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Essays in Management,
Economics and Ethics
Diego Matricano, Luigi Guadalupi,
Valerio Aniello Tutore, Francesco Andreottola,
Mario Sorrentino
The Creation of Academic Spin-offs:
Evidences from Italy
27
McGraw-Hill
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Essays in Management,
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Essays in Management,
Economics and Ethics
Diego Matricano, Luigi Guadalupi,
Valerio Aniello Tutore, Francesco Andreottola,
Mario Sorrentino
The Creation of Academic Spin-offs:
Evidences from Italy
n.
27
McGraw-Hill
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The Creation of Academic Spin-offs: Evidences from Italy
Diego Matricano1, Luigi Guadalupi2, Valerio Aniello Tutore3,
Francesco Andreottola4, Mario Sorrentino5
Abstract
Nowadays, Universities are trying to achieve their third mission, i.e. to foster the economic development of a specific context/Region, through Technology Transfer (TT)
activities and processes like licensing, patenting, joint venture or the creation of academic spin-offs. The present study focuses on the creation of academic spin-offs in Italy
and tries to investigate whether cognitive/personal characteristics of academics, Universities’ collaborations or environmental factors can affect this phenomenon. Data about
the ninety-two Italian Universities were collected (from MIUR and CINECA websites)
and multiple regression analyses were developed. Results show that both personal characteristics (academic status, age, gender) and cognitive factors (the amount of professors
and the fields of research) positively affect the creation of academic spin-off. Results
also show that the creation of academic spin-offs is positively correlated to the participation in and coordination of scientific collaborations. Environmental factors, do not present any significant relationship with the creation of academic spin-offs.
JEL Classifications: L26 – Entrepreneurship; M10 – General Business Administration;
M13 – New Firms, Startups.
Keywords: Academic spin-off, Venture creation, Knowledge Transfer, Entrepreneurial
University.
Contents
________________
1
Research Fellow, I.R.A.T. (Istituto di Ricerche sulle Attività Terziarie),
[email protected].
2
Researcher, I.R.A.T. (Istituto di Ricerche sulle Attività Terziarie), [email protected].
3
Research Fellow, I.R.A.T. (Istituto di Ricerche sulle Attività Terziarie), [email protected].
4
Researcher, I.R.A.T. (Istituto di Ricerche sulle Attività Terziarie), [email protected].
5
Full Professor of Management, Second University of Naples, [email protected].
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1. Introduction
2. The creation of academic spin-offs
3. Factors affecting the creation of academic spin-offs
4. Data collection
5. Methodology and results
6. Conclusions and discussion
References
Tables
3
3
6
14
17
21
23
29
Editorial notes
The present contribution expands and updates these previous works:
•
•
MATRICANO, D., GUADALUPI, L., TUTORE, V.A., ANDREOTTOLA, F. &
SORRENTINO, M. (2012), “The Creation of Academic Spin-offs: Evidences
from Italy”, working paper presented at the 4th E-LAB International Symposium of Entrepreneurship, University of Rome Tor Vergata, May 15th-16th
2012.
ANDREOTTOLA, F. (Ed.) (2011), Il Trasferimento Tecnologico e gli Spin-Off
Accademici in Italia, Napoli: Enzo Albano Editore.
Although the work is the result of the joint contribution of the authors,
section 1 has been written by Mario Sorrentino, sections 2 and 6 have been
written by Diego Matricano, section 3 has been written by Francesco Andreottola, section 4 has been written by Luigi Guadalupi, section 5 has been written
by Valerio A. Tutore.
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The Creation of Academic Spin-offs: Evidences from Italy 3
1. Introduction
Scholars seem to agree on the fact that Universities need to try and achieve
their third mission, i.e. to foster the economic development of the context or
Region they are settled in (Etzkowitz et al., 2000; Bramwell and Wolfe, 2008;
Shane, 2004; Chiesa and Piccaluga, 2000).
However, despite the agreement on the final aim, scholars question about
the possible means that can be used, properly named Technology Transfer (TT)
processes (Ratinho and Henriques, 2010; Abramo et al., 2009; Boardman and
Ponomariov, 2009; Hoye and Pries, 2009; Lucas et al., 2009; Chiesa and Chiaroni, 2005; Mowery et al., 2004; Agrawal and Henderson, 2002; Cohen et al.,
2002). Generally speaking, the TT processes can be classified as soft or hard activities (Philpott et al., 2011). The former group comprehends educational programmes, consultants’ training and scientific publications while the latter includes licensing, joint venture and the creation of academic spin-offs.
In the present work, attention is going to be focused on the creation of academic spin-offs and, above all, on the factors that can affect their creation by
the Italian Universities.
The structure of the paper is the following. In section two, we start from a
review of the entrepreneurial literature about the academic spin-offs in order to
define the phenomenon. Subsequently, in section three, the main factors affecting the creation of academic spin-offs are identified and explained and hypotheses are generated. Data about the previously identified factors are collected in section four and then, in section five, the results of multiple regression
analyses are presented. Final conclusions and discussion about these results are
reported in the last section.
2. The creation of academic spin-offs
Before proceeding, we are obliged to fix three very important points: the first
concerns the reasons why there is a growing interest about the creation of academic spin-offs in Italy; the second deals with the definition, as punctual as
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4 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
possible, of academic spin-off and the last one is about the level of analysis to
adopt for the present work.
In reference to the first point, we need to consider that the focus on the
third mission of Universities is very recent in Italy. Latest data (available on
NetVal website) show that most of the Italian Universities have just implemented or they are still implementing their Technology Transfer Offices (TTOs),
the offices aimed at promoting the economic exploitation of research results.
Even if this has inevitably generated a delay in the development of the academic spin-off phenomenon, as compared to other European Countries, an
increasing effort to develop the number of academic spin-off is currently on
the agenda of most of the Italian Universities. In fact, many universities are
currently involved in defining and addressing new paths to improve the number of new ventures generated by professors. As a consequence, it is necessary
to start inquiring the factors on which Italian Universities can leverage in order
to foster the creation of academic spin-offs thus reducing the existing gap with
other Countries.
