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The impact of habitual entrepreneurial experience on new firm closure outcomes

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The impact of habitual entrepreneurial experience on new firm closure outcomes

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In this paper, we argue that it is difficult for habitual entrepreneurs to use their experiential knowledge to develop a more viable new firm than novice entrepreneurs. Hindered by the difficulty of disentangling how actions lead to outcomes in low predictive environments such as new firm settings; hampered by the novelty and uncertainty of new firm closure; and misguided by subjective beliefs about their ability, we contend that habitual entrepreneurs close their new firm just as quickly as novice entrepreneurs and are just as likely to go bankrupt. Using large-scale panel data that track new firm closure amongst 7400 new German firms, we find that the new firms run by habitual entrepreneurs close just as quickly as those run by novice entrepreneurs. We also find that habituals are just as likely as novices to see their new business go bankrupt.
The impact of habitual entrepreneurial experience on new
firm closure outcomes
Sandra Gottschalk .Francis J. Greene .
Bettina Mu
¨ller
Accepted: 7 June 2016 / Published online: 17 August 2016
ÓThe Author(s) 2016. This article is published with open access at Springerlink.com
Abstract In this paper, we argue that it is difficult for
habitual entrepreneurs to use their experiential knowl-
edge to develop a more viable new firm than novice
entrepreneurs. Hindered by the difficulty of disentan-
gling how actions lead to outcomes in low predictive
environments such as new firm settings; hampered by
the novelty and uncertainty of new firm closure; and
misguided by subjective beliefs about their ability, we
contend that habitual entrepreneurs close their new
firm just as quickly as novice entrepreneurs and are
just as likely to go bankrupt. Using large-scale panel
data that track new firm closure amongst 7400 new
German firms, we find that the new firms run by
habitual entrepreneurs close just as quickly as those
run by novice entrepreneurs. We also find that
habituals are just as likely as novices to see their
new business go bankrupt.
Keywords Habitual entrepreneurs Novice
entrepreneurs Firm closure Panel data
JEL Classifications L26 L25
1 Introduction
Macmillan (1986) suggests that key to understanding
entrepreneurship is better comprehending habitual
entrepreneurship. This reflects that habitual
entrepreneurship is common (Westhead et al. 2005;
Parker 2013) and that habituals (here defined as serial
entrepreneurs who previously sold or passed a firm
onto a successor, and portfolio entrepreneurs who
concurrently run other firms besides the new firm) are
distinct from inexperienced novice entrepreneurs in
important ways. Chief amongst these is that habitual
experience provides valuable experiential learning
which augments entrepreneurial ability.
1
This has led
to the notion that habituals perform better in a
subsequent new firm than novices (Stuart and Abetti
1990; Starr and Bygrave 1992; Cope 2011).
But is this the case? Are habituals more likely to
achieve better outcomes in their new firm than
S. Gottschalk B. Mu
¨ller
Research Department Economics of Innovation and
Industrial Dynamics, Centre for European Economic
Research (ZEW), P.O. Box 103443, 68034 Mannheim,
Germany
e-mail: Gottschalk@zew.de
B. Mu
¨ller
e-mail: Bettina.mueller@zew.de
F. J. Greene (&)
Department of Management, University of Birmingham,
Edgbaston B15 2TT, UK
e-mail: f.greene@bham.ac.uk
1
We define ability in terms of causal (i.e. the ability to identify
the relationship between cause and effect) rather than procedural
(e.g. how well someone performs a task) ability. This is
important because enhanced procedural abilities developed
through repetition or trial and error may improve the ability to
do a task but may not give insights to the reasons why a certain
action works or how it can be improved.
123
Small Bus Econ (2017) 48:303–321
DOI 10.1007/s11187-016-9780-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
novices? In this paper, we argue that there are
instances where experience is a poor teacher. Firm
behavioural theorists have identified three conditions
which make it difficult to apply prior learning
successfully in a new business environment. First,
there is ambiguity about which actions lead to which
outcomes. This causal ambiguity leads individuals to
make inferential errors about cause–effect relation-
ships (Levitt and March 1988). Second, individuals are
prone to cognitive biases such as over-optimism or
over-confidence. The asymmetry between subjective
beliefs about ability and actual ability adds to the
likelihood of dysfunctional outcomes (Kim et al.
2009). Finally, individuals struggle to identify out-
comes because it is difficult to specify the dimensions
of success or failure (Zollo 2009). In sum, Levitt and
March (1988: 325) define situations in which ‘‘the
subjective experience of learning is compelling, but
the connection between actions and outcomes are
misspecified’’ as superstitious learning.
Our argument in this paper is that although there
ought to be gains from experiential learning, it is often
difficult to capitalize on prior learning because it is
frequently maladaptive in low predictive new firm
situations. We see that habituals cannot easily transfer
their past entrepreneurial experience to their new firm
because outcome uncertainty is high, it is difficult to
interpret accurately cause and effect, and because
entrepreneurs are prone to cognitive biases such as
over-confidence.
To test our theorizing, we investigate the impact of
novice and habitual experience on two new firm
closure outcomes. First, we focus on the speed of new
firm closure. This is important because the extant
literature provides divergent accounts of the relation-
ship between habitual experience and the speed of new
firm closure: for example, Holmes and Schmitz (1990)
suggest that, if prior learning enhances ability, then
habituals are better able to persist with their new firm
for longer, whilst Arora and Nandkumar (2011)
contend that habitual experience alters an entrepre-
neur’s opportunity costs and, as such, habituals may
decide to close a firm quicker than novices if it does not
meet their performance expectations. Using our mal-
adaptive learning framework, however, we contend
that both novice and habitual founders have similar
firm closure speeds, suggesting that habitual experi-
ence neither lengthens firm persistence, nor leads to the
swifter recognition that the firm is unviable.
For our second closure outcome, we distinguish
between three other closure outcomes: firm survival,
voluntary dissolution and firm bankruptcy. These
distinctions are crucial because closure is not a
synonym for failure, and can be distinguished into
the firm’s voluntary dissolution or its bankruptcy
(Headd 2003; Bates 2005). They are also important
because one presumption is that, if faced with closure,
habituals are better able to navigate the firm towards
dissolution: habitual experience prompts the redeploy-
ment of resources away from the unviable new firm to
another yet unrealized later firm or to wage employ-
ment, whilst bankruptcy imposes greater costs (Eesley
and Roberts 2012). We, however, contend that because
of the difficulties in translating experiences from one
context to another, novices and habituals are just as
likely to experience bankruptcy with their new firm.
To empirically examine the relationship between
habitual experience and our two failure outcomes, we
use large-scale representative panel data on around 7400
new German firms. These data are valuable for two main
reasons. First, these data contain a wide range of other
founder, finance and firm-level characteristics. These
are clearly important since, for example, a stylized
feature of firm closure is that under-resourced firms are
more likely to close (Parker 2009; Storey and Greene
2010). A second advantage of these data is that they
provide a wider view of the impact of habitual
experience than is often offered in other studies. Studies
of habitual experience typically focus on venture capital
or private equity-backed ventures (Gompers et al. 2010;
Arora and Nandkumar 2011). Our data allow us to
broaden the perspective because it is representative for
the whole population of start-ups in Germany. More-
over, it also allows us to examine—as a robustness
check—the impact of habitual experience onthe closure
outcomes of new firms in high-tech sectors.
Our findings suggest that habituals are just as likely
as novice founders to close their firm and go
bankrupt.
2
We also find that these relationships hold
when we only focus on high-tech firms. These results,
therefore, provide evidence for our theorizing that
when habituals and novices face closure, both are
liable to superstitious learning.
