Abstract
Over the last 20 years, universities have made steady progress in their efforts to foster the process of technology transfer through collaboration with industry. The establishment of technology transfer offices (TTO) has become routine for supporting the commercialization of academic research. However, the literature shows that there are many factors that affect the efficiency and effectiveness of these offices. Based on original data from interviews with 197 university departments in Italy, this paper investigates the determinants of universities university use of TTOs. We take account of the effects of universities’ and TTOs characteristics, of research and geographic indicators.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
1 Introduction
One of the key institutional challenges that governments face in their efforts to support innovation activity in firms is easing the process of technology transfer (TT) from research institutions to businesses (OECD 2003). Historically, bringing research results to market has not been a prime concern for academic institutions. However, since the late 1970s we have seen the emergence and consolidation of a third university mission—that of transferring knowledge to industry—which is in addition to the traditional missions of education and scientific research. This new emphasis on the commercialization of research output and increasing integration between researchers and industrialists has led to the term ‘entrepreneurial university’ (Branscomb et al. 1999). Many complementary factors have contributed to the strengthening of the entrepreneurial role of universities and university researchers.Footnote 1 In principle, aiding the transfer and commercialization of discoveries is in the interests of both inventors and society—the ultimate aim of applied scientific research being to improve the human condition (Litan et al. 2007).
From the universities’ perspective, the applicability of research to industry, and collaboration with firms has gained increased strategic relevance in terms of their potential as sources of funding. Universities are primarily motivated to collaborate with industry by the need to raise additional resources required to fund research and other university activities (Cohen et al. 1998). However, the benefits from university–industry collaborations for businesses and universities are reciprocal: as well as supporting firms’ innovation activities, collaboration with industry has positive effects on academic research, improving the performance of researchers (Guldbrandsen and Smeby 2005) without necessarily being detrimental to academics’ careers. Also, the desire to exploit scientific discoveries in an industry context and apply research outcomes to real-world scenarios are important drivers for universities (Muscio 2008).
Many universities have TT high on the agendas of their development plans in terms of promoting initiatives aimed at bridging between academic research and industry needs. However, universities vary enormously in the extent to which they are involved in the commercialization of their research, and in their success at generating additional income from third stream activities. The governance and management of university–industry interactions influences both their frequency and their success. In order to encourage scientists to consider commercialization, and to support them through the process, many universities have established technology transfer offices (TTO) (O’Gorman et al. 2008).
In the wake of the positive experience of northern European countries, academic institutions in Italy are rapidly increasing their involvement in TT processes. Since the late 1990s, the political—and economic—pressures on universities have increased, prompting several universities to embark on initiatives aimed at supporting the commercial exploitation of scientific research and university TTOs, business incubators, patent offices, industry liaison offices (ILOs) have proliferated in many Italian universities.
It is within this context that we try to assess the extent of TTO involvement in university–industry research collaborations in Italy, by estimating the effects of the presence of a TTO in a university on the frequency of collaboration, and identifying the factors that determine university collaboration with industry. We argue that, at least in the case of Italy, although university TTOs may play an important role in supporting universities in the establishment and maintenance of university–industry research collaborations, use of TTOs is constrained by specific factors that work to limit cooperative activity.
The paper is organized as follows. Section 2 sets the theoretical background to university governance of TT, university–industry collaboration, and the role of TTOs in easing this process. Section 3 presents our empirical results related to the determinants of university–industry collaboration in Italy, and use of TTOs. The data on universities are analyzed using both descriptive statistics and regression models. Section 4 discusses the results and their implications for policy.
2 Theoretical background
2.1 The role of universities in firms’ innovation activity
Academic research greatly contributes to innovation in several industries (Mansfield 1995, 1998) and is an important driver of industrial patents (Branstetter and Ogura 2005; Breschi et al. 2007). The innovation literature has analyzed in detail how universities contribute to firms’ innovation activity, and the modalities of university–industry interactions (D’Este and Patel 2007). TT from university to industry has become strategically important in many respects: it represents a source of funding for university research, a source of innovation for businesses, and is a source of economic development for policy-makers. Industrial policy relies increasingly on TT as a tool for the development of knowledge intensive economies and increased competitiveness (Bozeman 2000). In fact, whilst some countries are rethinking the role (and funding) of research institutions within their national innovation systems (Arnold et al. 2006), in several European countries there is increasing political pressure on universities to raise research funding from industry and to contribute actively to industrial innovation.
Universities have always made a significant contribution to economic development; however, the scale of current university research and the increased reliance on knowledge in production processes have created strong incentives to develop more efficient ways of transferring the discoveries made in academia to the business word. The heterogeneity among universities (and their success) in terms of maintaining knowledge transfer activities indicates that the amount of (financial and human) resources devoted to their support and the governance and the management of university–industry interactions have an influence on both the frequency and success of these activities (Geuna and Muscio 2008). The importance of appropriate ‘management’ of university–industry interactions becomes clearer when we consider the complexity of the knowledge transfer process.
Knowledge transfer between universities and industry takes place through a variety of mechanisms (D’Este and Patel 2007), ranging from recruitment of university graduates, to personnel exchanges, joint research, contract research, consulting, patents and publications, licensing, spin-off companies, industry funded laboratories and other facilities, and also informal contacts such as meetings and conferences. Flows of tacit knowledge (Grant and Gregory 1997) and informal contacts between industrialists and academics are relevant aspects of university–industry interactions (Bozeman et al. 1995). In principle, management can achieve little by fostering these mechanisms; in many cases, informal relations underlie the establishment of formalized collaborations. Furthermore, the partially tacit nature of knowledge and the complexity involved in putting a price on it (Arora et al. 2002) makes it difficult to design a governance structure that encompasses the right incentives for academics to improve knowledge transfer, but does not interfere with the traditional university missions of knowledge creation and higher education.