After explaining the reasons at the basis of the choice to focus on the creation of Italian academic spin-offs, it is time to move towards the definition of
the topic of research.
In reference to this, it is important to underline the missing of a generally
accepted definition of academic spin-offs. More specifically, scholars seem to
agree on some features but not on others (Matricano, 2011). On the one hand,
in fact, there is a general consensus on the idea that an academic spin-off is a new
venture that:
•
•
Starts thanks to a researcher, previously working in a University, who can
totally or partially move towards the role of entrepreneur (Compagno and
Pittino, 2006; Nicolau and Birley, 2003);
Leverages on the results achieved through previous academic research projects (Dell’Anno, 2010; Baglieri, 2008; Sorrentino, 2008; Rasmussen, 2006;
Lockett et al., 2003; Friedman and Silberman, 2003; Di Gregorio and
Shane, 2003; Piccaluga, 2001), i.e. on the exploitation of new knowledge
created inside Universities.
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The Creation of Academic Spin-offs: Evidences from Italy 5
On the other hand, instead, there is no agreement on the relationship between
the University and the new venture, especially in reference to property rights
and capital sharing. In Italy, for example, each University has a specific regulation so it is not possible to generalize any legal or property aspects.
So, for the present work, by academic spin-off is meant a new venture promoted and launched by a researcher that aims to exploit results of previous research projects (Mustilli and Sorrentino, 2009) without any reference to legal or
property aspects.
In reference to the levels of analysis addressed by scholars to study the academic spin-offs, Djokovic and Souitaris (2008) point out three possible levels:
macro, meso or micro.
The first level of analysis, the macro one, pays attention over the national
innovation systems and, particularly, on the role that policy makers can have in
the creation of academic spin-offs (O’Shea et al., 2005; Di Gregorio and Shane,
2003; Sorenson and Stuart, 2001; Florida and Kenney, 1988). According to
this, studies about the creation of academic spin-offs focus on the presence of
venture capitalists, the legal protection of innovations, the regional infrastructures and the industrial context in which universities are settled but, at the
same time, they give less importance to what happens inside the Universities
themselves. For this reason, the theoretical framework used to study academic
spin-offs at a macro level of analysis recalls the Industrial Organization (IO)
framework.
The second level of analysis, the meso one, focuses on the PRC (Public Research Center), Universities or part of them, like laboratories or researchers
team (Carayol and Matt, 2004; Stephan and Levin, 1997; Dasgupta and David,
1994) or academic departments (Muscio, 2008), to test the conditions or the
factors driving to the creation of academic spin-offs (Colombo et al., 2010;
Mustar and Wright, 2010; Lockett and Wright, 2005; O’shea et al., 2005; Powers and McDougall, 2005). The theoretical framework that is generally used to
develop this kind of analysis is the Resource-Based View (RBV) according to
which internal factors, like Penrose (1959) meant resources, determine or influence the output, i.e. the creation of academic spin-offs.
Eventually, the micro level of analysis converges on the characteristics of
the individual or of the group of individuals promoting the creation of aca-
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6 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
demic spin-offs. In this case, the theoretical framework has its roots in the field
of Entrepreneurial Theories (ET), investigating the individual attributes, or in the
Resource-Based View (RBV), inquiring the personal resources affecting the creation of academic spin-offs (Compagno et al., 2009; Landry et al., 2007).
The review of the above literature underlines a consistent heterogeneity in
studies about academic spin-offs (Mustar et al., 2006). The option to assume
three different levels of analysis requires each author to specify which is the
one, or even the ones, to be used when approaching the study of academic
spin-offs. For the present work, we adopt both the meso and the macro levels
of analysis. Thus, in order to inquire the factors affecting the creation of academic spin-offs we assume that:
•
•
An academic spin-off is a new venture launched by a researcher, previously
involved in academic research projects, to exploit the achieved results;
A meso level of analysis, in order to test the internal factors, and a macro
level of analysis, to test the environmental factors affecting the creation of
academic spin-offs.
3. Factors affecting the creation of academic spin-offs
As already anticipated, the main objective of this study is to identify the factors
affecting the creation of academic spin-offs in Italy. In order to achieve the
above-cited goal, the main factors that can probably affect academic spin-offs
have been considered. They can be referred to three different groups:
•
•
•
Cognitive/Personal factors;
Relational factors;
Environmental factors.
In order to identify the first group of factors to test, it is necessary to start
from a review of the entrepreneurial literature focusing on this topic. Even if
the cognitive/personal factors deal with professors/researchers involved in the
creation of academic spin-offs, we do not consider the individual as subject of
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The Creation of Academic Spin-offs: Evidences from Italy 7
the analysis. As anticipated, in fact, we are interested in identifying the main
factors that can affect the creation of academic spin-off according to a university-based perspective. For this reason, we adopt a meso level of analysis.
Evidences (Zucker et al., 1998; Di Gregorio and Shane, 2003; Powers and
McDougall, 2005) underline that one of the most cited factors is the amount of
structured professors.
Generally speaking, when the amount of structured professors grows up,
there is an increasing of both formal and informal relationships on which professors that aspire being entrepreneurs can leverage (Schillaci, 1992). The relevance of these formal and informal relationships is due to the exchange of already existing knowledge, skills, and expertise between professors (Bozeman,
2000; Feldman et al., 2002; Zucker et al., 2002; Di Gregorio and Shane, 2003)
and to the consequent creation of new knowledge that, as anticipated in section two when we defined the topic of research, lays at the basis of the creation
of academic spin-offs. Thus it is possible to hypothesize that:
H1: Universities with a higher presence of structured professors show a higher possibility to
create academic spin-offs.
Another factor that sounds very relevant in reference to creation of academic
spin-offs is the amount of unstructured researchers. As noticed by Powers
(2003) and Carayol and Matt (2004), unstructured researchers contribute to
carry out academic research.
