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CAREER PATHS, ORGANIZATIONAL AFFILIATION AND THE ENACTMENT

OF ENTREPRENEURIAL INTENTIONS

Riccardo Fini1

Draft version: 25/01/2010

ABSTRACT

How entrepreneurs’ career-paths relate to the enactment of entrepreneurial intentions? Does

entrepreneurs’ organizational affiliation influence their entrepreneurial intentions? In an

attempt to provide answers to such questions, we draw on intentional theory in order to

explain heterogeneity in entrepreneurs’ intentional processes. We study the impact that

entrepreneurs’ affiliation to public research institutions has on the enactment of their

entrepreneurial intentions, comparing the intentional processes of a matched-pairs sample of

52 public entrepreneurs and 52 private entrepreneurs. After employing multivariate regression

analyses and structural equation modelling techniques, the empirical results are confirmed,

showing that entrepreneurial intentions are differently determined within the two samples.

These findings provide us new theoretical and empirical evidences on the impact that

individual features, developed as a result of different career paths and organizational

affiliations, have on the enactment of entrepreneurial intentions.

Key words: Entrepreneurial intention, career-paths, organizational affiliation, public

employee, psychological characteristics, individual skills, academic entrepreneurship,

common method bias, bootstrap, structural equation modelling.

1 E-mail: [email protected]

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1. INTRODUCTION

Entrepreneurs’ intentions determine the form and the direction of nascent organizations

at their inceptions and affect the survival and growth of the organizations that they lead.

Although such actions can result from unintended and unplanned behaviors, intentions are

needed for them to become manifest (Bird, 1988).

In this article, we focus on the determinants of entrepreneurial intentions, studying the

heterogeneity in their enactment. We draw on intentional theory (Krueger, Reilly, & Carsrud,

2000), and we bring together psychological and contextual dimensions to explain such

differences.

Literature shows that entrepreneurial intentions are rooted in personal characteristics,

such as entrepreneurial self-efficacy (Zhao, Seibert & Hills, 2005) and risk-taking propensity

(Luthje & Franke, 2003), and in individual abilities, such as technical and managerial skills

(Wiklund & Shepherd, 2003). Contextual factors, such as environmental influences (Morris &

Lewis, 1995), are also needed for entrepreneurial intentions to become manifest.

As we acknowledge that several contributions have studied the determinants of

entrepreneurial intentions, still little is known on the dimensions that account for

heterogeneity in their enactment. In particular, it is still unclear if entrepreneurial intentions

arise following similar patterns for all entrepreneurs or if they are differently determined, as a

result of developed individual features or due to environmental exposures.

In an attempt to fill this void, in this contribution we compare the intentional processes

of two groups of entrepreneurs - who have established their firms after different career paths -

while employed in different organizations. In particular, we identify previous career and

organizational affiliation as the two dimensions responsible for the heterogeneity in the

enactment of entrepreneurial intentions. We compare public entrepreneurs, or individuals who

have established a new company - while employed in public research organizations - after a

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career as public servants, with a control sample of entrepreneurs without such affiliation and

background. We put forward a set of hypotheses on the differences in the determinants of

their entrepreneurial intentions. Adopting a quasi-experimental design, we compare a

matched-paired sample of 52 public entrepreneurs and 52 private entrepreneurs. We test for

differences using univariate statistics, multivariate regression analysis, and structural equation

modelling technique on bootstrapped data samples.

Our results show that entrepreneurial intentions are differently enacted within the two

samples. As for public entrepreneurs they are determined by risk-taking propensity and

technical skills, while for private entrepreneurs they are fostered by entrepreneurial self-

efficacy and managerial skills. In both samples the environmental support has positive and

significant impact on entrepreneurial intentions. These results hold when we bootstrap and we

apply structural equation modelling technique on the simulated data samples.

This study contributes in two ways to the debate on the psychology of entrepreneurs.

First, relying on intentional theory, it assesses the impact that career-paths and organizational

affiliation have on entrepreneurial intentions, showing the existence of heterogeneity in their

formation processes. Second, it empirically tests a behavioural model of the determinants of

entrepreneurial intentions, accounting for both personal and contextual dimensions.

Our results are of interest also for practitioners. Financial investors’ decisions, as well

as several norms put in place by policy makers, rely on the assumption that some

entrepreneurs, because of their career-paths and organizational affiliation, develop

idiosyncratic skills, specific mindsets and biased perceptions, that differently enact their

intentions, hindering entrepreneurial behaviours and, ultimately, their ventures’ growth. Being

aware of how entrepreneurial intentions differently arise might help policy makers and

institutional investors to better rationalize their efforts toward the support of technology

transfer activities from universities to business market. On top of that, the documented

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heterogeneity in intentional processes may possibly explain some differences in growth

patterns between academic spin-offs and private start-ups (Ensley & Hmieleski, 2005).

The rest of the paper is organized as follows: in section 2 we focus on intentional

theory. In section 3 we describe the role played by career paths and organizational affiliation

in the development of specific entrepreneurs’ features. We then put forward a set of

hypotheses on the differences between public and private entrepreneurs’ intentional

processes. In section 4 we present the methodology and the research design, while in section 5

we report the empirical results. A final section concludes with discussion and implications.

2. ENTREPRENEURIAL INTENTIONS

Intentions occupy a central position in cognitive approaches to understand human

behaviours (Tubbs & Ekeberg, 1991). According to Ajzen (1991) and Sutton (1998) most

behaviours of social relevance (i.e. health related behaviour and the establishment of new

organizations) are under volitional control. Intentions can be seen therefore as the immediate

determinants and the single best predictors of behaviours (Armitage & Conner, 2001).

Bird (1988), in her seminal work, studies entrepreneurial intentions. In particular, she

argues that the objective (or goal) of entrepreneurial intention can be identified in terms of

either the establishment of a new venture or the creation of new value within an existing

organization. Shane and Venkataraman (2000), building on her contribution, argue that the

two objectives that mainly characterize entrepreneurship are the establishment of new

independent firms, and the creation of new value in existing ones. Consistently,

entrepreneurial intentions can be seen as cognitive representations of the objectives and

actions to be implemented in order to either establish a new independent venture or create

new value within an existing organization.

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It is certainly true that entrepreneurial ideas begin with inspiration; though intentions

are needed in order for them to become manifest (Del Mar & Shane, 2003). Consistent with

this approach, Krueger et al. (2000) argue that individuals do not start a business as a reflex,

but they do it intentionally. Hence, especially at the birth of an organization, the impact of the

entrepreneur’s intention is predominant; also because the influences of external stakeholders,

corporate structure, politics, image, and culture have not yet been established (Bird, 1988). As

a consequence, entrepreneurs’ intention determines the form and the direction of a nascent

organization at its inception.

Entrepreneurial intentions also influence the actions of established organizations.

Different theories model organizational behaviours as the result of individual intentions,

emerging through social and political processes determined by individuals. As an example,

Mitchel (1981) argues that CEOs’ and entrepreneurs’ intentions directly affect the

organizations that they lead. Similarly, Stevenson and Jarillo (1986) state that, in established

firms, leading individuals, as a result of their intentional processes, pursue and exploit

opportunities. The role of individual intentions become even more critical as it goes to small

entrepreneurial firms. In particular the strategic orientation and entrepreneurial intentions of

CEOs and entrepreneurs are likely to be tantamount to the strategic and entrepreneurial

orientations of their firms (Baum & Wally, 2003). Consequently, existing organizations

embody and elaborate intentions that, ultimately, affect ventures’ growth.

Intentional theory roots entrepreneurial intentions into two macro-areas, defined as

personal domains and contextual variables (Bird, 1988). Scholars address the former in terms

of “internal dimensions” - or entities that co-exist inside the individual (i.e. psychological

characteristics and individual skills) -; the latter instead are seen as “external dimensions” - or

as influences coming from the outside world (i.e. environmental support and technological

opportunities) (Manstead & Van Eekelen, 1998) -.

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Internal dimensions are mostly identified in terms of psychological characteristics, and

individual skills and prior knowledge. Dropping a “trait-approach”, under which it has been

demonstrated that demographics and traits have resulted in a little explanatory power - failing

to predict entrepreneurship - (Gartner, 1989), scholars have mainly focused on psychological

characteristics, identifying them as responsible for determining entrepreneurial intention. In

particular, entrepreneurial self-efficacy (Zhao et al., 2005), or the individual’s confidence in

his or her ability to successfully perform entrepreneurial roles and tasks, and risk-taking

propensity (Forlani & Mullins, 2000), or a function of the variation in the distribution of

possible outcomes, the associated outcome likelihoods and their subjective values (Stewart &

Roth, 2001), have showed to determine entrepreneurial intentions2.