2.2 The emergence of TTOs
Reducing the barriers to university–industry interaction and the implementation of effective means to stimulate cooperation, have become priorities for university managers and for industrial policy in general. The emergence of intermediaries has been central in bringing university research to market: TTOs, patent offices, technology parks, business incubators and venture capital funds for business start-ups have been established in many countries, with the involvement of academic institutions.
Whilst contract research and consultancy still represent the largest share of knowledge transfer activities,Footnote 2 in recent years, there has been a substantial increase in public and private investment in TTOs (Link and Scott 2007). As evidenced in the AURIL and Proton surveys for Europe, and the AUTM surveys for the US, the number of TTOs in both Europe and the US has increased dramatically since the late 1990s. TTOs are usually established within universities or public research institutes, although in some countries, such as Canada, internal and external university TTOs coexist (Fisher and Atkinson-Grosjean 2002). Although there are indications of convergence across countries towards a model where the TTO is based within the university and is focused on intellectual property rights, there is still variety in terms of their organization, objectives and operations. Moreover, while there is a substantial agreement in the literature that the TTO is the gateway to university inventions, the TTO’s role also includes establishing a link between the university and industry (Rothaermel et al. 2007) to meet business needs. Colyvas et al. (2002) suggest that the marketing activities of TTOs are critical for inventions in technological areas where the links between academia and industry are weak. In reality, university researchers and firms are usually already embedded in the same formal and informal networks, which renders the TTO’s role in facilitating these relationships minimal. However, including representatives from university and industry in TTOs could contribute to enhancing and validating university–industry relationships.
Despite, divergences of opinion about what constitutes a viable indicator of TTO performance (Rothaermel et al. 2007), many countries have conducted assessments of TTOs’ efficiency. The US is the context that most economists and policy makers cite as the model for the implementation of TTOs. Overall, Coupé (2003) finds evidence that those US universities that established a TTO after the Bayh–Dole Act do seem to have increased their patenting activity more than those that did not.
However, the contribution of TTOs to TT processes is still being debated in the current literature on innovation. Lowe (2006) develops a theoretical model that examines how the relationship between the inventor’s knowledge and the effort to transfer that knowledge influences who develops an invention. The author comes to the perfectly reasonable conclusion that the mission of TTOs should be to assist inventors in the case that they are unable to market their inventions successfully. However, it could be that TTOs will not regard it worthwhile to intervene if the technology in question has only a small commercial value.
Accordingly, Siegel et al. (2004), based on interviews with several faculty members, conclude that in the US many academics do not disclose their inventions to their universities. Siegel et al. (2007) find evidence that the involvement of TTOs may slow down the commercialization process due to a keenness to safeguard researchers’ interests and maximize university returns. Choices related to commercialization are likely to be determined by the university’s perception of the expected financial returns from invention disclosure and their desire (or commitment) to generating economic/knowledge spillovers to the community. However, Markman et al. (2005b) find evidence that the active participation of university inventors is an important determinant of the speed of commercialization. When inventors collaborate actively with TTOs, technologies tend to be commercialized faster and earn higher revenues. These findings lead to the conclusion, which is supported by Friedman and Silberman (2003), that the effectiveness of TTOs depends on their management as much as on university regulations and scientists’ incentives (O’Gorman et al. 2008) to transfer technologies.
Litan et al. (2007) are concerned with what they define as the ‘revenue maximization model of TT’, which rewards university TTOs based on the revenues they generate rather than on the number of inventions that are transferred to industry. The authors find that too often this practice inhibits innovation dissemination. Despite revenue maximization being a central goal for most US TTOs (Markman et al. 2005a), Siegel et al. (2004) conclude that this patent-centered system oversimplifies the TT process as it entails a very linear vision of TT in which a scientific discovery is disclosed, passed to the TTO, licensed and paid for. An excessive focus on patents and licenses and the potential financial rewards for the TTOs (and universities) underestimates other (equally important) means of knowledge diffusion from universities to industry, when only a small fraction of the research conducted is codifiable in patents (Geuna and Muscio 2008).
In the case of the UK, Chapple et al. (2005) find heterogeneity in the relative performance of TTOs. Universities located in regions with higher levels of research and development (R&D) and GDP appear to be efficient at TT, implying that there may be regional spillovers. Chapple et al. (2005) also find relative inefficiency among older TTOs and those located in large academic institutions. Finally, Hatakenaka (2006) in her assessment of public funding for third-stream activities finds that although the ultimate policy objective should be to instil economic and social impact as ‘values’ within universities, one of the most strongly perceived benefits of university–industry relationships is seen in education.
2.2.1 TTOs in Italy
In the case of Italy, IPI (2005), a consulting body within the Ministry for Economic Development, conducted a survey of TT institutions, investigating their activities and how well their services match industry demand for services. IPI identified 305 organizations providing TT services in Italy most of which were not very specialized and were understaffed. Despite delays in the development of initiatives to support TT, the political pressure on universities to commercialize the results of academic research has increased.Footnote 3 Following progressive cuts in research funding, universities have been encouraged to collaborate with industry and to develop initiatives to support TT. By 2000–2005 the majority of Italian universities had established TTOs, and many of these offices dated from much earlier. In some cases, several offices within the same university, responsible for TT tasks (e.g., patent office, research office, incubator, etc.), had been merged into TTO-like offices in an attempt to rationalize their activities and increase efficiency.
According to the findings of the Netval survey (Piccaluga and Balderi 2006) the primary objectives of TTOs in Italy are to diffuse an entrepreneurial culture of research, support university spin-offs and promote economic valorization of research output and academic competencies. In 2005 TTOs were mainly focused on supporting spin-offs, managing intellectual property and licensing, and administering research contracts and university–industry collaborations.