The rationale assumed in reference to the amount of structured professors
can be assumed also in reference to unstructured researchers: the more unstructured researchers contribute to academic research, the more they contribute to the creation of new knowledge that is assumed to be exploited through
the launch of academic spin-offs. Also in this case, it sounds reasonable to assume that:
H2: Universities with a higher presence of unstructured researchers show a higher possibility
to create academic spin-offs.
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8 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
Beyond the amount of structured professors and unstructured researchers, a
very important factor that seems to affect the creation of academic spin-offs is
the nature of scientific research (O’shea, 2008), properly named Area Scientifico
Disciplinare (ASD) in Italy. As known, all the ventures need to leverage on R&D
activities (Lipparini, 2002) and to introduce innovation in order to compete
and to succeed in the market competition (Schumpeter, 1934). This is even
truer in reference to academic spin-offs that, by definition, are based on the
exploitation of the innovative results achieved through previous academic research projects.
Generally speaking, Universities with professors focused in technologybased ASD report a higher propensity to create academic spin-offs than Universities with professors in non-technology based ASD (like philologicalliterary or historical-artistic sciences). Previous empirical studies support this
assumption. Golub (2003) found out that spin-offs from Columbia University
exploit the results of previous research projects concerning with biomedicine
or electronics/software. Shane (2004) maintained that most of academic spinoffs launched by Massachusetts Institute of Technology (M.I.T.) are in the
biomedical industry. O’Shea et al. (2005) noticed that biotechnology, chemicals, and ICT are fields of research that can positively affect the creation of
academic spin-offs. Hence, it is possible to assume that:
H3: Universities with a higher presence of professors in technology-based ASD show a higher
possibility to create academic spin-offs.
The academic status, i.e. the distinction between full, associate and assistant
professors, seems to be another factor that can affect the creation of academic
spin-offs, even if the kind of correlation (direct or inverse) is not clear. On the
one hand, in fact, it is possible to assume that professors with higher academic
status, like full or associate ones, are more inclined to create academic spin-offs
than professors with lower one, like the assistant professors are (Bonaccorsi
and Daraio, 2002). This view is based on the assumption that full or associate
professors have a more developed network to leverage on. The previous academic career, in fact, makes higher academic status professors more experienced with research projects, better known by stakeholders and, consequently,
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The Creation of Academic Spin-offs: Evidences from Italy 9
less exposed to financial risk. In sum they seem to satisfy many of the requirements to start a new venture, thus it is possible to hypothesize that:
H4a: Universities with professors at higher academic status show a higher possibility to create
academic spin-offs.
On the other hand, however, it is possible to assume that professors with
higher academic status are less inclined to create academic spin-offs than professors with lower one. This view is based on the idea that lower academic
status professors feel a sort of disequilibrium between their research capabilities and the uncertainty linked to their academic status itself (Piccaluga, 2000).
This means that lower academic status professors could be more prone to exploit their own capabilities and to create academic spin-offs. Hence we hypothesize that:
H4b: Universities with professors at lower academic status show a higher possibility to create
academic spin-offs.
Age is another factor that can affect the creation of academic spin-offs. Generally speaking, it is easy to figure out that there is no kind of relationship between professors’ academic status and age. In fact it is possible to find professors: old and with high status, old and with low status, young and with high
status, young and with low status. According to this, age can be considered and
investigated as a standing alone variable.
By focusing only on age, several scholars (Kanazawa, 2003; Levin and
Stephan, 1991; Lehman, 1953) maintain that old professors show fewer possibilities to get interesting results from research projects. According to the
above-cited scholars, in fact, old professors are less involved in exploiting the
results of research projects in a very proactive way. Willingness to exploit the
results of their research projects seems to decrease as time passes by. Therefore, it is possible to hypothesize that:
H5: Universities with young professors show a higher possibility to create academic spin-offs.
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10 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
The last factor that according to the entrepreneurial literature can be included
in the cognitive/personal group is gender.
Several studies underline that female entrepreneurs are not so common in
technology-based fields of research since they often undergo a sort of segregation (Moore and Rickel, 1980; Anna et al., 1999; Brush et al., 2001). In reference to the creation of academic spin-offs, a peculiar phenomenon takes place.
According to the report “She Figures” (2009), edited by the European Commission, in the very last years the amount of women getting a Ph.D. and involved
in research activities has grown up consistently (the estimated rate is about
6,3% by year while the same rate about men is about 3,7% by year). However,
by the same report it emerges that, after concluding their studies, female researchers are mainly involved in universities (37%) and public research centers
(39%) rather then in creating their own ventures (17%) or other activities (7%).
These data are in line with an empirical study (Profumo and Schiavone, 2009)
demonstrating that male professors are more inclined to create academic spinoffs than female ones. Thus, the sixth research hypothesis is:
H6: Universities with a higher presence of male professors show a higher possibility to create
academic spin-offs.
In sum, the first group of factors, named cognitive/personal factors, includes:
1.
2.
3.
4.
5.
6.
The amount of structured professors;
The amount of non-structured researchers;
The field of research (Area Scientifico Disciplinare);
Academic status;
Age;
Gender.
In order to identify the second group of factors to test, the relational ones, we
proceed as done before: by reviewing the entrepreneurial literature focusing on
the academic spin-offs and by adopting a meso level of analysis.
Italian Universities, like all the Universities, can establish partnerships with external partners (both public and private) to carry out different research pro-
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The Creation of Academic Spin-offs: Evidences from Italy 11
jects. Heterogeneity in the kind of partnership prevents from investigating all
of them. For this reason, in the present paper, attention is paid only over
Progetti di Rilevante Interesse Nazionale (named PRIN) that are relevant research
projects announced by Ministry of Education.
The choice to focus on the PRINs is due to the fact that these research projects are usually proposed by two or more Universities and they are characterized by two important phases: the sharing experience and the conceptualization
(Dell’Anno, 2010).
