1 INDIVIDUALS' AGE AND ENTREPRENEURIAL ... - idem@uab.es

INDIVIDUALS’ AGE AND ENTREPRENEURIAL INTENTIONS:
RELATIONSHIPS AND IMPLICATIONS
Criaco Giuseppe
Department of Business Economics and Administration
Autonomous University of Barcelona (UAB)
Cerdanyola del Vallés, Barcelona, Spain
Email: Giuseppe.criaco@e-campus.uab.cat
Keywords: Age, entrepreneurial intention, desirability, feasibility, binary mediation.
ABSTRACT
Building upon the Theory of Planned Behaviour, the first objective of this study is to
test the entrepreneurial potential model in an adult population representative sample.
Moreover, inspired by recent studies on the potential implication of individuals’age in
entrepreneurship, the second, and most ambitious, objective of this research is to
investigate how individuals’ age affects both directly and indirectly entrepreneurial
intentions (EI). To establish such relationships within the entrepreneurial potential
model, this study assesses: 1) the direct effect of age on EI, 2) the mediating effect
two components of the entrepreneurial potential model, namely perceived desirability
(PD) and perceived feasibility (PF), play between individuals’ age and EI, and finally
3) the moderating effect of age on the PD-EI and PF-EI relationships. The database
used for this research is the 2004 Flash Eurobarometer survey. Findings reveal the
existance of an age effect that affects both directly and indirectly entrepreneurial
intentions.
1. INTRODUCTION
Enterprising individual is a critical component of venture creation (Shook et al 2003).
Research on individuals’ psychological and demographic characteristics is thus a
fundamental issue to understand how such critical factors influence the new venture
creation process. While demographic characteristics such as gender and ethnicity have
been a widely studied in entrepreneurship literature (e.g. Light 1984; Fischer et al
1993), very few researches have focused on age as a predicting attribute of
entrepreneurship (as suggested by Kazmi 1999; Lewis and Massey 2003; de Kok et al
2010). Despite recent attention to the role of age in entrepreneurial activity (Levesque
and Minniti 2006; Zissimopoulos and Karoly 2007; Kautonen, 2008; Kellermanns et
al 2008; de Kok et al 2010; Gielnik et al 2012), our current understanding is still too
fragmented. Nevertheless, age is a triggering factor of entrepreneurial behavior as it
is a crucial characteristic in the thought decision-making process (Levesque and
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Minniti 2006). As a result, it becomes important to shed light on the role of
individuals’ age in entrepreneurship.
Building upon the Azjen (1991) Theory of Planned Behaviour, the first objective of
this study is to test the entrepreneurial potential model in an adult (15-98) population
representative sample. Moreover, inspired by Levesque and Minniti (2006) work on
potential implication of age in individuals’ behaviors, the second, and most ambition
objective of this research is to investigate how individuals’ age affects both directly
and indirectly entrepreneurial intentions (EI). To establish such relationships within
the entrepreneurial potential model (Krueger and Brazeal 1994), this study assesses:
1) the direct effect of age on EI, 2) the mediating effect two components of the
entrepreneurial potential model, namely perceived desirability (PD) and perceived
feasibility (PF), play between individuals’ age and EI, and finally 3) the moderating
effect of age on the PD-EI and PF-EI relationships.
The dataset for our study is the Flash Eurobarometer survey. Our study confirms that
Krueger and Brazeal (1994) model of entrepreneurial intentions is a strong predictor
of entrepreneurial intention. Indeed, both perceived desirability and feasibility are
significantly and positively related to entrepreneurial intention (p