2.3 Hypotheses
We extend the literature by testing hypotheses related to the characteristics of both the TTO and the university where the TTO is located. First we analyze the determinants of university–industry research collaboration, investigating the contribution of university TTOs to research collaboration. Second, we look for empirical evidence for the effects of university and TTO characteristics, research performance and geographical indicators on the probability of TTOs being involved in collaborations.
We assume that the creation of a dedicated office is not a sufficient condition for promoting TT. Trust in the role of TTOs in TT processes is an important incentive for scientists to transfer technologies via these offices.
Some empirical works (Friedman and Silberman 2003) find evidence that rewarding faculty for involvement in university TT enhances its effectiveness. Markman et al. (2005b) conclude that the active participation of researchers in commercialization greatly increases the speed of innovation and the effectiveness of TTOs. Along these lines, we investigate how much faculty staff are convinced about the role of TTOs in TT, and whether greater trust in their effectiveness among university department directors encourages researchers to involve their university TTO when collaborating with industry. These aspects are particularly relevant in the case of Italy where in many cases, TTOs are quite new and are understaffed. It is important for scientists involved in TT to trust in the potential support of TTOs and collaborate with them for the commercialization of research.
Hypothesis 1
Academics’ confidence in TTOs drives university use of TTOs
Markman et al. (2005a) argue that persuading science and engineering faculty and their departments to disclose inventions is challenging, as researchers place high value on basic research leading to publication, and tend to ignore service activities such as technology commercialization. Siegel et al. (2003) provide evidence of the difficulty involved in bridging the cultural divide between university and industry. They recommend that the people hired by universities to manage TT should have a background in industry. This is fundamental for reducing the cognitive distance between managers and academics. In light of this argument, we suggest that to have credibility and work cooperatively with both scientists and industrialists, TTO managers need to understand and have the respect of academics and also have a good understanding and/or experience of the business world. Thus, TTO managers without an academic background can contribute greatly to TT, by scouting departments for marketable research, assessing the commercial potential of technologies and transferring it to firms. We argue that the involvement of industry in the management in TTOs affects their capability to mediate between scientists and companies and increases the probability of TTOs’ involvement in university–industry collaborations.
Hypothesis 2
When TTOs are managed by professional staff with industry background academics are more likely to collaborate with them
Jensen et al. (2003), find that scientists are reluctant to disclose their inventions and to allocate time to TT, with the result that only some 50% of inventions are disclosed, many of which are of questionable quality. Here, we argue that research performance can have a major influence on university use of TTOs. Those departments engaged in higher quality research are more likely to generate good research results that can be transferred to industry with the help of the TTO. Therefore, research performance drives the involvement of TTOs in university–industry collaborations. A secondary implication of this hypothesis is that despite the relevance of TTOs for TT, universities and policy makers need to support TTOs by improved academic research performance.
Hypothesis 3
Academic research performance drives academics’ use of TTOs
3 Empirical analysis
3.1 Description of the data
The empirical analysis is based on a web survey, carried out in June to September 2007,Footnote 4 which targeted university departments in Italy engaged in research in the Engineering and Physical Sciences (EPS).Footnote 5 Questionnaires were addressed to the department director of 1,047 EPS departments.Footnote 6 We received 197 completed questionnaires, a response rate of 18.8%. Survey respondents completed detailed questionnaires requesting information on TT activity and university–industry collaboration agreements signed by departments in the previous 3 years. Table 1 reports the distribution of the departments contacted across the nine EPS scientific areas (SA) and shows that the sample is highly representative. When we classify departments represented in the sample according to SA and size of academic institution we can see that the differences between the weights of each typology of department and the corresponding total population, in most cases are less than 3%.Footnote 7
Over 72% of departments confirm the presence of some kind of TT facilitator in their institution (Table 2). In the majority of cases this was an ILO, a TTO or a patent office. On average, TTOs are 4.5 years old. University TTOs are generally managed solely by the university; in a few cases other academic institutions or regional government bodies are involved in their management. Universities usually appoint a professor or a university administrator to be TTO manager. In less than 19% of cases professional non-academic managers are in charge of the TTO.
3.2 University–industry collaborations
In the 3 years previous to the survey, 85.8% of departments had collaborated with industry. In 43.1% of cases the frequency of collaborations has increased over time and in 38.6% of cases it has been stable. On average, there are four collaborations for every ten research staff in a department. Departments receive 21.0% of their research funding from industry. Scientific area and university size do not seem to influence the frequency of collaborations greatly.Footnote 8
The types of collaboration with industry vary widely (Table 3). In the period analyzed, university department collaboration with industry included research contracts (39.9% of cases), consultancies (22.2%) and joint research projects (11.5%).
Interviewees were asked to indicate the location and size of their main business partners (Table 4). Departments are not limited to collaboration with firms in their immediate area. They collaborate mainly with small and medium enterprises, either in their region or in the rest of Italy. There is also some interaction with foreign enterprises: in 31.5% of cases collaboration agreements had been signed with partners in other countries.
In 47.1% of cases, collaborations are established directly, between department and company, and do not involve a third party. Table 5 shows that collaborations are promoted directly by a professor (82.7%), a department (43.5%) or a company representative (48.2%). University TTOs were responsible for only 9.5% of collaborations and in only a very few cases were collaborations based on contacts provided by other TTOs.
Interviewees were asked to indicate the main barriers to interaction with industry (Table 6). Absence or low profile of a university TTO was found to be an issue, but not as major as some other aspects. One of the main barriers to interacting with industry is the lack of funding programmes for joint research. Also, departments find it difficult to identify appropriate business partners and make contact with them. Another barrier is the short-term orientation of industry research and lack of understanding on both sides about expectations and working priorities. Finally, the absence of established procedures for collaboration with industry limits university departments’ abilities to cooperate. Thus, whilst the low profile of TTOs may not constitute a barrier to interaction, there are many areas where their intervention—to find business partners and broker collaborations—would reduce the cognitive distance between university scientists and industry.