The sharing experience takes place when individuals, involved in TT processes,
exchange tacit knowledge (Polany, 1967; Dasgupta and David, 1994) and, contemporarily, start building trust based relationships that, hopefully, can become
knowledge based networks in the future. In these networks there are the crossfertilization of already existing knowledge and the creation of new knowledge
that can be both fruitfully exploited to start a new venture. Thus, it can be assumed that:
H7: Universities that participate in more scientific partnerships show a higher possibility to
create academic spin-offs.
The act of conceptualization is carried out in the last part of PRINs, before results
are going to be presented and discussed. As a matter of fact, the PRINs can be
divided into several parts. All the Universities participating in a PRIN, commonly named research units, can focus on one or on more parts of a PRIN. It
may happen that, even if all the involved Universities share the same results,
only the University that coordinates the PRIN has the key-role of providing
final results of the research projects and of presenting them. Implicitly, it happens that University that coordinates the PRIN can get to a wider and/or
deeper knowledge of the achieved results that, again, can be exploited to
launch a new venture. Therefore, it is possible to hypothesize that:
H8: Universities that coordinate more scientific partnerships show a higher possibility to create academic spin-offs.
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12 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
Participation in and coordination of PRINs is relevant also in reference to the
availability of funds that Universities can get.
These funds can be spent in several ways:
•
•
•
To acquire scientific equipment, like dedicated machines or specific components;
To provide research assistance, like partnerships with external high-skilled
researches or experts in specific fields;
To support technology transfer processes and activities, like the definition
of the entrepreneurial idea and of the business opportunity; the writing
down of the business plan; the research for potential investors and the
launch the new venture (Matricano, 2011).
In reference to the destination of PRINs funds, each of the three alternatives is
standing-alone since they seem to get to specific aims (equipment, partnerships, technology transfer). In point of fact, in reference to the creation of academic spin-offs, all the alternatives seem connected: investments in equipment
can be helpful for carrying out R&D activities and so to create new knowledge
to be potentially exploited by academic spin-offs; investments in partnerships
can be advantageous to share knowledge with other researchers or experts in
order to create new knowledge aiming at the same goal of starting a new venture; investments in technology transfer processes and activities are directly instrumental to create academic spin-offs. Since all the alternatives drive, implicitly or explicitly, toward the creation of academic spin-offs, we hypothesize
that:
H9: Universities that get more funds due to scientific partnerships show a higher possibility to
create academic spin-offs.
Briefly, the second group of factors, the relational ones, includes:
1. Participation in scientific partnerships;
2. Coordination of scientific partnerships;
3. Availability of funds due to scientific partnerships.
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The Creation of Academic Spin-offs: Evidences from Italy 13
In order to identify the third group of factors to test, the environmental ones,
we need to change the level of analysis and to use the macro one. Particularly,
for the present paper, we refer to a Regional level of analysis.
As noticed by Piccaluga (2000) and, successively, by other scholars (Bramwell and Wolfe, 2008; Moray and Clarysse, 2004), the external context can influence the creation of new ventures and, of course, also the creation of academic spin-offs.
One of the main environmental factors to be considered is the presence of
venture capitalists. The creation of new ventures in technology-based fields of
research, like the academic spin-offs are, requires a big amount of financial resources and to find them is not easy. Institutional investors, like banks, are not
involved in this kind of funding. The only chance that professors/aspiring entrepreneurs have is to count on venture capitalists (Heirman and Clarisse,
2004; Moray and Clarysse, 2004; Leland and Pile, 1997) that decide to settle
and to operate in a specific context (Italian Regions).
Other environmental factors that can affect the creation of academic spinoffs are linked to the innovative processes taking place in the Italian Regions.
Apparently, these processes are not directly combined with the ones taking
place inside the Italian Universities. Actually, this is not true if we consider the
spillover effect, i.e. the externalities of a specific activity that have implications for
subjects who are not directly involved in that activity.
A very important factor from which the spillover effect can derive is linked to
the amount of researchers employed in public research centres, not Universities. As noticed in reference to PRINs (see H7), both the cross-fertilization of
already existing knowledge and the creation of new knowledge can take place
also starting from the results of other research projects, like those promoted by
public research centres, thanks to researchers working there. This new knowledge can be fruitfully exploited by academics to start a new venture. So it
seems reasonable to admit that the higher presence of individuals working in
public research centres can positively affect the creation of academic spin-offs.
The innovation capacity in a Region, i.e. the average of R&D expenditure
by Public Administration, university, public and private ventures (as percentage
of regional G.D.P.), is another relevant factor linked to the creation of academic spin-offs. This is due to the fact that the results achieved by Regional
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14 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
R&D expenditure can be considered as middle-term results on which professors can leverage (thanks to spillover effect) in order to proceed with their research and, eventually, with the creation of academic spin-offs.
In the same way, the act of patenting, resulting from the innovation capacity
cited before, is very relevant. It communicates important information to the
stakeholders: to other researchers (like professors) it communicates whether it
is appropriate or not to proceed with a specific research project; to possible investors, it corresponds whether it is worth or not funding new ventures, also
including academic spin-offs, in a specific Region.
According to the above analysis about the environmental factors, the last
research hypothesis is:
H10: The presence of venture capitalists, the amount of researchers working in public research centres, the innovation capacity and the ability to patenting in a Region positively affect
the creation of academic spin-offs.
In conclusion, the third group of factors, so called environmental factors, includes:
•
•
•
•
The presence of venture capitalists;
The amount of researchers (employed in other research centres, not Universities);
The innovation capacity in the Region;
The ability to patenting.
4. Data collection
Data about all the ninety-two Universities existing in Italy have been collected
mainly from NetVal, MIUR and CINECA websites.
The dependent variable is the number of academic spin-offs (exactly 773)
created by Italian Universities over the years 2001-2009 and data about it have
been collected by NetVal website (Network per la Valorizzazione della Ricerca
Universitaria).