2
We also tested to see whether there were differences between
serial and portfolio founders, both in terms of new firm closure
speed and bankruptcy likelihoods. There were no qualitative
differences. These resultsare availablefrom the authorson request.
304 S. Gottschalk et al.
123
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This paper makes three contributions. First, it
advances current theorizing on how experiential
learning impacts on firm survival outcomes. The
presumption in some learning models is that by
specializing in entrepreneurship, habituals augment
their entrepreneurial ability and are more likely to
achieve superior outcomes (Cope 2011; Ericson and
Pakes 1995). The inference we draw from our results is
that although habituals may learn from experience or
suggest that they learn, experiential learning is of little
benefit when faced with noisy and complex new firm
closure contexts. This is germane since, as A
˚stebro
et al. (2011) and A
˚stebro and Thompson (2011) show,
enterprise populations are also made up of ‘‘misfits’
and ‘‘hobos’’ as well as ‘‘stars’’. Identifying, therefore,
some of the liabilities of habitual experience helps
build on prior studies that point to either no effects
(Dencker et al. 2009; Ucbasaran et al. 2006) or the
negative effects (Tornikoski and Newbert 2007;
Frankish et al. 2013) of such experience, thereby
extending the understanding of the ‘‘darker side’’ of
entrepreneurial learning (Miller 2015). A second
contribution is that we use large-scale, fine-grained,
longitudinal data. This is important because few
studies have access to ‘‘a large longitudinal panel
database containing a large number of entrepreneurs’’
(Ucbasaran et al. 2013: 187). Finally, our findings
make a practical contribution both for habituals
contemplating setting up a new firm and for external
stakeholders interested in supporting these entrepre-
neurs. For example, bank lending is tied to the
founder’s ability to repay the loan (plus interest).
Business closure outcomes, therefore, are key. Having
better insights on the role that experience plays in
closure outcomes may help banks provide guidance on
lending to such groups.
The rest of the paper proceeds as follows. In the
next sections, we develop our theoretical framework
and develop our hypotheses. We then detail our data,
measures and estimation approach. After the results,
we conclude by discussing the implications of our
findings for scholars, stakeholders and entrepreneurs.
2 The maladaptive effects of habitual experience
In the entrepreneurship literature, there has long been
the idea that entrepreneurs gain from prior experiential
learning because it helps to better estimate ability
(Jovanovic 1982), favourably alters expectations
(Frank 1988), or augments ability (Ericson and Pakes
1995). For example, Casson (1999) argues that those
with accumulated stocks of knowledge are better
placed to deal with market volatility and exogenous
shocks because they have greater experience of
processing and sifting through information. Minniti
and Bygrave (2001) further argue that prior entrepre-
neurial experience is pivotal in determining outcomes
and that ‘‘knowledge is cumulative. What is learned in
one period builds upon what was learned in an earlier
period’’ (p. 7). Such evidence suggests that entrepre-
neurial experience promotes the accretion of path-
dependent knowledge that can be successfully applied
to a new firm.
We note, however, that much of the evidence on the
efficacy of habitual experience rests on case studies in
which entrepreneurs often self-identify the positive
impact of entrepreneurial learning (e.g. Cope 2011;
Singh et al. 2015). We also note that applying prior
experiential learning is difficult (Levinthal and March
1993). Individuals may learn from experience, but
because learning involves the interpretation of these
experiences, individuals may struggle to apply prior
experiences correctly and appropriately to new situ-
ations. We see that such maladaptive learning is highly
salient in entrepreneurial settings for three main
reasons. First, of particular relevance is that the
conditions under which founders make decisions are
marked by their rarity, idiosyncrasy and complexity.
For example, unlike day-to-day operational issues
where there are evident gains from the repetition of
tasks (Thompson 2009), developing and implement-
ing strategies to about how to enter a market, thwart
the competition, or meet consumer needs are infre-
quent occurrences. Consequently, this increases the
likelihood of founders basing their decisions on small
sample sizes which limits opportunities to apply
relevant evidence to the current situation (Kim et al.
2009). Inferential errors are also likely because
individuals tend to focus on successful rather than
unsuccessful outcomes. Denrell (2003) shows that this
selection bias leads to inaccurate and faulty inferences
about the causes of business performance.
Similarly, no two business situations are identical.
Frankish et al. (2013) identify that new and old firms
of habitual entrepreneurs differ in significant ways,
perhaps because the new firm has a novel product, or
because the location or the sector differs. Evidence
The impact of habitual entrepreneurial experience 305
123
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suggests that entrepreneurs find learning to adjust to
such changes difficult, particularly because, like
everyone else, they are bounded rationalists and prone
to satisficing behaviours (Parker 2006). Instead,
entrepreneurs prefer to rely on prior learnt routines
to guide actions and strategies (Haynie et al. 2012).
Related to this is the complexity of the task environ-
ment. Entrepreneurship involves orchestrating com-
peting but interdependent activities such as marketing,
logistics, financial planning, sales and operations. This
makes it extremely difficult to disentangle which
actions lead to which particular outcomes. In sum, the
rarity, idiosyncrasy and complexity of new firm
settings create conditions for causal ambiguity.
Another reason for aberrant learning effects is the
presence of cognitive biases such as over-confidence
(i.e. the misplaced belief in an individual’s ability to
achieve a particular outcome, Forbes 2005) or over-
optimism (i.e. the tendency to overestimate achieving
positive outcomes and under-estimating negative
outcomes, Ucbasaran et al. 2010). Studies show that
both of these biases are common amongst entrepre-
neurs (Fraser and Greene 2006; Landier and Thesmar
2009) and persist even in the face of negative
information about likely returns from the market
(A
˚stebro 2003). Indeed, such biases help explain why
founders over-invest in risky projects, fail to plan for
contingencies and starve their new firm of the
resources required to achieve sustainability (Hayward
et al. 2006).
Finally, Zollo (2009) suggests one further impor-
tant reason for faulty or false inferences. Alongside
conditions that promote causal ambiguity and the
asymmetry between subjective beliefs about compe-
tency and actual competence, he suggests that it is
often difficult to discern outcomes easily.
3
In new firm
settings, such outcome ambiguity is likely because it
can be difficult to identify whether or not the
opportunity is a plum or a lemon. This outcome
fuzziness may be further exacerbated by cognitive
biases which cause the founder to overweigh their
chances of success and effectively downplay signs of
poor performance (Denrell 2003). For example,
tenacity may be seen as a virtue if the firm turns out
to be a success, but it may be a vice, resembling foolish
procrastination, if the firm turns out to be a failure.
Overall, the combination of causal ambiguity, out-
come ambiguity and the presence of cognitive biases
such as over-confidence can lead to harmful supersti-
tious learning outcomes in entrepreneurial settings.
3 Hypotheses
Our first argument is that, due to superstitious
learning, habitual and novice founders are equally
likely to speedily close their new firm. Novice
entrepreneurs have no entrepreneurial experience
and, as such, their past (non-entrepreneurial) experi-
ences may offer poor guidance for decision-making in
their new firm. Habituals may believe that their prior
experience provides them with a richer competence
repertoire, thus equipping them to identify and exploit
salient market opportunities (Gruber et al. 2012).
Because these experiences proved fruitful before,
habituals may replicate these earlier processes of
identifying and targeting key customers, competitors
and suppliers, believing that it helps shorten the time
and resources required to meet important develop-
mental start-up milestones (e.g. making sales, employ-
ing staff; Capelleras and Greene 2008). However,
because there is significant heterogeneity between
businesses, experience gained in one firm is likely to
be specific to that firm, resulting in limited opportu-
nities to apply experiential knowledge successfully
(Cassar 2014). Indeed, habituals may even mistakenly
assume that their prior actions were primarily respon-
sible for the performance of their previous firm when,
in fact, it may be that they were simply just lucky or
chose a high-risk strategy that paid off (Denrell 2004).