Interviewees were asked for a qualitative assessment of the share of their department’s research transferred with the support of the university TTO, and the TTO’s contribution to their work (Table 7). They were provided with a Likert scale from 1 = very low to 5 = very high. Overall, the share of research transferred via a TTO is relatively low (1.9): however, the importance of TTO to TT and its contribution to collaborations when involved is above average. As expected, there are significant differences in use of TTOs across different SA. AGR-VET and BIO clearly make the most use of TTOs: however, the importance of TTOs to TT is higher in CHIM and ICAR. Finally, TTOs contribute the most to collaborations in the area of GEO, MED, and ICAR. The involvement and the contribution of TTOs in the area ING IND-INF is modest, probably because of the more applied nature of the research carried out in this area to the industry context.
Interviewees were also asked to indicate the reasons for not involving TTOs (Table 8). In most cases departments did not involve the TTO in their collaboration arrangements because companies had contacted them directly. TTO experience was a negligible factor.
3.3 Econometric analysis
3.3.1 Description of the variables
The empirical evidence in this section is based on a set of regression models. The first part of the analysis investigates the determinants of university–industry collaboration. This section introduces the core of our investigation on the basis of which we estimate the determinants of TTO involvement in collaborations. Table 9 presents information on the variables used in the regressions.
COLLAB_R is the number of research collaborations signed by a department in 2004–2007 in the areas of: spin-offs, collaborative research agreements, research contracts and consultancies.
COLLAB_TTO is a dummy variable controlling for the involvement of university TTOs in collaborative agreements signed in 2004–2007. The independent variables control for four types of determinants: the characteristics of departments, research indicators, geographical indicators and TTO characteristics.
3.3.1.1 University department characteristics
The variable TTO accounts for the presence of a TTO in the university. TTOs can support research staff in the establishment and management of collaborations.
EXP_DIR_DEPT measures department director’s experience (number of years in post). Directors with longer experience might be more familiar with the research being conducted in the department and more knowledgeable about potential business partners. However, as TTOs are a relatively recent initiative to support TT, extensive experience in TT via particular activities or with specific (kinds of) partners could be detrimental to use of TTOs.
SIZE_DEPT and SIZE_UNI, respectively, account for department size and university size expressed in numbers of research staff. We assume that larger universities will potentially benefit from greater visibility, greater specialization of departmental research and more efficient procedures for the establishment and management of collaborations.
POLYTECHN controls for the location of the department in a polytechnic university (four in Italy). Polytechnic universities have numerous departments involved in research in the EPS. Universities with more EPS departments are likely to have more refined practices for collaborating with companies in EPS, and better services in this area.
3.3.1.2 Research indicators
RES_APPLIC controls for the impact of the applicability of the research to the industry context. Interviewees were asked to rank the applicability to industry of research carried out in their departments on a Likert scale ranging from 0 = not applicable, to 5 = very high. Mansfield (1998) proved that research applicability to an industry context has a positive effect on the frequency of university–industry collaborations.
RES_RATING measures research performance and the rating obtained in 2001–2003 according to VTR (MIUR 2007).Footnote 9 Mansfield finds evidence that research quality drives the frequency of university–industry collaborations (Mansfield 1995). If the mission of TTOs is to commercialize university research, better quality research output could generate more work for them.
COGN_DIST controls for the department director’s opinion as to the relevance of cognitive distance between professors and industrialists (low–medium–high). Cognitive distance refers to differences in work methodologies and use and interpretation of knowledge. Etzkowitz (1998) describes the cognitive effects of entrepreneurialism on academic culture. Nooteboom et al. (2007) provide evidence of the effects of differences in technology profiles between universities and firms on innovation performance and find an inverted-U shaped relationship in the case of alliances based on exploratory research.
3.3.1.3 Geographical indicators
SOUTH controls for location of the department in southern Italy. For universities located in rural areas, with low levels of industrialization, high unemployment and poor infrastructures could affect the capability of university departments to establish collaborations, and provide a greater challenge to TTOs in brokering collaborations.
SB_IND_PRO measures industrial activity in the area of the university and is expressed as the number of firms in the science based industries located in the university’s province.Footnote 10 The definition of a science-based industry is based on Marsili and Verspagen (2002) who updated the seminal classification of technological regimes proposed by Pavitt (1984). Science-based firms are assumed to make greater use of university knowledge; thus, being in an area where potential demand for TT services is high, could increase the frequency of university interactions with industry. This variable also controls for TTOs’ contribution to TT when the university is located near science-based firms.
3.3.1.4 TTO characteristics
TTO_AGE controls for the number of years of TTO activity. Longer experience is usually equated with their greater involvement in university departments’ collaborations with firms, or greater use of them to find appropriate business partners.
TTO_UNI_GOV controls for whether the TTO is governed exclusively by the university or is managed with the involvement of other universities, government bodies or companies. In some cases, a TTO may serve more than one academic institution or be promoted by a local government body. It is assumed that when a single university has sole control of the TTO, its mission will be better defined (e.g., to commercialize its own university’s research) and departmental use of it will be greater.
TTO_MNGMT controls for management of the university TTO, and determines whether the involvement of professional non-academic management encourages great use of the office. Non-academic management may be more appropriate for assessing the commercial potential of technologies and interaction with companies.
TTO_OPINION controls for the department director’s opinion of the importance of TTOs for TT, scored on a Likert scale (1 = very low to 5 = very high). Intuitively, the higher the director rates the services provided by the TTO for the purpose of TT, the higher will be the likelihood of the department contacting the office.
Finally, we control for departmental scientific research area (sa1mat to sa9ing) because differences in research areas can have an impact on frequency and ease of use of TTOs. Table 10 reports some descriptive statistics for the variables included in the regressions.