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The Creation of Academic Spin-offs: Evidences from Italy 15
There are two main aspects to be underlined:
1. It is not possible to have detailed information about the creation of academic spin-off by each University for each year. Thus, the dependent variable is the cumulated number of academic spin-offs over the years 20012009;
2. It is not possible to have detailed information about the promoter of each
academic spin-off. Because of this, the spin-offs included in the NetVal list
are considered as the result of the University activity. It derives that the
whole University is the unit of analysis of the present work and, consequently, it implies that all the independent variables are observed and considered from a University perspective and not from the promoter’s one.
The way independent variables have been constructed varies for each of them.
For this reason it is suitable to consider each of them separately.
In reference to H1, the considered variable includes the total amount of
structured professors for each University (Table 1).
Strictly connected to the above variable is the amount of unstructured collaborators (H2). They help structured professors with all their activities (from
teaching to researching). Especially in reference to the research activity, the unstructured collaborators represent a very important resource of cognitive capital. Collaborations can vary according to the kind of contract linking the unstructured collaborators to the University. In order to test the effect they can
have on the creation of academic spin-offs, different kinds of unstructured collaborators have been considered (Table 2).
Beyond the difference between structured professors and unstructured collaborators, a very important variable to be considered for the creation of academic spin-off is the ASD (H3). The Italian Ministry of Education (MIUR)
lists fourteen ASD that have been considered for the present work (Table 3).
In the category of “professors”, very important differences can come out
because of the academic status (H4). The positions each professor takes up in
the University (full, associate or assistant) can differently affect the creation of
academic spin-offs so they have been considered at a very deep level of analysis (Table 4).
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16 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
Another factor that can affect the creation of academic spin-off is age of professors involved (H5). For this reason, all the professors have been divided
into five groups according to age (Table 5).
Eventually, we assume that gender can affect the creation of academic spinoffs (H6). For this reason, male and female professors have been considered as
two different groups (Table 6).
From a methodological perspective, it is important to underline that, for all
the cognitive/personal factors, we have considered the average calculated on
the values reported over the same time span of the dependent variable.
Information collected about relational factors aims to catch the ability of
Universities to build scientific partnerships with other Universities or research
centres. As already anticipated, inclination towards networking activity positively affects the creation of academic spin-offs. Data about relational factors
refer to PRIN over the same time span of the dependent variable.
In order to test the effect on the creation of academic spin-offs, three independent variables have been considered.
In reference to H7, all the participations in PRIN have been considered. In
reference to H8, only the PRIN that have been coordinated by a specific University have been counted. Eventually, in reference to H9, the amount of financial resources attributed to a University to carry out a PRIN has been considered as a relational factor since it is representative of the networking activity
started by the University itself (Table 7).
The last group of factors includes the environmental ones. These factors reassume the specific conditions of each local context in order to test whether
and how they can affect the creation of academic spin-offs.
From a methodological perspective, it is important to underline that the environmental factors are expression of the context in each Italian Region. The
considered variables, included in Table 8 and referred to the same time span of
the dependent variable, are:
•
The average of the number of investments funded by venture capitalists in
the Italian Regions (named VC). These data were collected from AIFI (Associazione Italiana del Private Equity e Venture Capital);
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The Creation of Academic Spin-offs: Evidences from Italy 17
•
•
•
The average of the number of non-academic researchers per each thousand
inhabitants (called Researchers);
The average of intra-muros R&D expenditure funded by Public Administration, Universities, public and private ventures as a percentage of Regional
G.D.P. (named CapInn);
The average of patents registered at the European Patent Office (EPO) per
each million of inhabitants (named IntPat)
The values of the variables “CapInn”, “IntPat” and “Researchers” were collected from the Dipartimento per lo Sviluppo e la Coesione Economica del Ministero dello
Sviluppo Economico website.
5. Methodology and results
The present section can be divided into two main parts. The first includes descriptive statistics of the investigated population. The second, instead, shows
the results of hypotheses testing.
We start by describing the characteristics of the investigated population in
reference to their property assets (public or private universities), their size and
geographical location.
Table 9 shows that around 80% of all the Italian universities are public.
Considering the size of the universities, we assumed that “big university” are
those with more than 40.000 students, “medium university” those with 15.00040.000 students and “small university” those with fewer than 15.000 students.
Table 10 shows that “small universities” are the most frequent (60% of the
population), while the percentage of “big universities” in Italy is 12%.
Regarding the geographical location (Table 11), the universities are distributed not uniformly over the country. Most of them are located in central Italy.
Tables 12, 13, 14, and 15 report the distribution of the Italian professors according to ASD, academic status, age, and gender. For each of these distributions, besides average value and the standard deviation (σ ), tables report:
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18 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
•
•
The minimum value which is the lowest average value reported by a single
University over the years 2001-2009. This value always moves from zero
since in the observed population there are some new, very small Universities or telematic ones which can miss some independent variables.
The maximum value which is the highest average value reported by a single
University over the years 2001-2009.
By examining data about the number of professors disaggregated by scientific
disciplines (Table 12), we find that Medical Sciences (ASD06) is the scientific
area with the highest average value of professors.
Table 13 shows that, in the period considered, the average of no fixed term
full professors is 162 and the average of confirmed associate professors is 147,
while the average of no fixed term assistant professors is 183.
The analysis of data disaggregated by age (Table 14) shows that most of professors is 56-65, followed by the previous class 46-55. The under-35 age class
has a lower value, showing that the progress in academic career in Italian universities is only after researchers have gained experience over the years.
Finally, given the data on the gender of academics, we note that the average
number of male professors is almost double than that of the female ones (Table 15).
