Antonio messeni petruzzelli


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Antonio messeni petruzzelli

  1. 1. Paper to be presented at the Summer Conference 2009 on CBS - Copenhagen Business School Solbjerg Plads 3 DK2000 Frederiksberg DENMARK, June 17 - 19, 2009UNIVERSITY-INDUSTRY R&D COLLABORATIONS: A JOINT-PATENTS ANALYSIS Antonio Messeni Petruzzelli DIMeG - Politecnico di Bari a.messeni-petruzzelli@poliba.itAbstract:Empirical studies on R&D collaborations between universities and firms have mainly centred their attentionon universities and firms characteristics and strategies that favour the establishment of collaborative agreements.In this paper, we extend the current research framework investigating the role that specific relational attributesmay play on the relevance of such collaborations. Specifically, we focus on three relevant factors, namelytechnological relatedness, national culture similarity, and prior collaborations ties between universities andfirms. We develop testable hypotheses about their impact on the innovative performance of R&D university-industry collaborations, and test them on a sample of 796 university-industry collaborations, established by 27universities located in 12 different European countries.Our results suggest that innovation value has an inverted U-shaped relation with partners technologicalrelatedness. In addition, universities and firms belonging to similar cultural contexts and having had previousties are more able to achieve better innovative outcomes. JEL - codes: O32, -, -
  2. 2. University-Industry R&D Collaborations: A Joint-Patents Analysis ABSTRACTEmpirical studies on R&D collaborations between universities and firms have mainly centred their attentionon universities and firms’ characteristics and strategies that favour the establishment of collaborativeagreements. In this paper, we extend the current research framework investigating the role that specificrelational attributes may play on the relevance of such collaborations. Specifically, we focus on threerelevant factors, namely technological relatedness, national culture similarity, and prior collaborations tiesbetween universities and firms. We develop testable hypotheses about their impact on the innovativeperformance of R&D university-industry collaborations, and test them on a sample of 796 university-industry collaborations, established by 27 universities located in 12 different European countries.Our results suggest that innovation value has an inverted U-shaped relation with partners’ technologicalrelatedness. In addition, universities and firms belonging to similar cultural contexts and having hadprevious ties are more able to achieve better innovative outcomes.Key words: university-industry collaborations; innovation value; relational attributes 1. INTRODUCTIONNowadays, it is well understood that the creation and application of new knowledge are the primary factorsthat drive the economic growth. Moreover, it is also commonly accepted that universities are importantsources of new knowledge, especially in the areas of science and technology (e.g. Rosenberg and Nelson,1994; Nelson and Rosenberg, 1998; Etzkowitz and Leydesdorff, 2000). Thus, researchers have devoted agreat effort to investigate the nature and the importance of the relationships between university and industry,tying to build a clear picture of which mechanisms may favour universities and firms interaction, thuspromoting knowledge transfer and acquisition. A better comprehension of university-industry links hasassumed a great importance also at policy level, as shown by the several initiative launched by the EuropeanCommission to proactively enhance the transfer of technological knowledge from university to industry andidentify effective and efficient innovation policies.The aim of this study is to contribute to the analysis of university-industry relationships, focusing on the rolethat relational attributes may play on the relevance of such collaborations. In fact, studies on this topic havemainly centred their attention on the universities and firms’ characteristics and strategies that favour theestablishment of collaborative agreements. In particular, universities’ entrepreneurial orientation, faculty -1-
  3. 3. incentive mechanisms, national policies, government support, type of industry, and involvement incomplementary innovative activities have been described as the main important factors leading universitiesand firms to fruitful collaborate (e.g. Debackere and Veugelers, 2005; Veugelers and Cassiman, 2005;Rothaermel et al., 2007). Nevertheless, few attention has been devoted to understand how relational specificattributes may affect the value of university-industry R&D collaborations. In an attempt to fill this gap, weidentify three relevant such attributes, namely technological relatedness between partners, national culturesimilarity, and previous collaboration ties.Collecting data from the European Patent Office (EPO), we study university-industry collaborations in termsof joint patents, and present an econometric analysis examining the impact of the three relational variableson the value of the collaboration innovative output. 796 collaborations are considered, developed by 27universities located in 12 countries belonging to the European Union. Results show that the value associatedto university-industry joint innovations presents an inverted U-shaped relation with partners’ technologicalrelatedness, and it is favoured by national culture similarity and the existence of previous collaboration tiesbetween the organizations.The paper is structured as follows. Section 2 reports the theoretical background, analysing the relevance ofknowledge complementarity and collaboration agreements to innovate, and the role of universities asknowledge sources. Section 3 presents the hypotheses about the influence of technological relatedness,national culture similarity, and prior collaboration ties on the innovation value of university-industrycollaborations, whereas in Section 4 the research methodology and approach are described. Finally, Section5 and 6 discuss the main research results and conclusions, respectively. 2. THEORY2.1. Knowledge Complementarity & InnovationThere is a strong consensus in the literature (e.g. Hamel and Prahalad, 1994) that the development ofinnovation is strongly related to the organizations’ capability to collect and manage knowledge, since its useand combination provide the creativity and the novelty necessary to move outside existing paradigms. Infact, the innovation process can be conceived as an open process, where complementary and heterogeneousinputs (i.e. pieces of knowledge) are transformed into outputs (i.e. results of innovations) (Katz and Khan,1996).However, organizations are becoming more and more specialized on specific fields of knowledge and, then,rarely have all the required resources internally. Therefore, to successfully innovate they need to acquireknowledge from other external sources, such as customers, suppliers, competitors, universities, researchcentres, and other institutions (see also Freeman, 1987; Owen-Smith and Powell, 2004). -2-