Individual skills and abilities accumulated by each entrepreneur are also predictors of

entrepreneurial activities. Wiklund and Shepherd (2003) argue that entrepreneurial intentions

can be conceptualized as functions of entrepreneurs’ developed abilities. Prior knowledge,

defined by Shane (1999) as the stock of information generated through people’s idiosyncratic

life experiences, as well as technical, industry and organizational skills, defined by Baum,

Locke and Smith (2001) as competences required to perform a specific job, influence

entrepreneurial intention.

Moreover, entrepreneurship scholars have theorized that an array of external factors,

such as the social, political, and economic contexts influence entrepreneurial intentions

(Morris & Lewis, 1995; Fini, Grimaldi & Sobrero, 2009). Governments may intervene with

funding schemes, tax policies and other support mechanisms that are aimed at mitigating

market inefficiencies and promoting entrepreneurship (Lerner, 1999). As for local context,

several studies have focused on the ability that both tangible resources, such as financial

2 In this contribution we see risk-taking propensity, coherently with the classical decision theory (March & Shapira, 1987) as a situational psychological characteristics. Conversely, other scholars see risk-taking propensity as predispositional rather than situational (Jackson, Hourany & Vidmar, 1972; Plax & Rosenfeld, 1976), consistently with the big five personality theory, which suggest that risk-propensity is a facet of the traits of extraversion (Mount & Barrik, 1995).

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support (Beck, Demirguc-Kunt, & Maksimovic, 2005) and entrepreneurial support services

(Feldman, 2001; Foo, Wong, & Ong, 2005), and intangible resources, such as human capital

and routines, have in fostering entrepreneurial intention (Niosi & Bas, 2001).

Several contributions have thus studied the roots of entrepreneurial intentions.

Notwithstanding, scant attention has still been devoted to explain why entrepreneurial

intentions arise differently. In particular there is scant theoretical and empirical evidences

related to what factors account for differences in their enactment (Markman, Balkin, & Baron,

2002). In order to fill part of this void, in the next section, we focus on entrepreneurs’ career–

paths and organizational affiliations as the two dimensions responsible for such heterogeneity,

and we put forward a set of related hypotheses.

3. HETEROGENEITY IN ENTREPRENEURIAL INTENTIONS’ ENACTMENT

3.1 Career-paths, organizational affiliation and the development of intrinsic features

The extant literature acknowledges that individuals pick jobs as a result of their

abilities, intrinsic motivations and expectations of possible outcomes related to the specific

mansion (Jovanovic, 1979). On top of that, individuals self-select themselves into

organizations that have behavioural constraints consistent with their own inclinations and,

similarly, organizations select employees whose particular personal attributes are compatible

with organizational expectations (Baron, 1984). It follows that once individuals enter

organizations, they start to develop specific features, coherently with the missions of their

employers (Fujiwara-Greve & Greve 2000).

Public research institutions (PRIs) may be defined as ‘collections of interrelated rules

and routines that define appropriate actions in terms of relations between roles and situations’

(March & Olsen, 1989). According to this definition, both formal and informal aspects of

PRIs provide institutional influences to individuals that are part of them. PRIs’ work-related

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rules and norms shape not only the administrative behaviour of public researchers but also the

basic attitudes they hold about the value of PRIs themselves. Such organizations, in fact, are

not just a mean to produce outputs, they are also social institutions in which individuals

interact and influence each others in the context of a structured environment. We can

therefore hold that individuals who choose to serve PRIs self-select themselves, being

influenced by the nature of the PRIs they are part of (Moynihan and Pandey, 2007). Such

individuals own and develop specific intrinsic features that are coherent with public

organizations’ missions (Ozcan & Reichstein, 2009).

Public researchers also belong to the scientific community. Every community has its

distinctive culture, that is characterized by an ideological schema, which controls its self-

identification, knowledge, goal, and conduct and which is expressed in the conventional

actions of its members (Van Dijk, 1995). Members of the scientific community not only share

an acceptance of a great deal of knowledge, methods and criteria against which to evacuate

claims, they also share an acceptance of a culture shaped by social and historical forces

(Hyland, 1997). They are underpinned by PRIs’ values of science: communality, skepticism,

disinterestedness and universality of scientific knowledge. Merton (1958) argues that the goal

of scientists, coherently with the missions of their alma mater, is to establish priority in

discovery, by being first to communicate an advance in knowledge, getting the recognition

awarded by the scientific community for such accomplishment. Such recognition has varied

forms, depending upon the importance the scientific community attaches to the discovery.

Heading the list there is eponymy, or the practice of attaching the name of the scientist to the

discovery, then Nobel prizes, and finally scientific publications (Stephan, 1996).

We can therefore conclude that both the exposure to PRIs’ institutional environment

and the belonging to the community of science foster PRIs’ employees to develop

psychological idiosyncrasies, specific abilities and biased perceptions. Firstly, PRIs not only

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attract higher-quality employees (based on entrant individual skills - as specified by education

levels or aptitude test score (Crewson, 1995; Ozcan & Reichstein, 2009) -), but also foster

their employees to acquire a set of abilities and idiosyncratic skills that are coherent with their

tasks and missions (Ronstald, 1990). Secondly, PRIs’ employees shape their psychological

characteristics, developing specific mindsets and constructing beliefs based on what is

appropriate in light of their environment and the norms and behaviours of those around them

(Perry, 1996). Those who choose the public sector, originally, tend to have a greater interest

in altruistic and ideological goals; they decide to be employed as civil servants to make a

difference, having an impact on social welfare (Ozcan & Reichstein, 2009). Public

researchers also develop ‘professional’ and ‘organizational’ identities, through which they

seek to integrate their various statuses and roles, as well as their diverse experiences, into a

coherent image of themselves as a part of PRIs (Epstein, 1978). Finally, PRIs’ employees

differently interact with the environment, being biased by their institutional affiliation. As

they progress in their PRIs careers, they frame the external context, while the external context

perceive and interact with them as part of such organizations. For example, entrepreneurial

opportunities and market dynamism are then perceived differently by PRIs’ employees if

compared to private counterparts; pursuing such opportunities is indeed not consistent with

their parental institutions’ missions (Merton, 1988).

As a result of such developed psychological idiosyncrasies, specific abilities and biased

perceptions, individuals employed in PRIs enact entrepreneurial intentions differently, if

compared to other individuals with no such affiliation and background. Building on that, in

the next section, we put forward a set of hypotheses on differences in entrepreneurial

intentions’ determinants.

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3.2 Hypotheses

Coherently with the debate on the foundation of intentionality, we see entrepreneurial

intentions as determined by internal dimensions, in terms of psychological characteristics and

individual skills, as well as by external dimensions, in terms of environmental influences.

As for the former domain, risk-taking propensity represents one of the entrepreneurs’

psychological facets in which the existing literature roots entrepreneurial intentions (Forlani

& Mullins, 2000). To such extent, several contributions have studied risk-taking propensity in

relation to individuals’ career-choices. In particular, Bellante and Link (1981) point out that

individuals with a low level of risk-taking propensity, more likely than others, choose an

employment in public sectors. These findings are confirmed by Blank (1985), who shows that

public servants, consistently with the risk-aversion strategies of their parental organizations,

are motivated by job security and stability, rather than by independence and high returns. We

can therefore argue that public entrepreneurs, because of their previous choices to be part of

PRIs, have less risk-taking propensity if compared to the private counterparts.

On top of that, public entrepreneurs belong to PRIs for many of which technology

transfer activities are only a part of the their institutional mission, as their main objectives

remain teaching and research. For many of them, being entrepreneurs mean putting aside

(even if only for a limited period of time) a stable position in order to try the entrepreneurial

venture. Moreover, in many PRIs, entrepreneurship is still looked at as a non ‘desirable’

thing; this creates even more difficulties in events of failure. This suggests that for public

researchers getting engaged into a new venture is a even more risky activity, if compared to

other entrepreneurs, because of both the intrinsic entrepreneurial risk embedded in the new

venture and the decision to leave a risk-less wage in a public job for a risky entrepreneurial

career with volatile earnings3 (Ozcan & Reichstein, 2009). We then hold that for public

3 Same empirical evidences support this view. Data shows that both initial earnings and earnings growth rates of most entrepreneurs are lower than those for comparable wage earners (Hamilton 2000), and that more than 1/3

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researchers who decide to establish a new venture, risk-taking propensity plays a major role,

having a higher impact on entrepreneurial intentions than for private entrepreneurs.

Based on such arguments we put forward the following two hypotheses.

H1a: Public entrepreneurs have lower risk-taking propensity than private

entrepreneurs

H1b: The effect of risk-taking propensity on entrepreneurial intention is higher for

public entrepreneurs than for private entrepreneurs

Self-efficacy is another psychological facet proved to determine individual intentions.