In the passage from descriptive to inferential statistics, it is necessary to underline that the present work represents an exploratory study and so some
criticalities about the methodology aspects may rise up. The choice to use the
Ordinary Least Square (OLS) to get the estimation of the parameters can be ascribed as the first one. Even if the OLS is not the most fitting model with the
final aim, achieved results are presented since they seem to be interesting and
to constitute an important premise for further and deeper studies. The second
criticality is about endogeneity. This criticality prevents from drawing any conclusion about the causal relationship between regressors and spin-offs but it
helps to understand the relationship itself at an exploratory phase. The third
and last criticality concerns the stepwise approach. In an exploratory study like
this, the stepwise approach helps to understand the relationship between each
regressor and the spin-offs in a clear way. Once the relationship is clear, fur-
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The Creation of Academic Spin-offs: Evidences from Italy 19
ther studies will be carried out without the stepwise approach (including also
not-significant variables).
According to the criticalities underlined before, the following analysis does
not include any definitive results about the creation of academic spin-offs. The
results are a sort of guidelines to carry out future analyses.
Statistical elaborations support H1 according to which Universities with
a higher presence of structured professors show a higher possibility to create
academic spin-offs. Beta is different from zero in a significant way; it is
positive and its value is 0,017 (Table 16). According to this, the number of
structured professors positively affects the creation of academic spin-offs.
In H2 we predicted that Universities with a higher presence of unstructured
researchers show a higher possibility to create academic spin-offs. Statistical
elaborations show that Ph.D. and Ph.D. students do not affect the creation of
academic spin-offs. It is possible to maintain that only the collaborators who
receive a research grant (named Coll_Tot) positively affect the creation of academic spin-offs (Table 17). One possible explanation can be found in the
longer time spent in researching by collaborators with research grant as compared to Ph.D. The longer is the time spent in research activities, the more is
the new knowledge created and so the higher is the possibility to create academic spin-offs.
In reference to H3, based on the assumption that Universities with a higher
presence of professors in technology-based ASD show a higher possibility to
create academic spin-offs, results show that some fields of research, like the
number 07 (agriculture and veterinary science) and 09 (industrial and technological engineering), positively affect the creation of academic spin-offs. At
the same time, the fields of research number 08 (civil engineering and architecture) seem to affect in a negative way the creation of academic spin-offs
(Table 18).
Results about H4 (Table 19) refuses H4a and supports H4b since the regression analysis reveals that the only academic status affecting the creation
of academic spin-offs in a positive way is a low one: assistant professors not
confirmed. These professors work for the University with a fixed-term contract lasting three years and, after that term, their works are evaluated in order
to be confirmed. This might mean that when professors get a higher academic
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20 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
status, despite the major experience and better reputation, they are less inclined
to start academic spin-offs.
Statistical elaborations do not support H5 predicting that Universities with
young professors show a higher possibility to create academic spin-offs. The
professors that seem to affect in a positive way the creation of academic spinoffs are the ones aged 35-45 and the ones aged 46-55 (Table 20).
The regression analysis confirms H6 assuming that Universities with a
higher presence of male professors show a higher possibility to create academic spin-offs (Table 21).
The regression analysis seems to support H7 expecting that Universities
that participate in more scientific partnerships show a higher possibility to create academic spin-offs. It means that the more Universities participate in
PRIN, the higher is the possibility to create academic spin-offs (Table 22).
The achieved results, shown in Table 23, support H8 and drive to assume
that Universities that coordinate more scientific partnerships show a higher
possibility to create academic spin-offs. In fact, results show that the more
Universities coordinate PRIN, the higher is the possibility to create academic spin-offs.
By comparing the regression coefficients in Table 22 (Beta = 0,015) and
in Table 23 (Beta = 0,048) it results that when Universities coordinate
PRIN rather than participate in them, the possibility that academic spinoffs are created grows up. According to this, it derives that Universities
that coordinate PRIN are more inclined to create academic spin-offs.
Statistical results seem to support H9, according to which Universities that
get more funds due to scientific partnerships show a higher possibility to create
academic spin-offs, even if the relationship does not result so strong (Table
24).
As already anticipated, in order to test H10 the unity of analysis moves
from Universities to Regions where Universities are settled in. As a consequence, the dependent variable has changed too. In fact, the dependent
variable now is the total amount of academic spin-offs created at regional
level. Statistical elaborations drive to refuse H10. In fact, the prediction
that the presence of venture capitalists, the amount of researchers, the innova-
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The Creation of Academic Spin-offs: Evidences from Italy 21
tion capacity in a Region and the ability to patenting positively affect the creation of academic spin-offs is not statistically significant (Table 25).
6. Conclusions and discussion
The present contribution has tried to investigate the factors affecting the creation of academic spin-offs in Italy.
Methodological limitations (due to the use of OLS) and possible effects due
to endogeneity may affect the empirical results of this exploratory study. In any
case, results seem to suggest that cognitive/personal and relational factors affect the creation of academic spin-offs while environmental factors do not. In
reference to the cognitive/personal factors, statistical elaborations support the
following results:
•
•
•
•
•
•
The number of structured professors positively affects the creation of
academic spin-offs;
The collaborators who receive a research grant (research fellows) positively
affect the creation of academic spin-offs;
Carrying out research projects in some ASD, like the number 07 (agriculture and veterinary science) and 09 (industrial and technological engineering), positively affects the creation of academic spin-offs;
The presence of not confirmed assistant professors seems to affect the
creation of academic spin-offs in a positive way;
The professors that seem to affect in a positive way the creation of academic spin-offs are the ones aged between 35-45 and 46-55;
The presence of male professors seems to positively affect the creation
of academic spin-offs.
In reference to the relational factors, statistical elaborations show that the
creation of academic spin-offs is positively correlated to the number of scientific collaborations (PRIN) participated by Universities and, even more, to the
number of collaborations coordinated by each University. The availability of
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22 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
funds due to scientific partnerships seems to positively affect the creation of
academic spin-off but, statistically, the relationship does not result so strong.
Eventually, statistical elaborations about the environmental factors (including the number of investments funded by venture capitalists in each Region,
the number of researchers, the R&D expenditure by Public Administration,
University, public and private ventures and the patents registered at EPO) do
not present any significant relationship with the creation of academic spin-offs.
In order to comment the results of the present study, it is suitable to refer
to cognitive/personal and relational factors contemporarily and to focus on the
group of environmental factors alone.