al 2008) being such a major limitation in order to generalize findings on a population
level for all possible entrepreneurs (Shook et al 2003). Second, it advances the
understanding of the role of individuals’ age in entrepreneurship by providing both
theoretically and practically evidences of its importance within the entrepreneurial
process.
This research also offers theoretical and practical implications. At the microeconomic
level, our results show that individuals’ age is an important determinant of an
individual’s intention to become an entrepreneur. Thus, both the direct and indirect
relationship must be taken into account. At the macroeconomic level, our results
contribute to current understanding of the relationship between individuals’
characteristics and entrepreneurial perception and aggregate entrepreneurial activity.
As a result, not only the age distribution of a population may be an important issue for
the rate of potential entrepreneurs, but also how such age distribution interact with
individuals’ perception of desirability and feasibility towards entrepreneurship.
Understanding such dynamics helps tailoring programs and strategizing on policy
tools. Programs should indeed be implemented in order to raise perceived desirability
and feasibility in older individuals, especially in those regions that witness older than
average population.
The article is structured in the following way: session two deals with the literature
review, session three introduces the theoretical framework and develops the
hypothesis while session four explain the methodology of the research. Finally,
session five shows main findings, while session six highlights conclusion,
implications and limitations.
2. LITERATURE REVIEW
Several theoretical approaches have been developed to explain why some people
eventually become entrepreneurs. Among these, entrepreneurial intention has been
adopted as a solid stream of research. Becoming an entrepreneur is an intentionally
planned behavior (Krueger et al 2000) as it involves planning and risk analysis. Thus,
the intention to start a business is thought to be the best predictor of actual venture
creation (Ajzen 1991; Audet 2004). There exist one seminal theory-based model that
assess both general intention, namely theory of planned behavior (Ajzen 1991).
Morover, there also exists a theory-based model that assess specific entrepreneurial
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intention, called entrepreneurial event theory (Shapero and Sokol 1982). The theory
of planned behavior (TPB) is a theory about the relationship between attitudes and
behavior where intentions are formed as a result of three factors: attitude toward
performing the behavior, i.e. the degree to which an individual has a desirable or
undesirable consideration of performing that behavior, subjective norms, i.e.
perceived social pressure and consideration, and perceived behavioral control, i.e. an
individual’s personal judgment of the ability of performing a potential behavior.
According to Ajzen (1991), the TPB model is able to explain 30 percent of future
behavior. Finally, Shapero and Sokol (1982) formulated the entrepreneurial event
model that associates the intention of becoming self-employed and form a new
venture (an entrepreneurial event) to individuals’ perceptions of desirability,
feasibility (in relation to that activity) and propensity to act. Krueger et al (2000)
compared both models using the same sample and they found both models to be
strong predictors of entrepreneurial intention. Nevertheless, Shapero’s model was
found to better fit to the data by obtaining a higher R 2 and higher significance of the
components leading to intention.
While intentions models represent an important opportunity to increase the ability to
understand and predict entrepreneurial activity, situational (e.g. employment status or
informational cues) or individual (e.g. demographic characteristics or personality
traits) variables are poor predictors of entrepreneurial intentions (Krueger et al 2000).
Nevertheless, demographic characteristics influence employment status choice
indirectly, through the effects of those characteristics on the dimensions of intentions
model (Kolvereid 1996). Based on such findings, many studies assessed have been
investigated if demographic characteristics are determinants of entrepreneurial
intention. As far as gender is concerned, there exists numerous studies investigating
both the direct effect (Tkachev and Kolvereid 1999; Wilson et al 2007) and indirect,
i.e. mediating and moderating, effect (Díaz-García and Jiménez-Moreno 2010;
Shinnar et al 2012; Verheul et al 2012) such demographic characteristic plays on
entrepreneurial intention. Also family’s exposure’s influence over intentions has been
assessed (Tkachev and Kolvereid 1999; Carr and Sequeira 2007; Chlosta et al 2010;
Verheul et al 2012). Unfortunately, and perhaps, inexplicably, a different treatment
has been given to age. Indeed, many studies that use individual’s age as a control
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variable (e.g. Schwarz et al 2009 1 ; Kuckertz and Wagner 2010; Fitzsimmons and
Douglas 2011; Lee et al 2011; Zellweger et al 2011) or they use ANOVA analysis to
prove how intentions chances in different age brackets (see Hunjra et al 2011).
Nevertheless, they refrain to show a strong theoretical approach to discuss why such
demographic variable is to be considered. Other studies (see Gupta and York 2008;
Kautonen et al 2010) use age as sub-sampling variable, i.e. to divide the sample into
two o more categories (e.g. first age vs. third age individuals). To the best of our
knowledge, there exist only two studies that analyze the direct effect of age on
intention. Franco et al (2010) empirically tested the influence of age on
entrepreneurial intention of 988 university students from eastern and western
Germany and Portugal. Similarly, Ozyilmaz (2011) examined the effects of
demographic characteristics (age included) on undergraduate students’ pre-venture
entrepreneurial intentions. Nevertheless, both studies obtained no significant
relationship between the two variables. On the other hand, we found two studies
(Blanchflower et al 2001; Grilo and Irigoyen 2006) that found a negative linear
relationship between individuals’ age and self-employment preference 2 , having the
probability of preferring self-employment decreasing with age. It is finally worth to
be mentioned a study by i n et al (2011). While studying environmental cognitive
elements that may explain regional differences in start-up intentions between two
Spanish regions, authors found that individuals’ age (used as a control variable)
positively influences students’ subjective norms. Nevertheless, authors refrained to
explicitly motivate such relationship and discuss that finding, being not such issue the
main objective of their study.
Although, to the best of our knowledge, we found no studies in entrepreneurship
theorizing on the direct and indirect influence of individuals’ age on entrepreneurial
behavioral intention, there exist researches that accomplished so in other fields of
knowledge. For instance, Morris and Venkatesh (2000) studied how age influences
intention directly and indirectly, trough the TPB. Authors found that younger
workers’ technology usage decisions were more strongly influenced by attitude
toward using the technology, while older workers were more strongly influenced by
subjective norm and perceived behavioral control.
1 Although authors use individuals’ age as a control variable in the model, the study how
entrepreneurial intention varies with students’ age (bivariate analysis).
2 We divide here studies assessing entrepreneurial intentions from those that assess self-employment
preference, being both concepts slightly different.
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3. THEORETICAL FRAMEWORK AND HYPOTHESIS DEVELOPMENT
The theoretical framework used in this research (TPB) is operationalized through the
entrepreneurial potential model developed by Krueger and Brazeal in 1994. A recent
research by Guerrero et al (2008) analyzing the development of entrepreneurial
intention models since its early conception and adoption found the entrepreneurial
potential model to be one of the latest robust model as it integrates both Shapero and
Sokol (1982) entrepreneurial event model and on Ajzen (1991) TPB (Krueger and
Brazeal 1994; Guerrero et al 2008). According to such model (see Figure 1)
entrepreneurial intentions are determined by entrepreneurial potential that is in turn
defined on three critical constructs: perceived desirability (which takes into account
the two attractiveness components of the TPB, i.e. attitudes towards a behavior and
social norms), perceived feasibility (as the perceived ability to execute a target
behavior, i.e. perceived self-efficacy) and propensity to act, as the disposition to act
upon one’s decision (Shapero and Sokol 1982).
This research only considers perceived desirability and feasibility as the main
theoretical determinants of individuals’ intention to become entrepreneurs. Brannback
et al (2006) and Fitzsimmons and Douglas (2011) reinforce this choice since they
both consider entrepreneurial intentions models being primarily dependent on
perceived desirability and perceived feasibility.
Figure 1 Entrepreneurial potential model
3.1. The Role of Perceived Desirability and Feasibility on Entrepreneurial
Intention
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Many empirical studies on entrepreneurial intention mainly focus on young
individuals and students 3 (Guerrero et al 2008) being such a main limitation in
understanding the explicative power of intention-based model in general context
(Krueger et al 2000; Shook et al 2003). Moreover, there are studies that were not able
to see the influence of age on intentions due to its limited variae within the sample
used. An clear example is here made by Davidsson (1995) who, although he
theoretically included individuals’ age as a determinant of entrepreneurial intention,
he was not able to use such variable in the empirical analyses as the used sample was
drawn from a very narrow age span (general population of 35-40 years old
individual). There also exist studies that present both limitations listed above. A
recent example is here given by Fitzsimmons and Douglas (2011) who openly assume
that their results “may not be generalizable to the wider populations … since our
sample was better educated 4 and on average younger than the general populations” (p.
438). Moreover we must take into account that entrepreneurship as a career choice is
not only a choice undertaken only by students or young individuals. Indeed, studies
show that there is a recent trend of older individuals to engage in entrepreneurship,
the so-called third-age or grey entrepreneurship phenomenon (Weber and Schaper
2004; Kautonen 2008). Indeed, continued personal ambitions, longer and healthier
lives, and shrinking retirement security make later-life entrepreneurship an
increasingly attractive option for many older individuals (Rogoff 2007; de Bruin and
Firkin 2003; Minerd 1999). Nevertheless, intention models should maintain their high
explicatory power (Krueger et al 2000) regardless the sample used for the research.
Thus, the first two hypotheses of this study have the objective of testing the
entrepreneurial potential model within an adult population representative sample.
H1a: Individuals’ perceived desirability to start a business will have a positive effect
on their entrepreneurial intentions.
H1b: Individuals’ perceived feasibility to start a business will have a positive effect on
their entrepreneurial intentions.
3.2. Direct Effect of Age on Entrepreneurial Intention
3 Studies that assess entrepreneurial intention of young individuals and students often argue the high of
sample being such individuals facing an imminent career choice decision.
4 Authors use a sample of 414 MBA students taking an entrepreneurship course in Australia, China,
India or Thailand.
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Individuals’ age and entrepreneurial intentions
While empirically testing the TPB in the entrepreneurship context, Kolvereid (1996)
found demographic characteristics 5 to only indirectly affect entrepreneurial intentions
through their effect on attitude, subjective norm, and perceived behavioral control. As
stated in the session two, to the best of our knowledge there exist only two studies
(Franco et al 2010; Ozyilmaz 2011) that explicitly assess the direct relationship
between individuals’ age and entrepreneurial intention without finding any significant
association between the two. Nevertheless, there exist many studies that employ such
demographic variable as a control variable within the model. This somehow allows us
to have an idea on how age could possibly affect intention directly.
Studies sampling students or alumni mainly found no relationship between age and
entrepreneurial intention (Kuckertz and Wagner 2010; Fitzsimmons and Douglas
2011). Identical findings were obtained by Zellweger et al (2011) who sampled
students with a family business background. Authors found age not to be a
determinant for students’ intention to prefer self-employment to family business
succession. Exception are given here by Autio et al (2001) who found a positive
relationship between age and intention on a sample of Scandinavian and US students.
Moreover, Schwarz et al (2009) detected an inverse U-shaped relationship between
entrepreneurial intention and the students’ age, having intention declines as students
exceed the age of 35.
Concerning non-students samples, Lee et al (2011) found no relationship between IT
professionals’ entrepreneurial intentions and their age.
Finally, studies using adult population representative samples found contrasting
results. For instance, Sequeira 6 et al (2007) found individuals’ age to have a
significant and positive relationship with start-up intention. Not only the linear effect
of age was tested on entrepreneurial intention. Indeed both Henley (2007) and
Verheul et al (2012) tested the quadratic relationship. Findings were thought
completely contradictory: while the former study found a significant U-shape
relationship between individuals’ age and entrepreneurial intention (age is positively
associated with entrepreneurial aspiration until the mid-20s, but thereafter aspirations
decline as individuals get older), the latter discovered a significant and reverse U-
shape association (with a negative relationship up to the age of 46 and a positive
5 Gender and parents’ employment status.
6 Authors used three different sources to collect data.
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elationship afterwards) of the two variables. Due to contrasting results found in
empirical research, we decided to ground our hypothesis on the direct relationship
between individuals’ age and entrepreneurial intention based on Levesque and Minniti
(2006) model of optimal time allocations.
Authors depart from Becker’s (1965) time allocation theory that states that individuals
distribute their time between income producing activities and leisure. At any point in
time, the individual chooses whether to obtain income by becoming an entrepreneur
or by working for wages. Waged labor yields immediate and riskless earnings.
Starting a new firm, instead, does not benefit from instantaneous income, but rather
from a stream of future returns (Levesque and Minniti 2006). Discount rate attached
to a stream of payments by the same individual varies along her life span. On the
other hand time can be considered as a limited, and often scarce resource.
Nevertheless, such resource is relatively less scarce for younger individuals’ than
older as younger people generally dispose of more time. Thus, the discount rate
attached by younger individuals to a future stream of payments is lower than the
discount rate applied by older persons and, as a result, the present value of such a
stream is higher (Levesque and Minniti 2006). Since new firms yield income streams
over time, new firm creation is more likely to be preferred by younger individuals
who are more likely to collect the rewards of starting a new firm. In this sense, we set-
up our first hypothesis in the following terms:
H2: Individuals’ age negatively affects entrepreneurial intentions.
3.3. Indirect Effects
“Demographic characteristics may relate indirectly to entrepreneurial intentions”
(Shook et al 2003: 385). Kolvereid (1996) found demographic characteristics such
gender (being male) and having parents’ entrepreneurial background to influence
students’ entrepreneurial intentions trough their positive effect on attitude, subjective
norm, and perceived behavioral control. Similarly, Carr and Sequiera (2007)
discovered attitudes towards business ownership, perceived family support, and
entrepreneurial self-efficacy to positively mediate the relationship between prior
family business exposure and individuals’ entrepreneurial intentions. Recent
researchers have focused on the indirect effect of gender on entrepreneurial intention,
through intention models. Díaz-García and Jiménez-Moreno (2010) performed detect
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no differences of social norms and attitude towards entrepreneurship between males
and females students, while they detect women to show less optimistic perception of
self-efficacy than men. On the other hand, Verheul et al (2012) discovered women in
the sample appear to be less tolerant of risk, less likely to have an internal locus of
control, and more likely to feel that (a) there is a lack of financial support, (b) there
are administrative complexities, and (c) the economic climate for business start-ups is
unfavorable.
Moreover, if we strictly focus on desirability and feasibility as antecedents of
entrepreneurial intentions, some authors argued that “demographic characteristics”
(Shook et al 2003: 385) and more specifically “people’s stage of life, or their age”
(Peterman and Kennedy 2003: 140) may influence perceptions of desirability and
feasibility. In the next session, we develop reasoned hypotheses to support such view.
Individuals’ age and perceived desirability
Perceived desirability “subsumes the two attractiveness components of the theory of
planned behavior, attitude toward the act and social norms” (Krueger and Brazeal
1994: 96). Although they are typically related, we prefer to discuss the influence
individuals’ age plays on both, separately.
Attitude towards entrepreneurial behavior is determined by the expected risks and
rewards of starting a business as in the case of highly uncertain entrepreneurial
income, the attitude towards such variability (risk tolerance) becomes a crucial
element of the decision of whether or not to take the entrepreneurial path (Verheul et
al 2012). Moreover, risk tolerance has been found to be positively related to self-
employment preference (see Douglas and Shepherd 2002; Grilo and Irigoyen 2006;
Verheul et al 2012). Nevertheless, besides some exception (see Wang and Hanna
1997 7 ), recent risk tolerance decreases with age (Hallahan et al 2003, 2004; Yao et al
2011) as they accumulate experience, confidence, and know-how (Levesque and
Minniti 2006). Moreover, aging, a measure at the individual level, generally leads to a
decreased life expectancy and depreciation in human capital, which would be
expected to lead to a lower probability to recover from investment losses (Yao et al
2011). Attitude towards entrepreneurial behavior also depends on current job
satisfaction. While reviewing empirical findings on the relationship between
individuals’ age and job satisfaction, Rhodes (1983) found a predominant positive and
7 Authors calculate risk tolerance by the ratio of risky assets to total wealth.
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linear relationship between the two. Concerning those individuals who are in the job-
market, the older the individuals the more they would be satisfied with their job, so
the little their desirability to change job. Age-satisfaction relationship is a part of the
aging process and can be explained by changing needs, a mellowing process, and
changing cognitive structures associated with age (Gibson and Klein 1970). Thus,
younger individuals may consider more desirable to switch to an alternative job (e.g.
become entrepreneur) due of current job dissatisfaction 8 .
Social norms refer to peers’ influence on individuals about a certain behavior (Ajzen,
1991). In the case of the entrepreneurial behavior, the opinion and perceived social
pressure of family members may play an important role (Krueger et al. 2000).
Family’s exposure to entrepreneurship has been found to be a positive predictor of
entrepreneurial intentions (Chlosta et al 2010; Verheul et al 2012) as well as
positively related to subjective norms (Carr and Sequeira 2007). Concerning the
influence of age on career choice, Whiston and Keller (2004) provided a
comprehensive review of the research published related to family of origin influences
on career development and occupational. Authors conclude that “children seem to
strongly identify with their parents’ occupational area, but this proclivity seems to
attenuate as they age” (p. 554). Morover there exist other studies that claim that
parental influences on offspring’s career value decline after teenage years (Halaby
2003; Johnson 2002). Thus, those who could benefit from family’s exposure to
entrepreneurship in the formation of entrepreneurial intention would loose such
influence as their age increases. On the other hand, regardless those who could benefit
from family exposure to entrepreneurship, the growing awarness and social consensus
of entrepreneurship (e.g. in schools) bring youngers to have a tighter relationship with
peers who influence about becoming entrepreneurs (Peterman and Kennedy 2003). To
resume, both attitude toward the act and social norms, and thus perceived desirability,
seem to be negatively related to individuals’ age. In this sense, we aim at
corroborating the following hypothesis:
H3a: Perceived desirability will negatively mediate the relationship between
individuals’ age and entrepreneurial intentions.
8 Similar findings have been found while studying the influence of age on entrepreneurs’ exit. Indeed,
Van Praag (2003) found voluntary exits more likely for younger business owners. Such voluntary exit
is justified by the argument that younger starters are more likely to find (better) outside opportunities.
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Mungai and Velamuri (2011) found that the impact of parental exposure to
entrepreneurship on offspring’s (age 8-21) self-employment is moderated by
offspring’s developmental stage (i.e. age) at which the parents enter self-employment.
Authors found that the moderating effect is positive and stronger for young adult
offspring (age 18–21) than adolescents or younger children. Although this study
proves that age affect the relationship between role models and intentions, it only
includes individuals who experienced parents’ self-employment until the age of 21. In
this study we expect age to negatively moderate the influence of role models on
entrepreneurial intentions.
H3b: Individuals’ age and perceived desirability will have a negative interaction effect
in the formation of entrepreneurial intentions.
Individuals’ age and perceived feasibility
One of the correlate of perceived feasibility is self-efficacy, i.e. “person's perceived
ability to execute some target behavior” (Krueger and Brazeal 1994: 97). In the
context of careers option, such self-efficacy refers to t the perceived personal
capability to perform a specific job and related tasks. Self-efficacy should be focused
on a specific activity domain as the more task specific the measurement of self-
efficacy, the better the predictive role efficacy is likely to play in research on the task-
specific outcomes of interest (Bandura 1997). In our specific context we thus refer to
Entrepreneurial Self-Efficacy (ESE). McGee et al (2009) found ESE research to be
overly reliant on data collected from university students and practicing entrepreneurs.
Thus, evidences of the influence of age on self-efficacy on an adult no-entrepreneurial
population are rare. Nevertheless, Klyver and Thornton (2010) found a negative
correlation between ESE and individuals’ age. We argue that age is negatively related
to perceived self-efficacy because younger individuals may be overconfident about
their skills and abilities towards entrepreneurship.
H4a: Perceived feasibility will negatively mediate the relationship between
individuals’ age and entrepreneurial intentions.
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As individuals get older they eventually gain more experience. Thus, they get more
aware of the circumstances that typically precede a certain behavior. With this
experience comes an increased awareness of what is likely to happen in the future, as
well as increased contemplation of the behavior (Pomery et al 2009). Sommer and
Haug (2011) indeed found experience to positively mediate the relationship between
perceive behavioral control, which is conceptually related to perceived feasibility
(Krueger et al 2000), and entrepreneurial intention. Thus, in line with Sommer and
Haug (2011), we will test the following hypothesis.
H4b: Individuals’ age and perceived feasibility will have a positive interaction effect
in the formation of entrepreneurial intentions.
Figure 2 resumes our model, as well as the hypotheses and predicted signs.
Figure 2 Theoretical Model and Hypothesis
4. METHODOLOGY
4.1. The Data
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The database used for this research is the 2004 Flash Eurobarometer survey 9 . The
Flash Eurobarometer survey’s main aim is to examine the development of
entrepreneurship and entrepreneurial mindset in people. The survey also examines the
motivation, choices, experiences and obstacles linked to self-employment. Such
database has been recently used in entrepreneurship research (Grillo and Thurik 2005;
Van Der Zwan et al 2010; Verheul et al 2012). This survey, which has been issued by
the Directorate-General Enterprise and Industry of the European Commission,
interviewed a random sample of the general population from 29 countries, including
the ‘old’ 25 EU member states 10 , the United States, Iceland, Liechtenstein and
Norway. Each national sample is representative of the working-age population. Data
were collected by 29 EOS (European Omnibus Survey) Gallup ® Europe institutes. In
April 2004, a total of 21,051 people were interviewed by telephone for this survey.
Table 1 resumes the number of respondents classified by country of origin.
Table 1 Number of respondents by country of origin (N=21,051)
Country Freq. Percent Country Freq. Percent
Belgium 1,000 4.75 Cyprus 500 2.38
Denmark 503 2.39 Czech Republic 1,008 4.79
Germany 1,000 4.75 Estonia 503 2.39
Greece 1,000 4.75 Hungary 1,000 4.75
Spain 1,001 4.76 Latvia 510 2.42
France 1,007 4.78 Lithuania 500 2.38
Ireland 500 2.38 Malta 500 2.38
Italia 1,004 4.77 Poland 1,000 4.75
Luxembourg 500 2.38 Slovakia 504 2.39
Netherlands 1,000 4.75 Slovenia 500 2.38
Austria 500 2.38 Lichtenstein 500 2.38
Portugal 1,000 4.75 Iceland 501 2.38
Finland 501 2.38 Norway 500 2.38
Sweden 500 2.38 Usa 1,003 4.76
United Kingdom 1,006 4.78
The suitability of this database is extremely accurate for this research since it is
representative of the population of any age. For this study, we use data for all 29
countries in the Eurobarometer survey. The sample size for this study is of 16,783
observations. Those individuals whose current occupation was to be self-employed
9 A report showing main findings of the survey is available at
http://ec.europa.eu/public_opinion/flash/fl160_en.pdf.
10 Belgium, Netherlands, France, Italy, Germany, Denmark, Ireland, United Kingdom, Greece,
Portugal, Spain, Austria, Finland, Sweden, Slovakia, Lithuania, Cyprus, Estonia, Malta, Slovenia,
Latvia Czech, Republic, Hungary, Poland.
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(see Table 2), those individuals who already started to take steps to start a business 11
in the following order (see Table 3): those who were currently taking steps to start a
new business (530 observations), those who started or took over an existing business
in the last three year and it is still active (611 observations), and finally those who
started or take over an existing business more than three years ago and it is still active
(1231 observations) were dropped form the sample.
Table 2 Respondents’ current employment status
Freq. Percent
Self-employed 2,209 10.49
White collar worker 6,607 31.39
Blue collar worker 2,773 13.17
Not employed for pay 12 9,378 44.55
Refusal 84 0.40
Total 21,051 100.00
Table 3 Respondents’ engagement towards entrepreneurship
Have you started a business recently or are you taking to start one? Freq. Percent
No, I never thought about it 11,794 56.03
No, but I am thinking about it 3,712 17.63
I though about it or took steps, but I gave up 1,341 6.37
Yes, I am currently taking steps to start a new business 530 2.52
In the last three year I have started or take over an existing business and it is still
active
611 2.90
I have started or take over an existing business and it is still active more than 1,231 5.85
three years ago
I had previously started a business, but currently you are not a owner 1,298 6.17
Don’t Know / No Answer 534 2.54
Total 21,051 100.00
Such items were removed from the research because this study aims at assessing
entrepreneurial intention as an antecedent of behavior. Thus those individuals who
already took steps in order to become entrepreneurs switch such intention to action.
Indeed many of those entrepreneurs who are taking steps to become such they already
considered it as their current job (see footnote X). Nevertheless, there already exist
studies such as Grilo and Thurik (2005, 2008) and Van Der Zwan el al (2010) that
describe the different level of engagement individuals can have towards
entrepreneurship (e.g. entrepreneurial ladder). According to these authors,
11 Many, but not all, who stated their current situation as “started to take steps to start a business”
considered themselves as self-employed when asked about their current occupation.
12 Within this cohort we can found the following sub-categories: looking after home, student (full time)
retired, seeking a job.
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entrepreneurship can be view as a process made of six different engagement levels,
where such engagement can be considered as none, potential, intentional, nascent,
young and established entrepreneurship (Hessels et al 2011). Moreover, respondents
who answered ‘I had previously started a business, but currently you are not a owner’
were also dropped from the final sample (1,298). Indeed the fact that an individual do
not own the business they have started anymore does not mutually exclude that he/she
is thinking about starting a new business. We finally removed those observations that
include relevant questions for our analyses unanswered.
Concerning the sampled respondents’ age, we observe a very high variance of such
variable (see Figure 3), having individuals’ age distributing from 15 to 98. The
average age is of 43.84 years, while the standard deviation is of 18.12.
Figure 3 Age distribution in the sample
4.2. The Variables
Entrepreneurial intention
In order to assess entrepreneurial intention we used the following logical path. First,
participants were asked the following question: ‘Have you started a business recently
or are you thinking to start one?’ Respondents who answered negatively to such
question were subsequently asked: ‘Are you thinking about it?’ Respondents who
answered affirmatively were coded as having entrepreneurial intention 13 . Similar
dichotomous approaches to measure entrepreneurial intention have been adopted by
13 As already stated in the beginning of this session, those individuals who were already entrepreneurs
and those who already were taken ‘active’ steps to become were dropped from the sample.
16