Interpreting noisy prior performance signals may
be even harder when there are systematic biases in
inferences. Although some studies point to over-
optimism declining with experience (Fraser and
Greene 2006), other studies show that habituals are
prone to over-optimism and over-confidence (Landier
and Thesmar 2009; Ucbasaran et al. 2010). Such
biases may lead to a strategic myopia whereby the
habitual relies on pre-existing opportunity recognition
search or firm exploitation strategies that may be ill-
suited to the current context. This, consequently, is
likely to lead to competency traps in which entrepre-
neurs use strategies previously developed even though
3
Zollo (2009: 894) defines outcome ambiguity ‘‘as the degree
of uncertainty related to the assessment of the outcomes
consequent to a given decision or to the execution of a given
task’’.
306 S. Gottschalk et al.
123
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they are unsuited to the new context. This use of a
‘square peg’’ for a ‘‘round hole’’ may undermine firm
viability. Indeed, Cassar (2014) shows that experi-
enced entrepreneurs are no better at accurately fore-
casting their performance than inexperienced
entrepreneurs.
Both novices and habituals may be also prone to
maladaptive learning when faced with the novelty of
firm closure. Faced with a loss-making situation in
which they have little experience, it may be difficult
for either novices or habituals to discern how their
actions relate to the causes of closure and whether or
not they are actually facing a closure situation. Hence,
buoyed by over-confidence, and given that individuals
over-sample instances of success and downplay poor
performance, it is likely that both novices and
habituals rely on prior, but ill-suited, routines.
In sum, we see that novice and habitual founders
face similar maladaptive learning effects. When faced
with a context where causal ambiguity is high, novices
are likely to draw on non-entrepreneurial experiences.
Like habitual founders, we also see that these expe-
riences are likely to be ill-suited to the new firm.
Further, one impetus behind going into the new firm is
that, as with habituals, novices are likely to be over-
optimistic about their likely chances of success and,
again, may be subject to similar outcome ambiguities.
Hence, we first argue that due to superstitious learning
effects:
H1 Habitual entrepreneurs are just as likely to
speedily close their new firm as novice entrepreneurs.
One critique of H1 is that our maladaptive learning
framework is not the only explanation for any
similarities in firm closure speed. One alternative is
that H1 reflects regression to the mean effects: if past
experience is positive, then subsequent outcomes are
likely to be closer to the average outcome (closure).
Second, although the initial expectation might be that
experience prolongs firm persistence (Holmes and
Schmitz 1990),
4
subsequent research identifies coun-
tervailing influences on firm closure speed due to
‘job-matching’’ effects between the founder and the
firm (Holmes and Schmitz 1995). Illustrative of the
importance of these effects is that Gimeno et al. (1997)
find that the decision to persist with a firm is impacted
by opportunity cost thresholds. If a founder receives
low ‘‘psychic’’ (non-pecuniary) benefits from their
firm, they may shut it down even if it makes an
economic return. However, if the match or psychic
benefits are high, the founder is more likely to persist,
even if the economic returns are limited. Plehn-
Dujowich (2010) theorizes that although prior expe-
rience of business success may prolong firm survival
because founders have a more realistic comprehension
of opportunities, it may still lead them to cut short the
firm and seek alternative options that provide higher
returns. Indeed, Arora and Nandkumar (2011) empir-
ically demonstrate that if experienced founders have
higher opportunity costs, they are more likely to
quickly close their firm if the opportunity does not
meet their expected needs. In sum, therefore, an
alternative and plausible interpretation of H1 is that
the reason why habituals close their new firm just as
quickly as novices is that they realize that it does not
meet their expectations.
To better ascertain whether superstitious learning
effects are relevant, we therefore propose a further,
more stringent, test. We suggest that if the new firm is
facing the prospect of closure, both novice and
habituals are just as likely to see their new firm go
bankrupt. We see that this is valuable because it is
difficult to see how, if experience does impact on
ability and opportunity costs, a habitual founder would
end up in bankruptcy rather than voluntarily dissolu-
tion. In these frameworks, experience should make
habituals more alert to the dangers they face and make
it more likely to husband their resources ready for the
next opportunity. Of course, some founders will
deliberately choose bankruptcy as a mechanism for
sharing risks with creditors. However, the incidence of
such actions is very low, with the European Commis-
sion (2007) estimating that only 4 per cent of
bankrupts attempt to defraud their creditors. Indeed,
much more likely, particularly as bankruptcy is a clear
sign of business failure (Carter and Van Auken 2006),
is that the stigma of bankruptcy vastly outweighs the
small chances of deliberate bankruptcy amongst a few
novice and habitual founders.
Again core to our argument is that causal ambigu-
ities, cognitive biases and outcome uncertainties lead
to maladaptive learning effects that lead both the
novice and habitual entrepreneur to be just as likely to
4
Holmes and Schmitz Jr (1990: 269) suggest that ‘‘those that
are subsequently involved in a [business] transfer will on
average be of higher ‘quality’ and also survive than those that
are not transferred’’.
The impact of habitual entrepreneurial experience 307
123
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go bankrupt. Assessing closure outcomes objectively
is difficult when no relevant experience is available. In
such situations, as we have argued, founders are likely
to revert to earlier experiences that may be ill-suited to
the firm closure context. This is likely to lead to
inferential errors, particularly as founders overgener-
alize from examples of success and do not adopt
counterfactual thinking. Further exacerbating the
presence of causal ambiguity are cognitive biases.
For example, whilst habitual founders benefit from
greater levels of social and financial capital (A
˚stbro
and Bernhardt 2005) which they can leverage, for
example, to attract higher-quality employees, gain
trade credit from suppliers and attract outside finance
(Eesley and Roberts, 2012; Gompers et al. 2010), one
disadvantage of these resource benefits is the expec-
tation that ‘‘success’’ should breed ‘‘success’’. Habit-
uals may believe that by continuing with actions that
worked previously, they are more likely to be
successful. In doing so, they may persist with actions
that do little to turn the fortunes of the firm around and,
in fact, lead it further towards bankruptcy.
It may also be difficult for these founders to
recognize that the firm is in trouble. Although founders
may have developed vicarious learning from observing
others, the chances are that these learning experiences,
like their own experiences, are likely to oversample and
generalize successful contexts.
5
Accordingly, habitu-
als, like novices, may overestimate their chances of
rescuing the new firm, mistaking what is improbable
(firm survival) from the more likely (bankruptcy).
Furthermore, even if they do recognize that the firm is in
difficulties, because individuals become risk lovers in
loss situations (Kahneman 2011), habituals are likely—
as with novices—to be more prone to risk taking that
lead to deleterious outcomes such as bankruptcy (Miller
and Chen 2004). In sum, we propose that superstitious
learning has negative impacts both for novices and for
habituals. Stymied by experiences that are ill-suited to
firm closure contexts; hampered by the novelty and
uncertainty about likely closure outcomes; and
misguided by subjective beliefs about their compe-
tence, we argue that both are just as likely to go
bankrupt. Hence, we write:
H2 Faced with new firm closure, habitual entrepre-
neurs are just as likely to see their new firm go
bankrupt as novice entrepreneurs.