3.3.2 Estimation of the determinants of university–industry research collaboration
This section provides empirical evidence on the determinants of university–industry research collaborations. Since the dependent variable is based on count data and the distribution of collaborations is very skewed, we apply a negative binomial regression model. Table 11 reports the estimated parameters and marginal/impact effects.
The results for larger departments located in polytechnic universities show that they are more active in establishing research collaborations. Size of university does not have an impact on frequency, the estimated coefficient being close to zero. Presence of a TTO at the university has no discernible effect on collaborations. Research collaboration agreements are established mainly between departments or professors and firms directly, and rarely involve TTOs. These offices intervene in the “transfer” of research output outside academic institutions. Even were they prepared to provide services to valorize the research efforts of departments, they would not be able to respond to the broad spectrum of universities’ TT needs. Typically, as shown by Piccaluga and Balderi (2006), they are inadequately staffed, with employees with backgrounds in administration rather than technology. On the other hand, as expected there are differences among scientific areas: those departments carrying out research in Industrial Engineering are much more likely than medical departments to be involved in frequent collaborations with industry.
Academic institutions located in southern Italy are disadvantaged with respect to institutions located in central and northern Italy in terms of establishing collaborations with firms (SOUTH). However, the proximity of the university to science-based firms does not seem to affect the intensity of university–industry collaborations (SB_IND_PRO). Apparently, collaborations are not confined to the local industry: departments are also capable of looking beyond their own regions to find potential business partners.
Applicability of research (RES_APPLIC) has a positive impact on frequency of collaborations, with a very high estimated marginal effect (for a one-point increase in research applicability the frequency of collaborations increases by +5.7%). However, research performance (RES_RATING) has no significant impact of frequency of collaborations. Therefore, what really matters for frequency of collaborations is not how much research is conducted or how good the research is, but how applicable it is to the industry context. Department director’s experience is important for this purpose (EXP_DIR_DEPT): experienced directors sign more agreements than inexperienced ones.
Rather surprisingly, departments that considered there to be a great cognitive distance between researchers and industrialists collaborate more with firms than those that attributed a lower score to cognitive distance (COGN_DIST). This provides partial support for Nooteboom et al. (2007) findings of an inverted-U shaped relationship for university–industry collaborations with respect to cognitive distance. One possible explanation for this is that departments that perceive a greater cognitive distance are more experienced in interacting with industrialists and therefore have a deeper understanding of the different mindsets in industry.
3.3.3 Determinants of departments’ use of TTOs
This section investigates the determinants of departments’ use of TTOs. Since the dependent variable is a dummy variable that takes the value 1 if the university TTO is involved in collaborations and 0 if it is not, we chose a probit model to estimate the probability of an event. Table 12 reports the probit estimates and corresponding marginal/impact effects.
We run two regressions; one with and one without TTO characteristics. First, without TTOs’ characteristics, the model shows that the probability of using TTOs is driven by the applicability of the research and research performance. Agriculture and Veterinary Departments are more likely to contact TTOs.
Size of department and university, or location of the department in a polytechnic university, do not affect the probability of accessing a TTO. Location of universities in southern Italy does not reduce use of TTOs, while location in an area that has several science-based firms negatively affects the probability of accessing the TTO. Presumably, this is because, especially in the case of research collaborations, TTOs are likely to be involved with firms located away from the department. In some cases, especially in southern Italy, TTOs routinely identify demand from local firms. TTOs negotiating licensing out are typically not constrained by local demand for technology. Chapple et al. (2005) confirm this, finding that in some regions, due to lower levels of R&D and economic activity, universities are less efficient at the commercialization of technology.Footnote 11
When TTOs’ characteristics are included in the model, the results show that those departments that collaborate more frequently with industry are also more likely to contact TTOs (COLLAB_R).
In contrast to the case of university–industry research interactions, length of time in the post of department director will have a negative effect on the probability of a department contacting the TTO (EXP_DIR_DEPT). Arguably, less experienced and younger directors may be more likely to trust TTOs, while those with longer experience will be less likely (and have less need) to use these “new” forms of support for TT activities and collaboration.
Higher application of research has a positive effect on use of TTOs only in the regressions where TTO characteristics are not included. Therefore, TTO activities compensate for lower applicability of research outcomes, possibly by helping to find business partners interested in technologies based on more basic research or in turning those technologies into commercial products.Footnote 12 However, in order for departments to interact with TTOs it is necessary for there to be good research to transfer: the estimated coefficient for the variable RES_RATING is high and highly significant in both regressions. The significance of the coefficient increases when TTO indicators are included in the model (a 1% increase in the research rating generates a 2.8% in the probability that the department will contact the TTO). Thus, it is the best universities measured in terms of research that make the greatest use of TTOs. We can conclude, then, that departments access TTOs when they have good research to transfer, and seem not to require their services when research results are already close to market.
Cognitive distance has no significant impact on whether departments access TTOs and, in most cases, one of the responsibilities of the TTO is to broker between researchers and firms, and bridge between the cultures. In other words, use of TTOs is not influenced by how much university and industry mindsets differ. There is instead strongly significant evidence that universities make greater use of TTOs that are run by non-academic managers (TTO_MNGMT). Sloman (2007) underlines that TTO management requires strong administrative, technical and communication skills. In order to make informed decisions TTO managers need to analyze and interpret large quantities of very diverse information. Siegel et al. (2004), based on suggestions from university and business stakeholders about how to improve TTOs’ effectiveness, suggest that TTOs require marketing competencies. Thus, we can conclude that if TTO managers have some experience in company management in addition to an academic background they will have a better understanding of business needs which will improve the effectiveness of the TTO and the TT activity.