Krueger (1993: 11) and Peterman and Kennedy (2003: 135) who both assessed
entrepreneurial intention based on the following question ‘Do you think you’ll ever
start a business’ and by i n 14 et al (2011b) who relied on the following ‘Have you
ever seriously considered becoming an entrepreneur’ (p.214) to assess entrepreneurial
intentions in Spanish university students. This measurement slightly differed from
Verheul et al (2012) 15 . Indeed, authors used the following question: ‘Suppose you
could choose between different kinds of jobs; which one would you prefer: being an
employee or being self-employed?’ to measure preference for self-employment. In
order to justify this choice, authors claim that by using such measure “an individual
may choose self-employment as appealing due to favorable attributes (e.g., being your
own boss, flexible working hours) without the actual intention to engage in this
activity. This means that in fact, this variable is close to the concept of ‘‘wanting’’ but
does not necessarily factor in the ‘can’ element” (p.331). As this study aim to assess
entrepreneurial intention and not status choice intention (as for instance Kolvereid
1996 ETP) neither we are allow to use the same assumption of Verheul et al (2012),
nor using the same indicator.
Individuals’ age
Individuals’ age has been measured by the age of the respondents at the time the have
answered the survey. Although many studies prefer to use age in cohorts, some
consider it as a limitation (see Kautonen et al 2010). Therefore, in order to overcome
such eventual limitation, age was used as a continuum.
Perceived desirability
Alike Krueger 16 et al (2000) perceived desirability was assessed by the following
question ‘Personally, how desirable is for you to become self-employed within the
next 5 years?’ Respondents were able to answer such question on a 4-point likert
scale item, being 1 very undesirable and 4 very desirable.
Perceived feasibility
14 In this research entrepreneurial intention has been assessed as a combination of that binary variable
listed within the paragraph and other 7-point likert-type items.
15 Comparison is made with such study since authors have used the same database.
16 Authors assess Global Perceived Desirability on the following question ‘On a scale from 0 to 100,
how desirable is for you to start your own business?’
17