4 Data and methods
4.1 Data
We use data from five survey waves (2008–2012) of
the KfW/ZEW Start-Up Panel (KfW/ZEW-Gru
¨n-
dungspanel). These cover new firms founded in the
years 2005–2011. The panel was set up in 2008 by the
Centre for European Economic Research (ZEW),
KfW Bankengruppe (Germany’s largest state owned
promotional bank) and Creditreform (Germany’s
largest credit rating agency). Dencker et al. (2009:
1131) point out that ‘‘The ZEW panel study data are
considered a highly accurate source of statistical
information on newly founded firms (all legally
independent new firms founded in the private sector)
in Germany over time’’. New firms are sampled from
the Mannheim Enterprise Panel (Mannheimer Unter-
nehmenspanel, MUP). The MUP covers almost all
firms in Germany and contains annually updated
information on the population of new German start-
ups. The information collected in the MUP includes
data on firm name and address, legal form, industry
classification, start-up and closure dates as well as
information on firm dissolution and bankruptcy. One
key advantage of these data is that our firm closure
outcome measures (survival, dissolution, bankruptcy)
are taken from the independent parent MUP data and
not from the KfW/ZEW Start-up Panel, thus helping to
avoid common-method bias.
The KfW/ZEW Start-up Panel uses the MUP’s
unique identification code for each firm to sample
legally independent new firms. The panel excludes
new firms which are the result of merger activities or
which are subsidiary businesses and stratifies the
sample in three ways: year of firm formation, industry
and whether or not the firm received KfW financial
support. Each year, a random sample of new firms is
drawn from the MUP which has been founded in the
3 years prior to the year of the survey. Firms are
5
Arguably, there may be distinctions that can be drawn between
habitual founders, with portfolios being more likely to recognize
the prospects of bankruptcy than either serials or novice
founders. This may be because they are better able to switch
their focus from a failing firm to their other business interests.
However, if the focal firm is failing, a reasonable assumption is
that other firms owned by the portfolio may also be failing—we
thank one of the referees for this valuable last point.
308 S. Gottschalk et al.
123
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subsequently followed over successive panel waves
until they are 8 years old. These data are collected
using computer-aided telephone interviews and have a
target of approximately 6000 interviews each year.
The 2008–2012 panel waves contain self-reported
information on founder characteristics (e.g. educa-
tional background, gender, employment status, man-
agerial and leadership experience, entrepreneurial
experience) and firm characteristics (e.g. amount of
investments and current costs, financing sources and
structure, start-up size, number of employees). The
total sample is around 11,000 new firms. In this paper,
because the data do not allow us to specify whether or
not founders have entrepreneurial experience in team-
based new firms, we restrict our sample to the 7400
(68 % of total sample) solo founders of new firms.
4.2 Measures
4.2.1 Dependent variables
In common with a range of other studies (see reviews
by Parker 2009; Storey and Greene 2010), we measure
survival by recording annually whether or not a firm
has survived that specific year j. In formal terms, this
means that the dependent variable is: S
ij
=1 if firm
isurvives year jand S
ij
=0 if the firm idoes not
survive year j. In total, as Table 1shows, 89 % of the
focal firms survive. For our second dependent vari-
able, we differentiate the non-surviving firms between
those that go bankrupt (4 %) and those that quit by
dissolving their firm (7 %).
6
4.2.2 Habitual founders
In terms of the independent variables central to our
analysis, we distinguish between habitual and novice
founders by first asking ‘‘Have you ever set up one or
more firms before the founding of this firm?’’. If the
answer to this was yes, they were then asked whether
they had either serial or portfolio experience, and if they
did, we classified them as habitual founders (see
‘‘Appendix’ for a list of the definition of the variables).
Table 1shows that 17 % of all founders are habituals.
4.2.3 Founder human capital characteristics
Our panel data allow us to control for other factors
commonly associated with firm closure. In terms of
entrepreneurial characteristics, we consider the high-
est level of education, industrial and managerial
experience since entrepreneurs with higher endow-
ments in these respects are more likely to see their firm
survive (van Praag 2003; Lin et al. 2000; Bates 1995).
We also control for founder age, gender and start-up
motivations (opportunity, necessity and indepen-
dence). Table 1shows that 15 % of firm founders
are female, that the average age is 41 years and that
30 % has a university degree or a higher vocational
qualification, respectively. Mean years of industrial
experience are 15 years with 38 % having managerial
experience whilst 27 % are motivated by opportunity
and a further 19 % set the firm up out of necessity.
4.2.4 New firm characteristics
In terms of firm characteristics, we make use of
dummies for limited companystatus and R&D activity,
consider the size of the firms in terms of the number of
employees and control for the total amount of invest-
ment in the reporting year.
7
Further, we consider
external funding [access to government funding or
funding from external investors (share of external
financing from investors)], internal financing through
sales and retained earnings (share of cash flow on total
financing) and whether the firm experienced financial
problems. Prior studies show that these factors influ-
ence firm survival (Santarelli and Vivarelli 2007;
Geroski et al. 2010). Table 1shows that the average
firm size is 3.4 employees (including the founder) with
a quarter (26 %) being a limited company and 17 %
conducting R&D. The table also shows that 15 % of
founders experienced financial problems; that 35 %
used government support; that, on average, 66 % of
6
In Germany, there is a clear distinction between bankrupts and
dissolvers: debtors are required to file for bankruptcy if they are
financially distressed (i.e. if they are insolvent or cannot pay
their debts) (Prantl 2003). Failure to do so is punishable by up to
3 years in jail. German bankruptcy law also does not make
provision for voluntarily bankrupt because a German judge will
only open bankruptcy proceedings to those that are insolvent or
at serious risk of insolvency (i.e. firm bankruptcy is involun-
tarily, whilst dissolution is for reasons other than bankruptcy).
7
We replace investment =log(0.0001) if investments are
zero; otherwise, we would lose the observation of the firm in
the reporting year. We add a control variable indicating that
investments are zero for that observation.
The impact of habitual entrepreneurial experience 309
123
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Table 1 Summary statistics and correlation matrix
Mean SD NCorr. 1 Corr. 2 Corr. 3 Corr. 4 Corr. 5 Corr. 6 Corr. 7 Corr. 8 Corr. 9 Corr. 10
1. New firm dissolution 0.067 0.250 7369 1
2. New firm bankruptcy 0.037 0.189 7369 -0.052 1
3. Habituals 0.169 0.375 7369 -0.007 0.010 1
4. Female 0.148 0.355 7369 -0.001 0.018 -0.060 1
5. Degree qualification 0.300 0.458 7369 0.029 -0.017 0.145 -0.039 1
6. Mastercraft qualification 0.303 0.460 7369 -0.059 -0.016 -0.087 -0.074 -0.432 1
7. Age 40.515 9.820 7369 -0.015 0.002 0.200 0.024 0.195 -0.072 1
8. Industry experience 15.282 9.601 7369 -0.061 -0.017 0.082 -0.117 -0.033 0.168 0.564 1
9. Managerial experience 0.383 0.486 7369 -0.032 0.022 -0.111 -0.056 0.103 0.061 0.123 0.165 1
10. Start-up motivation: opportunity 0.267 0.442 7369 -0.011 0.017 0.121 0.006 0.115 -0.070 0.014 -0.071 0.021 1
11. Start-up motivation: necessity 0.193 0.395 7369 0.022 0.025 -0.101 0.015 -0.083 0.021 0.133 0.084 -0.048 -0.295
12. Size (employee) 3.407 5.724 7369 -0.035 0.065 0.073 -0.021 0.054 0.015 0.061 0.065 0.117 0.058
13. Limited company 0.258 0.438 7369 -0.058 0.036 0.307 -0.092 0.323 -0.144 0.201 0.051 0.082 0.183
14. R&D 0.169 0.375 7369 0.011 0.019 0.146 -0.085 0.210 -0.100 0.046 0.004 0.056 0.187
15. Investment (000s) 36.545 324.591 7369 -0.010 0.006 0.063 -0.018 0.028 -0.005 0.034 0.034 0.007 0.011
16. Retained earnings 65.821 41.017 7369 -0.110 -0.052 -0.025 -0.065 0.033 0.028 0.095 0.149 0.053 -0.040
17. External finance 12.702 27.861 7369 -0.036 0.025 -0.026 0.023 -0.033 0.051 -0.062 -0.042 0.032 0.018
18. Government funding 0.348 0.476 7369 -0.021 0.035 -0.103 0.034 -0.055 0.073 -0.111 -0.065 0.047 -0.019
19. Financial problems 0.148 0.355 7369 0.045 0.154 0.039 0.014 -0.010 -0.029 0.007 -0.041 -0.006 0.050
Corr. 11 Corr. 12 Corr. 13 Corr. 14 Corr. 15 Corr. 16 Corr. 17 Corr. 18 Corr. 19
11. Start-up motivation:
necessity
11
12. Size -0.068 1
13. Limited company -0.118 0.222 1
14. R&D -0.079 0.062 0.280 1
15. Investment (thousand )-0.024 0.280 0.068 0.049 1
16. Retained earnings 0.011 0.068 0.019 -0.029 -0.031 1
17. External funding -0.014 0.067 -0.011 -0.013 0.055 -0.221 1
18. Government funding 0.037 0.071 -0.054 -0.002 0.042 -0.198 0.237 1
19. Financial problems 0.025 0.043 0.043 0.073 0.009 -0.104 0.091 0.049 1
Correlations which differ significantly (1 % level) from zero are marked in italics
