneurship and knowledge creation – dynamics in globalising learning economies”,
financed by the Swedish Scientific Council. The authors are grateful for the helpful contributions
from two anonymous reviewers in the course of developing this work. They are also grateful for
the comments received from Sofia Avdetchikova, Goya Harichi, Hans Landstro
¨m and Craig
Mitchell.
IJEBR
18,1
48
Received 10 November 2010
Revised 1 July 2010
6 December 2010
Accepted 4 February 2011
International Journal of
Entrepreneurial Behaviour
& Research
Vol. 18 No. 1, 2012
pp. 48-74
qEmerald Group Publishing Limited
1355-2554
DOI 10.1108/13552551211201376

Introduction
There is little disagreement among entrepreneurship scholars that the accumulated
work experience that enterprising individuals gain during their course of life is an
important source for the generation of new business ideas (Shane, 2000; Politis, 2005;
Shepherd and DeTienne, 2005). The general explanation is that work experience
exposes individuals to personal and often unique insights into customer problems,
viable markets, product availability and competitive resources that ultimately
influence their ability to identify shortcomings or inefficiencies in current ways of
doing things. These insights may then trigger ideas for new or better ways of serving
customers and markets, which ideally connect to unmet needs that other individuals
and firms are willing to pay for.
The observation that work experience plays a crucial role in the process of
entrepreneurial discovery has in recent research been linked to human capital theory
explanations (e.g. Davidsson and Honig, 2003; Ucbasaran et al., 2008; Wiklund and
Shepherd, 2008). Originating from the work of Becker (1964) and Mincer (1974), this
theoretical perspective suggests that the ability to successfully engage in
entrepreneurial activities is largely a function of the education, training and
practical learning that people experience throughout their careers and professional
lives. Thus, the theory emphasizes the value of personal investments in human capital
via education and work experience for explaining performance differentials and higher
success in entrepreneurial settings.
However, although human capital theory has contributed much to our
understanding of how education and experience may lead to a higher likelihood of
entrepreneurial success (Cooper et al., 1994; Gimeno-Gascon et al., 1997), it can also be
observed that current theorizing fails to fully explain why entrepreneurs with largely
similar amounts of accumulated work experience still can show distinct differences in
terms of performance. This critique relates to research within the field of
entrepreneurial learning which emphasizes that there are differences in the extent to
which entrepreneurs have a mindset that makes them more open to learn from
exploration activities and search for variety in experience (e.g. Minniti and Bygrave,
2001; Politis, 2005), something which in turn may influence their motivation and ability
to spot and seize new business ideas. Thus, there seems to be a need to acknowledge
that entrepreneurs differ not only in terms of their accumulated work experience but
also in terms of preferences for learning (Politis, 2005), and that a combination of these
two dimensions can better explain performance differentials.
To address this observed research gap and further advance our scholarly
knowledge in the field, we will in this study build an integrated framework to examine
the role of work experience for the generation of new business ideas among
entrepreneurs. In the framework we combine insights from human capital theory
(Becker, 1964; Mincer, 1974), especially as it has been applied to the entrepreneurship
domain (e.g. Davidsson and Honig, 2003; Ucbasaran et al., 2008; Wiklund and
Shepherd, 2008), with theory and research on entrepreneurial learning (e.g. Minniti and
Bygrave, 2001; Politis, 2005). Based on our framework we develop hypotheses
concerning how entrepreneurs can be expected to benefit from investments in human
capital via work experience, and how this may be influenced by individual differences
among entrepreneurs in their preferred way of learning from experience. Our
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hypotheses are tested on a sample of 291 Swedish entrepreneurs that started a new
independent firm between 1998 and 2002.
The study makes three important contributions. First, we present an integrated
framework for understanding how entrepreneurs’ work experience is associated with
the generation of new business ideas, by combining human capital theory with theory
and research on entrepreneurial learning. This combination is a novel approach that
responds to recent calls in entrepreneurship research emphasizing the need to focus on
the role of knowledge and the acquisition and processing of information in the early
stages of the entrepreneurial process (Corbett, 2007; Dimov, 2007; Westhead et al.,
2005). Second, we make an analysis of various sources of work experience on business
idea generation, thus differentiating not only between industry-level and firm-level
influences (Shane, 2003) but also whether the entrepreneur has more of a specialist or a
generalist career path. This differentiation provides a more fine-grained analysis of
work experience than usually is applied in entrepreneurship research, where we
acknowledge that the career trajectory of entrepreneurs can vary both in depth and in
breadth (Marvel and Lumpkin, 2007). Third, we examine the effect that a learning
mindset that favors exploration (Minniti and Bygrave, 2001; Politis, 2005) has on the
generation of new business ideas, thereby advancing our scholarly knowledge of how
accumulated work experience and individual learning preferences interact in the
entrepreneurial process.
The rest of the paper proceeds as follows. The next section presents a literature
review where we discuss the generation of business ideas based on investments in
human capital via different kinds of work experience, and how this process can be
expected to be influenced by entrepreneurs’ preferred way of learning from experience.
Thereafter follows the method section where we describe our sample and variables.
After this, there is a section presenting the results. This is followed by a discussion of
the results in relation to theory and research on the generation of business ideas. The
study ends with a concluding section.
Literature review
Entrepreneurship and the generation of new business ideas
Shane and Venkataraman (2000) describe entrepreneurship as the process through
which future goods and services come to be in existence. Past research (e.g. Bhave,
1994; Ardichvili et al., 2003; Klofsten, 2005) suggests that this process starts with the
initial perception of an opportunity for recombining resources on the market, and
where a new business idea eventually develops as the entrepreneur shapes these
elemental insights into an emerging business concept that he or she thinks will yield
future profit. The generation of new business ideas can in this respect be seen as an
important part of the entrepreneurial process, where entrepreneurs – based on their
ability to identify and anticipate unmet customer needs (i.e. opportunities for
entrepreneurial profit) – can come up with and offer solutions in the form of emerging
ideas for new potential business ventures.
Even if a business idea forms the basis for a new potential business venture, it
should be acknowledged that this can be in a more or less developed state. A highly
developed business idea encompasses a representation of the whole business in terms
of its market niche, its production system and its organization (Klofsten, 2005;
Davidsson, 2006), thus basically describing a model of how the business is supposed to
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work, what will be offered at the market, and how the business intends to make money.
However, before reaching this stage of development the entrepreneurial process often
starts with one or more diffuse ideas of how to meet customer needs, which
subsequently emerge and develop into a basic understanding of what the future
business venture will offer. The generation of a new business idea can thus largely be
understood as a development process (Ardichvili et al., 2003) where the idea can be
elaborated and subsequently refined during its path of development. Eventually, if not
terminated due to a lack of motivation, interest or other resources in developing the
idea further, the process will generally result in a reasonably well-formed business idea
which the entrepreneur judges to have some possibility of success ( Jones and Holt,
2008)[1].
Human capital and entrepreneurship
Entrepreneurship scholars have long been interested in why some people are able to
come up with more business ideas than others. A number of recent studies points
toward the value of using human capital theory to explain differences in outcomes in
the early stages of the entrepreneurial process. In a general sense, the concept “human
capital” refers to the stock of knowledge and experience embodied in the ability to
perform labor so as to produce economic value (Becker, 1964; Mincer, 1974). The
concept has in entrepreneurship research more specifically been used to refer to the
knowledge, skills and problem-solving abilities that come from education and work
experience, and that assist entrepreneurs in the creation of new business ventures
(Davidsson and Honig, 2003; Ucbasaran et al., 2008).
A general assumption in human capital theory is that expenditures on education
and work experience are costly and thus should be considered as investments, since
they are undertaken with a view to yield additional output and increase personal gain
(Becker, 1964). The theory contends that human capital is a means of production and
that additional investments raise the productivity of individuals through increases in
their cognitive abilities. Investments in human capital can thus be seen as an “asset”
where those with more (or higher-quality) human capital are expected to achieve more
desirable outcomes in various work-related undertakings. In our case, this means that
we would expect an observable link between the accumulated stock of knowledge that
entrepreneurs gain through their work experience and differences in their performance
with respect to the generation of new business ideas.