  4. 4. The importance of complementarity in the organizations’ innovation strategy is also well analysed byCassiman and Veugelers (2007), who demonstrate the tight relationship between organizations’ internalR&D activities and external knowledge acquisition to effectively develop innovations, and access andcapture their benefits. Further light on the complementarity issue can be added quoting the Philips CEOGerard Kleisterlee (Economist, 2002), who stated that “we used to start by identifying our core competenciesand then looking for market opportunities. Now we ask what is required to capture an opportunity and theneither try to get those skills via alliances or develop them internally to fit”.Thus, internal knowledge, mainly resulting by R&D activities, is not the only kind of knowledge managedby organizations, which can also acquire new knowledge from the external environment by activatingcollaborative R&D agreements with upstream and downstream sources of knowledge (such as suppliers, andcustomers), and with other firms and scientific organizations (such as universities and research centres).2.2. R&D CollaborationCollaborative relationships are defined to include the direct and voluntary participation of two or more actorsin designing and/or producing a product or process (Polenske, 2004). The importance of collaboration in thedevelopment of R&D activities has been extensively investigated by several scholars and literature streams.In the Transaction Costs Economics (TCE), collaborative relationships are seen as hybrid forms oforganization between hierarchical transactions and arms length transactions in the market place (e.g.Williamson, 1975; Pisano, 1990). Following this perspective, collaboration allows organizations to acquirenew competencies and to reduce the uncertainty and opportunistic behaviours associated to the developmentand creation of new knowledge. In fact, organizations must constantly seek out new opportunities forupgrading and renewing their capabilities. Nevertheless, acquiring capabilities entails uncertainty regardingthe value of the capability and the extent to which it can benefit the firm. Consequently, organizations maybenefit from having a network of knowledgeable collaborations that provides a reliable source ofinformation about options for enhancing competitive capabilities and minimizes opportunism, being thepartners involved in mutual knowledge exchanges (Nooteboom, 1999; Hagedoorn, 2002; Freel, 2003).The importance of R&D collaboration to reduce opportunism has been also discussed by the OrganizationalTheory, which analyses how inter-organizational ties are effective means to favour the diffusion and transferof complex knowledge, since they contribute to create a mutual trust, embeddedness, and social cohesionbetween partners, necessary to overcome opportunistic problems and enhance innovation rise (e.g.Granovetter, 1973; Reagans and McEviliy, 2003; Burt, 2004).The Strategic Management literature has dealt with R&D collaborations, underlining how they can be usedby organisations as channels to reach and acquire external competencies, necessary to innovate and achievea sustainable competitive advantage. In fact, R&D alliances are often aimed at expanding an organisation’sset of distinctive capabilities through inter-organisational learning, so to shape or respond to competitivedynamics in a market (e.g. Mowery et al., 1998; Colombo, 2003; Goerzen and Beamish, 2005). -3-
  5. 5. Finally, the Industrial Organisation literature has investigated the R&D collaboration issue, focusing on theappropriability hazards. Specifically, knowledge presents the features of a public good, since the use by oneorganisation of the information and new knowledge produced by R&D activities does not reduce the amountof information available to other organizations. Furthermore, R&D activities are generally characterised byan externality problem, since organisations involved in these activities cannot fully appropriate and exploitthe benefits for the occurrence of involuntary knowledge spillovers (Spence, 1984; d’Aspremont andJacquemin, 1988; Alcacer and Chung, 2007). Therefore, the establishment of collaborative R&D agreementsbetween organisations can contribute to control knowledge spillovers and, then, to internalize the positiveeffects arising from R&D investments (e.g. Cassiman, 2000).2.3. Universities as Sources of Knowledge ComplementarityIn the previous sections the complementarity character of knowledge and the importance to establish R&Dcollaborations as means to acquire such complementarity has been highlighted. Therefore, it is nowinteresting to understand which organisations can represent effective sources of knowledgecomplementarity.It is commonly recognized that universities are important sources of new knowledge, especially in the areaof science and technology (see also Agrawal, 2001). In particular, several studies have shown the relevanceof universities as explorative organizations, stressing how they can act as bridges, allowing otherorganisations to reach dispersed and heterogeneous information and pieces of knowledge (e.g. Saxenian,1994; Varga, 2000; Adams, 2005; Audretsch et al., 2005). The knowledge gatekeeper character of universityis strictly related to its research activity, which gives the opportunity to i) access to a wide range ofindustries, ii) learn the different knowledge from many industries, and ii) link knowledge across industriesand sectors.Such gatekeeper character can make universities as ad hoc partners for firms to acquire heterogeneous andcomplementary knowledge. In fact, universities have the ability to recombine and integrate such externalknowledge (Henderson and Cockburn, 1994) and act as knowledge brokers that span multiple markets andtechnology domains and bring knowledge from where it is known to where it is not.Recent studies have revealed an increasing attention towards university-industry R&D collaborations, aschannels through which knowledge can be transferred and acquired (e.g. Rothaermel and Thursby, 2005),mainly focusing on firms and universities’ characteristics favouring such collaborations.With this regard, Veugelers and Cassiman (2005) have empirically demonstrated that firms’ size, type ofindustry, government support, and the involvement in complementary innovative activities positively affectthe likelihood to establish R&D collaborations with universities (see also Bercovitz and Feldman, 2007).Regarding universities, the entrepreneurial orientation and the existence and productivity of technologytransfer offices (TTOs) are generally seen as the most important factors affecting the universities’ capabilityto collaborate and develop joint innovations with the industrial environment (Rothaermel et al., 2007). -4-