Despite methodological limitations, cognitive/personal and relational factors show results that are in line with existing literature. They remark that Universities need to have distinct capabilities that facilitate the creation of academic spin-offs (Rasmussen and Borch, 2010).
Results about environmental factors, instead, show that none of the four
factors considered seem to affect the creation of academic spin-offs in Italy.
The possible causes at the basis of the above results are linked to methodological aspects, especially to the used methodology (OLS) and to the shift of
the unit of analysis from University level to Regional level. This shift makes the
statistical relationships between the independent variables and the dependent
one less punctual and, moreover, it implies a decrease of information variability
about the investigated phenomenon. However, beyond methodological aspects, the possible implications due to the achieved results recall the idea of the
“ivory tower” (Mowery et al., 2004; Etzkowitz et al., 2000). Italian Universities
still seem to be apart from the context they are settled in. They seem to ignore
the presence of VC and of other researchers, the results of other public R&D
activities and the amount of patents that, instead, could contribute to the creation of new ventures promoted by academics.
In order to increase the creation of academic spin-offs, policy makers
should thus drive the academics towards the external context from which new
possible synergies can come out and to which new ideas can be transferred.
Policy makers in Italy should act like a bridge able to connect the academic
world with the entrepreneurial one in order to reduce or even to eliminate any
possible gap.
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The Creation of Academic Spin-offs: Evidences from Italy 23
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The Creation of Academic Spin-offs: Evidences from Italy 29
Tables
Table 1: Data used to test H1
Tot_Prof
Number of professors (full, associate and assistant) for each University
Table 2: Data used to test H2
Coll_Tot
PhD_Tot
PhD_Stud_Tot
Number of collaborators who receive a research grant (post-doc, scholarship)
Number of Ph.D.
Number of Ph.D. students
Table 3: Data used to test H3
Prof_ASD01
Prof_ASD02
Prof_ASD03
Prof_ASD04
Prof_ASD05
Prof_ASD06
Prof_ASD07
Prof_ASD08
Prof_ASD09
Prof_ASD10
Prof_ASD11
Prof_ASD12
Prof_ASD13
Prof_ASD14
27 Sorrentino epod_v2.indd 29
Number of professors of mathematics and information technology
Number of professors of physics
Number of professors of chemistry
Number of professors of geology
Number of professors of biology
Number of professors of medical sciences
Number of professors of agriculture and veterinary science
Number of professors of civil engineering and architecture
Number of professors of industrial and technological engineering
Number of professors of ancient times, philological-literary and historicalartistic sciences
Number of professors of historical, philosophical, pedagogical and psychological sciences
Number of professors of law sciences
Number of professors of economics and statistics
Number of professors of politics and social sciences
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30 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
Table 4: Data used to test H4
Full Professors
Associate Professors
Assistant Professors
Number of full professors at no fixed term
Number of full professors at fixed term
Number of temporary full professors
Number of confirmed associates professors
Number of non-confirmed associate professors
Number of associate professors on contract
Number of assistant professors at no fixed term
Number of assistant professors at fixed term
Number of assistant professors not confirmed
Table 5: Data used to test H5
Prof_under35
Prof_35_45
Prof_46_55
Prof_55_65
Prof_over65
Number of professors younger than 35 years old
Number of professors aged between 35 and 45 years old
Number of professors aged between 46 and 55 years old
Number of professors aged between 56 and 65 years old
Number of professors older than 65 years old
Table 6: Data used to test H6
Prof_M
Prof_F
Number of male professors
Number of female professors
Table 7: Data used to test H7, H8 and H9
Partic_PRIN
Coord_PRIN
Funded_PRIN
27 Sorrentino epod_v2.indd 30
Number of PRIN participated by the university (H7)
Number of PRIN coordinated by the university (H8)
Financial resources assigned to the university for participation to PRIN (H9)
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The Creation of Academic Spin-offs: Evidences from Italy 31
Table 8: Data used to test H10
VC
Researchers
CapInn
IntPat
The average of the number of investments funded by venture capitalists in
each Region
The average of the number of researchers per each thousand inhabitants
The average of R&D expenditure by Public Administration, university, public and private ventures (as percentage of regional G.D.P.)
The average of patents registered at EPO per each million inhabitants
Table 9: Distribution of the Italian Universities according to property assets
Property assets
Private Universities
Public Universities
Total
Frequency
19
73
92
Percentage
20,7
79,3
100,0
Cumulative percentage
20,7
100,0
Table 10: Distribution of the Italian Universities according to size
Size
Big Universities
Medium Universities
Small Universities
Total
Frequency
11
25
56
92
Percentage
12,0
27,2
60,9
100,0
Cumulative percentage
12,0
39,1
100,0
Table 11: Distribution of the Italian Universities according to geographic area
Geographic area
North
Center
South
Total
27 Sorrentino epod_v2.indd 31
Frequency
28
40
24
92
Percentage
30,4
43,5
26,1
100,0
Cumulative percentage
30,4
73,9
100,0
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32 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
Table 12: Distribution of the Italian professors according to ASD.
(ASD) Area Scientifico Disciplinare
Professors in ASD01
Professors in ASD02
Professors in ASD03
Professors in ASD04
Professors in ASD05
Professors in ASD06
Professors in ASD07
Professors in ASD08
Professors in ASD09
Professors in ASD10
Professors in ASD11
Professors in ASD12
Professors in ASD13
Professors in ASD14
Minimum
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Maximum
244,1
178,2
248,0
87,1
361,3
1.392,0
288,0
468,4
543,9
385,4
385,2
297,8
290,1
138,5
Average
35,7
27,6
34,9
13,7
56,0
119,7
34,4
40,9
53,4
62,6
54,1
51,4
45,3
17,6
σ
50,0
39,2
52,7
20,6
78,5
209,4
65,7
80,2
95,0
81,4
73,2
61,9
53,6
26,0
Table 13: Distribution of the Italian professors according to academic status.