The development of human capital through different kinds of work experience
Work experience has in prior research been found to play a prominent role in
increasing entrepreneurs’ ability to assess, evaluate and combine resources in relation
to the process of entrepreneurial discovery (Shane, 2000; Shepherd and DeTienne,
2005). This experiential influence can come from a range of different sources. However,
two kinds of work experience that have been recognized as important inputs for
triggering an entrepreneurial conjecture among entrepreneurs are industry experience
and business function experience (Cooper, 1985; Cooper et al., 1994; Stuart and Abetti,
1990; Shane, 2003). Entrepreneurs are often faced, for example, with uncertainty about
the value of the goods and services they plan to produce, and industry experience may
in this respect help to create a better understanding of how to meet demand conditions
in the market place. Industry experience may also help prospective entrepreneurs to
Generation of
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conduct the due diligence necessary to evaluate the merits and potential risks
connected with product availability and competitive resources. Work experience from
performing certain business functions, on the other hand, can give individuals valuable
training in important business activities such as planning, organizing and
communicating. New business ideas can in this respect originate from perceptions
of everyday mistakes related to production, investments, distribution etc., which in
turn can lead to a better understanding of resources that are not coordinated in an
effective way. Hence, the extent to which individuals are able to seize potential
opportunities for entrepreneurial profit and generate new business ideas can be
conjectured to be influenced by both industry experience and business function
experience acquired throughout their careers.
However, it should also be acknowledged that the above arguments based on
human capital reasoning are, for the moment, somewhat general in application to the
entrepreneurship field. It is for example not clear whether entrepreneurs during their
careers benefit most from being specialists where they develop human capital within a
particular field of work, or from being generalists where they develop human capital
across different fields of work[2] (Marvel and Lumpkin, 2007). This conceptual
distinction can be linked to literature that has acknowledged that people can be
described as following either a specialist or a generalist career path (Parker, 1994;
Greenberg, 1998; Pashke, 2004; Smits, 2007). A specialist career path consists of
accumulated work experience within specific fields of work, and people within this
career trajectory are more likely to become experts within one or a few subjects. A
generalist career path consists on the other hand of accumulated work experience
across different areas of work, and people within this career trajectory can thus be
described as “jacks-of-all-trades” who know a little about a lot of things. Each
respective career path can thus be described as resulting in a different kind of
experientially acquired knowledge base (Greenberg, 1998), which in turn may have
different effects on the ability to generate new business ideas.
Although we can identify some studies in the entrepreneurship field that have based
their theoretical reasoning on both of those alternatives (e.g. Shane, 2000; Marvel and
Lumpkin, 2007), the most common approach is to argue primarily for the value of
becoming a specialist and developing human capital within specific fields of work,
while ignoring the potential gains from becoming a generalist and developing human
capital across different fields of work. To meet this potential shortcoming and
acknowledge the distinction between generalist and specialist career paths, we will
thus separate these different lines of reasoning in our theoretical framework.
The specialist career path – depth in work experience. One explanation for the ability
to come up with new business ideas is through human capital investments aimed at
becoming a specialist and developing experience within a particular field of work. The
main argument in this line of reasoning would be that the longer individuals operate
within a given field, the better they become in absorbing new knowledge and
combining concepts within that particular field (e.g. Cohen and Levinthal, 1990), thus
stimulating innovation and the generation of novel business ideas. Past research
suggests that increased knowledge in a particular field allows individuals to acquire
important advantages in their capacity to process information (Shepherd and
DeTienne, 2005). For example, as individuals start to know more about a particular
task or field, they are likely to focus on the most important dimensions of the
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information available and process this information in a more efficient manner (Chase
and Simon, 1973), which may permit associations and linkages that have not been
considered before (Gustafsson, 2006). This pattern-seeking is thus not necessarily
about knowing more facts about something, but rather about knowing (and thus
perceiving) things differently.
Adding to this, it has also been pointed out that individuals with more knowledge in
a domain appear to think in a more intuitive way, and make decisions in a more
automatic manner, than through the more conscious and “step-by-step” systematic
processing that often characterize novices (Shepherd and DeTienne, 2005).
Decision-making based on such automatic processing is also generally faster, which
can lead to the discovery of a much greater number of opportunities for action.
Experienced specialists can thus be expected to have a more rapid, fluid and involved
behavior in their domain, compared to the relatively slow and detached reasoning that
characterizes the novice practitioner – and this makes them more likely to see complex
pictures and meaningful patterns (DeGroot, 1965). In sum, both deeper experience from
a particular industry and deeper experience from a particular functional work area can
from this discussion be expected to lead to a higher number of ideas for new businesses
among entrepreneurs. Based on these arguments, we propose the following two
hypotheses:
H1. Deeper work experience from a particular industry is associated with a higher
number of new business ideas.
H2. Deeper work experience from a particular function is associated with a higher
number of new business ideas.
The generalist career path – breadth in work experience. Another explanation for the
ability to come up with new business ideas comes from human capital investments
aimed at becoming a generalist and developing experience across different fields of
work. Based on the general notion of entrepreneurship as new combinations carried out
by the entrepreneur (Schumpeter, 1934), the main underlying argument in this
explanation is that experience across different fields makes individuals come up with
novel solutions and entrepreneurial insights (e.g. Baron, 2006; Cliff et al., 2006;
Johansson, 2004). Past research suggests that the process of problem-solving and
learning from diverse knowledge bases is likely to lead to innovation and new thinking
as it provides a combination of knowledge from different domains (Simon, 1985). For
example, Ward (2004) argues that merging and combining concepts or images from
separate fields can lead to novel properties that were not obviously present in either of
the separate components. This effect is particularly strong for dissimilar or divergent
concepts. When people attempt to make sense out of these novel combinations, the
process can yield emergent properties that do not come to mind when considering
either concept in isolation.
It has also been suggested that the production of novel ideas regarding products,
services, and processes is guided by existing knowledge elements that are available for
combination into new variations (Amabile et al., 2005; Simonton, 1999). Experience
from a broader array of different areas can in this respect stimulate cognitive processes
that contribute to creative combinations, which in turn may result in the consideration
of more alternatives as well as more careful evaluation of alternatives (Milliken and
Vollrath, 1991; Ward, 1995). The combination of knowledge from different domains
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may thus increase the likelihood that individuals are able to connect seemingly
unrelated events or trends, which may result in something new emerging from the mix
(Baron, 2006). From this point of view, work experience across different industries and
business functions can be expected to lead to a higher number of new business ideas
among entrepreneurs which can be exploited in the form of new products or services.
Based on these arguments, we propose the following two hypotheses:
H3. Work experience across different industries is associated with a higher
number of new business ideas.
H4. Work experience across different business functions is associated with a
higher number of new business ideas.
Work experience and the experiential learning process
Our theoretical framework has so far assumed that entrepreneurs with largely similar
amounts of accumulated work experience show similar results in terms of
performance. This assumption is intuitive and largely in line with human capital
theory reasoning. However, such a simple and direct relationship has been increasingly
questioned by scholars who have applied theory and research on experiential learning
to better understand the role of knowledge and the acquisition and processing of
information in the early stages of the entrepreneurial process (e.g. Politis, 2005; Corbett,
2007; Dimov, 2007). Rather than seeing wok-related performance gains only as a direct
learning outcome that automatically results from the accumulation of experience, this
stream of research focuses also on the influence from the intermediate learning process
where experience is continually acquired and subsequently transformed into
knowledge. It is furthermore emphasized in this research that the transformation
process can have different outcomes depending on the manner in which people prefer
to learn from experience. On this view, it is argued that the knowledge base of
entrepreneurs may develop differently over time depending on their individual
preferences for learning (Politis, 2005), which in turn may explain why entrepreneurs
with largely similar amounts of accumulated work experience still can show distinct
differences in terms of work-related performance.
A particular stream of studies has emphasized that entrepreneurs show differences
in the extent to which they prefer to transform experience into knowledge by more or
less intensive searching for variation in experience (Minniti and Bygrave, 2001; Politis,
2005). This conceptualization can be related back to March (1991) who introduced the
idea that there is a trade-off between searching for standardization (exploitation) and
diversity (exploration) in experience. Both search strategies are essential for learning
from experience, but they also compete for scarce resources (such as time, effort etc.),
which means that people have a tendency to specialize in one of the two. The returns to
exploitation activities are generally more certain, closer in time and closer in space than
are the returns to exploration. In contrast, exploration activities are associated with
larger performance variation, as it includes chances of both substantial success and
major failure. Searching for variation in experience through exploration will in this
respect lead to increased experimentation and more alternative ideas at the cost of
reduced expertise within a particular area, and vice versa (Ghemawat and Costa, 1993;
Holmqvist, 2004; see also Cyert and March, 1963). In the context of this study this
reasoning implies that some entrepreneurs may prefer to employ a learning mindset
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where they put lower emphasis on exploration activities, thereby to a larger extent
seeking actions that exploit their pre-existing knowledge base and replicate things they
already know. Or else, they may prefer to employ a learning mindset where they put
higher emphasis on exploration activities and thereby seeking actions that are distinct
from the ones that they have already taken, thus supporting the development of new
insights, ideas and competences.