Self-efficacy refers to a person’s belief in his or her capability to perform a given task

(Bandura, 1997) and to attain certain goals (Boyd & Vozikis, 1994). As a task oriented

behaviour, entrepreneurial self-efficacy can be defined as entrepreneurs’ beliefs and

confidence in their capabilities to affect the environment and being successful in

implementing entrepreneurial behaviors (Luthans & Ibrayeva, 2006).

Coherently with the aforementioned literature on public employment, individuals who

choose the public sector, consistently with the missions of their employers, tend to have a

greater interest in altruistic and ideological goals. As they progress in their careers, they

become gradually involved in PRIs and develop a strong sense of public service (the so-called

“public service motivation”), mutuating public service as their ultimate goal (Moynihan &

Pandey, 2007). As reviewed earlier by Wood and Bandura (1989) and by Zhao et al. (2005),

high self-efficacy expectations regarding performance in a specific behavioral setting lead

individuals to approach that setting, whereas low self-efficacy expectations lead individuals to

avoid that setting. It follows that individuals who firstly choose to being employed as public

of entrepreneurial activities likely fail during the first year of operations (based on Panel Study for Income Dynamics (PSID), Quadrini 1999).

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servant, and only subsequently being entrepreneurs, will feel more confident in the

performance of altruistic and ideological behaviors rather then in attaining entrepreneurial

goals.

Moreover, consistent with previous research on career related self-efficacy, Boyd and

Vozikis (1994) and Chen, Greene and Crick (1998) view entrepreneurial self-efficacy as a

determinant of both the strength of entrepreneurial intentions and the likelihood that those

intentions will result in entrepreneurial actions. According to them, entrepreneurial intentions

are more likely to be positively affected by those individuals who have high beliefs in their

ability to influence the achievement of business goals (Chen et al., 1998). It follows that

entrepreneurial intentions are more likely predicted by entrepreneurial self-efficacy for those

individuals who consider themselves more efficacious in performing entrepreneurial roles and

tasks.

Based on that, we then put forward the following two hypotheses.

H2a: Public entrepreneurs have lower entrepreneurial self-efficacy than private

entrepreneurs

H2b: The effect of entrepreneurial self-efficacy on entrepreneurial intention is lower

for public entrepreneurs than for private entrepreneurs

Scholars also show that PRIs attract higher-quality individuals (Crewson, 1995; Ozcan

& Reichstein, 2009), helping them to acquire a set of abilities and idiosyncratic skills that are

coherent with PRIs tasks and missions – which have been identified primarily with research

related activities and teaching - (Ronstald, 1990). The public research system is designed so

to incentivate and recompensate mainly public knowledge and research development. As a

consequence PRIs’ employees through the years have come to develop mainly research skills,

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contributing to the development of frontier knowledge, becoming themselves repository of

specific technical knowledge. We then expect that public entrepreneurs will develop cutting

edge abilities in technical-related fields, such as in product and process designs, if compared

to entrepreneurs with no such affiliation.

Building on this last point, scholars have also acknowledged the great relevance of

technical skills as important inputs for recognizing entrepreneurial opportunities, and more

generally, for triggering the entrepreneurial processes. Several PRIs’ employees, especially

the ones who research in high-technology fields, might see a commercialization potential of

their knowledge (Shane, 2004). Greater knowledge will directly provide greater awareness

about the existence of career options based on that knowledge (Ronstald, 1990). Coherently

with this view we see technical skills as critical in triggering entrepreneurial intentions for

public entrepreneurs.

Based on these arguments we put forward the following two hypotheses.

H3a: Public entrepreneurs have higher technical skills than private entrepreneurs.

H3b: The effect of technical skills on entrepreneurial intention is higher for public

entrepreneurs than for private entrepreneurs

Since the early 1980s, several PRIs have promoted closer linkages with the market,

adopting a more outward looking attitude on technology transfer and commercialization

related activities (Etzkowitz, 1983). As Slaughter and Leslie (1997) concluded ‘faculty

professional officers and administrators where reshaping their epistemology of science to

accommodate professional interactions with the market’. The investments that many PRIs

have done to foster entrepreneurial culture within their settings allowed researchers to

increase the frequency of interactions with industry people, and to develop more market-

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oriented abilities. Notwithstanding, the development of managerial competences, such as

planning, organizing and executing entrepreneurial activities, are still under emphasized

within PRIs - if compared to private organizations -. Being employed in organizations with

mandates mainly focused on research related activities do not therefore foster the creation of

managerial competences among employees. We might then expect that public entrepreneurs

will develop lower levels of managerial skills if compared to entrepreneurs with no such

affiliations.

Besides that, there are several studies focused on technology transfer activities that

have highlighted that the lack of managerial competencies is often identified as one of the

main obstacles to academic spin-offs’ growth (Colombo & Grilli, 2005; O'Shea, Allen,

Morse, O'Gorman & Roche, 2007). Several researchers support this view and see public

entrepreneurship mainly fostered by industry skills and technical abilities rather then by

managerial skills. Sharing this approach, we hold that the impact of managerial skills on

entrepreneurial intentions for public entrepreneurs will be lower than for private ones.

We then propose the following two hypotheses.

H4a: Public entrepreneurs have lower managerial skills than private entrepreneurs

H4b: The effect of managerial skills on entrepreneurial intention is lower for public

than for private entrepreneurs

Finally, sharing the idea that intentions are determined by both internal and external

factors, we see environmental influences as direct predictor of entrepreneurial intentions

(Bird, 1988). Environment is framed as a result of affiliation to groups, organizations and

institutions (Saegert & Winkel, 1990; Charness, Rigotti & Rustichini, 2007). Public

researchers, as a result of their university affiliation, interpret the external environment

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coherently with their roles as members of institutions. As public servant, they are biased in

scanning the environment for market opportunities (Burton et al., 2002). In particular, the

absence of (intense) competition in public markets increases the degree to which

organizations focus inward and erect information barriers that make it difficult for public

employees to glean information from the market for exploit entrepreneurial opportunities

(Ozcan & Reichstein, 2009).

In order to mitigate this, policy makers have put in place a set of norms and

infrastructures, aimed at supporting the technology transfer activities from PRIs to market.

The vast majority of these options, such as the access to business incubators (Mian, 1996) and

to science parks (Feldman, 2001), the presence of business plan competitions (Foo, Wong &

Ong, 2005) and the possibility to obtain financial incentives from governments (Beck et al.,

2005) are available to all firms. However, in several countries, in the attempt to bring research

to the market, a lot of effort has been specifically targeted to support entrepreneurial activities

spun-off from PRIs - in particular to the creation of academic spin-offs (Mustar, 1997;

Lockett, Siegel, Wright & Ensley, 2005). Specifically, the possibility to access public

laboratories and to be hosted in university incubators represent such examples (Mian 1996;

Feldman, 2001).

On top of that, public researchers are also more proactive in searching for government

support. This is again coherent with the idiosyncratic skills developed during their PRIs-

careers. Writing grants proposals or filing government applications in order to access public

infrastructures and equipments are activities in which public researchers are daily involved. It

follows that public entrepreneurs have more expertise in dealing with such bureaucratic

activities. The awareness about the availability of a highly supportive environment, together

with the developed abilities to deal with public bureaucracy will play a critical role in

determining their entrepreneurial intentions.

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Coherently with that we put forward the two following hypotheses.

H5a: Public entrepreneurs receive greater support from the government than private

entrepreneurs

H5b: The effect of government support on entrepreneurial intention is higher for

public entrepreneurs than for private entrepreneurs

In Figure 1 we report the conceptual model summarizing the set of hypotheses

proposed in this section.

---------------------------------

Insert Figure 1 about here

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4. METHODOLOGY

4.1 Context

In order to test our assumptions, we adopted a quasi-experimental design. Coherently

with the definition provided by Cook and Campbell (1979), we identified a treatment (i.e.

starting a new venture while employed in a PRI), an outcome measure (i.e. the enactment of

entrepreneurial intentions), and some experimental units (i.e. a matched-pair sample of public

and private entrepreneurs). Beside that, we avoided using random assignment to create the

comparisons from which treatment-caused change is inferred. We used a passive

observational method and we drew causal inference based on measure taken all at one time,

with differential levels of both effects and exposures to presumed causes (i.e. entrepreneurs’

career-paths and organizational affiliation), being measured as they occur naturally, without

any experimental intervention. Moreover, in order to better describe individuals’ intentional

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processes we selected a context characterized by high entrepreneurial dynamism within an

extremely static institutional landscape.