  6. 6. Nevertheless, few attention has been devoted to investigate and understand the role played by relationalattributes in explaining university-industry collaborations and their influence on the collaborations’ value.Specifically, we are interested at analysing how technological relatedness, national culture similarity, andprior collaboration ties may contribute to clarify why certain university-industry collaborations are morevaluable than others. 3. HYPOTHESESIn the present section, we develop a set of theoretical arguments that lead to the development of specifichypotheses regarding how the three relational variables affect the innovation value of university-industryR&D collaborations.3.2.Technological Relatedness and Innovation ValueThe notion of technological relatedness is based on shared technological experiences and knowledge basesbetween organizations. It refers not to the technologies themselves, in terms of tools and devices used tocreate new products and services, but to the knowledge actors possess about these technologies (Jaffe, 1986;Mowery et al., 1996; Knoben and Oerlemans, 2006).The importance of technological proximity is strictly related to the notion of absorptive capacity. In fact, asshown by Cohen and Levinthal (1990), in order to successfully collaborate, the prior (technological)knowledge of an organization must be similar to the new knowledge on the basic level, but fairly diverse onthe specialized level. Basic knowledge refers to the general understanding of the techniques upon which ascientific discipline is based, whereas specialized knowledge refers to the specific knowledge used by theactors in its everyday functioning. With this regard, Lane and Lubatkin (1998) show that organizations withgreater technological relatedness in basic technologies have greater relative absorptive capacity, and henceare more likely to learn from each other.This has to do with the technical and market competencies organisations own and have acquired whendealing with specific technologies and markets. If these are not sufficient, search and imitation cost willincrease too much. In this vein, Perez and Vein (1988) stress a negative relationships between the currentknowledge base of an organization and the costs firms have to sustain to acquire the required knowledge of anew technology. In fact, the authors argue that for each new technology exists a minimum level ofknowledge under which firms are incapable of bridging their knowledge gap.However, when partners’ technological bases are too similar, it can be detrimental for learning andinnovation (Noteboom, 2000). In fact, it may result in a technological lock-in, in the sense that similarknowledge bases limit the rising of new technologies or new market possibilities (Knoben and Oerlemans,2006). -5-
  7. 7. Divergences in technological specializations can be an important condition to establish R&D collaborations,since it can allow partners to reach new and distinctive resources and capabilities (Colombo, 2003). In fact,the exposure to partners’ different cognitive and technological frames may yield novel insights, as firmsbenefit from “external economies of cognitive scope” (Nooteboom, 1999; Wuyts et al., 2005).For instance, Sakakibara (1997) analyses the motivations of Japanese firms in participating in government-sponsored R&D consortia and shows that firms perceive obtaining complementary knowledge and sharingspecialized skills as the most important objectives of such projects. Similarly, Brockhoff et al. (1991) findthat the possibility of capturing synergistic gains from the exchange of complementary technical knowledgeis the most important reason for collaborative R&D in Germany.This reasoning leads to state that there may be an optimal amount of technology overlap between partnersthat affects both the potential benefits (higher when partners are technologically distant) and the ability tocollaborate (higher when partners are close). Following Nooteboom (2000), it is possible to argue that toolittle technological distance may imply a lack of sources of novelty, whereas too much technologicaldistance implies problems of communication and mutual understanding.Thus, a non-monotonic relation between the technological relatedness and the value of the innovationdeveloped through university-industry collaborations may be expected.Therefore, following this analysis, we argue that:Hypothesis 1. Technological relatedness between universities and firms collaborating in R&D activities hasa curvilinear effect (inverted U) on the value of joint innovations.3.2. National Culture and Innovation ValueCulture can be defined as the “complex whole which includes knowledge, beliefs, art, moral, laws, customs,and any other capabilities and habits acquired by a man as a member of a society” (Taylor, 1871, p. 38).Therefore, it is reasonably to assume that people belonging to the same community have a common cultureand system of opinions. Consequently, people of a same culture share the same tacit background andideology, adopt similar ways of thinking, behaving, deciding, and do not need to communicate a lot toexplain their opinion to other members of their culture, since the whole community grounds on the samesocial awareness pre-existing and accumulated knowledge base.In order to investigate the influence of cultural proximity on the knowledge transfer processes andinnovations development, we adopt a macro-level approach, focusing on the differences between continents,nations, or regions’ culture, assuming that organisations located within the same geographical areas share thesame culture (Hofstede, 1980; Gerler, 1995).In the business literature, several empirical studies have highlighted the importance of cultural proximity atthe macro-level, showing that this similarity can contribute to explain knowledge flows and partnershipsbetween organisations (e.g. Kogut and Singh, 1988; Folta and Ferrier, 2000; Hargadoorn, 2002; VanEverdingen and Waarts, 2003). This depends on the tight relation between culture and institutions (Zuckin -6-