Exploration is associated in the literature with behaviors such as experimentation,
discovery, risk-taking and playfulness (March, 1991; Benner and Tushman, 2002). It
thus seems fair to argue that entrepreneurs who have a learning mindset that to a
larger extent favors exploration also will have a higher likelihood of coming up with
possible new business ideas. We believe that this influence comes both from a direct
effect of an exploration mindset on business idea generation and from a moderating
effect through its influence on the way entrepreneurs invests in human capital via
work experience. The main argument underlying the first conjecture is that a higher
emphasis on exploration activities gives entrepreneurs an inherent interest in trying
out new things, thus motivating them to be consistently open for voluntary
investigations of new alternatives and non-conforming perspectives (Politis, 2005).
Exploration can in this respect largely be seen as an emotional-motivational and
curiosity-driven desire to allocate personal attention to novel and challenging
experiences (Kashdan et al., 2004). Such intrinsic motivation is, furthermore, an
important driver of individual-level creativity and increases the ability to break away
from current perspectives by generating novel, original and unique ideas (Prabhu et al.,
2008).
In addition, a higher emphasis on exploration activities can be expected to moderate
the relationship between work experience and business idea generation, as the
knowledge gained from previous experience plays a central role in the process of
entrepreneurial learning (Corbett, 2005). Exploration will thus not merely stimulate
and direct entrepreneurs towards curiosity-driven search (e.g. Kashdan et al., 2004), but
may also increase the likelihood that they search for variety within their particular
knowledge domains (Holmqvist, 2004; Politis, 2005). This search for variety would in
turn be in effect largely irrespective of whether they have a more general or specialist
career path. A higher emphasis on exploration activities would in this respect imply a
more playful, trial-and-error mode of experiential learning where entrepreneurs
actively seek out varied sources of novelty and challenge before integrating these new
experiences with what was learned in earlier periods (Minniti and Bygrave, 2001;
Politis, 2005). This emphasis on experimentation, creativity and play in the learning
process (e.g. Kolb and Kolb, 2010) may in turn improve their ability to perceive new or
emergent relationships and to discern possibilities to improve current situations that
have not been identified previously, something which ultimately could stimulate
creative solutions and the generation of novel ideas (Mainemelis and Ronson, 2006). In
all, our theoretical reasoning leads us to the following hypotheses:
H5. A learning mindset with a higher emphasis on exploration is associated with
a higher number of new business ideas.
H6. A learning mindset with a higher emphasis on exploration moderates the
association between work experience and the number of reported new
business ideas.
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In detail, this means:
H6a. The number of reported new business ideas will increase with deeper
experience from a particular industry, but the effect will be enhanced with a
higher emphasis on exploration.
H6b. The number of reported new business ideas will increase with deeper
experience from a particular business function, but the effect will be
enhanced with a higher emphasis on exploration.
H6c. The number of reported new business ideas will increase with wider
experience across different industries, but the effect will be enhanced with a
higher emphasis on exploration.
H6d. The number of reported new business ideas will increase with wider
experience across different business functions, but the effect will be
enhanced with a higher emphasis on exploration.
Data and methods
Sample and data collection
We collected empirical data through a questionnaire survey sent out to a randomly
selected group of 1,000 individuals who started a new independent firm registered
during 1998-2002 across all industries according to the Swedish Standard Industrial
Classification (SNI2002). Contact addresses were administrated by Statistics Sweden.
A control question verified that the respondent had relevant start-up experience.
Fifteen unanswered questionnaires were returned shortly after the first send-out due to
business closures, mergers, ownership changes and unknown addresses, which
reduced our initial sample to 985 entrepreneurs. After two reminders we received
303 responses resulting in a valid response rate of approximately 30.8 percent.
Thereafter we carefully examined the data with respect to missing and incomplete
information, and this examination led to a final sample of 291 cases with complete and
accurate data.
Chi-square and t-tests were conducted to assess whether the results from our sample
of entrepreneurs can be generalized to the population. These tests did not reveal any
statistically significant differences between respondents and non-respondents with
respect to contingency variables such as industry, geographical location, firm size and
firm age. On these criteria the sample appears to be representative of the population
from which it was drawn. Prior studies have shown that late respondents are a
reasonable substitute for non-respondents (Clausen and Ford, 1947; Goudy, 1976,
1978), which suggests that comparisons of early and late respondents may provide a
reasonable approximation of potential non-response bias. Analysis of early (69.6
percent) and late respondents (30.4 percent) did not show any differences with respect
to the contingency variables (see above) and the variables used in the study (see below).
Thus, in all we have no reason to suspect that there are any major response biases in
our sample.
Variables and measures
Number of new business ideas. Our dependent variable was based on Singh (2000) and
gauged by an item measuring the number of new ideas which the entrepreneur had in
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the last year and which could lead to a new business or a significant part of an existing
business. A higher score on this item indicates a higher number of business ideas. Due
to a skewed distribution in the data, the variable was transformed using a logarithmic
transformation.
There are at least three reasons why we believe that our self-report measuring the
number of new ideas can be considered satisfactory in this study. First, we are not
primarily interested in the objective viability or potential in a business idea, but rather
in respondents’ subjective alertness towards new business ideas. Second, we can
assume that there is value in generating more rather than fewer business ideas, as
there is greater likelihood that one (or some) of these ideas will develop into a viable
profit-generating business concept (Marvel and Lumpkin, 2007; Ucbasaran et al., 2008).
Third, the dependent variable is positively and significantly associated with the
number of business opportunities (defined as unmet customer needs) that the
entrepreneur had seized during the last five years ( p,0.01). This suggests that the
reported business ideas to a large extent are coupled with efforts to seize a perceived
opportunity for entrepreneurial profit, something which supports our general
conceptualization of the opportunity development process in our theoretical framework
(i.e. Ardichvili et al., 2003; Klofsten, 2005).
Depth and breadth of work experience. Our integrative framework involves four
independent variables related to the depth and breadth of respondents’ industry and
functional work experience. We used the Swedish Standard Industrial Classification
(SNI2002) to identify and distinguish between different industries (see the Appendix
for an overview). Moreover, we use six functional areas to identify and distinguish
between different work functions:
(1) general management;
(2) R&D;
(3) manufacturing/production;
(4) sales/marketing;
(5) finance; and
(6) legal.
This was based on earlier work by Stuart and Abetti (1990), McGee et al. (1995), and
Entrialgo (2002). Thereafter, we reviewed past entrepreneurship research that has
applied human capital theory, for guidance on how to measure the depth and breadth
of respondents’ industry and functional work experience. In this endeavor we found
that work experience often has been measured by numbers of years of professional
work experience (e.g. Evans and Leighton, 1989; Bru
¨derl et al., 1992; Marvel and
Lumpkin, 2007). Following this advice, deep industry experience was measured as the
longest number of years the respondent has worked in a particular industry. Likewise,
deep functional work experience was measured as the longest number of years the
respondent has worked in a particular work function.
There was, however, less guidance in the literature on how to measure experience
across different industries and work functions. Some studies have measured
entrepreneurs’ number of prior full-time jobs, or number of employers the entrepreneur
had worked for (Gimeno-Gascon et al., 1997; Marvel and Lumpkin, 2007), but this
operationalization does not fit very well in entrepreneurial settings. Instead, experience
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across different industries was measured in this study as the total number of industries
that the entrepreneur has experience from. Likewise, experience across different work
functions was measured as the total number of different work functions that the
entrepreneur has experience from.
Exploration mindset. Appropriate scales for measuring the extent to which
entrepreneurs have a learning mindset that emphasizes exploration were not available.
Instead, we carefully examined existing literature on both exploration theory (e.g.
March, 1991) and entrepreneurial learning (e.g. Minniti and Bygrave, 2001; Politis,
2005) in order to tap the domain of this construct and start generating a pool of
potential items. From this work we developed five items on a five-point scale, aimed to
capture the extent to which entrepreneurs have an exploration mindset. To further
validate our self-generated scale, we employed an exploratory factor analysis, which
showed that all items loaded strongly on a single factor explaining 45.7 percent of the
variance and with an eigenvalue of 2.29. Cronbach’s alpha (
a
) for this construct was
0.69 and the item-to-total correlations were between r ¼0.55 and r ¼0.78. The five
items are presented together with the results from the factor analysis in the Appendix.