TTO’s age is not a relevant factor (TTO_AGE). Longer established TTOs appear to be just as efficient as newer ones, suggesting an absence of learning effects (Chapple et al. 2005; Friedman and Silberman 2003). We also find that when TTOs are managed solely by the university with no participation from regional government bodies, chambers of commerce, etc., departments are more likely to contact them (TTO_UNI_GOV). One explanation might be that university-governed TTOs are likely to have better-defined missions that respond to the needs of one research institution, and the transfer of research outcomes to possibly a smaller number of industry sectors. And Friedman and Silberman (2003) find evidence that universities whose TTOs have a clear mission and well defined objectives for generating licenses and license income are generally more productive in these areas.
Finally, as expected, if the department director has a good opinion of the TTO, the office he or she will be more likely to involve it in collaborations (TTO_OPINION). Research shows that there is significant variation in the use and perceived value of the support offered by TTOs and incubators (Hackett and Dills 2004). A crucial first step in TT is to persuade academic staff to disclose their potentially valuable innovations to the TTO (Owen-Smith and Powell 2001). Siegel et al. (2004) provide evidence that in the majority of cases company managers and scientists see the TTO as an obstacle rather than a facilitator. Therefore, convincing university staff of the potential utility of a TTO’s services is the first step towards successful commercialization. Especially in the case of Italy, most TTOs lack the resources and competencies necessary to search a wide range of laboratories and research groups for commercially viable technologies. Thus, the involvement of a TTO in collaborations largely depends on faculty perceptions of the benefits that the TTO brings to the university.
4 Conclusions
The evidence provided in this paper shows that, in the case of Italy, TTOs’ contributions to university–industry collaborations are marginal. However, TTOs in Italy are recent initiatives and efforts are needed to integrate their activities into academic institutions and establish effective procedures to support TT. Whilst TTOs’ inefficiencies are not a barrier to interactions, there is scope for TTO intervention in several areas, which could provide dramatic improvements to academic interactions with industry. Departments need help with finding and mediating with business partners.
Overall, our evidence shows that research performance, business-oriented management of TTOs and greater receptiveness of university departments to TTO services, positively affect the probability of the TTO being involved in university–industry collaboration. Managing a TTO requires special skills to facilitate the matching of academic knowledge, competencies and resources to business needs, and provide assistance in the commercialization and pricing of technology. The involvement of professional, non-academic managers in TTOs will support these activities and help to bridge the cultural gap between university and industry. There is significant evidence that universities make greater use of TTOs when they are run by non-academic managers and would explain why some TTOs are more effective than others in managing university intellectual property (Siegel et al. 2003). However, TTO managers also need the support of university departments to be effective. Our investigation shows that working through the TTO is more common for departments directed by younger academics that have more trust in TTO activities. Arguably, more effort should be made to promote the activities of these offices and to increase their visibility within their universities.
This paper also provides evidence that the university’s research performance affects scientists’ use of TTOs. Those departments with good research output are more likely to contact TTOs to transfer their results to market, and companies are more likely to contact those TTOs located in universities with a good research ranking, in order to access their results. This has some implications in light of recent cuts in government funding for research in Italy. If encouraging universities to create TTOs improves academic capabilities to commercialize research results and raise third-stream funding, then cutting research funding will undermine research performance and jeopardize the effectiveness of TTOs to transfer university research. These conclusions are supported by the findings of a recent Inno-Metrics report produced for the European Commission (Hollanders and Celikel Esser 2007), which measures innovation efficiency in Europe and other peer countries. The report defines Italy as a ‘moderate innovator’ combining above average innovation efficiency scores—in terms of both efficiency of transforming innovation inputs into applications outputs, and intellectual property outputs. However, the report concludes that it may be difficult for Italy to improve its innovation performance without increasing its innovation inputs.
Finally, most universities manage their own TT operations, but very few have sufficiently strong research bases to allow the establishment of high-quality offices. The Lambert (2003) review found that a barrier to commercializing university intellectual property lies in the variable quality of TTOs. This paper confirms that research performance drives academic use of TTOs and therefore affects the licensing out process. In accordance with the Lambert review, in the case of small universities located in rural areas policy-makers should encourage the development of shared TT services established on a regional basis. This thesis is supported by Litan et al. (2007)who suggests that groups of universities should form consortia that will share commercialization costs among participants and maximize the number of contracts.
Our study has some limitations. First, our findings are based on cross-section analysis and we do not have information on trends in university use of TTOs. Second, we our qualitative information on the management of TTOs and university regulations on research commercialization is limited which inevitably affects our ability to control for the effectiveness of university initiatives supporting TT. Third, this analysis, similar to others in the same area, only elicits the opinions of university faculty. A more complete picture of the role of TTOs in university–industry collaborations would require complementary fieldwork on how industrialists perceive the role of TTOs in the TT process. Our study could be extended by comparing the data presented here on frequency of collaborations with university department revenues. This would provide a picture of the role of TTOs in supporting departments’ capability to generate revenue to finance future research.
Finally, our results cast some doubt on the impact of cognitive distance on university–industry interactions. They show that cognitive distance has a positive impact on the frequency of interactions but no significant impact on university use of TTOs. In other words, as differences in mindsets between stakeholders grow bigger, so does the frequency of collaborations. These results partially support the findings of Nooteboom et al. (2007) who find an inverted-U relationship between cognitive distance and innovation performance. However, their work is based on patent data and not qualitative information. Helping scientists to communicate with business partners (and vice versa) is a key mission for many TTOs; therefore TTOs will influence the cognitive distance between stakeholders. We would argue that the relationship between cognitive distance, and innovation and commercialization needs to be investigated in more depth in order to provide a better understanding of the role of TTOs in facilitating communication and supporting collaboration.
Notes
See Landry et al. (2007) for an overview.
See, e.g., the results of the UK HE-BCI survey or Muscio (2008).