Academic status
Not confirmed assistant professors
No fixed term Assistant professors
Fixed term assistant professors
Not confirmed associate professors
Confirmed associate professors
Temporary full professor
No fixed term full professor
Fixed term full professor
Minimum
0
0
0
0
0
0
0
0
Maximum
309,2
1.666,2
36,0
257,6
1.027,3
192,9
1.182,0
7,8
Average
62,6
183,2
2,9
49,5
147,4
34,5
162,4
0,2
σ
71,5
262,0
6,2
53,6
191,0
38,6
214,8
0,9
Table 14: Distribution of the Italian professors according to age.
Minimum
Maximum
Average
σ
Prof_under35
0
36,9
8,6
9,5
Prof_35_45
0
678,3
142,1
156,6
Prof_46_55
0
1.237,7
189,4
220,2
Prof_56_65
0
1.691,0
204,7
284,0
Prof_over65
0
1.103,7
117,2
179,4
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The Creation of Academic Spin-offs: Evidences from Italy 33
Table 15: Descriptive statistics of professors by gender
Gender
Female professors
Male professors
Minimum
0
0
Maximum
1.636,0
2.920,3
Average
218,7
435,9
σ
286,8
540,0
Table 16: Regression Coefficients about H1
Model
(Constant)
Tot_Prof
Unstandardized
Coefficients
B
Standard Error
,249
1,332
,017
,003
Standardized
Coefficients
Beta
1,258
t
Sig.
,187
6,806
,852
,000
t
Sig.
1,736
8,277
,086
,000
t
Sig.
3,689
8,990
4,269
8,954
-4,540
2,977
8,827
-4,675
4,445
,000
,000
,000
,000
,000
,004
,000
,000
,000
Table 17: Regression Coefficients about H2
Model
(Constant)
Coll_Tot
1
Unstandardized
Coefficients
B
Standard Error
2,014
1,160
,017
,002
Standardized
Coefficients
Beta
,657
Table 18: Regression Coefficients about H3 – Stepwise method
Model
1
2
3
(Constant)
Prof_ASD09
(Constant)
Prof_ASD09
Prof_ASD08
(Constant)
Prof_ASD09
Prof_ASD08
Prof_ASD07
27 Sorrentino epod_v2.indd 33
Unstandardized
Coefficients
B
Standard Error
3,673
,996
,082
,009
3,855
,903
,146
,016
-,088
,019
2,587
,869
,134
,015
-,083
,018
,050
,011
Standard
Coefficients
Beta
,688
1,221
-,619
1,114
-,581
,290
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34 D. MATRICANO, L. GUADALUPI, V.A. TUTORE, F. ANDREOTTOLA, M. SORRENTINO
Table 19: Regression Coefficients about H4 – Stepwise method
Model
1
(Constant)
AssP_noconf
Unstandardized
Coefficients
B
Standard Error
1,937
1,253
,098
,013
Standardized
Coefficients
Beta
,616
t
Sig.
1,546
7,418
,126
,000
t
Sig.
1,182
7,887
1,525
5,601
-2,341
1,444
5,368
-3,082
-2,238
,844
3,986
-3,006
-3,029
2,133
,240
,000
,131
,000
,021
,152
,000
,003
,028
,401
,000
,003
,003
,036
t
Sig.
2,149
6,887
1,896
3,844
-2,525
,034
,000
,061
,000
,013
Table 20: Regression Coefficients about H5 – Stepwise method
Model
1
2
3
4
(Constant)
Prof_35_45
(Constant)
Prof_35_45
Prof_under35
(Constant)
Prof_35_45
Prof_under35
Prof_56_65
(Constant)
Prof_35_45
Prof_under35
Prof_56_65
Prof_46_55
Unstandardized
Coefficients
B
Standard Error
1,469
1,243
,046
,006
1,868
1,225
,075
,013
-,513
,219
1,733
1,200
,110
,020
-,719
,233
-,015
,007
1,031
1,222
,089
,022
-,689
,229
-,037
,012
,042
,020
Standardized
Coefficients
Beta
,639
1,026
-,429
1,513
-,601
-,381
1,225
-,576
-,934
,812
Table 21: Regressions Coefficients about H6 – Stepwise method
Model
1
2
(Constant)
Male prof
(Constant)
Male prof
Female prof
27 Sorrentino epod_v2.indd 34
Unstandardized
Coefficients
B
Standard Error
2,674
1,245
,012
,002
2,309
1,218
,035
,009
-,043
,017
Standardized
Coefficients
Beta
,587
1,652
-1,085
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The Creation of Academic Spin-offs: Evidences from Italy 35
Table 22: Regressions Coefficients about H7
Unstandardized
Coefficients
Model
1
B
Standard Error
(Constant)
2,284
1,176
Partic_PRIN
,015
,002
Standardized
Coefficients
t
Sig.
1,942
,055
7,882
,000
t
Sig.
2,902
6,759
,005
,000
t
Sig.
2,355
7,768
,021
,000
t
Sig.
1,603
-,940
1,398
-1,012
,301
,130
,362
,183
,328
,767
Beta
,639
Table 23: Regressions Coefficient about H8
Model
(Constant)
Coord_PRIN
1
Unstandardized
Coefficients
B
Standard Error
3,451
1,189
,048
,007
Standardized
Coefficients
Beta
,580
Table 24: Regressions Coefficients about H9
Model
1
(Constant)
Funded_PRIN
Unstandardized
Coefficients
B
Standard Error
2,715
1,153
4,201E-7
,000
Standardized
Coefficients
Beta
,634
Table 25: Regressions Coefficients about H10
Model
1
(Constant)
CapInn
Researchers
IntPat
VC
27 Sorrentino epod_v2.indd 35
Unstandardized
Coefficients
B
Standard Error
25,642
15,996
-47,070
50,058
26,485
18,951
-,282
,278
,154
,512
Standardized
Coefficients
Beta
-,688
1,166
-,444
,111
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