Control variables. We included three control variables in our research model that are
in line with arguments in human capital theory and research on experiential learning.
First, we include a measure of respondents’ formal educational experience, as this can
facilitate the integration and accumulation of new knowledge and thereby provide
entrepreneurs with improved information-processing skills, a higher capacity to access
and assess firm-specific information, and a larger opportunity set (Davidsson and
Honig, 2003). To measure this variable, we asked the respondents to indicate the total
number of years from which they have formal educational experience. In the sample, 12
percent of the entrepreneurs had compulsory school education (7 or 9 years) as their
highest educational level, 33.7 percent had education also from senior high school, and
50.8 percent experience from higher education (university studies).
In addition, we included a binary variable related to whether the entrepreneurs have
past experience from starting up another firm, which can be seen as a proxy for their
amount of specific human capital that can assist them in new entrepreneurial
undertakings (Davidsson and Honig, 2003; Ucbasaran et al., 2006; Politis, 2008). The
data show that the majority of the entrepreneurs in our sample (69 percent) have
multiple start-up experience, and thus can be labeled “habitual entrepreneurs”
(Westhead and Wright, 1998). The average number of start-ups per respondent
(including their current firm) is 2.5, with min ¼1, and max ¼15.
Finally, we included a binary variable related to whether the respondents currently
are operating their firms in a service industry. Industries in the service sector have
been the fastest-growing in the Swedish economy in the past decade, and this
industry-level growth can thus be expected to represent larger levels of opportunity
present in these industries (Shane, 2003). These industries were identified according to
the classification of service industries made by the Swedish Standard Industrial
Classification[3].
Common method variance
An issue that deserves attention is that the majority of the variables used in this study
come from the same source of self-report data. This may result in a “common method
variance” problem where respondents seek out consistency in their responses and yield
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random measurement error (Podsakoff et al., 2003). We took the following steps to
reduce this risk. First, we placed the different variables in different sections of the
questionnaire, to reduce the likelihood that respondents could cross-check for their own
internal consistency. Second, as advised by Podsakoff and Organ (1986), we ran
Harman’s one-factor test which suggests that a substantial amount of common method
variance is present if a single factor emerges from the factor analysis, or if one general
factor accounts for the majority of the covariance among the variables. Rather than a
single factor, the analysis revealed three distinct factors with an eigenvalue greater
than 1.0 and an account of total variance of 58.3 percent. As no general factor was
apparent and the largest factor did not account for a majority of the variance (24.8
percent), there were consequently no signs that common method variance posed any
serious problem in our data.
Analysis and results
We have a metric measure as the dependent variable and several metric and binary
independent variables in our research model, and for this reason we chose to apply
linear multiple regression analysis (Hair et al., 1998). Before running the analysis, we
made careful observations to judge the quality of our data set. In these efforts we
controlled that the residuals were distributed randomly within a rectangle, which
indicates that the independent and control variables have a linear relation with the
dependent variable in the model (Pallant, 2001). Examination of normal probability
plots in SPSS also indicated normal distribution of the residuals about the predicted
DV scores. Controlling for heteroscedasticity, we did not detect any presence of
unequal variances. The Durbin-Watson value was 1.875, which indicates independence
of residuals[4]. In sum, we judged the data set to meet the assumptions for the
forthcoming regression analyses. A description of the variables used in the analysis
(correlations, means and standard deviations) is displayed in Table I.
We expected relatively strong correlations between the various independent
variables in our data set. For this reason we carefully checked our data set for potential
problems of multicollinearity by examining the correlation matrix in Table I as well as
the variance inflation factors (VIF) for each explanatory variable in the regression
analysis. As can be seen in Table I, all correlation coefficients are less than r ¼0.70,
which according to Nunnally (1978) is the standard threshold used to determine high
correlation. Moreover, all explanatory variables in the regression analysis have low
VIF’s (between 1.03 and 1.65). This examination leads us to conclude that no problems
of multicollinearity exist in our data set.
We also conducted additional analyses in order to control for the possibility that our
experience variables could be associated with the number of business ideas in a
curvilinear or nonlinear way. It could, for example, be that the slope is initially positive
as entrepreneurs’ work experience increases, but becomes negative as experience
becomes excessive. There may also be a decreasing but never negative marginal utility
of entrepreneurs’ work experience. To control for these potential outcomes, we
examined scatter plots of the relationships between each of our independent variables
and the dependent variable. However, this exercise did not reveal any such evidence.
We also controlled for possible interaction effects between depth and breadth of
experience on business idea generation. These tests did not reveal any significant
results and are therefore not reported.
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Variables
1 2 3 4 5 6 7 8 9 Means SD
1 No of new business ideas 1.00 – – – – – – – – 0.91 0.82
2 Higher educational experience 0.14 *1.00 – – – – – – – 13.6 2.55
3 Habitual entrepreneurship experience 0.17 ** 0.03 1.00 – – – – – – 0.69 0.46
4 Service industry 20.02 0.26 ** 0.07 1.00 – – – – – 0.66 0.48
5 Deep industry work experience 20.17 ** 20.13 *20.01 20.07 1.00 – – – – 21.0 10.1
6 Deep functional work experience 20.01 20.11 *0.17 ** 20.04 0.56 ** 1.00 – – – 16.0 10.1
7 Cross industry work experience 0.17 ** 20.02 0.23 ** 0.04 20.05 0.20 ** 1.00 – – 2.68 1.77
8 Cross functional work experience 0.26 ** 20.00 0.25 ** 20.07 20.03 0.12 *0.31 ** 1.00 – 3.06 1.62
9 Exploration 0.33 ** 0.09 0.18 ** 0.03 20.10 *0.11 *0.28 ** 0.30 ** 1.00 3.52 0.71
Notes: Significance level *p,0.05, and **
p,0.01
Table I.
Descriptive statistics and
correlations
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We have included exploratory self-generated measures in our analysis. Although this
novel approach contributes to the exploration of new theoretical domains, novelty may
also influence measure reliability and thus lead to lower statistical power and a risk of
Type I errors. To cope with this potential problem, we have chosen in our analysis to
sacrifice some significance for statistical power by interpreting statistical results not
only at the usual 1 percent and 5 percent levels of significance, but also at the 10
percent level. This was done in order to improve the informativeness of our study and
obtain a more reasonable balance between the risks of having Type I and Type II
errors[5], which can be of particular benefit for exploratory survey-based studies like
this (e.g. Westhead et al., 2005).
Results from the analysis
To be able to identify the separate effects of the control variables, the independent
variables, and the interaction variables, we introduced them in different steps in the
regression analysis. Following recommendations by Aiken and West (1991), we
centered the variables before including them in the analysis, to reduce multicollinearity
between main effects and interaction terms, and to facilitate interpretation of
potentially significant interaction effects. The results of the various steps are presented
in Table II.
Generation of new business ideas
Step Model and variables Model I Model II Model III Model IV
Control
1 Higher educational experience 0.15 *0.14 *0.12 þ0.13 *
Habitual entrepreneurship experience 0.17 ** 0.10 0.08 0.08
Service industry 20.03 20.02 20.02 20.03
Independent
2 Deep industry work experience – 20.16 *20.12 þ20.16 *
Deep functional work experience – 0.04 0.01 0.04
Cross industry work experience – 0.08 0.03 20.02
Cross functional work experience – 0.19 ** 0.13 *0.14 *
Moderator
3 Exploration mindset – – 0.24 ** 0.25 **
Interactions
4. Exploration £deep industry work experience – – – 20.11
Exploration £deep functional work experience – – – 0.13 þ
Exploration £cross industry work experience – – – 0.12 þ
Exploration £cross functional work experience – – – 20.07
– – – 0.08
R
2
0.05 0.12 0.17 0.19
Adj R
2
0.04 0.10 0.14 0.16
DAdj R
2
– 0.06 *0.04 *0.02 *
F (sign) 4.7 ** 5.0 ** 6.5 ** 5.1 **
Notes: The table reports
b
(partial standardized coefficients), R
2
, adjusted R
2
, and significance levels
+p,0.10, *p,0.05, and **
p,0.01
Table II.
Regression analysis
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Models with independent effects. The control variables were first entered in an initial
model which is reported as “control” in Table II. As can be seen in the table, the model
is significant but does not explain a large share of the variance in the dependent
variable. Next, we enter the independent variables related to depth and breadth in
industry and functional work experience. The results are reported as step 2 in the
“independent” columns in Table II. The resulting model has a significant contribution
compared to the initial model, with an adj. R
2
-change of 0.06, p,0.01. Within this
model we can see that deeper experience from a particular industry is negatively
associated with a higher number of new business ideas, at p,0.05. Although
significant, it is contrary to what we expected, and H1 is thus not supported.