We thus located our study in the Emilia Romagna region, in Italy’s northeast. Emilia

Romagna, with an annual pro capita GDP of €28,684 - which is among the highest in Europe

(the European average is €22,400) (Eurostat 2005) -, represents one of the leading region in

EU for entrepreneurial activities. One of the peculiar characteristics of the Emilia Romagna

production system is represented by the presence of geographically concentrated clusters of

SMEs operating in specific sectors such as: industrial machinery (especially the packaging

sector), the agricultural and food sector (including well-known products such as Parmigiano

Reggiano (Parmesan) cheese, traditional balsamic vinegar and Parma ham), the mechanical

sector (with great strength in the motor industry, which includes Ducati, Ferrari, Lamborghini

and Maserati), the ceramic tile industry (the Sassuolo area is the world leader for both the

production of tiles and related machinery), and the bio-medical sector (specifically the

districts of Ferrara and Medolla) (Fini et al., 2009). Moreover, Emilia Romagna has been

identified by the EU commission as one of the leading regions in Europe for its increasing

number of academic spin-offs and, more generally, for its proactive role in supporting

research-to-industry technology transfer (Eurostat, 2007). With 89 firms, Emilia Romagna

leads Italy in terms of number of academic spin-offs (Piccaluga & Balderi, 2007) and

represents one of the few Italian regions where the commercialization of public research is

actually taking place.

Overall, the Italian context is characterized by the 26.4% of self-employment

(of total employed civilians); this percentage is above the 16.1% - average of OECD countries

(OECD, 2006) -. Notwithstanding, Italy is still characterized by a set of highly bureaucratic

governmental norms – that remained unchanged during the last decades – that are perceived

in most of the cases as obstacles to entrepreneurial activities (Baldini, Grimaldi & Sobrero,

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2007). Beside that, government investments in new technologies and advanced knowledge are

fairly low. In 2006, the gross domestic expenditure (GDP) on R&D, as a percentage of GDP,

has been 1.14% - lower than OECD average of 2.26% -; similarly in 2003 the

investment in knowledge as a percentage of GDP has been 2.4% - lower than OECD average

of 4.9% -.

On top of that, Italian academics’ careers are very similar in all PRIs, showing no

idiosyncrasies due to the affiliation to specific institutions. They are all ruled by the same set

of norms, providing academics with a similar array of incentives. Moreover, in Italian PRIs,

and more generally in all Italian public organizations, there is almost no horizontal work force

mobility. Once an individual starts a career as a public employee, he/she remains employed in

the public sector and in that specific institution until retirement.

Given the availability of a such vivid entrepreneurial landscape – to a certain extent

fostered by PRIs technology transfer activities – and an highly static institutional landscape –

that provided all individuals with very similar set of incentives – we decided to compare the

intentional process of public and private entrepreneurs who established their firms in the

Emilia Romagna region. In the next section we describe the sampling strategy we relied on.

4.2 Sample and data gathering

4.2.1 Questionnaire

Based on the theoretical and empirical research about the foundation of

entrepreneurship, we developed a survey to collect primary data directly from entrepreneurs.

We gathered demographical information, such as gender, age and education, as well as

information on previous research and entrepreneurial activities, such as the number of patents

granted and the number of other firms established. We gathered information on the

entrepreneurs’ psychological characteristics, in terms of risk-taking propensity and

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entrepreneurial self-efficacy, on developed skills, in terms of technical skills and managerial

skills, and on perceptions of entrepreneurial support received, in terms of government, context

and university support. We gathered some information on the motivational factors that have

fostered individuals to establish their own companies. We finally assessed the measurement

scale of entrepreneurial intention4 (scales and items are reported in appendix).

We ran a small-scale field pre-test to gather feedback on question phrasing and to find

out if other relevant facets of the domains under study remained untapped. Subsequently, a

panel of ten experts (professors and technology transfer managers) and ten entrepreneurs (5

public and 5 private entrepreneurs) validated the questionnaire. They provided very helpful

insights with regard to the questionnaire’s completeness and clarity, as well as an evaluation

of the time needed to complete it. No major inconsistencies emerged from this pre-test phase.

4.2.2. Sampling strategy

Our sampling strategy relies on a two-pronged approach. In order to control for

organizational and institutional dimensions, we first adopted a matching procedure at firm-

level, aimed at identifying a matched-pair sample of academic and private firms5. Secondly,

relying on the firm-level sampling strategy, we selected a matched-pair sample of public and

private entrepreneurs, comparing their intrinsic features and intentional processes6.

4 We also gathered some other information at both individual (i.e. personal traits and environmental influences) and firm level (i.e. turnover, innovative performance, debt and equity financing, and collaborations). The complete questionnaire has been translated and it’s available from the author. 5 We define academic spin-off as a company with either a PRI or at least one academic (full, associate, assistant professor, PhD student, research fellow or technician) among the founders. Such a definition encompasses situations where: a) there is a formal commitment of a PRI (the spin-off has passed through the spin-off regulation approval and the PRI is involved as one of the founders); b) there is no formal commitment of the PRI (except for individuals affiliated to PRI who decide to share equity) (Fini et al., 2009). We do not include in our definition those firms established by surrogate academic entrepreneurs based on a university technology licensing (Radosevich, 1995). On the contrary, we define private start-up a company without either PRI or public affiliated individuals between the founders, that at the establishment was not controlled by another private organization, either business or private institution (although other private organizations may have held minority shareholdings in the new firms (Colombo, DelMastro & Grilli, 2004). 6 We define a public entrepreneur as an individual who has founded and currently shares equity in an academic spin-off and he/she is employed (either tenure or not-tenured) in a PRI. With private entrepreneur we refer to an

20

4.2.2.1 Matching firms

Relying on existing research (Piccaluga & Balderi, 2007; Fini et al., 2009) and

through the regional PRIs7’ websites and technology transfer offices (where available), we

retrieved basic information on the population of 89 academic spin-offs, such as name,

telephone and e-mail contacts.

First, we targeted the regional population of academic spin-offs. After a first round of

e-mails at the end of November 2006, a second reminder sent to non-respondents at the

beginning of December 2006, and several phone calls, we set up face-to-face interviews with

134 individuals (132 founders and 2 CEOs) involved in 72 academic spin-offs. The data

collection was closed at the beginning of February 2007 with a total number of 72 academic

spin-offs visited and 132 entrepreneurs interviewed (we excluded the CEOs), corresponding

to an overall firm level response rate of 81% (=72/89) and an overall individual level response

rate of 39% (= 132/337).

We then matched the 72 academic spin-offs with 72 private start-ups in term of:

industry (ATECO code), year of establishment and localization8. The data collection started at

the end of February 2007 and was closed at the beginning of May 2007 with a total number of

61 private start-ups visited and 75 individuals interviewed (68 founders and 7 CEOs),

corresponding to an overall individual level response rate of about 37% (= 68/186).

We than dropped 9 pairs because for 9 private start-ups we collected information only

on CEOs and/or founders with an on-going relationship (as adjuncts) with PRIs9. Our final

matched-pairs sample included 52 academic spin-offs and 52 private start-ups10.

individual who founded and currently shares equity in a private start-up and he/she has not been employed (neither now nor in the past) in a PRI. 7 Five public universities and three public research centers. 8 The information on the regional population of private start-ups were retrieved through the databases of the Chamber of Commerce of Bologna. 9 These individuals started their commitments with PRIs after the establishment of their firms, as a result, based on our taxonomy, they are private entrepreneurs with an on-going temporary affiliation with public institutions. 10 As a roboustness check we also perfomed a matching procedure using a probit estimate (probability of being an academic spin-offs) by looking at industry, year of establishment and localization, with a one-to-one (nearest-

21

All the interviews were conducted by the same interviewer on the basis of the same

structured questionnaire and lasted, on average, two hours.

4.2.2.2 Matching individuals

We then implemented the matching procedure at individual level. Specifically, we had

information on 92 public entrepreneurs and 63 private entrepreneurs, among the founders of

the matched sample of 52 academic spin-offs and 52 private start-ups. The response rate for

academic spin-offs’ founders was 52% (=92/176) and for private start-ups’ founders 40%

(=63/158). In both sample no differences were recorded in terms of gender and year of birth

between respondents and non-respondents.

Relying on the collected evidences, we matched public entrepreneurs (treated

individuals) with private entrepreneurs (symmetric un-treated ones). We adopted three

different matching procedures, aimed at identifying two samples of individuals, founders of

comparable firms, with similar demographic profiles and likely to have been exposed to the

same set of entrepreneurial opportunities.

Coherently with a multiple respondents research design, we start considering the two

full samples of 92 public and 63 private entrepreneurs. We calculated the individual

propensity score (probability of being a public entrepreneur) using a probit estimate,

performing a one-to-one (nearest-neighbour) matching with replacement (Rosenbaum &

Rubin, 1983). As for the predictors, we encompassed both firm level dimensions, in terms of

industry, year of establishment, localization, and the presence of a private firm among the

founders, and individual level dimensions, in terms of gender and age. This procedure

neighbor) matching without replacement. The procedure resulted in a mean difference between academic and private firms of 4.26% (Std. Dev. .079), equaling a matching under a Caliper at. 30, confirming the goodness of the performed matching procedure.