  8. 8. and Di Maggio, 1990). In fact, organisations located in countries sharing similar cultures, are alsocharacterised by similar institutional frameworks, such as legislative conditions, labour relations, andbusiness practices, that can reduce transaction costs and, then, favour the likelihood of collaborations inR&D activities, for instance providing analogous norms and laws for protecting intellectual property rights(Capello, 1999; Kirat and Lung, 1999; Knoben and Oerlemans and, 2006).These findings are also supported by some theoretical studies, suggesting that a similar culture encouragescoordination and facilitates transfer and feedbacks of information, and leads to a high rate of trust amongmembers, thus allowing communication and learning to proceed relatively smoothly (e.g. Maskell andMalmberg, 1999; Knoben and Oerlemans, 2006).The specificity of culture is seen as an important factor also for explaining university-industry collaborations(Juniper, 2000). Specifically, studies on knowledge transfer between universities and firms in the Alsatianregion show the existence of few partnerships between French firms and German universities, due to thecultural distance between the organisations (Heraud and Nanopoulous, 1994; Levy and Woessner, 2001). Infact, when universities and companies collaborate in research activities institutional differences maygenerate a great complexity in terms of coordination and arrangements, that can be mitigated by thesimilarity between the cultural frameworks of the organizations’ countries.Thus, we hypothesize that:Hypothesis 2. Similar national culture between universities and firms collaborating in R&D activities has apositive effect on the value of joint innovations.3.3. Prior Collaborations Ties and Innovation ValueStrategic alliances and collaborations between organizations are now considered as a ubiquitousphenomenon, that has received a great deal of attention from a number of perspectives.Recently, scholars have focused on various path-dependent and sociological factors affecting theperformance of such collaborations, especially referring to innovation processes. With this regard, authorshave shown that higher level of familiarity, trust, and mutual understanding make existing relationshipsefficient to establish and easy to maintain. Thus, prior collaboration ties have a clear and persistent influenceon the choice of future partners (Gulati, 1995; Hagedoorn et al., 2003; Goerzen, 2007; Kim and Song, 2007).Moreover, it has been empirically demonstrated that this embeddedness has a positive effect on the transferof knowledge between actors, since it favours economies of time, integrative agreements, Paretoimprovements in allocative efficiency, and complex adaptation (Uzzi, 1997).The underlying mechanisms of repeated collaborations are related to the establishment of inter-personal tiesthat tend to increase over time, giving a greater understanding of each others’ needs and capabilities (Gulati,1995). The existence of prior ties contributes to rise trust between management teams, which is transferred atthe level of inter-organizational trust (Zucker, 1986), and increases the transaction efficiency, in terms oflower transaction costs (Zollo et al., 2002; Dyer and Chu, 2003; Goerzen, 2007; Kim and Song, 2007). -7-
  9. 9. Given the specific nature of academic knowledge, R&D collaborations between universities and firms aregenerally affected by high uncertainty, information asymmetries, transaction costs, and appropriabilityhazards) (Hall et al., 2001; Veugelers and Cassiman, 2005), which can hamper the development ofinnovations. Therefore, repeated collaborations may mitigate these problems for two main reasons. First, thereputation effect (in terms of character, skill, reliability, competence, and other attributes) is essential toexchange and it is an important platform to mitigate problems of information asymmetry and causalambiguity. Second, trust indicates a willingness to have openness to trade partners for value creation throughexchange and combination. Referring to the governance structure of R&D collaborations, trust offers asociological element of exchange giving more flexibility in operation and reducing coordination costs byproviding the ability to smooth conflicts (Murray, 2004; Lin, 2006).Consequently, we suggest that:Hypothesis 3. Prior collaboration ties between universities and firms collaborating in R&D activities have apositive effect on the value of joint innovations. 4. METHODOLOGY4.1. Research SettingTo empirically test our hypotheses we analyse the university-industry R&D collaborations, in terms of jointpatents, carried out by different universities belonging to the European Union (EU). In particular, weconsider the industry R&D relationships created by the three most innovative universities for each EUcountry, identified on the basis of the overall number of patents registered at the EPO. The choice toconsider only the three most innovative universities is leaded by two main reasons. First, to investigate howthese organizations, generally considered as a benchmark in research activities at both the national andinternational level, manage relationships to fully capture the benefits arising from industry collaborations.Second, since we use patents as proxy for innovations, only the most innovative universities present asufficient set of relationships with the industrial environment for testing our hypotheses.The use of patents as a proxy to evaluate innovations has been largely adopted in the literature, as shown byseveral empirical works evaluating organizations’ innovative performance and the diffusion and transfer ofknowledge (e.g. Jaffe et al., 1993; Flor and Oltra, 2004; Singh, 2005; Fritsch and Slavtchev, 2007;Nooteboom et al., 2007). Several factors can explain their intensive use (Ratanawaraha and Polenske, 2007).First, patent data are readily available in most countries, thus permitting cross-country comparisons. Second,the extensiveness of patent data enables researchers to conduct both cross-sectional and longitudinalanalysis. Third, patent data contain detailed useful information, such as the technological fields, theassignees, the inventors, and some other market features. Finally, patents provide a measure of innovationthat is externally validated through the patent examination process (see also Griliches, 1990; Schilling and -8-