Furthermore, we cannot find any significant association between either deeper
experience from a particular work function or experience across different industries
and a higher number of new business ideas in the model. Neither H2 nor H3 is thus
supported. However, experience across different work functions is associated with a
higher number of new business ideas, at p,0.01. Thus, we find support for H4 in our
model.
Thereafter, we introduce the exploration mindset variable to examine its
independent effect. The results are reported as step 3 in the “moderator” column in
Table II. The results indicate that the number of reported new business ideas is
significantly higher for individuals who have a higher exploration mindset, with an
adj. R
2
-change in the model of 0.04, p,0.01. In sum, these findings suggest that a
learning mindset which emphasizes exploration has a strong and direct influence on
the generation of new business ideas, regardless of entrepreneurs’ previous amount of
work experience. Thus, we find support for H5 in our model.
The full model including interaction effects. The full model including interaction
effects is reported as step 4 in the “interaction” column in Table II, and it includes both
independent and moderating variables. In short, an interaction effect exists if the
interaction term gives a contribution over and above the independent effects model
(Aiken and West, 1991; Cohen and Cohen, 1983). As can be seen in Table II, the
addition of the new interaction variables increases adj. R
2
from 0.14 to 0.16[6] and the
increase is statistically significant at p,0.01, which suggests an interaction effect. A
closer look reveals that the interaction effect has a weak but significant positive
association between deeper experience from both a particular work function, as well as
experience across different industries, and a higher number of new business ideas, at
p,0.10. These findings partly support the overall assumption emphasized in H6 that
a learning mindset which favors exploration moderates the association between work
experience and the number of reported new business ideas. Thus, accepting a 10
percent level of significance, the findings suggest that H6b and H6c are supported
while we find no support for H6a and H6d.
To further examine the nature of the observed interaction effects, we followed the
procedures outlined by Aiken and West (1991) by calculating the high and low points
for each of the variables (plus and minus one standard deviation from their mean) and
then crossing these levels to graph the interaction. The results are shown in Figure 1.
The interaction effects of exploration mindset and depth of functional work
experience on the number of business ideas is graphed in Figure 1 with two lines. The
first line represents individuals with a high exploration mindset, and the second line
represents individuals with a low exploration mindset. As can be seen, the extent to
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which respondents have an exploration mindset has an interaction effect on the
amount of depth in functional work experience within the context of the number of
reported business ideas. Simply stated, individuals with deeper functional work
experience report a higher number of new business ideas if they also have a high
exploration mindset.
The interaction effects of exploration mindset and breadth in industry work
experience on the number of business ideas is graphed with two lines in Figure 2. Here
Figure 1.
Interaction of exploration
mindset and depth in
functional work
experience on the number
of business ideas
Figure 2.
Interaction of exploration
mindset and breadth in
industry work experience
on the number of business
ideas
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we can see that the extent to which respondents have an exploration mindset has an
interaction effect on the amount of breadth in industry work experience within the
context of the number of reported business ideas. Simply stated, individuals with
higher experience across different industries report a higher number of new business
ideas if they also have a higher exploration mindset.
Discussion and conclusions
In this article we have developed an integrative framework that combines human
capital theory with theory and research on entrepreneurial learning, to examine how
entrepreneurs’ work experience is associated with the generation of new business
ideas. The integration of these two intellectual traditions is a novel approach, which is
in line with recent developments in entrepreneurship research that focus on the role of
knowledge and the acquisition and processing of information in the early stages of the
entrepreneurial process (e.g. Corbett, 2007; Dimov, 2007; Westhead et al., 2005). In line
with our initial theoretical expectations, the empirical data show that entrepreneurs
benefit both directly from investments in human capital through work experience and
from employing a learning mindset that favors exploration. Our empirical data also
indicate that the effect from investments in human capital through work experience
can be enhanced with a learning mindset that favors exploration. Adding to this, we
show how these influences vary depending on the extent to which the entrepreneurs
have industry-level and firm-level experience, and whether they have developed more
of a specialist or a generalist career path. Thus, in all our study contributes a better
understanding of how different kinds of work experience among entrepreneurs,
together with individual learning preferences, interact in the process of generating new
business ideas. A more elaborate discussion of these findings will be further developed
below.
The value of depth in work experience for business idea generation
Our integrative framework developed arguments about how human capital
investments in a specialist career path, where the entrepreneur develop experience
within a particular field of work, are associated with the generation of new business
ideas. Contrary to what we hypothesized, however, our analysis failed to find evidence
of any value from developing human capital via depth in work experience. Instead, the
findings show a negative significant association between deep industry work
experience and the number of new business ideas, which suggests that the longer
entrepreneurs have operated within an industry the less likely they are to come up with
new business ideas. Although this is somewhat counter-intuitive with respect to the
ability to find patterns and process information that often is associated with specialists
and experts, our findings may on the other hand perhaps be explained by the
institutionalized and highly shared worldview that often is developed within industries
(e.g. Spender, 1989; Cliff et al., 2006). Spender (1989), for example, suggests that
managers and entrepreneurs operating in the same industry develop a set of shared
beliefs, or “industry recipes”, that impose order and limits on the information with
which they must cope. The perceived understanding of the industrial environment is
thus much the same among firms producing similar goods or services, and this may in
turn trigger the adoption of taken-for-granted behavior through socialization processes
at the industry level. The tendency to reproduce an industry’s prevailing practices and
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routines is also emphasized in the work of Cliff et al. (2006) who report that extensive
work experience in the core of an industry among entrepreneurs influence the degree of
novelty exhibited by their newly established firms. Interestingly, they also find that the
reproduction of established routines is in effect even if the legitimacy of established
routines is questioned. The observed negative association in our data set may in this
respect be explained by assuming that entrepreneurs have a general tendency to
routinely apply problem-solving heuristics and memorized practices to reduce the
complexities of decision-making in the process of new venture creation. Since these
routines function as carriers of knowledge and experience they are moreover fairly
difficult to change (e.g. Cyert and March, 1963) which over time may constrain the
ability to break out of established patterns of thinking and acting. Given our findings,
it thus seems fair to argue that experientially acquired knowledge gained from longer
tenure within a particular industry in many cases may become a fence that blocks the
path to novel solutions and new business ideas.
The value of breadth in work experience for business idea generation
Our integrative framework also developed arguments about how human capital
investments in a generalist career path, where the entrepreneur develop experience
across different areas or fields of work, are associated with the generation of new
business ideas. The analysis failed to find evidence of any value from moving across
different industries. However, the findings suggest that the possession of relevant
information and inputs gained from experience across different business functions can
permit associations and linkages that ultimately lead to the generation of more new
business ideas. Developing human capital through work experience across different
business functions can in this respect be regarded as a valuable asset for practicing
entrepreneurs, as it makes them come up with a higher number of new business ideas.
Interestingly, this also speaks in favor of the “jack-of-all-trades” hypothesis developed
by Lazear (2005), where individuals who are knowledgeable in many skill areas are
expected to have a higher probability of becoming self-employed and successfully
developing a new business venture. In all, it seems fair to argue that the transfer of
information and knowledge between different business functions seems to be one of the
keys to successful entrepreneurial insights.
The value of an exploration mindset for business idea generation
Our empirical findings emphasize the importance of taking differences in how
entrepreneurs prefer to acquire and transform information gained from work
experience into account for a better understanding of how work experience influence
the generation of new business ideas. In support of our initial expectations, the
empirical findings suggest that there is value in employing a learning mindset that
favors exploration, due to its direct influence on business idea generation.
Interestingly, there are prior empirical studies in the entrepreneurship field that
seem to back up this finding. For example, it has long been argued that entrepreneurs
who are highly explorative and alert also tend to become more effective in identifying
and acting on entrepreneurial opportunities (e.g. Hills et al., 1997; Zietsma, 1999). It has
also been pointed out that entrepreneurs who are continuously involved in new
entrepreneurial undertakings, so called habitual entrepreneurs, often strive for
variation and new challenges with the overall aim to learn something new (Katz, 1994;
Generation of
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Hall, 1995; Westhead and Wright, 1998; Politis, 2008). Thus, an exploration mindset
seems to be a favorable characteristic for entrepreneurs who seek involvement in new
potential ventures through self-generated business ideas. In addition, our empirical
findings also provide indications that a learning mindset that favors exploration can
have a multiplicative effect on the ability to generate new business ideas, if combined
with certain sources of work experience. In our case, we can observe this positive and
significant association for investments in breadth in industry experience and depth in
business function experience respectively. Given these findings, it seems fair to
suggest that entrepreneurs who make human capital investments aimed at becoming a
business function specialist and/or an industry generalist, should employ a learning
mindset that search for variety in their particular knowledge domains to increase their
learning efforts in relation to business idea generation.