22

resulted in a sample of 92 pairs, matched with a mean difference probability of 1.1% (Std.

Dev. .026), equalling a matching under Caliper at .15.

We then adopted a multiple-respondent approach characterized by a matching

procedure with no-replacement. This procedure allowed us to identify 63 pairs with a mean

difference probability of 4.01% (Std. Dev. .061), equalling a matching under Caliper at .36.

We finally adopted a single-respondent procedure, matching with no-replacement only

one entrepreneur for each firm. We identified 52 pairs, with a mean difference probability of

6.79% (Std. Dev. .107), equalling a matching under Caliper at .39.

As for all three specifications, mean difference probabilities and Caliper radius

resulted in acceptable values (Rosenbaum & Rubin 1983). These results allowed us to choose

the most parsimonious single respondent approach (52 vs. 52), avoiding the non-

independence of the regressors. Notwithstanding, as a robustness check, we tested our

regression models with both the 92 and 63 specifications. In Table 1 we report the group

means of the pre-treatment observable variables for the 52 pairs.

---------------------------------

Insert Table 1 about here

-----------------------------------

4.3 Measures and common method bias

We measured entrepreneurial intentions through an individual-level version of the

strategic posture scale (Covin & Slevin 1989). We characterize the entrepreneurial intention

concept in terms of willingness to create new value within an existing organization. This is

accomplished through the engagement in innovative, proactive and risky actions.

Innovativeness reflects an entrepreneur’s intention to have his/her firm engaged in new

23

experimentation and creative processes that may result in new products, services, or

technological processes. Proactiveness suggests the entrepreneur’s forward-looking

perspective, which is supposed to be a characteristic of a marketplace leader who has the

foresight to act in anticipation of future demand and shape the environment. Riskiness

measures the entrepreneur’s willingness to engage in risky projects and his/her preferences

for bold versus cautious acts in order to achieve firm objectives. For all other domains we

used scales used in the existing literature (see Table A1 in appendix for scales and items).

We evaluated the internal consistency of the constructs, checking for convergent

validity, through the assessment of the Composite Reliability (CR). CR is calculated as the

sum of the square roots of the item-squared multiple correlations squared and divided by the

same quantity plus the sum of the error variance (Werts, Linn, & Joreskog, 1974). Estimates

of CR above .60 and statistically significant concept-to-domain coefficients (t>2.0, p<.05) are

usually considered supportive of convergent validity (Bagozzi & Yi, 1988). All values had

CR significantly higher than the stipulated criteria, and all items were statistically significant.

Table 2 summarizes the measurement model latent variables, the number of measurement

items, the measurement description and format, and the composite reliability (CR).

---------------------------------


-----------------------------------

We also verified the discriminant validity of the constructs using a three-pronged

approach. First, we computed the 95% confidence interval for each off-diagonal element of

the phi matrix, showing that in no case the interval included the value of 1.00. Second, we

performed a more formal test of discriminant validity, comparing the model under scrutiny

24

with a series of more restricted models in which the correlation between each pair of latent

constructs (or traits) has been, for one pair of constructs at a time, constrained to unity. The

significant differences in chi-square (one degree of freedom), between the null model and the

more restricted ones, points to a rejection of the hypothesis that any two constructs are not

mutually distinct. Finally, we determined that the average variance extracted by each latent

variable’s measure was larger than its shared variance with any other latent variable. This

index estimates the amount of variance captured by a construct’s measure relative to random

measurement error (Fornell & Larcker, 1981).

We also dealt with common method bias with both procedural and statistical methods

(Podsakoff, MacKenzie, Lee & Podsakoff, 2003). From a procedural standpoint, great care

was taken in the design of the questionnaire (i.e. proximal separation between the

measurement of the predictor and criterion variables, and use of different scale formats, such

as Likert-style scales, semantic differential scales, reverse coded and negatively worded

items) and in the execution of the interviews (i.e. same interviewer and protocol). For some

predictors, such as environmental support related dimensions, as we report in Table A1 in the

appendix, we checked the consistency of the primary data gathered assessing the correlation

with existing secondary sources. Unfortunately, the “behavioral nature” of the research made

impossible, in some cases, to collect data from different sources. Moreover, limitations due to

the entrepreneurs’ tight schedules also inhibited the temporal distancing of different parts of

the questionnaire.

From a statistical standpoint, additional remedies were implemented to control for the

method biases that might occur in this particular research setting; more specifically, a

conservative single-method bias was assumed. According to the specifications of the

hierarchically nested co-variance structure models, originally recommended by Widaman

(1985) and subsequently used by Williams, Cote, and Buckley (1989), we used confirmatory

25

factor analysis to test four alternative measurement models. Model 1 (Null) was a null

measurement model assuming that no factors underlie the data and that inter-correlations

between measures could be explained by random error only. Model 2 (Trait) was a full

measurement model, in which the 6 traits of interest, plus random error, were assumed to

underlie the data. Model 3 (Method) posited that variation in measures could be explained by

a single method factor plus random error. Finally, Model 4 (Trait-Method) assumed that the

data could be accounted for by the 6 traits in Model 2, plus a single uncorrelated method

factor, plus random error. The results are presented in Tables 3 and 4. Table 3 shows the

results for Models 1-4, while Table 4 reports the comparisons between models needed to test

for the significance of trait and method effects.

---------------------------------

Insert Tables 3 and 4 about here

-----------------------------------

In order to assess the presence of a trait effect we compared the Trait and Null models

(Δχ2 (38)=1309.86, p<.001) and Trait-Method and Method models (Δχ2 (40)=589.98, p<.001),

revealing significant trait effects. Then, we proceeded to evaluate the presence of a common

method bias, checking for possible method effects, through the assessment of the differences

between Models 3 vs. 1 and Models 4 vs. 2. Both the comparisons between Method and Null

models (Δχ2 (25)=811.07, p<.001) and Trait-Method and Trait models (Δχ2 (27)=91.19,

p<.001) revealed that the method effect was significant.

Conclusively, we can state that the variance in the entrepreneurs’ responses can be

explained by the simultaneous effect of traits, method, and random error. The highly

unsatisfactory fit of the Method model (M3) and the small (albeit significant) gain in fit

26

achieved by adding the method factor to the Trait model (Method-Trait model, M4) support

the idea that common method bias accounts for a small variance in the data.

In particular as a result of Method-trait model, we decomposed the total variance in

trait effect (.44), method effect (.06) and error effect (.50). The impact of method bias

therefore is quite low, accounting for just 6% of the overall variance. A comparison to other

empirical benchmarks by Williams et al. (1989) gives further support to this assumption (trait:

.50; method: .27 and error: .23). Moreover, looking at individual measures (data are available

upon request), the method bias was uniformly low: the only exceptions were some of the

indicators underlying entrepreneurial intention that exhibit a moderate amount of bias (.10).

Therefore, in this specific case, we can safely state that the method bias will not play a

relevant role in inflating the structural relations between predictor and criterion variables.

In sum, the results of the independence test, the assessment of the convergent and

discriminant validities and the satisfactory estimation of the common method bias enabled us

to proceed to the estimation of multivariate models.

5. ANALYSES

5.1 Multiple sample analyses

We first compare the paired samples under a set of observable characteristics. We

firstly assess entrepreneurs’ educational level. Our results show that public entrepreneurs

have better educational profiles (in terms of years devoted to higher education), if compared

to private ones (11.69 vs. 8.31; p<.01). Similar patterns are reported for patenting activities,

showing that public entrepreneurs have obtained a higher number of patents than private

entrepreneurs, from the Italian Patent Office (1.27 vs. .12; p<.05), from the European Patent

Office (1.04 vs. .06; p<.05) and from the United States Patent Office (.94 vs. 04; p<.05). Also

27

the entrepreneurial activities differ between the two samples. Public entrepreneurs have

established fewer companies11 than private ones (.31 vs. .98; p<.01).

---------------------------------


-----------------------------------

We also look for differences in factors fostering individuals to start their own

companies. Among the most important factors, they acknowledge the possibility to obtain

financial gains (.56 vs. .52; n.s.), to exploit new business opportunities (.56 vs. .56; n.s.) and

to create new jobs (.65 vs. 56; n.s.). Coherently with their PRIs employees positions, public

entrepreneurs haven’t started their companies aiming for independence and freedom, as

private ones did (.33 vs. .56; p<.05), but in order to commercialize the outputs of their own

research (.81 vs. .44; p<.01).