310 S. Gottschalk et al.
123
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firms used retained earnings to fund their investments;
that 13 % used outside financing from external sources
(banks, funding or private investors); and that firms
invested on average 37,000 annually.
Finally, we include a range of sector dummies from
the standard European Union NACE sectoral classi-
fication: new technology-based manufacturing, non-
high-tech manufacturing, high-tech services, con-
struction, knowledge-intensive services, and con-
sumer-related services, other services and retail, and
controls for the start-up year and the reporting year in
the estimations (see ‘Appendix’ for definitions). The
most common sectors are new technology-based
services (19 %), retail (17 %), consumer services
(13 %) and construction (13 %), whilst the most
common years of founding are 2006 and 2007.
4.3 Estimation approach
To identify the length of time a firm survives and if the
non-surviving firm either voluntary dissolves or goes
bankrupt, we use two duration models. The first is a
single risk model where we account for whether or not
the firm survives. Whilst survival time is continuous,
we estimate a model for interval-censored data because
we only observe whether the firm still exists at the end
of the year (i.e. spell lengths are only observed in yearly
intervals). The relevant hazard rate is the probability of
exit during year jgiven survival up to year j-1
hjXðÞ¼Pj1\TjjT[j1;XðÞ;
where jdenotes the half-open interval ðyearj1;yearj.
As already mentioned above, the dependent variable
contains the information whether or not firm isurvived
year j
Sij ¼1 if firm isurvives year j
0 if firm idoes not survive year j
Duration models based on this type of data can be
estimated by applying methods for standard binary
outcome models (Sueyoshi 1995). As functional form
for the hazard rate, we use a complementary log–log
specification and allow for both time-invariant and
time-variant covariates.
8
The hazard rate can then be
expressed as
log log 1 hij

¼b0þb0
1Xiþb0
2Zij þui;
where X0is a matrix of time-invariant explanatory
variables and Z0
tis a matrix of time-variant explana-
tory variables, and uiis the random intercept of firm i:
In order to allow the hazard rate to vary with survival
time (duration dependence), year dummies are added
to the list of regressors. To account for at least part of
the firm heterogeneity not captured by the observed
variables, the complementary log–log model is esti-
mated with random effects so that for binary variables,
the unobservable firm effect uiis sampled along with
the dependent variable and observable independent
variables and it is removed by integrating it out
(Wooldridge 2002: 482). Here, the distribution of uiis
assumed to be N0;ru
ðÞ.
9
Our second duration model is a competing risk
model in which we assess whether habitual experience
has different impacts on voluntary dissolution and
bankruptcy. Because of the way survival time is
reported in our data, we again use a model for interval-
censored data. The dependent variable is:
Sij ¼
0 if firm isurvives year j
1 if firm ivoluntarily exits in year j
2 if firm igoes bankrupt in year j
8
<
:
It can be shown that a competing risk model with
interval-censored data can be estimated by applying a
standard multinomial logit model. The destination-
specific hazard for the two exit states is:
hvd ¼exp b0
vdX

1þexp b0
vdX

þexp b0
bX

and
hb¼exp b0
bX

1þexp b0
vdX

þexp b0
bX

;
where vd =voluntary dissolution and b =bank-
ruptcy. Duration dependence is accounted for by
including year dummies in the list of regressors.
8
In principle, any continuous distribution function can be used,
but Greene (2000: 815) shows that the results of binary choice
Footnote 8 continued
models are not very sensitive to the functional form of the dis-
tribution functions.
9
As it is usual with this type of models, we can only control for
unobserved firm heterogeneity that does not change over time.
The impact of habitual entrepreneurial experience 311
123
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5 Results
We organize our results in four tables. Besides the
summary statistics and correlations shown in Table 1,
we present in Table 2univariate ttests to identify
whether there are differences between habitual and
novices in terms of their human capital and firm
characteristics. Table 2shows that habituals are more
likely to be males, university graduates, be older, have
more industry experience and be opportunity moti-
vated. In terms of their firm, it is likely to be bigger,
have greater investment levels, be a limited company,
be more likely to be involved in R&D and active in
high-tech or knowledge-intensive sectors (high-tech
manufacturing, new technology-based services, soft-
ware, knowledge-intensive services) but be less likely
to be involved in construction or retail.
Although more habitual founders report experienc-
ing finance problems, getting less government funding
and less external financial capital, Table 2provides a
profile of habitual founders that is consistent with
them having higher than average human capital
attributes and firm-level advantages that are likely to
promote superior closure outcomes. Yet, there are no
significant differences in the survival and bankruptcy
rate between novices and habituals.
These results also appear in the multivariate
regressions (Table 3). As column 1 of Table 3shows,
there is no difference between the speed at which
habitual and novice founders close their new firm. This
supports H1. With respect to the other covariates, we
find that there is evidence of an inverted U-shaped
pattern effect for entrepreneurial age [age: marginal
effect (m.e.) =0.406, p\0.05; age
2
: m.e. =
-0.057, p\0.05) and that those with mastercraft
level qualifications (m.e. =0.016, p\0.01) and
industry experience (m.e. =0.011, p\0.01) are
more likely to survive longer. We also find that
necessity-motivated founders (m.e. =-0.012,
p\0.05) survive for shorter periods and that financial
resources influence persistence: founders with greater
levels of investment (m.e. =0.005, p\0.01),
received government financial support (m.e. =
0.007, p\0.1) and used greater shares of retained
earnings (m.e. =0.0001, p\0.01) are all more likely
to persist. In contrast, those with financial problems
persist for shorter periods (m.e. =-0.043, p\0.01).
In sum, although human capital attributes (e.g. indus-
trial experience and age) and financial resources are
important, Table 3shows that habitual experience has
no effect.
Columns 2 and 3 of Table 3present the marginal
effect results of the competing risk model where we
compare survival (base category) with dissolution and
bankruptcy. We find that habituals are just as likely as
novices to dissolve or go bankrupt. This supports H2.
Again there is evidence that other covariates are
important in explaining these outcomes. Table 3
shows that necessity-based founders (m.e. =0.008,
p\0.01), and those experiencing financial problems
(m.e. =0.023, p\0.01) are more likely to go
bankrupt. Those with industrial experience (m.e. =
-0.003, p\0.01), that are graduates (m.e. =0.006,
p\0.05) or have a higher vocational qualification
(mastercraft; m.e. =0.005, p\0.1) are less likely to
go bankrupt.