Concerning perceived feasibility, we assess it through the following question
‘Regardless of whether or not you would like to become self-employed, how feasible is
would it be for you to become self-employed within the next 5 years?’ As for the
previous measurement, participants were able to answer such question on a 4-point
likert scale item, being 1 very unfeasible and 4 very feasible.
Control variables
The model is control by different factors. First, we control for gender. There exist
several studies that assess that individuals’ gender plays a fundamental role in
assessing entrepreneurial and self-employment career choice intentions (Verheul et al
2012). Men usually show higher entrepreneurial intentions compared with women
(Gupta and York 2008). Individuals’ education and experience are key control factor
in this study. Indeed, many studies have used individuals’ age as a proxy for human
capital (see Coleman 2007) and more generally experience (see Littunen and Virtanen
2009). Thus, if we control our model for both education and experience, we overcome
the eventual problem of having age measuring education or experience. Following
Verheul et al (2012), education was measured by two dummy variables: low
education (with a value of 1 if age when finished full-time education 21 and 0 otherwise).
Concerning experience, we relate to the current job of the respondents. We select
those jobs from which experience can be gathered and employed to establish new
venture. We thus create two dummy variables that assess if the current employment of
the respondents is respectively that of a professional or a manager. Table 4 shows the
variables used in the research with respective measuraments.
Table 4 Variables Description
Dimension Variable Measurement
Entrepreneurial
Intention
Are you thinking about starting
a business?
Taking 1 if yes, 0 if not.
Individuals’ Age What’s your age? Age of the respondent in years.
Perceived
Desirability
Personally, how desirable is
for you to become selfemployed
within the next 5
years?
Regardless of whether or not
Four-point Likert scales (1 very undesirable, 2
somewhat undesirable, 3 somewhat desirable, 4
very desirable).
you would like to become self- Four-point Likert scales (1 very unfeasible, 2
Perceived Feasibility employed, how feasible is somewhat unfeasible, 3 somewhat feasible, 4 very
would it be for you to become
self-employed within the next 5
feasible).
18