---------------------------------


-----------------------------------

We finally look for some differences in individuals’ unobservable characteristics. As

for psychological characteristics, our result show that academics’ risk-taking propensity is

lower than private entrepreneurs’ one (5.29 vs. 6.02; p<.01), finding support for hypothesis

H1a. On the contrary, no statistical differences are registered in terms of entrepreneurial self-

efficacy; hypothesis H2a is therefore not supported. As we look for differences in individual

11 Other than the ones under scrutiny.

28

skills we find that the two samples do not differ in terms of technical skills (4.36 vs. 4.35;

n.s.), while they differ in terms of managerial skills (5.80 vs. 5.30; p<.01). Hypotheses H3a

and H4a are then not supported. Finally, as for contextual dimensions, we find that

government support is perceived to be significantly higher for academic entrepreneurs than

for private ones (3.35 vs. 1.56; p<.01). This result supports hypothesis H5a.

---------------------------------


-----------------------------------

5.2 OLS regression models

As reported in Table 8, we use multivariate analyses to test for differences in

entrepreneurs’ intentional paths. As result of our quasi-experimental design, we managed to

collect information only on a small sample of individuals, being then unable to compare the

two samples applying structural equation modeling technique (Bollen, 1989). Therefore,

relying on the previously described confirmatory factor analysis, we specify both predictors

and dependent variable as first order latent factors and, in order to include them in linear

probability OLS models, we construct one macro indicator per latent factors, averaging the

items loading on that factor. We then regress entrepreneurial intention on the set of

covariates. In the regression table, for both samples, we report the mean and standard

deviation of the dependent variable, showing that no statistical differences occur between the

entrepreneurial intentions of the two types of entrepreneurs (5.28 vs. 5.42; n.s).

Conversely, the regression paths show that entrepreneurial intentions are differently

enacted within the two samples. We assess the different impacts of risk-taking propensity and

entrepreneurial self-efficacy on entrepreneurial intention. In particular, public entrepreneurs’

29

intentions are mainly explained by risk-taking propensity (βacad=.14; p<.05) while private

entrepreneurs’ intentions are mainly fostered by entrepreneurial self-efficacy (βpriv=.05;

p<.01). We also assess some differences with regard to developed skills. Our results show that

technical skills of public entrepreneurs better predict entrepreneurial intentions (βacad=.12;

p<.1), than for private ones. On the contrary, managerial skills of private entrepreneurs have

stronger impact (βpriv=.38; p<.01) than for public counterparts. Finally, in both samples the

government support has a highly positive effect for both public (βacad=.09; p<.1) and private

entrepreneurs (βpriv=.27; p<.05).

---------------------------------

Insert Tables 8, 9, 10 about here

-----------------------------------

5.3 Structural equation models on bootstrapped samples

In order to check for both stability and replicability of OLS results we implement

bootstrap analysis. Bootstrap is probably the best-known re-sampling method, that instead of

relying on theoretical assumptions to derive sampling distributions for statistical estimators, it

attempts to estimate these distributions empirically, using information drawn from the

original sample12 (Fan, 2003).

We employ a nonparametric bootstrap to perform statistical inference and to test for

the robustness of the obtained results. Re-samples of size N=52 are drawn with replacement

from the two original samples (i.e. public and private). To investigate the accuracy of the

bootstrap with respect to the number of bootstrap samples drawn (BS), each simulated data

12 In our case the paired-sample used to estimate the OLS models.

30

set has been repeatedly modeled in LISREL 8.80 (Joreskog & Sorbom, 2006), using 250, 500,

1000 and 2000 BS, drawn from the original sample (Nevitt & Hancock, 2001). All

bootstrapped samples are mutually independent of one another (i.e. none of the bootstrap

samples drawn for a particular value of BS are used as samples for any other value of BS).

For each bootstrapped sample we compute the corresponding correlation matrix and

we re-estimate the structural equation model, computing the structural coefficients for both

samples. If the structural equation model estimation procedure does not converge, we drop the

improper solutions, without replacing them with new samples. In Table 11 we report the

number of converged modellization (n). In our case, the occurrence of improper solution is

mainly due to small sample size conditions (N<100) (Nevitt & Hancock, 2001). In Table 11,

for both samples, we report the average values and standard deviations of gamma coefficients

under different bootstrapping conditions.

---------------------------------


-----------------------------------

As Table 11 shows, the patterns within the two samples are confirmed and are more

evident as the bootstrapped sample sizes increase. Public entrepreneurs’ intentions are mostly

predicted by risk-taking propensity and technical skills, while for private entrepreneurs by

entrepreneurial self-efficacy and managerial skills. In both cases the impact of the

government support is positive and significant.

Differences between the two samples are coherent with the results obtained in OLS

regression models. As for psychological characteristics the impact of risk-taking propensity

becomes significantly higher for public (p<.01, as the sample size increases), while the

31

entrepreneurial self-efficacy is lower (p<.01). Similarly, the impact of technical skills is

higher for public (p<.01) while the influence that managerial skills have on entrepreneurial

intentions is lower (p<.01, as the sample size increases). Coherently with the previous

findings no differences are recorded in terms of government support.

5.4 Test of hypotheses on the determinants of entrepreneurial intentions

The obtained results allow us to test for the second set of hypotheses we put forward.

In Table 12 we provide a summary of the test of hypotheses, assessing the differences in the

determinants of entrepreneurial intentions.

As for the OLS specifications, in order to test for path differences, we run a test of

equality between the sets of coefficients in the two linear regressions (Chow test; Chow,

1960). In the table we report the path differences (βdiff), calculated as β(public) - β(private),

and the corresponding statistical significances (F-test results). Conversely, for structural

models we report, for each bootstrapped replication, the average path difference (γdiff),

estimated as γ(public) - γ(private), and the corresponding statistical significances (T-test

results).

In the OLS models, the path from risk-taking propensity to entrepreneurial intentions

does not differ between samples (βdiff=.212; n.s), although results show a higher impact for

public entrepreneurs than for private entrepreneurs. On the contrary, the pattern is statistically

different in all structural models (.514<γdiff<.94; p<.05). This implies that Hypothesis 1 is

only partially supported.

In both OLS and structural models, the path linking entrepreneurial self-efficacy to

entrepreneurial intention shows statistical differences between public and private

entrepreneurs. In particular, the influence of entrepreneurial self-efficacy is significantly

smaller for public entrepreneurs than for private entrepreneurs (βdiff=-.035; p<.05 and

32

-.771<γdiff<-.667; p<.01). Hypothesis 2 is then supported.

Similarly, we find some differences in the impact of both technical and managerial

skills on entrepreneurial intention. As for the former, the impact is higher for academic

entrepreneurs than for private entrepreneurs (βdiff=.29; p<.01 and .963<γdiff<.99; p<.01).

Hypothesis 3 therefore turns out to be supported. As for the latter, inequality is also registered

in terms of the impact of managerial skills on entrepreneurial intention. In particular the

impact of managerial skills is lower for academic entrepreneurs, both in OLS (βdiff=-.385;

p<.05) and structural models (-1.45<γdiff<-.859; p<.01). Hypothesis 4 is thus verified.

Finally we test for differences in the impact of government support on entrepreneurial

intention. Our results do not show any differences between the two paths, both in OLS

analysis (βdiff=-.175; n.s.) and in structural models (.086<γdiff<.486; n.s.). Hypothesis 5 is not

supported13.

---------------------------------


-----------------------------------

6. CONCLUSION

6.1 Discussion

Public culture, which is an invisible but existing part of PRIs, promotes a sense of

organizational identity, trough a commitment to common values and beliefs such as mission,

ethics and trust. This culture affects members beyond their defined jobs and roles (Moon,

1999). Being affiliated to PRIs endow public researchers with a public service oriented 13 We adopt different OLS specifications in order to test for differences in regression paths. We assess the same differences between public and private entrepreneurs either comparing 92 public vs. 63 private (full sample), 63 public vs. 63 private (under matching procedure 63 vs. 63 with no-replacement) and 92 public vs. 92 private (under matching procedure 92 vs. 92 with replacement). The impact of entrepreneurial self-efficacy, technical skills and managerial skills systematically differ between the two samples.

33

mindset, as they come to share PRIs specific beliefs, values and culture (Henkel, 2005). All

their actions and behaviours are thus fostered by motivations that are coherent with their alma

mater’ missions.

In this contribution, we draw on the public service literature and we study how the

individuals’ specificities, developed as a result of their affiliation to PRIs, affect their

intentional process. In particular, relying on intentional theory, we compare the intentional

paths of individuals affiliated to PRIs - who decided to start a venture -, with a matched-pairs

sample of entrepreneurs without such previous career and affiliation.