Although our univariate results showed that habit-
uals are more likely to be R&D active and in particular
high-tech sectors, it may still be the case that very
many founders in our sample are lifestyle firms. To
examine whether the hypothesized outcomes persist
for more innovation-orientated firms, we conducted a
robustness check by restricting our sample to high-
tech firms (new technology-based manufacturing, new
technology-based services and software). As Table 4
shows, the marginal effects for prior experience were
the same: habitual founders are no more likely to
persist with the new firm (column 1: survival time
model), nor are they more or less likely to close their
firm voluntarily or avoid bankruptcy (column 2 and 3:
competing risk model). As with Table 3, Table 4
shows that those with greater levels of industrial
experience are able to persist for longer
(m.e. =0.018, p\0.1) with their new firm, that
graduates are less likely to go bankrupt
(m.e. =-0.008, p\0.1), whilst those who experi-
ence financial problems are both more likely to quit
sooner (m.e. =-0.040, p\0.1) and go bankrupt
(m.e. =0.015, p\0.1).
6 Discussion and conclusions
The two key findings from this study are that habitual
and novice founders share similar firm closure speeds
and that both are just as likely to see their new firm go
bankrupt. These findings persist despite habitual
founders having higher levels of human capital (e.g.
312 S. Gottschalk et al.
123
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are graduates), better access to resources, and their
firms having more favourable characteristics (e.g.
being bigger, limited company status). These are
attributes more commonly associated with better
survival outcomes. Our results also persist when we
examine high-tech firms.
Table 2 Univariate ttests
for novice and habitual
founders
Novices Habituals Sig.
Mean SD Mean SD
Survival rate 0.896 0.305 0.896 0.244
Bankruptcy rate 0.036 0.187 0.041 0.198
Founder human capital characteristics
Degree qualification 0.270 0.444 0.447 0.497 ***
Mastercraft qualification 0.321 0.467 0.215 0.411 ***
Founder’s age 39.627 9.617 44.870 9.646 ***
Experience in industry 14.925 9.298 17.030 10.801 ***
Managerial experience 0.407 0.491 0.264 0.441 ***
Female 0.157 0.364 0.101 0.301 ***
Motive: opportunity 0.243 0.429 0.385 0.487 ***
Motive: necessity 0.211 0.408 0.105 0.307 ***
Motive: independence 0.459 0.498 0.443 0.497
New firm characteristics
Number of employees 3.219 4.775 4.331 8.979 ***
Financing problem 0.141 0.349 0.179 0.383 **
Government funding 0.370 0.483 0.240 0.427 ***
Retained earnings 66.292 40.814 63.512 41.942 **
External financial capital 13.032 28.245 11.086 25.846 **
Investments (thousand ) 27.265 87.723 82.059 763.061 ***
R&D 0.144 0.351 0.290 0.454 ***
Limited company 0.198 0.398 0.555 0.497 ***
Sectors
New technology-based manufacturing 0.088 0.283 0.119 0.324 ***
New technology-based services 0.184 0.387 0.226 0.418 **
Software 0.049 0.217 0.084 0.278 ***
Other manufacturing 0.119 0.324 0.106 0.308
Knowledge-intensive services 0.049 0.217 0.071 0.257 **
Other business services 0.057 0.231 0.059 0.236
Consumer services 0.132 0.338 0.134 0.341
Construction 0.147 0.354 0.067 0.251 ***
Retail 0.175 0.380 0.133 0.340 ***
Foundation years
2005 0.138 0.345 0.091 0.288 ***
2006 0.170 0.376 0.128 0.334 ***
2007 0.181 0.385 0.190 0.392
2008 0.139 0.346 0.150 0.357
2009 0.147 0.354 0.175 0.380 **
2010 0.129 0.335 0.143 0.351
2011 0.096 0.294 0.123 0.328 **
Number of observations 6121 1248
The impact of habitual entrepreneurial experience 313
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Table 3 Marginal effects of habitual experience on new firm survival
Dep. var.: survival of the following year (y/n) Dep. var.: survival/voluntary dissolution/bankruptcy of/in the
following year
Failure event 1: voluntary dissolution Failure event 2: bankruptcy
(1) (2) (3)
Habituals -0.002
(0.005)
0.002
(0.004)
0.001
(0.003)
Female 0.001
(0.005)
-0.002
(0.004)
0.002
(0.003)
Degree qualification 0.002
(0.004)
0.004
(0.004)
-0.006**
(0.003)
Mastercraft qualification 0.016***
(0.004)
-0.010**
(0.004)
-0.005*
(0.003)
Age 0.406**
(0.162)
-0.234*
(0.121)
-0.114
(0.101)
Age
2
-0.057**
(0.022)
0.033**
(0.017)
0.016
(0.014)
Industrial experience 0.011***
(0.002)
-0.008***
(0.002)
-0.003**
(0.001)
Managerial experience -0.001
(0.004)
-0.003
(0.003)
0.004
(0.002)
Opportunity -0.001
(0.004)
-0.002
(0.004)
0.001
(0.003)
Necessity -0.012**
(0.005)
0.003
(0.004)
0.008***
(0.003)
Size -0.008
(0.005)
0.002
(0.005)
0.011***
(0.003)
Size
2
-0.001
(0.002)
0.000
(0.002)
-0.001
(0.001)
Limited company 0.005
(0.005)
-0.016***
(0.004)
0.009***
(0.003)
R&D -0.003
(0.005)
0.001
(0.004)
0.003
(0.003)
Investment 0.005***
(0.002)
-0.004***
(0.001)
-0.002
(0.001)
Investment dummy 0.033**
(0.015)
-0.023*
(0.012)
-0.011
(0.010)
Retained earnings 0.000***
(0.000)
-0.000***
(0.000)
-0.000
(0.000)
External funding 0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
Government funding 0.007*
(0.004)
-0.006*
(0.003)
-0.001
(0.002)
Financial problems -0.043***
(0.004)
0.016***
(0.004)
0.023***
(0.002)
314 S. Gottschalk et al.
123
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We see that these key focal results are valuable in
several ways. First, they challenge the idea of a ready
association between habitual experience and firm
performance. This theorizing suggests that because
entrepreneurship is experiential, habitual experience
provides learning opportunities which augment the
human capital of habitual entrepreneurs (Cope 2011;
Minniti and Bygrave 2001). This leads to predictions
that firm survival is more likely or, alternatively, that
greater experience increases the chances of speedier
firm shut down since habituals are more likely to
quickly realize that the new firm does not meet their
performance expectations. It also suggests that when
faced with closure, greater experience is likely to
guide founders away from bankruptcy.
In contrast, our argument is that it is difficult to
successfully apply earlier experiences in low predic-
tive new firm environments. In our account, prior
performance provides noisy feedback which does little
to disentangle the heterogeneous causes of perfor-
mance. Further, because of biases which lead individ-
uals to overgeneralize experiences of prior success, we
argue that this exacerbates the likelihood of beha-
vioural ruts and competency traps which, in the novel
context of firm closure, does little to inhibit inferior
outcomes such as bankruptcy. This theorizing and our
subsequent results, therefore, build on and extend the
evidence from a growing number of studies which
suggest that habitual experience can lead to costly
errors. For example, Parker (2013) finds that habitual
entrepreneurship provides only temporary perfor-
mance effects, whilst other studies, like ours, find that
there are few benefits from habitual experience
(Frankish et al. 2013; Rocha et al. 2015). Such
evidence is valuable because, by pointing to the
‘darker’’ side of habitual experience, it helps to
correct the prevailing bias towards the assets rather
than the liabilities of entrepreneurial experience.