Control
years?
Gender Taking value 1 if respondent is male, 0 if female.
Taking value 1 if age when finished full-time
Low Education
education 21, 0 if not.
Self-Employed Parents
Taking value of 1 if the mother, father or both are
self-employed, 0 if not.
Professionals
Taking value of 1 if the current job is as a
professional (e.g. doctor, lawyer…), 0 if not.
Managers
Taking value of 1 if the current job is as a senior or
middle manager, 0 if not.
Our analysis is constrained by the single item measurement of the Eurobarometer
method (also experienced by Verheul et al 2012). This is not necessarily a limitation.
Indeed, there exist some advantages of using single-item measures over multiple-item
measures can be identified such as including the minimization of respondent refusal
and the reduction of common method bias (Bergkvist and Rossiter 2007). Moreover,
it can be argued that single-item measures need not lead to distorted results being the
dependent variable a very clear concept. Lastly, in our specific case, it provides room
to compare the results with those of other studies using similar single-item measures
for the dependent variable, including Krueger (1993) who studied entrepreneurial
intentions, as well as Grilo and Thurik (2008) and Verheul et al (2012) who assess
preference for self-employment.
4.3. The Model of Analysis
Being our dependent dichotomous, we used a probit model revealing the probability
of having entrepreneurial intention to various explanatory and control variables 17 .
More precisely we estimate the following equation:
Pr (y1 = 1X) = F (Xb1)
where y1 = 1 if the individual has entrepreneurial intentions and = 0 if the individual
has not and X both explanatory and control variables.
5. RESULTS AND DISCUSSION
Means, standard deviations, Spearman and Kendall correlations are provided in Table
5. All our correlations are below the 0.60 cut-off value, which indicates that there is
17 An exhaustive description of such variables has been provided in the previous section.
19