Our results show that both types of entrepreneurs intend to equally pursue the creation

of new economic value within their businesses. Notwithstanding, their entrepreneurial

intentions are enacted following two markedly different intentional paths. Our proposed

behavioural model helps us to characterize such heterogeneity, allowing us to test for the two

sets of five hypotheses we put forward. After employing multivariate regression analyses and

structural equation modelling techniques, we show that entrepreneurial intentions are

differently rooted and differently determined within the two samples. For public

entrepreneurs, intentions are mainly predicted by risk-taking propensity and technical skills,

while for private entrepreneurs are strongly influenced by entrepreneurial self-efficacy and

managerial skills. In both groups, government support has a significant impact on the

entrepreneurial intentions. The two patterns highly differ in terms of technical skills - that

strongly predict public entrepreneurs intentions – as well as in terms of entrepreneurial self–

efficacy and management skills - that foster private entrepreneurs’ intentions -.

We can conclude that public entrepreneurship is then fostered by the awareness of a

superior knowledge in technical fields. Such entrepreneurs, before being involved in their

firms, have been excellent researchers – or at least they have developed cutting edge skills as

a result of their careers as researchers -. Conversely, private entrepreneurs appear to be

34

individuals who root their entrepreneurial intentions in intrinsic motivational factors and in

the ability to motivate and manage their employees and collaborators. Such motivational

aspects turn out to be critical in triggering their intentions.

Public entrepreneurship seems therefore a matter of abilities developed within the

PRIs and transferred to the market; these individuals act because they know how to do it.

Private entrepreneurship emerges instead from individuals’ belief of being able to perform

entrepreneurial actions through the management of the right resources. Private entrepreneurs

act therefore because they know they can do it.

6.2 Limitations

Despite we carefully design our study, this research still suffers from some limitations.

First of all, the cross-sectional design this research rests upon is robust enough to grant the

internal consistency of the obtained results, while greater care (and more research) is needed

in order to generalize the results to a broader entrepreneurial population. The cross-sectional

nature of our quasi-experiment allowed us to test for direct antecedents of intentions, without

being able to assess their impact on entrepreneurial behaviors and firms performances.

Secondly, we root our behavioral model on both individual and contextual variables.

As for the former we focus on psychological characteristics and developed skills, while for

the latter, we target environmental support. Besides these domains, we acknowledge that

some other factors, such as personal traits, social ties and technological opportunities, have

been proposed as antecedents of entrepreneurial intentions. Notwithstanding, we have decided

not to model them. We exclude demographics and personal traits because of their proved

inefficacy in predicting entrepreneurial intentions. We do not include social ties and network,

as we see social relations as direct predictor of entrepreneurial behaviors (Stam & Elfring,

2008). Finally, as we acknowledge the relevance of industry opportunities, market

35

heterogeneity (Miller & Friesen, 1982) and organizational factors (Covin & Slevin, 1988) in

predicting entrepreneurial intentions, we exclude them from our conceptual model as we

control for these dimensions with the firm-level matching-procedure.

36

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EXHIBITS

TABLE 1 Matched-pairs comparison: pre-treatment observables

Observables

Mean public

entrepreneurs (N=52)

Mean private


Difference T-test

Firm level Year of establishment 2003.19 2003.31 -.12 -.27 (.31) (.29) (.42) Localization: North-east .15 .13 .02 .28 (.05) (.05) (.07) Localization: South-east .06 .06 .00 .00 (.03) (.03) (.05) Localization: North-west .13 .17 -.04 -.54 (.05) (.05) (.07) Sector: Environment .27 .19 .08 .93 (.06) (.06) (.08) Sector: Biomedical .17 .13 .04 .54 (.05) (.05) (.07) Sector: Material .27 .29 -.02 -.22 (.06) (.06) (.09) Sector: Advanced services .04 .02 .02 .58 (.03) (.02) (.03) Private firm among founders .19 .15 .38 .34 (.05) (.10) (.11)

Individual level Male .85 .92 -.08 -1.22 (.05) (.04) (.06) Age 57.69 57.12 .58 .26 (1.62) (1.56) (2.25)

Standard errors in parentheses. *** p<.01, ** p<.05, * p<.1. Baseline dummy for Localization is South-west. In the South-west category we include Bologna and Modena, in the Nort-east one we include Ferrara, in the South-east one we include Forlì-Cesena and Ravenna, while in the Nort-west one we include Reggio Emilia, Parma and Piacenza. Baseline dummy for Sector is Electronics. In the Electronics category we include aerospace, computers, electronic components, internet and telecommunication services and software; in the Environment one we include environment related services and energy; in the Biomedical one we include biochemistry, biotechnology, medical and pharmaceuticals; in the Material one we include, mechanical equipment, optical equipment, advanced mechanics and automation; in the Advanced Services one we include architectural, civil engineering and statistical services.

TABLE 2 Predictor Measures (N=104)

Domain and predictor

Item Scale format Research reference CR

Entrepreneurial intention Entrepreneurial intention 9 1 to 7 forced choice scale Covin & Slevin, 1989 .88

Psychological characteristics Entrepreneurial self-efficacy 2 0 to 7 scale Baum, Locke & Smith, 2001 .92 Risk-taking propensity 4 1 to 7 likert like scale Gomez & Meija, 1989 .85

Skills and prior knowledge Technical skills 3 1 to 7 scale Gupta & Govindarajan, 2000 .80 Managerial skills 5 1 to 7 likert like scale Roberts & Fusfeld, 1981 .89

Contextual dimension Government support 2 1 to 7 likert like scale Fini, Grimaldi & Sobrero, 2009 .73

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TABLE 3

Common method bias nested models: goodness-of-fit statistics

Model χ2

Df

RMSEA

CFI NFI

M1: Null 1689.92 a 300 .212 .37 .31 M2: Trait 380.06 a 262 .066 .89 .75 M3: Method 878.85 a 275 .146 .61 .52 M4: Trait-Method 288.87 a 235 .047 .93 .80 a p < .001; Root Mean Square Error of Approximation (RMSEA); Comparative Fit Index (CFI); Normed Fit Index (NFI)

TABLE 4 Common method bias nested models: χ2 differences

Effect

Δ Model Δχ2 Δdf p-value

M2 – M1 1309.86 38 < .001 Trait M4 – M3 589.98 40 < .001 M3 – M1 811.07 25 < .001 Method M4 – M2 91.19 27 < .001

TABLE 5 Multiple sample analysis: Observable characteristics

Variable

Mean public


Mean private


Difference T-test

Education Years of higher education 11.69 8.31 3.38 7.13*** (1.57) (3.05) (.47)

Patenting activity Number of Patent issued (ITA) 1.27 .12 1.16 2.60** (3.15) (.38) (.44) Number of Patent issued (EPO) 1.04 .06 .98 2.35** (2.97) (.24) (.42) Number of Patent issued (USPTO) .94 .04 .90 2.27** (2.83) (.20) (.40)

Entrepreneurial activity Number of other firms established .31 .98 -.67 -2.93***

(.08) (.22) (.23) Standard errors in parentheses; *** p<.01, ** p<.05, * p<.1

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TABLE 6 Multiple sample analysis: Motivation fostering individuals to start-up a new venture

Dummy variables (1=yes; 0=no)

Mean public

entrepr. (N=52)

Mean private entrepr. (N=52)

Difference T-test

Higher income .56 .52 .04 .39 (.50) (.50) (.10) Independence and freedom .33 .56 -.23 -2.41** (.47) (.50) (.10) No interest in my ideas from the former employer .08 .17 -.10 -1.48 (.27) (.38) (.06) Exploitation of new business ideas in new markets (outside the core market of the former employer) .56 .56 .00 .00

(.50) (.50) (.10) Too many obstacles toward the advancements in the previous career .27 .25 .02 .22

(.45) (.44) (.09) No tenure in the previous job .25 .17 .08 .96 (.44) (.38) (.08) Uncertainties in the future of previous employer (i.e. failure or buyouts) .15 .10 .06 .88

(.36) (.30) (.07) No occupational opportunities .06 .06 .00 .00 (.24) (.24) (.05) Possibility to exploit the results of my research .81 .44 .37 4.12*** (.40) (.50) (.09) Creation of job opportunities .65 .56 .10 1.00 (.48) (.50) (.10) Standard errors in parentheses; *** p<.01, ** p<.05, * p<.1

TABLE 7

Multiple sample analysis: Unobservable characteristics

Variable

Mean public


Mean private


Difference T-test Hp. Support

H1a Risk-taking propensity 5.29 (.16)

6.02 (.13)

-.72 (.21) -3.35*** Yes

H2a Entrepreneurial self-efficacy 5.38 (.15)

5.20 (.15)

.18 (.21) .82 No

H3a Technical skills 4.36 (.21)

4.35 (.20)

.01 (.29) .04 No

H4a Managerial skills 5.80 (.11)

5.30 (.12)

.49 (.16) 2.95*** No

H5a Government support 3.35 (.23)

1.56 (.15)