In sum, it seems that just as entrepreneurs can be both behaviorally and cognitively
different (e.g. Wright et al., 1997; Baron and Ensley, 2006; Ucbasaran et al., 2006) they
also show differences when it comes to their preferences for learning (Minniti and
Bygrave, 2001; Politis, 2005; Corbett, 2007). These differences can moreover be related
to subsequent performance differences in the early stages of the entrepreneurial
process. Hence, taking the above findings into account it seems fair to argue that
differences in how entrepreneurs prefer to acquire and transform experience
throughout their career has a subsequent influence on the ability to generate new
business ideas.
Intersection of career path and type of work experience
The discussion has so far focused on how the career path of entrepreneurs, in this
study conceptualized as depth versus breadth in industry and firm level work
experience, interact with their individual learning preferences in the process of
generating new business ideas. Here we can observe that our empirical findings
suggest an interesting overall pattern. As previously discussed, a career path
characterized by depth in industry experience has a direct negative effect on business
idea generation while a career path characterized by breadth in business function
experience has a direct positive effect. These associations seem consequently to be in
effect irrespective of entrepreneurs’ learning mindset. However, the other two
combinations – breadth in industry work experience and depth in business function
work experience – seem to require a high emphasis on exploration to realize a
significant influence. To be in effect, these career paths thus seem to require both
investments in work experience as well as the more intensive search for variety in
experience that an exploration mindset offers. On the aggregate, our findings thus
suggest that entrepreneurs with a higher exploration mindset are the ones who can
make the most out of investments in work experience throughout their careers with
respect to the generation of new business ideas. The findings are summarized and
illustrated in Table III.
Limitations and suggestions for future research
There are some limitations in the present study that we need to acknowledge. One
potential limitation is that our study has employed the perspective that business idea
discovery is very much an individual-level phenomenon. Although this assumption is
largely in line with both human capital theory and experiential learning theory some
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scholars have suggested that there is also a need to take into account how the evolution
from an initial idea into a more developed business concept interacts with the
entrepreneurs’ local surroundings and social context (e.g. De Konig, 1999; Davidsson
et al., 2006). The relationship between individualized and contextualized views on
entrepreneurial discovery is consequently something that deserves further attention in
order to develop more interactive and holistic models of the early stages in the
entrepreneurial process. Another potential limitatin refers to that our dependent
variable is biased towards the quantity rather than the quality of new business ideas.
We have argued, in line with previous research (e.g. Marvel and Lumpkin, 2007;
Ucbasaran et al., 2008), that it is better to come up with more rather than fewer new
business ideas as this increases the likelihood that one (or some) of them will turn out
to be highly successful. However, this is to the best of our knowledge still a largely
untested assumption, and future studies should thus incorporate measures of quality in
their research designs. One way to do this would be to associate generated business
ideas with their innovativeness (Shepherd and DeTienne, 2005). Another way could
perhaps be to assess the degree to which generated business ideas attract interest from
potential stakeholders, such as investors or customers (Der et al., 2005). Incorporating
such measures would be a substantial leap forward and a great contribution to the
research field. A third potential shortcoming is that we have treated entrepreneurs’
learning mindset as an independent variable alongside their work experience.
Although this was made in order to follow the logic in our theoretical framework we
also know from previous studies that there may be feedback loops between learning
preferences and work experience that over time may strengthen and reinforce certain
kinds of entrepreneurial behavior (Politis, 2005). For example, just as entrepreneurs’
with certain career preferences may self-select into certain contexts that call for a
specific decision logic (Gabrielsson and Politis, 2009), it can in a similar vein be
conjectured that there is a significant bias in the type of work experience that is sought
by people with a learning mindset that favors exploration. How feedback loops
between an exploration mindset and work experience interact in the process of
entrepreneurial learning is thus an interesting line of inquiry that needs further
investigation. A final potential limitation is that we have only tested our hypotheses on
a sample of practicing entrepreneurs. While we believe our findings are robust across
similar populations, we advise caution when generalizing to nascent entrepreneurs or
to people in general. Further studies with samples that include both practicing, nascent
and non-entrepreneurs should thus be conducted to extend the findings from this
study.
Specialist career path – depth in
work experience
Generalist career path – breadth in
work experience
Industry level work
experience
Negative direct effect irrespective
of learning mindset
Positive effect if entrepreneur
employs a learning mindset that
favors exploration
Firm level business
function experience
Positive effect if entrepreneur
employs a learning mindset that
favors exploration
Positive direct effect irrespective of
learning mindset
Table III.
The intersection of career
path and type of work
experience: effects on
business idea generation
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Implications for practice
When it comes to practical implications our findings speak in favor of developing and
nurturing an explorative learning mindset for the successful pursuit of an
entrepreneurial career. This recommendation relates both to entrepreneurs who
self-manage their own careers as well as to course organizers who want to support the
development of an entrepreneurial mindset among students in the curriculum. Our
findings also suggest that movements across different functional work areas have
great potential as sources of ideas for new products and markets. Individuals who aim
for an entrepreneurial career should consequently aim for a “generalist” career path by
favoring more project-oriented work situations, rather than trying to become experts in
a particular industry or functional work area. The knowledge gained from such
experiences may in turn favor the generation of new business ideas. This
recommendation relates both to individuals who start up their own firms and to
managers working with corporate entrepreneurship and innovation.
Concluding remarks
To conclude, the aim of this study was to develop an integrated framework to examine
how entrepreneurs’ work experience is associated with the generation of new business
ideas. On the aggregate our findings seemingly contribute to a greater understanding
of how work experience and individual learning preferences interact in the process of
generating new business ideas. Based on these contributions, we believe that our study
provides an important step in research that seeks to understand how variations in
work experience and individual learning preferences influence performance outcomes
in the early stages of the entrepreneurial process.
Notes
1. At this point we want to emphasize that we do not mean that a developed business idea
means that the idea will be fully viable and lead to a profitable and successful start-up.
Business ideas are primarily cognitive outcomes created in the minds of prospective
entrepreneurs, and they may therefore not necessarily correctly reflect external conditions,
such as customer demand or the availability and cost of needed resources. For an extended
discussion of this issue, see for example Davidsson (2006, p. 233).
2. We are aware that human capital theory acknowledges the value of both depth and breadth
in work experience for the development of human capital (Becker, 1964). However, this
original conjecture is related to increases in wage earnings and may thus not be directly
applicable to studies of entrepreneurial discovery.
3. These include hotel and catering trade, transportation, storage and communication,
financing, real estate and rental service, consulting and other business services, public
administration and defense, compulsory social insurance, education, health and medical
service, veterinary and other social and personal services.
4. The rule-of-thumb says that a value between 1 and 3 is acceptable (Field, 2005).
5. A Type I error is to incorrectly reject the null hypothesis of no effect, while a Type II error is
to falsely not reject the null hypothesis.
6. The adj. R
2
value in the final statistical model suggests that we can explain 16 percent of the
variance in our dependent variable. This can be seen as satisfactory since we are focusing on
a focused set of variables that relate to human capital reasoning and theory and research on
entrepreneurial learning, rather than an all-encompassing framework that seeks to
incorporate all possible influences on entrepreneurs’ ability to generate new business ideas.
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References
Aiken, L.S. and West, S.G. (1991), Multiple Regression: Testing and Interpreting Interactions,
Sage Publications, Newbury Park, CA.
Amabile, T.M., Barsade, S.G., Mueller, J.S. and Staw, B.M. (2005), “Affect and creativity at work”,
Administrative Science Quarterly, Vol. 50 No. 3, pp. 367-403.
Ardichvili, A., Cardozo, R. and Ray, S. (2003), “A theory of entrepreneurial opportunity
identification and development”, Journal of Business Venturing, Vol. 18 No. 1, pp. 105-23.
Baron, R.A. (2006), “Opportunity recognition as pattern recognition: how entrepreneurs connect
the dots to identify new business opportunities”, Academy of Management Perspectives,
Vol. 20 No. 1, pp. 104-19.