  10. 10. Phelps, 2007), thus giving a certain degree of confidence to the relevance and result of the R&Dcollaborations.4.2. SampleFirst, we identified all the universities, both public and private, located in each of the 27 countries of the EU,thus defining a list of 812 universities. Then, we identified the three most innovative universities in eachcountry on the basis of the overall number of patents registered at the EPO between 1998 and 2003. Fromthis analysis, 81 universities have been classified. Finally, for each of the 81 universities, we analysedpatents jointly registered with firms. Thus, 29 universities have been selected, located in 12 differentcountries and establishing 796 R&D university-industry collaborations.To assess the value of the collaborations’ innovative output, we considered the patents registered between1998 and 2003, since a moving window of five years is the appropriate time frame for assessingtechnological impact (Stuart and Podolny, 1996; Henderson and Cockburn, 1996). In fact, studies aboutR&D depreciation (e.g. Griliches, 1985) suggest that knowledge capital depreciates sharply, losing most ofits value within five years.4.3. Dependent VariableThe analysis and assessment of patent value is a very debated and controversial topic, occupying a numberof pages on scientific journals. In the literature, several empirical strategies have been used to approximatethe patent’ value. Despite the strong heterogeneities across studies, in terms of indicators adopted, datasources, time spans, and research methodologies, some similarities emerge. The most important one is thatthe patent’s value is closely associated with the number of forward citations.The use of forward citations has been introduced by the pioneer work of Trajtenberg (1990) and fullydeveloped by Jaffe et al., (1993) and validated as measure of patent’s value in numerous subsequent studies(e.g. Hirschey and Richardson, 2001; Harhoff and Reitzig, 2002; Gittleman and Kogut, 2003; Harhoff et al.,2003; Hall et al., 2005; Bonaccorsi and Thoma, 2007; Giuri et al., 2007; Singh, 2008).Thereby, we measure the value (InnValue) associated to each innovation as the number of citations receivedby each patent.4.4. Independent VariablesTechnological relatedness. The technological relatedness (TechRel) is evaluated by means of the degree ofoverlapping between the organizations’ technological bases, in terms of technological fields in which theypatent. In particular, in this research the technological similarity is evaluated following the measureproposed by Jaffe (1986), who uses the patent technological class information to construct a measure of thecloseness between two actors in the technology space. In this case the technology space is represented by the -9-
  11. 11. 129 patent classes (three-digit) assigned by the International Patent Classification (IPC). Hence, thetechnological relatedness is evaluated as: f i f jTech Re li , j = (1) ( f f )( f f ) i i j j where the vectors fi and fj (apex indicates the transposed vector) are constituted by all the patents registeredby the university (i) and the company (j) at the EPO from the previous five years up to date of thecollaboration, respectively, and allocated to the patent class n (n=1,…,129). Thus, the firms’ patent portfoliois compared to the patent portfolio of each university has developed a patent with it. TechReli,j, whichrepresents the uncentered correlation between the two vectors, assumes value one, if i and j’s patentactivities perfectly coincide (fi = fj). On the contrary if they do not overlap at all, i.e. the two vectors areorthogonal, it assumes value 0.National Culture Distance. This variable aims at capturing the differences and similarities between nationalcultural frameworks at the macro-level, in terms of norms and values of conduct. To achieve this goal, weadopt the Kogut and Singh (1988) modified index of Hofstede that measures the cultural distance (CultDist)between universities and companies collaborating in R&D activities (see also Clodt et al., 2006). Inparticular, this index analyses four distinct dimensions: i) power distance (as the extent to which the lesspowerful members of organisations and institutions accept and expect that the power is distributedunequally), ii) individualism (as the degree to which individuals are not integrated into groups), iii)masculinity (as the distribution of roles between the genders), iv) and uncertainty avoidance (as the society’stolerance for uncertainty and ambiguity). Through the analysis of these four key issues, a positive continueindex (CDij) is identified, which measures the institutional distance between actors i and j as:CDij = ∑ { I di − I dj ) 2 / Vd }/ 4 4 ( (2) d =1where Idj stands for the index for the d-th considered dimension and j-th actor, Vd is the variance of the indexof the d-th dimension.Prior collaboration ties. To evaluate the existence of prior ties between universities and firms jointlydeveloping a patent, we account for previous research experiences between the partners. In particular, wemeasure this variable as a binary one (PriorTies), assuming value one if, before the partnership underanalysis, the two actors have established previous R&D collaborations, in terms of other patents jointlyassigned. Otherwise, the variable assumes value zero. To identify such prior collaborations, we use a five-year moving window following previous studies suggesting that the lifespan for alliances is usually no morethan five years (Kogut, 1988; Gulati, 1995; Kim and Song, 2007).4.5. Control VariablesWe include several variables to control for alternative factors that can explain the value of innovationsjointly developed by universities and firms. - 10 -
  12. 12. We introduce dummy variables to control for industry fixed effects, since university-industry relationshipscan be strongly affected by specific sector capabilities and competences (see also Pfeffer and Novak, 1978;Pavitt, 1984). In particular, 14 main different industrial sectors are identified according to the standardindustrial classification (SIC): pharmaceuticals; engineering services; chemicals; industrial and commercialmachinery; electric services, measuring, analysing, and controlling systems; fabricated metal products;transportation equipments; textile mills products; rubber and miscellaneous plastic products; food andkindred products; business services; agriculture; fishing.Then, we control for the firms absorptive capacity (Cohen and Levinthal, 1990) measured by means of firmssize (FirmSize), in terms of natural logarithm of number of employees, and natural logarithm of the overallnumber of patents successfully filled from the previous five years up to date of the collaboration withuniversity (FirmPatents), which can be used also to take into account the technological capital owned by thesampled companies (e.g. Phene et al., 2006; Nooteboom et al., 2007; Rothaermel and Boeker, 2008).Regarding universities, we control for their entrepreneurial orientation and the existence of TTOs (see alsoDebackere and Veugelers, 2005; Rothaermel et al., 2007). The entrepreneurial orientation