1.78 (.28) 6.25*** Yes

Standard errors in parentheses; *** p<.01, ** p<.05, * p<.1

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TABLE 8 Linear probability OLS models

Entrepreneurial intention

Public entrepreneurs

Private entrepreneurs

5.28 (.09)

5.42 (.13)

Risk-taking propensity .140** -.072 (.068) (.119) Entrepreneurial self-efficacy .016* .051*** (.008) (.010) Technical skills .127* -.163** (.064) (.072) Managerial skills .001 .386*** (.119) (.137) Government support .099* .274** (.049) (.102) Constant 2.869*** 1.676* (.757) (.860) Observations 52 52 R-squared .37 .48 F test 5.2 8.5 prob>F 0 0 Standard errors in parentheses; *** p<.01, ** p<.05, * p<.1;

TABLE 9

Correlation table: Public entrepreneurs (N=52)

Mean Std. Dev. 1 2 3 4 5

1. Entrepreneurial intention 5.28 .70 2. Risk-taking propensity 5.29 1.22 .31 3. Entrepreneurial self-efficacy 5.38 1.12 .38 .13 4. Technical skills 4.36 1.57 .41 .08 .24 5. Managerial skills 5.80 .82 .23 .13 .06 .52 6. Government support 3.35 1.73 .34 .04 .17 .17 .14

TABLE 10

Correlation table: Private entrepreneurs (N=52)

Mean Std. Dev. 1 2 3 4 5

1. Entrepreneurial intention 5.42 .95 2. Risk-taking propensity 6.02 .97 .25 3. Entrepreneurial self-efficacy 5.20 1.11 .57 .30 4. Technical skills 4.35 1.48 -.13 .01 .05 5. Managerial skills 5.30 .89 .24 .25 .16 .31 6. Government support 1.56 1.12 .20 .23 .01 -.05 -.34

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TABLE 11 Summary of means and standard deviations for Gamma in SEM

Bootstrap (BS=250)

Means Bootstrap (BS=500)



Means

Path specification Public

entrepr. Private entrepr.

Public entrepr.

Private entrepr.

Public entrepr.

Private Entrepr.

Public entrepr.

Private entrepr.

n=199 n=164 T-test n=395 n=334 T-test n=801 n=668 T-test n=1574 n=1348 T-test Risk-taking propensity Entrepreneurial intention .334 -.18 2.33** .314 -.39 4.70*** .324 -.616 8.80*** .329 -.58 12.74*** (1.066) (2.878) (1.146) (2.7) (1.136) (2.752) (1.182) (2.524) Entrepreneurial self-efficacy Entrepreneurial intention .038 .809 -5.29*** .085 .814 -9.25*** .117 .824 -15.99*** .129 .796 -25.6*** (1.814) (.473) (1.317) (.632) (1.011) (.583) (.825) (.523) Technical skills Entrepreneurial intention .805 -.158 6.94*** .813 -.165 10.18*** .774 -.216 15.39*** .771 -.215 21.82*** (1.427) (1.165) (1.242) (1.347) (1.19) (1.271) (1.326) (1.077) Managerial skills Entrepreneurial intention -.402 .457 -2.24** -.528 .602 -4.99*** -.597 .851 -10.09*** -.546 .854 -15.12*** (3.738) (3.485) (2.843) (3.263) (2.364) (3.126) (2.281) (2.723) Government support Entrepreneurial intention .964 .478 1.24 .989 .715 1.05 1.02 .949 .4 1.017 .931 .71 (2.917) (4.449) (2.225) (4.538) (2.271) (4.287) (2.455) (4.002) Standard deviations in parentheses; *** p<.01, ** p<.05, * p<.1; Bootstrap samples, both for Public and Private entrepreneurs, are of the same size (N=52) of the two original samples; BS=number of bootstrapped samples; n=number of bootstrapped samples that converge to a solution.

TABLE 12 Summary of the test of hypotheses

OLS (βdiff) SEM (γdiff) BS=250 SEM (γdiff) BS=500 SEM (γdiff) BS=1000 SEM (γdiff) BS=2000 Hp. Path specification Path diff Sig. Path diff Sig. Path diff Sig. Path diff Sig. Path diff Sig. support H1b Risk-taking propensity

Entrepreneurial intention .212 - .514 ** .70 *** .94 *** .909 *** Part.

H2b Entrepreneurial self-efficacy Entrepreneurial intention -.035 ** -.771 *** -.73 *** -.71 *** -.667 *** Yes

H3b Technical skills Entrepreneurial intention .290 *** .963 *** .98 *** .99 *** .986 *** Yes

H4b Managerial skills Entrepreneurial intention -.385 ** -.859 ** -1.13 *** -1.45 *** -1.4 *** Yes

H5b Government support Entrepreneurial intention -.175 - .486 - .27 - .07 - .086 - No

*** p<.01, ** p<.05, * p<.1; Path differences are computed for OLS as β(public) - β(private) and for SEM as γ(public) - γ(private)

48

FIGURE 1 Summary of hypotheses

49

APPENDIX TABLE A1

Details of Measures Latent variable Items description Entrepreneurial intention In the next year I want my firm:

1. (1) favors a strong emphasis on the marketing of tried and true products or services or (7) favors a strong emphasis on R&D, technological leadership and innovation

2. (1) favors the introduction of no new lines of products or services or (7) favors the introduction of very many new lines of products or services

3. (1) favors changes in product or services lines mostly of a minor nature or (7) favors quite dramatic changes in product or services line

4. (1) responds to action which competitors initiate or (7) initiates actions which competitors then respond to

5. (1) would be very seldom the first businesses to introduce new products/services or (7) would be the first business to introduce new products/services

6. (1) seeks to avoid competitive clashes, preferring a “live and let live” posture or (7) adopts a very competitive, “undo the competitors” posture

7. (1) has a strong proclivity for low risk projects (with normal and certain rates of return) or (7) has a strong proclivity for high risk projects (with chances of very high returns)

8. (1) explores the environment gradually, via timid, incremental behavior or (7) acts bold, wide-ranging in order to achieve the firm’s objectives

9. (1) adopts a cautious, “wait and see” posture in order to minimize the probability of making costly decisions or (7) adopts a bold, aggressive posture in order to maximize the probability of exploiting potential opportunities.

Risk-taking propensity

Please indicate how strongly you agree or disagree with each statement by circling the appropriate number (1=strongly disagree; 7=strongly agree):

1. I’m not willing to take risks when choosing a job or a company to work for

2. I prefer a low risk/high security job with a steady salary over a job that offers high risks and high rewards

3. I prefer to remain in a job that has problems that I know about rather than take the risk of working at a new job that has unknown problems even if the new job offers greater rewards

4. I view risk on a job as a situation to be avoided at all costs.

Entrepreneurial self-efficacy

Thinking about your skills, write a number from the confidence scale below (1 to 7) to show how sure you are that you can beat the % change in 2007 (compared with 2006) [the same for 2008 compared with 2007]:

1. Up 100% or better 2. Up 50% or better 3. Up 20% or better 4. Up 5% or better 5. No change or better 6. Down 5% or better 7. Down 10% or better 8. Down 25% or better.

Technical skills Please assess the skill levels you have now (1=no skills at all; 7=very skilled): 1. Product designs 2. Process designs 3. Production systems.

Managerial skills

Please indicate how strongly you agree or disagree with each statement by circling the appropriate number (1=strongly disagree; 7=strongly agree):

1. I’m good at problem solving 2. I’m good at communicating my point of view and supporting my ideas 3. I’m good at motivating people and leading teams 4. I’m good at the maintaining interpersonal relationships and coordinating people

5. I’m good at developing resources and creating new competencies within the organization

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Government support b

To what extent do you think the following factors are supporting your entrepreneurial behavior (1=no support; 7=high support):

1. National public funding 2. International (EU) public funding. Context support c

To what extend do you think the following factors are supporting your entrepreneurial behavior (1=no support; 7=high support):

1. Regional founding (ex. PRRIITT, Spinner) 2. Existence of a business plan competition 3. Existence of regional technology transfer offices 4. Existence of regional patent support offices

University support d

To what extend do you think the following factors are supporting your innovation activities and helping you in pursuing a significant venture growth (1=no support; 7=high support):

1. Interest of public research institutions in investing in firms’ equity 2. Possibility to access academic laboratories and equipments 3. Possibility to be hosted in a university incubator 4. Synergies between public research institutions and private firms Notes: a These items were reverse coded; b r=.21 (p<.05) with received amount of public funds; c r=.37 (p<.001) with participation to regional entrepreneurial support programs; d r=.51 (p<.001) with received incubation support; Government support and Context support (r=.57; p<.001); Context support has a composite reliability =.85 Government support and University support (r=.58; p<.001); University support has a composite reliability =.84

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