One further advantage of responding to calls for
more fine-grained panel data that track the effects of
habitual experience on performance (Ucbasaran et al.
2013), is that we are able to examine other pertinent
human capital attributes associated with closure
outcomes. Our findings show that those with industrial
experience are more likely to survive for longer and
avoid bankruptcy. This suggests that, although habit-
ual experience provides few opportunities to apply
learning in new firm settings, relevant industrial
experience increases opportunities for founders to
draw on and repeat earlier learning. Specifically, it
suggests that industrial experiences reduce uncertain-
ties surrounding the identification and exploitation of
an opportunity by giving insights into trends in market
segments, and the cost and pricing structures in such
markets (Cassar 2014). Equally, our findings also
expose two other well-established phenomena:
Table 3 continued
Dep. var.: survival of the following year (y/n) Dep. var.: survival/voluntary dissolution/bankruptcy of/in the
following year
Failure event 1: voluntary dissolution Failure event 2: bankruptcy
(1) (2) (3)
Sector dummies Yes Yes
Cohort dummies Yes Yes
Firm age dummies Yes Yes
Number of firms 7369 7369
Number of observations 15,400 15,400
Wald v
2
(39) 272.46***
LR v
2
(78) 562.58***
Pseudo-R
2
0.080
Standard errors in parentheses
***, **, * Depict significance at the 1, 5 and 10 % level, respectively, and correspond to the test of the underlying coefficient being
zero. Reference categories, resp.: formal education: apprenticeship and minor formal education, motive for foundation:
independence. For data protection reasons, the coefficient for the explaining variable ‘‘financial support by KfW’’ is not reported
in the table
The impact of habitual entrepreneurial experience 315
123
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Table 4 Marginal effects of habitual experience on new firm survival—high-tech firms (new technology-based manufacturing, new
technology-based services, software)
Dep. var.: survival of the following year (y/n) Dep. var.: survival/voluntary dissolution/bankruptcy of/in the
following year
Failure event 1: voluntary dissolution Failure event 2: bankruptcy
(1) (2) (3)
Habituals 0.005
(0.008)
-0.007
(0.008)
0.001
(0.004)
Female 0.001
(0.011)
-0.004
(0.009)
0.002
(0.005)
Degree qualification -0.002
(0.007)
0.010
(0.006)
-0.008**
(0.004)
Mastercraft qualification 0.010
(0.008)
-0.007
(0.008)
-0.003
(0.004)
Age 0.662***
(0.252)
-0.470**
(0.196)
0.010
(0.158)
Age
2
-0.093***
(0.034)
0.066**
(0.027)
0.001
(0.021)
Industrial experience 0.018***
(0.005)
-0.015***
(0.004)
-0.003
(0.002)
Managerial experience 0.002
(0.006)
-0.005
(0.006)
0.003
(0.003)
Opportunity -0.006
(0.007)
0.006
(0.006)
-0.002
(0.003)
Necessity -0.004
(0.008)
0.007
(0.007)
-0.005
(0.005)
Size 0.005
(0.009)
-0.002
(0.010)
0.001
(0.004)
Size
2
-0.001
(0.004)
-0.003
(0.005)
-0.000
(0.001)
Limited company 0.003
(0.007)
-0.013*
(0.007)
0.010***
(0.004)
R&D -0.000
(0.007)
-0.006
(0.006)
0.004
(0.003)
Investment 0.001
(0.003)
0.001
(0.002)
-0.000
(0.001)
Investment dummy -0.006
(0.025)
0.010
(0.021)
0.005
(0.013)
Retained earnings 0.000
(0.000)
-0.000
(0.000)
-0.000
(0.000)
External funding -0.000
(0.000)
-0.000
(0.000)
0.000
(0.000)
Public funding 0.008
(0.007)
-0.009
(0.006)
0.002
(0.003)
Financial problems -0.040***
(0.008)
0.021***
(0.007)
0.015***
(0.003)
316 S. Gottschalk et al.
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necessity founders and financially under-resourced
firms are more likely to experience deleterious
survival outcomes.
We see that these results have important practical
implications. Our findings challenge habituals to
carefully evaluate any new opportunity and to exam-
ine the routines that they have previously adopted. It
also challenges novices to think carefully about the
challenges faced by new firm entry. As our study
shows, one form of experience that promotes firm
survival benefits is relevant industrial experience since
this provides valuable and relevant information about
opportunities. Moreover, one other potential way of
avoiding closure outcomes is to use deliberative forms
of learning such as business planning activities that
improve decision-making processes in entrepreneurial
settings (Burke et al. 2010). We also would join with
Ucbasaran et al. (2013) in urging habituals to consider
how they make sense of their experiences. We know
that cognitive biases are persistent features of
entrepreneurial activity and, as such, experienced
founders are perhaps better advised to develop
heterophily rather than homophily by building firms
that rely on diverse teams and outside mentors. This
may aid the development of counterfactual thinking
and allow for the development of more realistic sense-
making activities about the likelihood of firm closure.
Our findings also have implications for debt
financiers. Banks may be persuaded that prior habitual
experience is a valuable proxy for a founder’s
underlying entrepreneurial competence. One potential
temptation, therefore, is to target habituals in the belief
that they are more likely to repay their loans (plus
interest). We do not find support for this strategy:
banks are better off focusing on relevant industrial
experience and those that do not have financial
problems.
Although this study uses large-scale panel data to
test whether habituals can learn from their experience,
it is subject to a number of caveats. One limitation is
that our German habitual new firm founders may be
atypical re-entrants. Since we are unable to sample
habituals that decided not to return to entrepreneur-
ship, we may be under-estimating the likely experi-
ential learning benefits of prior experience amongst
those habitual founders that elect not to re-enter.
Rocha et al. (2015), however, do control for selection
bias in their Portuguese study and show that habitual
experience in itself provides few performance bene-
fits. Further, because our findings relate to habituals as
a group, this does not preclude that individual
habituals may learn from their experiences and
successfully apply these experience in their new firm.
Our data are also context specific. It may be that in
Table 4 continued
Dep. var.: survival of the following year (y/n) Dep. var.: survival/voluntary dissolution/bankruptcy of/in the
following year
Failure event 1: voluntary dissolution Failure event 2: bankruptcy
(1) (2) (3)
Sector dummies Yes Yes
Cohort dummies Yes Yes
Firm age dummies Yes Yes
Number of firms 2519 2519
Number of observations 5275 5275
Wald v
2
(33) 115.47***
LR v
2
(66) 235.25***
Pseudo-R
2
0.107
Standard errors in parentheses
***, **, * Depict significance at the 1, 5 and 10 % level, respectively, and correspond to the test of the underlying coefficient being
zero. Reference categories, resp.: formal education: apprenticeship and minor formal education, motive for foundation:
independence. For data protection reasons, the coefficient for the explaining variable ‘‘financial support by KfW’’ is not reported
in the table
The impact of habitual entrepreneurial experience 317
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Germany, there are particular reasons for our results,
reflecting, for example, German bankruptcy proce-
dures and laws which may incentivize firm founders to
act inappropriately or place undue restrictions on the
founder which stymie firm survival outcomes, com-
pared to countries such as the USA. In response, we
note that in the World Bank Doing Business index,
Germany was ranked third out of 184 countries for the
ease of its bankruptcy practices. The USA was ranked
fifth (World Bank 2015). Nonetheless, it is true that
societal attitudes, which we are unable to capture in
our data, are likely to influence unobserved attitudes to
closure. A stereotypical view is that in the USA, for
example, there is a greater acceptance of closure,
whilst in Europe, the stigma surrounding new firm
closure is more pronounced (Storey and Greene 2010).