has been widelydiscussed in relation with the aptitude of universities to create new firms, such as spin-offs and incubators.Thus, we introduce two binary variables, Spin-Off and Incubator, assuming value one if the universities havecreated spin-offs or firms incubators, respectively. To control for the existence of TTOs, another dummyvariable (TTO) is introduced, which takes value one if the university has at least one technology transferoffice.Other potential explanations to successful university-industry collaborations can be represented byuniversity’s reputation (UnivReputation) and university’s capability to be involved in scientific projects withthe industrial environment (UnivProjects). The former is measured following the Academic Ranking ofWorld Universities, compiled by the Shanghai Jiao Tong University’s Institute of Higher Education. Thereport includes major institutes of higher education ranked according to a formula that takes into accountdifferent criteria, such as teaching quality, staff quality, and research productivity, quality and efficiency. Wecode UnivReputation as a dummy variable assuming value one if the sample universities are ranked in thefirst ten positions.UnivProjects is measured by means of the number of market-oriented and industrial R&D projectsdeveloped by the sample universities during the observation period (1998-2003). Data are collected throughthe EUREKA database, which provides several financial and technical information about Europeanuniversity-industry joint projects aimed at creating innovative products, processes and services.We control also for the university’s patenting propensity, as the natural logarithm of the overall number ofpatents successfully filled by universities from the previous five years up to the date of the industrycollaboration (UnivPatents), and for their size, in terms of natural logarithm of number of full-timeresearchers (UnivSize). In addition, we take into account the university’s country fixed effects. In particular,country dummies are included to control for universities located in Belgium, Germany, Netherland, UK, that - 11 -
  13. 13. count for about 80% of the overall number of university-industry relationships (see Table 1), and othercountries (Austria, Czech Republic, Denmark, France, Ireland, Italy, Poland, and Spain). Exogenous shockscharacterising the year of the relationship are also controlled.Finally, we evaluate the effects of geographical distance between partners. Following a broad literature onthe effect of geography on learning and innovation rise (e.g. Audretsch and Stephan, 1996; Lublinski, 2003;Siegel et al., 2003; Alcacer, 2006), we measure geographical distance (GeoDist) as a continue positivevariable, evaluated by the spatial distance (expressed in kilometres) between the location sites of universitiesand companies jointly registered patents. To avoid problems related to companies’ multiple locations,especially referring to multinationals, information about the site where the patents have been developed areobtained analysing inventors’ addresses. Given the skewed distribution of the variable, also this variable hasbeen transformed using a log transformation.In Table 1, all the model variables are described. Table 1. Definition of variables.Dependent variable InnValue Number of citations received by each university-firm joint patentIndependent variables Degree of overlapping between the technology profile of univeristy and firm jointly developing a TechRel patent. The technology profile is represented by all patents registered by the university and the firm from the previous five years to the date of the collaboration, and assigned to the 129 IPC (three-digit). TechRel2 Squared term of the previous variable. CultDist Degree of overlapping between the national cultures of unveristy and firm jointly developing a patent. Dummy variable assuming value 1 if university and firm jointly developing a patent have registered PriorTies another patent in the previous five years.Control variables FirmSize Number of full time employers of each firm jointly developing a patent with university (Source:..). Number of patents that each firm firm jointly developing a patent with university has registered from FirmPatent the previous five years up to the date of the collaboration. UnivSize Number of full time researchers of each university. Number of patents that each university has registered from the previous five years up to the date of UnivPatent industry collaboration. Incubator Dummy variable assuming value 1 if university has at least one incubator. Spin-off Dummy variable assuming value 1 if university has at least one spin-off. TTO Dummy variable assuming value 1 if university has a technology transfer office. Dummy variable assuming value 1 if university is ranked in the first ten positions of the Academic UnivReputation Ranking of World Universities. UnivProjects Number of EUREKA projects developed by university during the observation period. Natural logaritm of the physical distance expressed in kilometres between the location sites GeoDist (headquarter of local affiliates) of university and firm jointly developing a patent. Industry dummies Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Pharma pharmaceutical industry (SIC codes 2833, 2834, 2835, 2836). - 12 -
  14. 14. Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the EngServices engineering services industry (SIC codes 8711, 8712, 8713, 8748). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Chem chemicals industry (SIC codes 281-, 282-, 285-, 286-, 287-, 288-, 289-). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the IndusMachinery industrial and commercial machinery industry (SIC codes 3531, 3552, 3556, 3559, 3565, 3568, 3569, 3682, 3585, 3589, 3599). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the ElectricServices electric services industry (SIC codes 4931, 4939). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the MeasurSystems measuring, analysing, and controlling systems industry (SIC codes 3823). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Metal fabricated metal products industry (SIC codes 3443, 3449, 3479, 3498, 3499). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Transp transportation equipments industry (SIC codes 3715, 3732, 3743, 3799). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Textile textile mills products industry (SIC codes 2211, 2221, 2241, 2273, 2295, 2299). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Rubber rubber and miscellaneous plastics products industry (SIC codes 3011, 3021, 3052, 3053, 3061, 3069). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Food food and kindred products industry (SIC codes 2011, 2013, 2032, 2038, 2041, 2043, 2087, 2099) Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the BusinessServices business services industry (SIC codes 7335, 7336, 7363, 7389). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Agric agriculture industry (SIC codes 01-, 02-, 07-). Dummy variable assuming value 1if firms jointly developing a patent with universities operate in the Fish fishing industry (SIC codes 0919, 0921) University country dummies BE Dummy variable assuming value 1if university is located in Belgium. DE Dummy variable assuming value 1if university is located in Germany. NL Dummy variable assuming value 1if university is located in Netherland. UK Dummy variable assuming value 1if university is located in United Kingdom. Dummy variable assuming value 1if university is located in Austria, Czech Republic, Denmark, Others France, Ireland, Italy, Poland, and Spain. Year dummies Dummy variable assuming value 1 if the patent jointly developed by university and firms is dated 1998 1998. Dummy variable assuming value 1 if the patent jointly developed by university and firms is dated 1999 1999. Dummy variable assuming value 1 if the patent jointly developed by university and firms is dated 2000 2000. Dummy variable assuming value 1 if the patent jointly developed by university and firms is dated 2001 2001. Dummy variable assuming value 1 if the patent jointly developed by university and firms is dated 2002 2002. Dummy variable assuming value 1 if the patent jointly developed by university and firms is dated 2003 2003.4.6. Estimation Model - 13 -
  15. 15. The dependent variables of this study are represented by a nonnegative, integer count variable. Verified by astatistical test of overdispersion (Gourieroux et al., 1984), the negative binomial estimation provides asignificant better fit for the data than the more restrictive Poisson model. Negative binomial regressionaccounts for an omitted variable bias, while simultaneously estimating heterogeneity (Hausman et al., 1984;Cameron and Trivedi, 1986). Thus, the following model is adopted:P ( nit / ε ) = e − λit exp( ε ) λinit / nit −1!where n is a nonnegative integer count variable, representing the value associated to each university-industryrelationship (patent). Therefore, P( nit / ε ) indicates the probability that each relationship (patent) hasreceived n citations in year t.The application of a negative binomial estimation, jointly with a rich set of detailed control variables, allowsus to effectively address any potential endogeneity (Hamilton and Nickerson, 2003; Rothaermel and Hess,2007). 5. RESULTSIn Table 2 basic descriptive statistics and pairwise correlations are reported. All the correlations between theindependent variables fall below the 0.70 threshold, thus indicating acceptable discriminant validity (Cohenet al., 2003). Table 2. Descriptive statistics and correlation matrix (N=796). Panel (A): independent variables Variables Mean S.D. Min Max 1 2 3 4 1. InnValue .477 1.304 0 12 1.000 2. CultDist .583 1.054 0 4.435 -.099 1.000 3. PriorTies .797 .417 0 1 -.033 -.070 1.000 4. TechRel .556 .308 0 .991 .071 -.026 .124 1.000 Panel (B); main control variables Variables Mean S.D. Min Max 1 2 3 4 5 6 7 8 9 10 11 1. InnValue .477 1.304 0 12 1.000 2. FirmSize 7.134 2.706 1.792 13.541 .025 1.000 3. FirmPatent 3.709 3.081 0 11.278 -.032 .512 1.000 4. UnivSize 7.861 .832 5.561 8.854 -.006 .030 .184 1.000 5. UnivPatent 5.484 .898 3.178 6.942 .040 -.038 .124 .172 1.000 6. Spin-off .987 .111 0 1 .041 .015 -.025 -.042 .137 1.000 7. Incubaor .739 .438 0 1 .019 .150 .150 .363 .053 .113 1.000 - 14 -
  16. 16. 8. TTO .930 .255 0 1 .063 .157 .176 .300 .251 -.031 .184 1.0009. UnivReputation .373 .484 0 1 .024 .013 .178 .537 .333 .087 .457 .100 1.00010. UnivProjects 5.308 3.035 0 10 .056 .005 .070 .294 .309 .097 .652 .154 .432 1.00011. GeoDist 4.006 3.433 0 9.343 .018 .212 .346 .050 -.189 .015 .148 -.005 .024 -.046 1.00 Panel (C): firms’ industries Variables Obs. Mean S.D. ScienValue (correlation) 1. Pharmaceuticals 437 .550 .498 .099 2. Engineering services 58 .071 .258 .014 3. Chemicals 76 .094 .292 -.092 4. Industrial and commercial machinery 51 .064 .245 -.005 5. Electric services 56 .069 .254 -.058 6. Measuring, analysing, and controlling systems 38 .048 .213 -.028 7. Fabricated metal products 25 .031 .174 -.044 8. Transportation equipments 28 .035 .184 -.069 9. Textile mills products 4 .005 .071 -.026 10. Rubber and miscellaneous plastic products 5 .006 .079 .081 11. Food and kindred products 4 .005 .071 -.026 12. Business services 1 .001 .035 -.013 13. Agriculture 8 .010 .099 .156 14. Fishing 5 .006 .079 -.029 Panel (D): universities’ countries Variables Obs. Mean S.D. InnValue (correlation) 1. Austria 7 .009 .093 -.034 2. France 9 .011 .106 -.039 3. Denmark 7 .009 .093 -.024 4. Ireland 13 .016 .127 .120 5. Germany 120 .151 .358 -.006 6. Netherland 143 .180 .384 -.028 7. Poland 33 .041 .199 -.076 8. Italy 36 .045 .208 .050 9. Czech Republic 26 .033 .178 -.067 10. Spain 11 .013 .117 -.043 - 15 -
  17. 17. 11. Belgium 105 .132 .339 .048 12. UK 286 .359 .480 .027The results of the negative binomial regression are reported in Table 3. Model 1 loans only the controlvariables, whereas in Models 2-5 the impact of technological relatedness, national culture distance, and priorcollaboration ties on innovation value is investigated. Regarding firm industry, university country, andcollaboration year fixed effects, the omitted industry is pharmaceutical, the omitted country is others, and theomitted year is 1998. Table 3. Negative binomial estimates of joint innovations’ value. Dependent variable ScienValue Model 1 Model 2 Model 3 Model 4 Model 5 Independent variables TechRel 1.796** 1.230* TechRel2 -1.439* -1.134* CultDist -.312*** -.332*** PriorTies -.232* -.251* Control variables FirmSize .003 .015 -.001 .005 .005 FirmPatent .007 -.002 .015 .001 .004 UnivSize -.821*** -.711*** -.528** -.761*** -.370 UnivPatent -.673 -.423 -.668 -.547 -.482 Incubator -1.575*** -1.303*** -1.448*** -1.443*** -1.129*** Spin-Off -1.313*** -1.346*** -1.285*** -1.473*** -1.388*** TTO 1.799*** 1.528** 1.401** 1.676** 1.127** UnivReputation 5.524*** 5.622*** 5.364*** 5.792*** 5.619*** UnivProjects 0.163*** .142** .146** .149*** .122** GeoDist .041** .049** .084*** .034* .089*** Industry dummies included included included Included included University country dummies included included included Included included Year dummies included included included Included included Log likelihood -236.199 -234.303 -232.438 -235.160 -229.794 (*, **,***) ρ < 0.10 (0.05, 0.01).Regarding control variables, firms’ characteristics have no impact on the innovation value, whereasuniversities’ attributes seem to significantly affect it. Specifically, Table 3 shows that the presence of TTO inacademic organizations has a significant and positive impact on the scientific value, whereas the existence of - 16 -