no apparent concern that our data suffer from shared variance. In order to corroborate
such statement, we calculated the Variance Inflation Factor (VIF) founding an
average value of 1.12 with a maximum of 1.31. Multicollinearity is thus not a concern
being both values below the critical cut-off of 10 (Hair et al 2006).
Our sample presents an overall average of entrepreneurial intention of 21.93 percent.
This means that 1 out of five individuals have the intention to start a business. In
2004 18 Global Entrepreneurship Monitor (GEM) found that, on average, 14 percent of
the adult working population has the intention to start a new business within the next
three year 19 (Bosma et al 2009). The difference between the two indicators may be
due because of two different reasons. First, while GEM only surveyed individuals
whose age is between 18-64, Flash Eurobarometer takes into the whole adult
population (age between 15 and 98). The second reason may be because the
dependent variable used in the two researches has been assessed through different
questions. Indeed, while GEM asks for entrepreneurial intentions in a 3 years time
length, Eurobarometer does not specifically set a precise time length for the assessing
intention. In this way, individuals who have the intentions to start a firm in period
longer than 3 years from when asked would have answered positively to
Eurobarometer survey and negatively to GEM.
Table 6 displays in different age cohorts both the number of sampled respondents and
their entrepreneurial intention. We can observe how entrepreneurial intention
increases until the 21-26 years interval and it further decreases until reaching 5.94
percent in individuals whose age is older than 68. The highest drop in entrepreneurial
intention happens between the 27-32 and 33-38 cohorts, having intention decreasing
by 5.13 percent.
18 We use 2004 as a reference year since our data were gathered in the same year.
19 Individuals between 18-64 years and from efficiency-driven economies.
20