For, e.g., the two national laws D.L. 27/7/1999 no. 297 and D.M. 8/8/2000 no. 593 encourage and regulate the creation of university TTOs. Art. 65 of the Codice dei Diritti di Proprietà Industriale, 10/02/2005, introduced Bayh-Dole act-like regulation of intellectual property, granting intellectual property rights to scientists for their scientific discoveries. You took out qualification of the acro IPR in the text so either it must be reinstated or you must establish it in the footnote so that you continue to use the short form in the fn or spell out in footnotes as well, which would be more consistent.
The survey was carried out by Gruppo di Ricerche Industriali e Finanziarie (GRIF)—Università Luiss Guido Carli (Roma) as part of the research project “The Governance of Technology Transfer in Italy”, funded by the Italian Ministry of University and Research (MIUR), Progetto FIRB: “A Multidimensional Approach to Technology Transfer”.
The National University Council (CUN) classification of scientific areas is similar to that applied by the OECD in its Frascati Manual (OECD 2002). The scientific areas considered here correspond to the areas identified by the Frascati Manual as: (1) Natural Sciences, (2) Engineering & Technology, (3) Medical Sciences, (4) Agricultural Sciences.
The list of departments is available at: www.cineca.it. Contact details were found for 1,047 out of 1,116 department directors.
This is supported by the results of the econometric analysis: estimates were similar with and without population weighting.
Apart from Industrial Engineering, where there is around one collaboration per research staff member.
In 2005 CIVR conducted the Valutazione Triennale della Ricerca (VTR), the first national evaluation of research activity in Italy, covering 2001–2003 (MIUR 2007). The review targeted the research performance of all universities and public research centres. The 102 research institutions involved in the review submitted a predefined number of research outputs (books, book chapters, journal articles, patents and other types of economic valorization of research results, manufactured and artistic products) to an expert panel for peer review. The number of research outputs per institution was defined based on university size (full-time-equivalent research staff). The VTR rated and ranked university research performance and assessed the impact factor of outputs.
Provinces correspond to NUTS 3 units in the Eurostat classification of administrative units in Europe. The average dimension of an Italian province is 2,926.6 km2. The data refer to the 2001 Census carried out by ISTAT.
Chapple et al. (2005) suggest that in these geographical contexts, government should establish regional TTOs to offer additional assistance to both universities and businesses.
Jensen and Thursby (2001) in their study on university licensing found that the majority of licensed technologies are not ready for commercialization and require significant development to turn them into commercial products.
References
Arnold, E., Brown, N., Eriksson, A., Jansson, T., Muscio, A., Nählinder, J., & Zaman, R. (2006). The Role of industrial research institutes in the national innovation system: A report to VINNOVA, Technopolis, October.
Arora, A., Fosfuri, A., & Gambardella, A. (2002). Markets for technology—The economics of innovation and corporate strategy. Cambridge: MIT Press.
Bozeman, B. (2000). Technology transfer and public policy: A review of research and theory. Research Policy, 29, 627–655.
Bozeman, B., Papadakis, M., & Coker, K. (1995). Industry perspectives on commercial interactions with federal laboratories: Does the cooperative technology paradigm really work? Report to the National Science Foundation. Research on Science and Technology Program.
Branscomb, L. M., Kodama, F., & Richard, F. (1999). Industrializing knowledge: University–industry linkages in Japan and the United States. London: MIT Press.
Branstetter, L., & Ogura, Y. (2005). Is academic science driving a surge in industrial innovation? Evidence from patent citation. National Bureau of Economic Research, NBER Working Paper 11561, August.
Breschi, S., Lissoni, F., & Montobbio, F. (2007). The scientific productivity of academic inventors: New evidence from Italian data. Economics of Innovation and New Technology, 16(2), 101–118.
Chapple, W., Lockett, A., Siegel, D., & Wright, M. (2005). Assessing the relative performance of UK university technology transfer offices: Parametric and non-parametric evidence. Research Policy, 34, 369–384.
Cohen, W. M., Florida, R., Randazzese, L., & Walsh, J. (1998). Industry and the academy: Uneasy partners in the cause of technological advance. In R. G. Noll (Ed.), Challenges to research universities (pp. 171–200). Washington, DC: The Brookings Institution. (ch. 7).
Colyvas, J., Crow, M., Gelijns, A., Mazzoleni, R., Nelson, R. R., Rosenberg, N., et al. (2002). How do university inventions get into practice? Management Science, 48(1), 61–72.
Coupé, T. (2003). Science is golden: Academic R&D and university patents. Journal of Technology Transfer, 28, 31–46.
D’Este, P., Nesta, L., & Patel, P. (2005). Analysis of university–industry research collaborations in the UK: Preliminary results of a survey of university researchers. SPRU Report for a project funded by EPSRC/ESRC.
D’Este, P., & Patel, P. (2007). University–industry linkages in the UK: What are the factors underlying the variety of interactions with industry? Research Policy, 36, 1295–1313.
Etzkowitz, H. (1998). The norms of entrepreneurial science: Cognitive effects of the new university–industry linkages. Research Policy, 27, 823–833.
Fisher, D., & Atkinson-Grosjean, J. (2002). Brokers on the boundary: Academy-industry liaison in Canadian universities. Higher Education, 44, 449–467.
Friedman, J., & Silberman, J. (2003). University technology transfer: Do incentives, management, and location matter? Journal of Technology Transfer, 28, 17–30.
Geuna, A., & Muscio, A. (2008). The governance of university knowledge transfer. SPRU Electronic Working Paper Series, September, SEWP173.
Grant, E. B., & Gregory, M. J. (1997). Tacit knowledge, the life cycle and international manufacturing transfer. Technology Analysis and Strategic Management, 9(2), 149–161.
Guldbrandsen, M., & Smeby, J. C. (2005). Industry funding and university professors’ research performance. Research Policy, 34, 932–950.
Hackett, S., & Dills, D. (2004). A systematic review of business incubation research. Journal of Technology Transfer, 29(1), 55–82.
Hatakenaka, S. (2006). Development of third stream activity: Lessons from international experience. Oxford: Higher Education Policy Institute.
