the creation of academic spin-offs - università di roma "tor vergata"

27 Sorrentino epod_v2.inddMario Sorrentino
Evidences from Italy
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Diego Matricano, Luigi Guadalupi, Valerio Aniello Tutore, Francesco Andreottola, Mario Sorrentino
The Creation of Academic Spin-offs: Evidences from Italy
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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, Univer- sities’ 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
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1. Introduction 3 2. The creation of academic spin-offs 3 3. Factors affecting the creation of academic spin-offs 6 4. Data collection 14 5. Methodology and results 17 6. Conclusions and discussion 21 References 23 Tables 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 Sympo- sium 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 Andreot- tola, section 4 has been written by Luigi Guadalupi, section 5 has been written by Valerio A. Tutore.
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 Chi- aroni, 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 pro- grammes, 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
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 imple- mented 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.
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 regula- tion 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 infrastruc- tures 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 Re- search 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; Pow- ers 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-
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
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 rele- vance 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.
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 technology- based ASD report a higher propensity to create academic spin-offs than Uni- versities with professors in non-technology based ASD (like philological- literary 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 spin- offs launched by Massachusetts Institute of Technology (M.I.T.) are in the biomedical industry. O’Shea et al. (2005) noticed that biotechnology, chemi- cals, 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 experi- enced with research projects, better known by stakeholders and, consequently,
less exposed to financial risk. In sum they seem to satisfy many of the require- ments 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. Gener- ally 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 possi- bilities 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. There- fore, it is possible to hypothesize that:
H5: Universities with young professors show a higher possibility to create academic spin-offs.
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 segrega- tion (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 Commis- sion, 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 spin- offs 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. The amount of structured professors; 2. The amount of non-structured researchers; 3. The field of research (Area Scientifico Disciplinare); 4. Academic status; 5. Age; 6. 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-
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 character- ized 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 cross- fertilization 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, com- monly 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.
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 com- ponents;
• 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 in- strumental 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.
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 (Bram- well 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 spin- offs 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 Universi- ties. 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
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 Uni-
versities); • 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).
There are two main aspects to be underlined: 1. It is not possible to have detailed information about the creation of aca-
demic spin-off by each University for each year. Thus, the dependent variable is the cumulated number of academic spin-offs over the years 2001- 2009;
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).
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 spin- offs (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 Uni- versity 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 re- assume 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 (As- sociazione Italiana del Private Equity e Venture Capital);
• 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 Administra- tion, 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 15.000- 40.000 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 distribu- tions, besides average value and the standard deviation (σ ), tables report:
• 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 Universi- ties 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 (Ta- ble 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 as- cribed 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-
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
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 spin- offs 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 spin- offs 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-
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 (agricul-
ture 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
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 Uni- versities 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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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 Number of collaborators who receive a research grant (post-doc, scholar- ship)
PhD_Tot Number of Ph.D. PhD_Stud_Tot Number of Ph.D. students
Table 3: Data used to test H3
Prof_ASD01 Number of professors of mathematics and information technology Prof_ASD02 Number of professors of physics Prof_ASD03 Number of professors of chemistry Prof_ASD04 Number of professors of geology Prof_ASD05 Number of professors of biology Prof_ASD06 Number of professors of medical sciences Prof_ASD07 Number of professors of agriculture and veterinary science Prof_ASD08 Number of professors of civil engineering and architecture Prof_ASD09 Number of professors of industrial and technological engineering
Prof_ASD10 Number of professors of ancient times, philological-literary and historical- artistic sciences
Prof_ASD11 Number of professors of historical, philosophical, pedagogical and psycho- logical sciences
Prof_ASD12 Number of professors of law sciences Prof_ASD13 Number of professors of economics and statistics Prof_ASD14 Number of professors of politics and social sciences
Full Professors Number of full professors at no fixed term Number of full professors at fixed term Number of temporary full professors
Associate Professors Number of confirmed associates professors Number of non-confirmed associate professors Number of associate professors on contract
Assistant Professors Number of assistant professors at no fixed term Number of assistant professors at fixed term Number of assistant professors not confirmed
Prof_under35 Number of professors younger than 35 years old Prof_35_45 Number of professors aged between 35 and 45 years old Prof_46_55 Number of professors aged between 46 and 55 years old Prof_55_65 Number of professors aged between 56 and 65 years old Prof_over65 Number of professors older than 65 years old
Prof_M Number of male professors Prof_F Number of female professors
Table 7: Data used to test H7, H8 and H9
Partic_PRIN Number of PRIN participated by the university (H7) Coord_PRIN Number of PRIN coordinated by the university (H8) Funded_PRIN Financial resources assigned to the university for participation to PRIN (H9)
VC The average of the number of investments funded by venture capitalists in each Region
Researchers The average of the number of researchers per each thousand inhabitants
CapInn The average of R&D expenditure by Public Administration, university, public and private ventures (as percentage of regional G.D.P.)
IntPat The average of patents registered at EPO per each million inhabitants Table 9: Distribution of the Italian Universities according to property assets
Property assets Frequency Percentage Cumulative percentage Private Universities Public Universities Total
19 20,7 20,7 73 79,3 100,0 92 100,0
Table 10: Distribution of the Italian Universities according to size
Size Frequency Percentage Cumulative percentage Big Universities Medium Universities Small Universities Total
11 12,0 12,0 25 27,2 39,1 56 60,9 100,0 92 100,0
Table 11: Distribution of the Italian Universities according to geographic area
Geographic area Frequency Percentage Cumulative percentage North Center South Total
28 30,4 30,4 40 43,5 73,9 24 26,1 100,0 92 100,0
Table 12: Distribution of the Italian professors according to ASD.