Table 5 Means, standard deviations, and correlations
Mean S. D. 1 2 3 4 5 6 7 8 9 10 VIF
1. Intention a,b .2193477 .4138173 -
2. Age b 43.84139 18.11772 -.235 ** - 1.32
3. Perceived Desirability b 1.954278 1.022777 .383 ** -.376 ** - 1.34
4. Perceived Feasibility b 1.947067 .9781119 .314 ** -.313 ** .464 ** - 1.34
5. Gender a,b .434964 .495767 .033 ** -.053 ** .094 ** .132 ** - 1.03
6. Low Education a,b .1352559 .3420069 -.036 ** .334 ** -.098 ** -.171 ** -.024 ** - 1.21
7. High Education a,b .2477507 .4317191 .020 ** -.017 .038 ** .163 .016 ** -.067 ** - 1.15
8. Self Employed Parents a,b .258626 .4378932 .003 .033 ** .032 ** .043 ** -.007 .018 ** .014 ** - 1.01
9. Professionals a,b .0423049 .20129 .010 -.014 .036 ** .101 ** -.000 -.011 ** .033 ** -.000 - 1.06
10. Managers a,b .0690522 .2535504 .008 ** -.026 * .023 * .093 ** .021 ** -.017 ** .041 ** .000 -.006 ** - 1.06
a : Correlation has been assessed by using Kendall tau rank correlation coefficient (dummy vs. dummy)
b : Correlation has been assessed by using Spearman correlation coefficient (continuous vs. dummy, continuous vs. continuous)
* : p

5.1. The Role of Perceived Desirability and Feasibility on Entrepreneurial
Intention
Model III tests hypotheses H1a and H1b (see Table 7). Both perceived desirability and
feasibility are significantly and positively related to entrepreneurial intention
(p

Table 5 Entrepreneurial intention and its determinants (Probit model)
MODEL I MODEL II MODEL III MODEL IV
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Age -.0194966 *** .0007373 -.0079256 *** .0008498
Perceived Desirability .4205579 *** .013679 .5636054 *** .0311933
Perceived Feasibility .2309566 *** .0144105 .2481386 *** .0330012
Age x Perceived
Desirability
-.0035811 *** .0007082
Age x Perceived
-.0004234 .0007339
Feasibility
Gender .2052679 *** .0228019 .1889556 *** .0233381 .0853726 ** .0257505 .0795184 ** .0257785
Low Education -.6090748 *** .0417175 -.2564806 *** .0449068 -.3120106 *** .049826 -.2943544 *** .0491881
High Education .0402641 .0268878 .1019808 *** .0275542 .0165343 .0303738 .0245927 .0304139
Self-Employed Parents .055716 * .0259392 .0631168 * .0266412 -.0009538 .0290889 -.004711 .0291146
Professionals .3188234 *** .0528438 .3317936 *** .0534189 .2435594 *** .0583115 .255699 *** .0583371
Managers .1098456 * .0430645 .121397 ** .0434902 .0856938 † .0473391 .099176 * .0473872
Constant -.8587258 *** .0189665 -.1109193 ** .0334483 -1.887567 *** .0576121 -2.225987 *** .0401348
χ2 437.55 *** 1183.44 *** 2842.70 *** 2882.46 ***
Prob > χ 2 0.000 0.000 0.000 0.000
−2 log likelihood 16,033.548 15,287.657 12,568.306 12,528.549
Hosmer–Lemeshow
goodness of fit (χ 2 )
45.34 **
1588.52 *** 6182.38 6067.23
Pseudo R 2 0.0266 0.0718 0.1845 0.1870
N 15,705 15,705 14,651 14,651
† : significance level

espondents imply a smaller variance of the variable age and thus a limited scope in
understanding its dynamics.
Moreover, as both Henley (2007) and Verheul et al (2012) found non-linear
significant relationship between age and intention, we tested the quadratic term of
age. No significant results were found, thus reinforcing our theoretical approach on a
linear relationship between the two variables. The discrepancy between our study and
may be due to two reasons. Vis-à-vis Verheul et al (2012) who found a U-shaped
relationship between preference for self-employment and age, we claim that the
discrepancy between the two results happens because of difference in the dependent
variable. Indeed, while we studied entrepreneurial intention, Verheul et al (2012)
assessed preference for self-employment.
5.3. Indirect Effect (Mediation)
Mediator variables are variables that sit between independent variable and dependent
variable and mediate the effect of the IV on the DV. Figure 4 shows a model with two
mediators.
Figure 4 Mediation Model
If dependent, independent and mediating variables were all continuous, the
calculation of the indirect effects would require a combination of two OLS
regressions. Since our dependent variable is dichotomous, the calculation of the
indirect effects would require a combination of an OLS regression along with a probit
model. Thus computing indirect effects in our study involves multiple models, each
with different variables. To facilitate the comprehension of this concept and the math
involved, we considerate only one mediator (see Figure 5).
24

Source: Herr (2012)
Figure 5 Segmented mediation model
We thus have three equations that describe our mediating model:
DV' = cIV + E1
MV" = aIV + E2
DV" = bMV + c'IV + E3
Primes are added to mediating variable (M) or dependent variable (DV) to show that
M' is on a different scale than M since different econometric models has been used to
assess them. Because DV is the outcome variable in two equations, it gets the coveted
"double-prime" to show that the scale of DV differs from DV' which differs from
DV".
Thus, as we have different models, we should standardize the coefficients (Kenny
2008). Indeed, while in OLS regression the residual variance for the model changes as
variables are entered or removed from the regression equation, in probit regression the
residual variance is fixed. Since the residual is fixed the scaling of the coefficients
varies. Coefficients from OLS models are rescaled using the standard deviations of
the observed variables. For logit or probit models the rescaling involves the standard
deviation of the underlying latent variable for the binary variable. Once the
coefficients are rescaled (standardized) the indirect effects can be computed as the
product of coefficients 20 . Table 8 shows the coefficient of both mediating variables,
20 In order to perform the binary mediation, we use a Stata program called binary mediation
(http://www.ats.ucla.edu/stat/stata/faq/binary_mediation.htm) based on Kenny (2008) and wrote by
25