  18. 18. incubators and spin-offs has a negative influence. Moreover, the development of more valuable innovationsis favoured by the universities’ involvement in applied R&D projects and by their reputation.Also geographical distance between partners matters, as showing by the positive and significant coefficients.Probably, it is due to the spatial stickiness of knowledge. Thus, technological knowledge coming frompartners located in distant areas are generally characterised by different paradigms, providing a potential fornon-overlapping knowledge bases and favouring the creation of more radical and scientific valuableinnovations.Firms developing rubber and miscellaneous plastic products are more able to achieve greater innovationperformance than pharmaceutical companies. Differently, the electric services sector is characterised bylower values than the pharmaceutical one.Universities located in Belgium and Netherland seem to scientifically perform better than academicorganizations located in Austria, Czech Republic, Denmark, France, Ireland, Italy, Poland, and Spain.Finally, no statistical differences occur between dummy years.Considering the independent variables, data reveal that technological relatedness has an inverted U-shapedrelationship with the innovation value, thus confirming Hypothesis 1. In fact, it emerges that it is necessary aminimum threshold of technological similarity to favour mutual understanding, but an excessive value maybe harmful for discovering the novelty necessary to improve the scientific relevance of innovations.Similarity between national cultures has a positive and significant impact on the innovation value, as shownby β coefficients of cultural distance in Models 3 (-.312) and 5 (-.332), thus supporting Hypothesis 2.Finally, also Hypothesis 3 is confirmed, since the existence of prior collaborations between universities andfirms positively affects the value of innovation. Thereby, it emerges that universities and firms that havebeen previously involved in R&D collaborations have a greater likelihood to develop more valuableinnovations. 6. DISCUSSION & CONCLUSIONSThe present study wants to shed further light on the university-industry R&D collaborations, exploring howrelational attributes may influence the value of the innovations jointly developed. Previous works havemainly investigated the role played by specific universities and firms’ attributes, such as universities’entrepreneurial orientation, national policies, government support, types of industry, and the involvement incomplementary innovative activities, devoting few attention to the dyadic properties, rising from theinteraction between path-dependent partners characteristics. In particular, we have focused our study onthree key aspects: i) technological relatedness, ii) national cultural similarity, and iii) prior collaboration ties,in order to show their impact on the collaboration innovative output. - 17 -
  19. 19. Our results suggest that technological relatedness between universities and firms presents an inverted U-shaped relation with the value of innovation. This finding reveals that to increase the relevance ofinnovations a certain threshold of similar technological competencies is required. Nevertheless, too muchsimilarity may be detrimental since the development of valuable innovations requires dissimilar andcomplementary bodies of knowledge, generally available in different technological partners.In addition, confirming our second hypothesis, national cultural similarity between partners seems to be afundamental condition to improve the innovation value. In fact, the similarity between countries’ rules, laws,norms, and values can provide a common ground on which technological strategies can be based, thusfavouring goals alignment and the achievement of innovative results.Finally, also prior ties positively contribute to enhance the value associated to joint innovations. In fact,previous collaborations may promote the creation of an initial base of inter-partner trust, so developing suchrelational routines useful to proceed further to the joint development and ownership of technologies.The present study contributes to the existing literature on university-industry relationships, stressing therelevance of specific relational attributes and how these may predict the development of successful jointinnovations. With this regard, our findings seem to suggest that policy makers should promote and supportthe establishment of university-industry collaborations, considering also partners’ specific relationalfeatures. Thereby, founds and aids destined to sustain collaborative R&D projects between academicorganizations and companies should be allocated not only evaluating the specific project and partners’characteristics, but also taking into account how these characteristics interact. In fact, we have shown thatthe relation between organizations’ technological bases, cultural frameworks, and the degree of past mutualexperiences may significantly impact on the value of the resulting innovations.Of course the paper presents some limitations. First, the use of joint patents is not able to capture all theuniversity-industry collaborations. However, since we are interested in analysing successful collaborations,joint patents can describe with a certain degree of confidence the success of such partnerships in terms ofinnovations development (see also Kim and Song, 2007). Second, joint patents between universities andcompanies are often registered only with the name of the researcher(s) and firms engaged in the innovationsdevelopment. Nevertheless, we have not considered these cases, since our focus is represented by theinteractions between universities and firms at the institutional level. To include also collaborations of singleprofessors and researchers with the industrial environment, other aspects, more devoted to capture the socialdynamics occurring between the academic and industrial environment, should be analysed.Third, the study focuses only on the impact that three specific relational attributes, dealing withtechnological competencies, culture, and embeddedness, exert on the value of resulting innovations. Futurestudies could complement the present work investigating how these attributes may differently affect theinnovation value, according to both its scientific or economic relevance and the more explorative orexploitative collaboration aim. - 18 -
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