Baron, R.A. and Ensley, M.D. (2006), “Opportunity recognition as the detection of meaningful
patterns: evidence from comparisons of novice and experienced entrepreneurs”,
Management Science, Vol. 52 No. 9, pp. 1331-44.
Becker, G.S. (1964), Human Capital, Chicago University Press, Chicago, IL.
Benner, M.J. and Tushman, M.L. (2002), “Process management and technological innovation: a
longitudinal study of the photography and paint industries”, Administrative Science
Quarterly, Vol. 47 No. 4, pp. 676-706.
Bhave, M. (1994), “A process model of entrepreneurial creation”, Journal of Business Venturing,
Vol. 9 No. 3, pp. 223-42.
Bru
¨derl, J., Preisendorfer, P. and Zeigler, R. (1992), “Survival chances of newly founded business
organizations”, American Sociological Review, Vol. 57 No. 2, pp. 227-42.
Chase, W. and Simon, H. (1973), “Perception in chess”, Cognitive Psychology, Vol. 4, pp. 55-81.
Clausen, J.A. and Ford, R.N. (1947), “Controlling bias in mail questionnaires”, Journal of the
American Statistical Association, Vol. 42 No. 240, pp. 497-511.
Cliff, J.E., Jennings, P.D. and Greenwood, R. (2006), “New to the game and questioning the rules:
the experiences and beliefs of founders who start imitative versus innovative firms”,
Journal of Business Venturing, Vol. 21 No. 5, pp. 633-63.
Cohen, J. and Cohen, P. (1983), Applied Multiple Regression/Correlation Analysis for the
Behavioral Sciences, Lawrence Erlbaum Associates, Hillsdale, NJ.
Cohen, W.M. and Levinthal, D.A. (1990), “Absorptive capacity: a new perspective on learning and
innovation”, Administrative Science Quarterly, Vol. 35 No. 1, pp. 128-52.
Cooper, A. (1985), “The role of incubator organizations in the founding of the growth oriented
firms”, Journal of Business Venturing, Vol. 1 No. 1, pp. 75-86.
Cooper, A.C., Gimeno-Gascon, F.J. and Woo, C.Y. (1994), “Initial human and financial capital as
predictors of new venture performance”, Journal of Business Venturing, Vol. 9 No. 5,
pp. 371-95.
Corbett, A.C. (2005), “Experiential learning within the process of opportunity identification and
exploitation”, Entrepreneurship: Theory and Practice, Vol. 29 No. 4, pp. 473-91.
Corbett, A.C. (2007), “Learning asymmetries and the discovery of entrepreneurial opportunities”,
Journal of Business Venturing, Vol. 22 No. 1, pp. 97-118.
Cyert, R.M. and March, J.G. (1963), A Behavioral Theory of the Firm, Prentice Hall, Englewood
Cliffs, NJ.
Generation of
new business
ideas
69

Davidsson, P. (2006), “The entrepreneurial process”, in Carter, S. and Jones-Evans, D. (Eds),
Enterprise and Small Business: Principles, Practices and Policy, 2nd ed., Prentice Hall,
Harlow, pp. 129-51.
Davidsson, P. and Honig, B. (2003), “The role of social and human capital among nascent
entrepreneurs”, Journal of Business Venturing, Vol. 18 No. 3, pp. 301-31.
Davidsson, P., Hunter, E. and Klofsten, M. (2006), “Institutional forces: the invisible hand that
shapes venture ideas”, International Small Business Journal, Vol. 24 No. 2, pp. 112-5.
DeGroot, A.D. (1965), Thought and Choice in Chess, Mouton, New York, NY.
De Konig, A. (1999), “Opportunity formation as a socio-cognitive perspective”, in Bygrave, W.D.,
Autio, E., Brush, C.G., Davidsson, P., Green, P.G., Reynolds, P.D. and Sapienza, H.J. (Eds),
Frontiers of Entrepreneurship Research, Babson College, Wellesley, MA.
Der, F.M., Wong, P.K. and Ong, A. (2005), “Do others think you have a viable business idea?
Team diversity and judges’ evaluation of ideas in a business plan competition”, Journal of
Business Venturing, Vol. 20 No. 3, pp. 385-402.
Dimov, D. (2007), “From opportunity insight to opportunity intention: the importance of
person-situation learning match”, Entrepreneurship: Theory and Practice, Vol. 31 No. 4,
pp. 561-84.
Entrialgo, M. (2002), “The impact of the alignment of strategy and managerial characteristics on
Spanish SMEs”, Journal of Small Business Management, Vol. 40 No. 3, pp. 260-70.
Evans, D.S. and Leighton, L.S. (1989), “Some empirical aspects of entrepreneurship”, The
American Economic Review, Vol. 79 No. 3, pp. 519-36.
Field, A. (2005), Discovering Statistics Using SPSS, 2nd ed., Sage Publications, London.
Gabrielsson, J. and Politis, D. (2009), “Career motives and entrepreneurial decision making:
examining preferences for causal and effectual logics in the early stage of new ventures”,
Small Business Economics, DOI: 10.1007/s11187-009-9217-3.
Ghemawat, P. and Costa, J. (1993), “The organizational tension between static and dynamic
efficiency”, Strategic Management Journal, Vol. 14, S2, pp. 59-73.
Gimeno-Gascon, F.J., Folta, T.B., Cooper, A.C. and Woo, C.Y. (1997), “Survival of the fittest?
Entrepreneurial human capital and the persistence of underperforming firms”,
Administrative Science Quarterly, Vol. 42 No. 4, pp. 750-83.
Goudy, W.J. (1976), “Nonresponse effects on relationships between variables”, Public Opinion
Quarterly, Vol. 40 No. 3, pp. 360-9.
Goudy, W.J. (1978), “Interim response to a mail questionnaire: impacts on variable relationships”,
Sociological Quarterly, Vol. 19, Spring, pp. 253-65.
Greenberg, E.R. (1998), “The mobile manager”, Management Review, Vol. 87 No. 3, pp. 9-10.
Gustafsson, V. (2006), Entrepreneurial Decision Making: Individuals, Tasks and Cognitions,
Edward Elgar, Cheltenham.
Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate Data Analysis, 5th
ed., Prentice Hall, Englewood Cliffs, NJ.
Hall, P.J. (1995), “Habitual owners of small businesses”, in Chittenden, F., Robertson, M. and
Marshall, I. (Eds), Small Firms: Partnership for Growth, Paul Chapman, London, pp. 217-30.
Hills, G.E., Lumpkin, G.T. and Singh, R.P. (1997), “Opportunity recognition: perceptions and
behaviors of entrepreneurs”, in Reynolds, P.D., Carter, W.D., Davidsson, P., Gartner, W.B.
and McDougall, P. (Eds), Frontiers of Entrepreneurship Research, Babson College,
Wellesley, MA, pp. 168-82.
IJEBR
18,1
70

Holmqvist, M. (2004), “Experiential learning processes of exploration and exploitation within and
between organizations: an empirical study of product development”, Organization Science,
Vol. 15 No. 1, pp. 70-81.
Johansson, F. (2004), The Medici Effect: Breakthrough Insights at the Intersection of Ideas,
Concepts, and Cultures, Harvard Business School Press, Boston, MA.
Jones, O. and Holt, R. (2008), “The creation and evolution of new business ventures: an activity
theory perspective”, Journal of Small Business and Enterprise Development, Vol. 15 No. 1,
pp. 51-73.
Kashdan, T.B., Rose, P. and Fincham, F.D. (2004), “Curiosity and exploration: facilitating positive
subjective experiences and personal growth opportunities”, Journal of Personality
Assessment, Vol. 82 No. 3, pp. 291-305.
Katz, J.A. (1994), “Modeling entrepreneurial career progressions: concepts and considerations”,
Entrepreneurship Theory and Practice, Vol. 19 No. 2, pp. 23-39.
Klofsten, M. (2005), “New venture ideas: an analysis of their origin and early development”,
Technology Analysis and Strategic Management, Vol. 17 No. 1, pp. 105-19.
Kolb, A.Y. and Kolb, D.A. (2010), “Learning to play, playing to learn: a case study of a ludic
learning space”, Journal of Organizational Change Management, Vol. 23 No. 1, pp. 26-50.
Lazear, E.P. (2005), “Entrepreneurship”, Journal of Labor Economics, Vol. 23 No. 4, pp. 649-80.
McGee, J.E., Dowling, M.J. and Megginson, W.L. (1995), “Cooperative strategy and new venture
performance: the role of business strategy and management experience”, Strategic
Management Journal, Vol. 16 No. 7, pp. 565-80.