Whilst we accept that levels of stigma differ between
countries, our response is that higher stigma levels in
Germany should dissuade individuals from bank-
ruptcy. Further, our study only examines those with
habitual experience rather than the nature of these
experiences. Hence, our data are silent on how long or
how many times these founders were involved in
earlier new firms. We note, though, that Greene et al.
(2008) found that few founders set up more than two
new firms over the course of their entrepreneurial
careers. Finally, because we only examine solo
founders, we are unable to assess how experiential or
vicarious learning gained from prior experience
impacts on firm outcomes for team-based
entrepreneurship.
We would welcome research that investigated
whether founder teams have either more or less
difficulties in applying previously acquired knowledge
to new situation. Team-based settings may encourage
vicarious learning and counterfactual thinking, but it
may also lead to ‘‘group think’’ whereby there is a
(misguided) consensus about their (superior) compe-
tence, what are the (inaccurate) cause–effect relations,
and what outcomes are possible (but unlikely).
Similarly, although our German study adds to recent
British (Frankish et al. 2013) and Portuguese (Rocha
et al. 2015) studies that also find habitual experience
has little bearing on performance, we see that one way
of extending our research is to consider other coun-
tries. Another future research direction is to examine
firm growth. Survival may be a cardinal business
objective for any firm, but scholars, policy makers and
practitioners are often keen to establish what factors
are likely to promote firm performance. Future
research could fruitfully follow our approach by
examining, whilst controlling for survivorship bias,
whether habitual experience enhances firm
performance.
In conclusion, in this paper we argued that it is
difficult for habituals to successfully apply their
experiential knowledge in another firm so that the
new firm is more viable than that of a novice
entrepreneur. Using large-scale German panel data
that track individual firm founders, we find that both
habitual and novice founders have similar firm closure
speeds and both are prone to similar bankruptcy
outcomes. Such findings are important in challenging
the view that habitual experience necessarily leads
superior firm closure outcomes.
Acknowledgments We would like to thank the SBE editorial
team who were responsible for papers for this special issue. In
particular, we would like to thank Mariagrazia Squicciarini and
her team as well as an unnamed referee. We would also like to
thank Kiran Trehan for her comments on the paper. As usual, all
omissions and errors are ours.
Compliance with ethical standards
Conflict of interest The authors declare that they have no
conflict of interest.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrest-
ricted use, distribution, and reproduction in any medium, pro-
vided you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons license, and
indicate if changes were made.
Appendix
See Table 5.
318 S. Gottschalk et al.
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 5 Variable description
Exit Survival status of the firm at the end of each year—information available in the MUP: 0—active. 1—
voluntarily closed (liquidated or dissolved). 2—bankruptcy
Habitual experienceavailable in the KfW/ZEW Start-up Panel
Habitual 1 =If either PORTFOLIO (1 =founder currently running more than one firm besides the new focal
firm) or SERIAL (1 =Founder previously sold/transferred a firm)
Founder human capital characteristicsavailable in the KfW/ZEW Start-up Panel
Female 1 =Female. Male =0
Degree qualification 1 =Founder is a university graduate. 0 =otherwise
Mastercraft qualification 1 =Founder has a German master craftsman diploma. 0 =otherwise
Age/age
2
Logarithm of the founders’ age in years
Industrial experience Logarithm of the years of sectoral experience of the founder
Managerial experience 1 =Founder has previously been a senior manager in another firm. 0 =otherwise
Opportunity 1 =Main motivation to set up the firm was based on a precise business idea or market gap.
0=otherwise
Necessity 1 =Main motivation to set up the firm was because it was a way out of unemployment or the absence
of adequate employment. 0 =otherwise
Independence 1 =Main motivation for setting up the firm was to be independent. 0 =otherwise (base category)
New firm characteristicsavailable in the KfW/ZEW Start-up Panel
Size/size
2
Logarithm of number of employees (full-time equivalent) (including the founder)
Limited company 1 =Firm is private or public limited company. 0 =otherwise (information available from the MUP
dataset)
R&D 1 =Firm conducted research and development activities. 0 =otherwise
Investments/investment
dummy
Logarithm of the amount of investments (without leasing and rents)/dummy of investments
Retained earnings Percentage share of firm cash flow from sales and retained earnings
External funding Percentage share of financial capital from external sources
Government funding 1 =Received government financial support. 0 =otherwise
Financial problems 1 =Had problems acquiring financial funding from external sources. 0 =otherwise
Sectorsavailable in the KfW/ZEW Start-up Panel
New technology-based
manufacturing
1=Firm sector is new technology-based manufacturing. 0 =otherwise (NACE Rev. 2: 20.20.
21.10. 21.20. 24.46. 25.40. 26.11. 26.20. 26.30. 26.40. 26.51. 26.60. 30.30. 30.40. 32.50/20.13.
20.14. 20.16. 20.17. 20.41. 20.51. 20.53. 20.59. 22.11. 22.19. 23.19. 26.70. 27.11. 27.12. 27.20.
27.40. 27.90. 28.11–15. 28.23. 28.24. 28.29. 28.30. 28.41. 28.49. 28.92–96. 28.99. 29.10. 29.31.
29.32. 30.20)
New technology-based
services
1=Firm sector is new technology-based service industries excluding software. 0 =otherwise
(NACE Rev. 2: 61.1–3. 62 (without 62.01). 63.1. 71.1–2. 72.1)
Software 1 =Firm sector is the software industry. 0 =otherwise (NACE Rev. 2: 62.01)
Other manufacturing 1 =Firm sector is other manufacturing industries other than NTB manufacturing industries.
0=otherwise (NACE Rev. 2: 10–33 (without NTB manufacturing))
Knowledge services 1 =Firm sector is knowledge-intensive service industries. 0 =otherwise (NACE Rev. 2: 69.1–2.
70.2. 72.2. 73.1–2)
Other business services 1 =Firm sector is other firm-related service industries. 0 =otherwise (NACE Rev. 2: 49.2. 49.5.
50.2. 50.4. 51.2. 52. 53. 61.9. 63.9. 64. 74.1. 74.3. 74.9. 77.1. 77.3–4. 78. 80–82)
Consumer services 1 =Firm sector is other consumer-related service industries. 0 =otherwise (NACE Rev. 2: 49.1.
49.3–4. 50.1. 50.3. 51.1. 55. 56. 58–60. 65–66. 68. 74.2. 77.2. 79. 85.5-6. 90–93. 95–96)
Construction 1 =Firm sector is construction. 0 =otherwise (NACE Rev. 2: 41–43)
Retail 1 =Firm sector is the retail sector. 0 =otherwise (NACE Rev. 2: 45–47 (without 46.1))
Foundation year Year of firm founding
Reporting year Reporting year of the survey
The impact of habitual entrepreneurial experience 319
123
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... Although there is some dwindling literature in this phenomenon, substantial knowledge about the behaviour of entrepreneurs is lacking. Gottschalk et al. (2017) argue that policymakers should discourage second chance entrepreneurs as they are more likely to fail than highly skilled entrepreneurs. The main argument by Gottschalk et al. (2017) in their paper titled "The Impact of Habitual Entrepreneurial Experience on New Firm Closure ...
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... In a sister paper, we analyze the effect of non-failure entrepreneurial experience, i.e., restart after sale of a business and opening another business next to an already existing one (portfolio entrepreneurship(Gottschalk et al, 2017).3 Lucas (1978), for example, analyzes the implications of heterogeneous individuals that differ with respect to entrepreneurial ability for the choice between wage work and self-employment. ...
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