Hollanders, H., & Celikel Esser F. (2007). Measuring innovation efficiency. INNO-Metrics Thematic, December.
IPI—Istituto per la Promozione Industriale. (2005). Indagine sui centri per l’innovazione e il trasferimento tecnologico in Italia, a cura del Dipartimento Centri e Reti Italia, Direzione Trasferimento di Conoscenza e Innovazione, Novembre, Roma.
Jensen, R., & Thursby, M. (2001). Proofs and prototypes for sale: The tale of university licensing. American Economic Review, 91(1), 240–259.
Jensen, R. A., Thursby, J. G., & Thursby, M. C. (2003). The disclosure and licensing of university inventions: The best we can do with the s**t we get to work with. International Journal of Industrial Organization, 21(9), 1271–1300.
Lambert, R. (2003). Lambert review of business–university collaboration: Final report. HM Treasury, December.
Landry, R., Amara, N., & Ouimet, M. (2007). Determinants of knowledge transfer: Evidence from Canadian university researchers in natural sciences and engineering. Journal of Technology Transfer, 32, 561–592.
Link, N., & Scott, J. T. (2007). The economics of university research parks. Oxford Review of Economic Policy, 23(4), 620–639.
Litan, R. E., Mitchell, L., & Reedy, E. J. (2007). Commercializing university innovations: Alternative approaches. Available at SSRN: http://ssrn.com/abstract=976005
Lowe, R. A. (2006). Who develops a university invention? The impact of tacit knowledge and licensing policies. Journal of Technology Transfer, 31, 415–429.
Mansfield, E. (1995). Academic research underlying industrial innovation: Sources characteristics and financing. Review of Economics and Statistics, 77, 55–65.
Mansfield, E. (1998). Academic research and industrial innovation: An update of empirical findings. Research Policy, 26, 773–776.
Markman, G. D., Gianiodis, P. T., Phan, P. H., & Balkin, D. B. (2005a). Innovation speed: Transferring university technology to market. Research Policy, 34, 1058–1075.
Markman, G. D., Phan, P. H., Balkin, D. B., & Gianiodis, P. T. (2005b). Entrepreneurship and university-based technology transfer. Journal of Business Venturing, 20(2), 241–263.
Marsili, O., & Verspagen, B. (2002). Technology and the dynamics of industrial structure: An empirical mapping of Dutch manufacturing. Industrial and Corporate Change, 11(4), 791–815.
MIUR—Ministero dell’Università e della Ricerca. (2007). CIVR, Comitato per la Valutazione della Ricerca, VTR 2001–2003, Relazione Finale, Roma.
Muscio, A. (2008). Il trasferimento tecnologico in Italia: risultati di un’indagine sui dipartimenti universitari. L’Industria Numero Speciale, pp. 245–268.
Nooteboom, B., van Haverbeke, W., Duysters, G., Gilsing, V., & van den Oord, A. (2007). Optimal cognitive distance and absorptive capacity. Research Policy, 36, 1016–1034.
O’Gorman, C., Byrne, O., & Pandya, D. (2008). How scientists commercialise new knowledge via entrepreneurship. Journal of Technology Transfer, 33, 23–43.
OECD. (2002). Frascati manual: Proposed standard practice for surveys on research and experimental development. Paris: OECD.
OECD. (2003). Turning science into business: Patenting and licensing at public research organisations. Paris: OECD.
Owen-Smith, J., & Powell, W. W. (2001). To patent or not: Faculty decisions and institutional success at technology transfer. Journal of Technology Transfer, 26, 99–114.
Pavitt, K. (1984). Sectoral patterns of technical change: Towards a taxonomy and a theory. Research Policy, 13, 353–369.
Piccaluga, A., & Balderi, C. (2006). La valorizzazione della ricerca nelle università italiane, Quarto rapporto annuale (dati relativi al periodo 2002–2005). Netval.
Rothaermel, F. T., Agung, S., & Jiang, L. (2007). University entrepreneurship: A taxonomy of the literature. Industrial and Corporate Change, 16(4), 691–791.
Siegel, D. S., Veugelers, R., & Wright, M. (2007). Technology transfer offices and commercialization of university intellectual property: Performance and policy implications. Oxford Review of Economic Policy, 23(4), 640–660.
Siegel, D. S., Waldman, D., Atwater, L., & Link, A. N. (2004). Toward a model of the effective transfer of scientific knowledge from academicians to practitioners: Qualitative evidence from the commercialization of university technologies. Journal of Engineering and Technology Management, 21(1–2), 115–142.
Siegel, D. S., Waldman, D., & Link, A. (2003). Assessing the impact of organizational practices on the relative productivity of university technology transfer offices: An exploratory study. Research Policy, 32, 27–48.
Sloman, R. G. (2007). Technology transfer data management. In intellectual property management. In A. Krattiger, R. T. Mahoney, L. Nelsen, et al. (Eds.) Health and agricultural innovation: A handbook of best practices. Oxford, UK: MIHR, and Davis, California, USA: PIPRA. Available at http://www.ipHandbook.org
Acknowledgments
This work benefited from valuable comments from Francesco Crespi, Jean-Michel Dalle, Francesco Ferrante, Aldo Geuna, Georg Licht, Francesco Lissoni, Mauro Mallone, Laura Ramaciotti and two anonymous referees. The author would like to acknowledge the financial support of the Italian Ministry of University and Research (FIRB Project 2003—Prot. RBNE033K2R: “A multidimensional approach to technology transfer for more efficient organizational models”). Finally the author is grateful to the late Professor Fabio Gobbo for his guidance and encouragement throughout this work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Muscio, A. What drives the university use of technology transfer offices? Evidence from Italy. J Technol Transf 35, 181–202 (2010). https://doi.org/10.1007/s10961-009-9121-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10961-009-9121-7