Mainemelis, C. and Ronson, S. (2006), “Ideas are born in fields of play: towards a theory of play
and creativity in organizational settings”, in Staw, B.M. and Brief, A.P. (Eds), Research in
Organizational Behavior, Vol. 27, JAI Press, Oxford, pp. 81-131.
March, J.G. (1991), “Exploration and exploitation in organisational learning”, Organization
Science, Vol. 2 No. 1, pp. 71-87.
Marvel, M.R. and Lumpkin, G.T. (2007), “Technology entrepreneurs’ human capital and its
effects on innovation radicalness”, Entrepreneurship Theory and Practice, Vol. 31 No. 6,
pp. 807-28.
Milliken, F. and Vollrath, D. (1991), “Strategic decision making tasks and group effectiveness:
insights from theory and research on small group performance”, Human Relations, Vol. 44
No. 12, pp. 1-25.
Mincer, J. (1974), Schooling, Experience and Earning, Columbia University Press, New York, NY.
Minniti, M. and Bygrave, W. (2001), “A dynamic model of entrepreneurial learning”,
Entrepreneurship Theory and Practice, Vol. 25 No. 3, pp. 5-16.
Nunnally, J.C. (1978), Psychometric Theory, McGraw-Hill Book Company, New York, NY.
Pallant, J. (2001), SPSS Survival Manual, Open University Press, Milton Keynes.
Parker, G. (1994), “Cross-functional collaboration”, Training and Development, Vol. 48 No. 10,
pp. 49-53.
Pashke, G.F. (2004), “Bring back the generalist”, Strategic Finance, Vol. 86 No. 4, pp. 33-6.
Podsakoff, P.M. and Organ, D.W. (1986), “Self-reports in organizational research: problems and
prospects”, Journal of Management, Vol. 12 No. 4, pp. 531-44.
Generation of
new business
ideas
71

Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases
in behavioural research: a critical review of the literature and recommended remedies”,
Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903.
Politis, D. (2005), “The process of entrepreneurial learning: a conceptual framework”,
Entrepreneurship Theory and Practice, Vol. 29 No. 4, pp. 399-424.
Politis, D. (2008), “Does prior start-up experience matter for entrepreneurs’ learning? A
comparison between novice and habitual entrepreneurs”, Journal of Small Business and
Enterprise Development, Vol. 15 No. 3, pp. 472-89.
Prabhu, V., Sutton, C. and Sauser, W. (2008), “Creativity and certain personality traits:
understanding the mediating effect of intrinsic motivation”, Creativity Research Journal,
Vol. 20 No. 1, pp. 53-66.
Schumpeter, J.A. (1934), The Theory of Economic Development, Harvard University Press,
Cambridge, MA.
Shane, S. (2000), “Prior knowledge and the discovery of entrepreneurial opportunities”,
Organization Science, Vol. 11 No. 4, pp. 448-69.
Shane, S. (2003), A General Theory of Entrepreneurship: The Individual – Opportunity Nexus,
Cheltenham, Edward Elgar.
Shane, S. and Venkataraman, S. (2000), “The promise of entrepreneurship as a field of research”,
Academy of Management Review, Vol. 25 No. 1, pp. 217-26.
Shepherd, D.A. and DeTienne, D.R. (2005), “Prior knowledge, potential financial reward, and
opportunity identification”, Entrepreneurship Theory and Practice, Vol. 29 No. 1,
pp. 91-112.
Singh, R.P. (2000), Entrepreneurial Opportunity Recognition through Social Networks, Garland
Publishing, New York, NY.
Spender, J.-C. (1989), Industry Recipes: The Nature and Sources of Managerial Judgement, Basil
Blackwell, Oxford.
Simon, H.A. (1985), “What we know about the creative process”, in Kuhn, R.L. (Ed.), Frontiers in
Creative and Innovative Management, Ballinger, Cambridge, MA, pp. 3-20.
Simonton, D.K. (1999), Origins of Genius: Darwinian Perspectives on Creativity, Oxford
University Press, New York, NY.
Smits, W. (2007), “Industry-specific or generic skills? Conflicting interests of firms and workers”,
Labour Economics, Vol. 14 No. 3, pp. 653-63.
Stuart, R.W. and Abetti, P.A. (1990), “Impact of entrepreneurial and management experience on
early performance”, Journal of Business Venturing, Vol. 5 No. 3, pp. 151-62.
Ucbasaran, D., Westhead, P. and Wright, M. (2006), Habitual Entrepreneurs, Edward Elgar,
Aldershot.
Ucbasaran, D., Westhead, P. and Wright, M. (2008), “Opportunity identification and pursuit: does
an entrepreneur’s human capital matter?”, Small Business Economics, Vol. 30 No. 2,
pp. 153-73.
Ward, T. (1995), “What’s old about new ideas?”, in Smith, S.M., Ward, T.B. and Finke, R.A. (Eds),
The Creative Cognition Approach, MIT Press, Cambridge, MA, pp. 157-78.
Ward, T. (2004), “Cognition, creativity, and entrepreneurship”, Journal of Business Venturing,
Vol. 19 No. 2, pp. 173-88.
Westhead, P. and Wright, M. (1998), “Novice, portfolio, and serial founders: are they different?”,
Journal of Business Venturing, Vol. 13 No. 3, pp. 173-204.
IJEBR
18,1
72

Westhead, P., Ucbasaran, D. and Wright, M. (2005), “Decisions, actions, and performance: do
novice, serial, and portfolio entrepreneurs differ?”, Journal of Small Business Management,
Vol. 43 No. 4, pp. 393-417.
Wiklund, J. and Shepherd, D.A. (2008), “Portfolio entrepreneurship: habitual and novice founders,
new entry, and mode of organizing”, Entrepreneurship Theory and Practice, Vol. 32 No. 4,
pp. 701-25.
Wright, M., Robbie, K. and Ennew, C. (1997), “Serial entrepreneur”, British Journal of
Management, Vol. 8 No. 3, pp. 251-68.
Zietsma, C. (1999), “Opportunity knocks – or does it hide?”, in Reynolds, P.D., Bygrave, W.D.,
Manigart, S., Mason, C., Meyer, G.D., Sapienza, H.J. and Shaver, K.G. (Eds), Frontiers of
Entrepreneurship Research, Babson College, Wellesley, MA, pp. 242-56.
Further reading
De Koning, A.J. (2003), “Opportunity development: a socio-cognitive perspective”, in Katz, J. and
Shepherd, D. (Eds), Advances in Entrepreneurship, Firm Emergence and Growth, Vol. 6,
Emerald Group Publishing Limited, Bingley, pp. 265-314.
Park, J.S. (2005), “Opportunity recognition and product innovation in entrepreneurial hi-tech
start-ups: a new perspective and supporting case study”, Technovation, Vol. 25 No. 7,
pp. 739-52.
Ucbasaran, D., Westhead, P. and Wright, M. (2009), “The extent and nature of opportunity
identification by experienced entrepreneurs”, Journal of Business Venturing, Vol. 24 No. 2,
pp. 99-115.
Venkataraman, S. (1997), “The distinctive domain of entrepreneurship research”, in Katz, J. and
Brockhaus, R. (Eds), Advances in Entrepreneurship, Firm Emergence and Growth, JAI
Press, Greenwich, CT, pp. 119-38.
Appendix
Industries according to the Swedish Standard Industrial Classification (SNI2002)
.Agriculture, hunting and forestry.
.Fishing.
.Winning of mineral.
.Manufacturing.
.Electricity, gas, heating- and water supply.
.Construction.
.Wholesale- and retail trade.
.Hotel- and catering trade.
.Transportation, storage and communication.
.Financing.
.Real estate- and rental service, consulting and other business services.
.Public administration and defense, compulsory social insurance.
.Education.
.Health and medical service, veterinary.
.Other social and personal services.
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Corresponding author
Jonas Gabrielsson can be contacted at: Jonas.gabrielsson@circle.lu.se
Items *Exploration **
I prefer to use experimentation as a learning technique 0.72
To learn things it is important that I try to think in new ways 0.69
I prefer to explore new fields rather than repeating old ones 0.77
I prefer to use experimentation as a learning technique 0.56
I very much like to be in fields where I have not been before 0.79
I associate learning with search for new information 0.53
Eigenvalue 2.29
Percentage of variance explained 45.7
Cronbach’s alpha 0.69
Notes: *Items follow a five-point scale (1=very low emphasis vs 5=very high emphasis); **
Absolute
loadings of 0.50 or higher are significant
Table AI.
Factor analysis of items
employed in the
exploration variable
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