(ASD) Area Scientifico Disciplinare Minimum Maximum Average σ Professors in ASD01 0 244,1 35,7 50,0 Professors in ASD02 0 178,2 27,6 39,2 Professors in ASD03 0 248,0 34,9 52,7 Professors in ASD04 0 87,1 13,7 20,6 Professors in ASD05 0 361,3 56,0 78,5 Professors in ASD06 0 1.392,0 119,7 209,4 Professors in ASD07 0 288,0 34,4 65,7 Professors in ASD08 0 468,4 40,9 80,2 Professors in ASD09 0 543,9 53,4 95,0 Professors in ASD10 0 385,4 62,6 81,4 Professors in ASD11 0 385,2 54,1 73,2 Professors in ASD12 0 297,8 51,4 61,9 Professors in ASD13 0 290,1 45,3 53,6 Professors in ASD14 0 138,5 17,6 26,0
Table 13: Distribution of the Italian professors according to academic status.
Academic status Minimum Maximum Average σ Not confirmed assistant professors 0 309,2 62,6 71,5 No fixed term Assistant professors 0 1.666,2 183,2 262,0 Fixed term assistant professors 0 36,0 2,9 6,2 Not confirmed associate professors 0 257,6 49,5 53,6 Confirmed associate professors 0 1.027,3 147,4 191,0 Temporary full professor 0 192,9 34,5 38,6 No fixed term full professor 0 1.182,0 162,4 214,8 Fixed term full professor 0 7,8 0,2 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
Table 15: Descriptive statistics of professors by gender
Gender Minimum Maximum Average σ Female professors 0 1.636,0 218,7 286,8 Male professors 0 2.920,3 435,9 540,0
Table 16: Regression Coefficients about H1
Model Unstandardized
Coefficients Standardized Coefficients t Sig.
B Standard Error Beta (Constant) ,249 1,332 ,187 ,852 Tot_Prof ,017 ,003 1,258 6,806 ,000
Table 17: Regression Coefficients about H2
B Standard Error Beta
1 (Constant) 2,014 1,160 1,736 ,086 Coll_Tot ,017 ,002 ,657 8,277 ,000
Table 18: Regression Coefficients about H3 – Stepwise method
Coefficients Standard
1 (Constant) 3,673 ,996 3,689 ,000
Prof_ASD09 ,082 ,009 ,688 8,990 ,000
2 (Constant) 3,855 ,903 4,269 ,000
Prof_ASD09 ,146 ,016 1,221 8,954 ,000 Prof_ASD08 -,088 ,019 -,619 -4,540 ,000
3
(Constant) 2,587 ,869 2,977 ,004 Prof_ASD09 ,134 ,015 1,114 8,827 ,000 Prof_ASD08 -,083 ,018 -,581 -4,675 ,000 Prof_ASD07 ,050 ,011 ,290 4,445 ,000
Table 19: Regression Coefficients about H4 – Stepwise method
1 (Constant) 1,937 1,253 1,546 ,126
AssP_noconf ,098 ,013 ,616 7,418 ,000 Table 20: Regression Coefficients about H5 – Stepwise method
1 (Constant) 1,469 1,243 1,182 ,240 Prof_35_45 ,046 ,006 ,639 7,887 ,000
2 (Constant) 1,868 1,225 1,525 ,131 Prof_35_45 ,075 ,013 1,026 5,601 ,000
Prof_under35 -,513 ,219 -,429 -2,341 ,021
3
(Constant) 1,733 1,200 1,444 ,152 Prof_35_45 ,110 ,020 1,513 5,368 ,000
Prof_under35 -,719 ,233 -,601 -3,082 ,003 Prof_56_65 -,015 ,007 -,381 -2,238 ,028
4
(Constant) 1,031 1,222 ,844 ,401 Prof_35_45 ,089 ,022 1,225 3,986 ,000
Prof_under35 -,689 ,229 -,576 -3,006 ,003 Prof_56_65 -,037 ,012 -,934 -3,029 ,003 Prof_46_55 ,042 ,020 ,812 2,133 ,036
Table 21: Regressions Coefficients about H6 – Stepwise method
1 (Constant) 2,674 1,245 2,149 ,034 Male prof ,012 ,002 ,587 6,887 ,000
2 (Constant) 2,309 1,218 1,896 ,061 Male prof ,035 ,009 1,652 3,844 ,000
Female prof -,043 ,017 -1,085 -2,525 ,013
Table 22: Regressions Coefficients about H7
1 (Constant) 2,284 1,176 1,942 ,055
Partic_PRIN ,015 ,002 ,639 7,882 ,000 Table 23: Regressions Coefficient about H8
1 (Constant) 3,451 1,189 2,902 ,005
Coord_PRIN ,048 ,007 ,580 6,759 ,000 Table 24: Regressions Coefficients about H9
Funded_PRIN 4,201E-7 ,000 ,634 7,768 ,000 Table 25: Regressions Coefficients about H10
1
(Constant) 25,642 15,996 1,603 ,130 CapInn -47,070 50,058 -,688 -,940 ,362
Researchers 26,485 18,951 1,166 1,398 ,183 IntPat -,282 ,278 -,444 -1,012 ,328
VC ,154 ,512 ,111 ,301 ,767

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