i.e. perceived desirability and feasibility. Age affects negatively both perceived
desirability and feasibility. Moreover, we can appreciate that the influence of age is
much stronger in the case of desirability than feasibility. Indeed, the mediating
coefficient in the former case is of -0.134 and -0.053 in the latter. Moreover, we can
observe how the indirect effect that age plays on entrepreneurial intention is 1.56
times greater than the direct one.
Table 6 Indirect effects with binary response variable intention (Mediation)
Statistic Value Observation
Indirect effect 1 -.13388477 (Desirability, continuous)
Indirect effect 2 -.05256539 (Feasibility, continuous)
Total indirect effect -.18645017
Total direct effect -.11901529
Total effect -.30546546
c path -.36320568
Proportion of total effect mediated .61038054
Ratio of indirect to direct effect 1.5666069
Ratio of total to direct effect 2.5666069
As the model does not produce any standard error or confidence interval on its own,
we thus must apply a standard bootstrapping procedure (Yung and Bentler 1996) in
order to obtain standard errors for the direct and indirect effects as well as for getting
95 percent percentile confidence intervals, and thus to determine the statistical
significance of each estimated path. The number of resample was set at 500. Table 9
shows 95 percent confidence interval calculated with both percentile bootstrap and
bias-correct bootstrap (it adjusts for bias in the bootstrap distribution) methods. In
looking at the bootstrap results, we see that both of the indirect effects, along with
direct and total indirect effect, are significant having their confidence interval not
contain zero. Thus, hypotheses H3a and H4a are supported, having both perceived
desirability and feasibility negatively mediating the relationship between individuals’
age and entrepreneurial intentions.
Observed
Coef.
Bias
Bootstrap
Std. Err.
b1 (Indirect effect 1) -.13388477 -.0001515 .00489637
[95 percent Conf. Interval]
-.1442392 -.1247539 (P)
-.1432881 -.1242449 (BC)
UCLA Academic Technology Services. The program allows performing mediation models with
multiple mediator variables in any combination of binary or continuous along with either a binary or
continuous response variable. Moreover, it automatically calculates standardized mediation coefficients
depending on the technique used.
26

1 (Indirect effect 1 ) -.05256539 -.0000491 .00336716
b3 (Total ind. effect) -.18645017 -.0002006 .00515805
b4 (Total direct effect ) -.11901529 .0002626 .01243337
b5 (Total effect) -.30546546 .000062 .01135402
(P) Percentile confidence interval
(BC) Bias-corrected confidence interval
Table 9 Bootstrap analysis for mediating coefficients
5.4. Indirect Effect (Moderation)
-.0592862 -.04579 (P)
-.0592862 -.04579 (BC)
-.19666 -.17654 (P)
-.1959327 -.1759634 (BC)
-.1449329 -.0937942 (P)
-.1452305 -.0943935 (BC)
-.3304094 -.2827478 (P)
-.3316875 -.2839217 (BC)
Finally, Model IV corroborates H3a. Individuals’ age negatively moderates the
relationship between perceived desirability and entrepreneurial intention (p

eduction of the likelihood confirms a better suitability of the model in respect of the
data. The Chi-square (χ 2 ) test proves that such reduction is statistically significant
with 99.99 percent level of confidence in all models. Additionally, Hosmer and
emeshow Chi-square test of goodness of fit was performed. This test is considered to
be more robust than the traditional Chi-square test where a finding of non-significance
generally granted the model to adequately fit the data. Nevertheless, Model I and II
show a very high significance of the test (see Table X) suggesting the inadequacy of
the model for the study. However, a significant Hosmer and emeshow test does not
necessarily mean that a predictive model is not useful (Kramer and Zimmerman
2007). Caution has to be exercised when using this test, as it depends on the sample
size of the data. Indeed, this test will likely indicate that the model fits for small
sample size and this test may “fail” for a large dataset even if the model fits (Chan
2004). As we count with more than 15,000 observations, we witness such problem for
Models I-II. Table 9 finally resumes the collaboration of our hypotheses and the
model used to support them.
Table 7 Results of the contrasts of hypothesis
Probit Regression
H1b: Individuals’ perceived desirability to start a business will have a
positive effect on entrepreneurial intentions.
Supported Model III
H1b: Individuals’ perceived feasibility to start a business will have a
positive effect on entrepreneurial intentions.
Supported Model III
H2: Individuals’ age negativly affects entrepreneurial intentions. Supported Models II and III
H3a: Individuals’ age and perceived desirability will have a negative
interaction effect in the formation of entrepreneurial intentions.
Supported Model IV
H4a: Individuals’ age and perceived feasibility will have a positive
Rejected Model IV
interaction effect in the formation of entrepreneurial intentions.
Probit Regression with dichotomous outcomes
H3b: Perceived desirability will negatively mediate the relationship
between individuals’ age and entrepreneurial intentions.
H4b: Perceived feasibility will negatively mediate the relationship
between individuals’ age and entrepreneurial intentions.
6. CONCLUSIONS
Supported Bootstrap analysis
Supported Bootstrap analysis
Building upon Ajzen (1991) theory of planned bahaviour, our study assesses the
direct and indirect relationship between indidivuals’ age and entrepreneurial
intentions. Alike Guerrero et al (2008), our study confirms that Krueger and Brazeal
(1994) model of entrepreneurial intentions is a strong predictor of entrepreneurial
intention. Indeed, both perceived desirability and feasibility are significantly and
positively related to entrepreneurial intention (p

entrepreneurial intention both directly and indirectly. Individuals’ age is negatively
related to entrepreneurial intention (p

This study presents some limitations. First, the cross-sectional data used in this
analysis limit the demonstration of causation. Future studies should thus seek to
develop longitudinal research designs. Indeed, by having longitudinal information we
can assess individually how entrepreneurial intentions, perceived desirability and
feasibility evolve in people as they get older. Second, more accurate measurements of
individuals’ experience are needed. Indeed, as we aim at disintagling the intrinsic
effect of age, we must be sure to control all those individuals’ level determinants that
are correlated with age.
By establishing theoretical linkages and providing first empirical evidence for the
mediating effect of perceived desirability and feasibility between age and intention,
we consider our study as a starting point for further research on the impact of aging on
entrepreneurial intention. Future studies should move on the first stage of the
entrepreneurial process (Cassia et al 2012) and investigate the role of age in
entrepreneurial behavior 21 and performance. Moreover, future contributions should
also aim at investigating how age may moderate the relationship between
entrepreneurial intention and actual behavior.
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