R&D collaborations and innovation performance the case of argentinean biotech firms
R&D collaborations and innovation performance. The case ofArgentinean biotech firms Lilia Stubrin1AbstractMany emerging countries are encouraging firms to enter into biotechnology, as it is seen as awindow of opportunity to generate a descommoditization of their patterns of productspecialization. We analyze the case of biotechnology in Argentina. We assess what strategies dofirms display to sustain their technological dynamisms and update their knowledge bases in orderto compete in this knowledge-intensive sector. In particular, we study the Argentinean biotechfirms’ network of collaborations in order to evaluate how knowledge diffuses within and to localfirms. Our main results suggest that the knowledge network structure of the Argentinean biotechfirms is different from the ones found in biotech leading regions, but similar to those in othernon-leading ones. The salient features are the scarcity of collaborations among co-located firms,the key role that local PROs play in knitting the local network together and the striking relevanceof non-local partnerships predominantly forged with partners in leading regions. Collaborationswith local scientific and technological institutions as well as with foreign partners are shown tobe valuable to enhance firms’ innovation performance. Our study contributes to provide newevidence regarding how-high tech activities develop in emerging countries, and the role of localand non local knowledge flows to promote firms’ learning and technical change. PhD Fellow UNU-MERITUNU-MERITKeizer Kareplain 196211 TC MaastrichtThe Netherlandsstubrin@merit.unu.edu
1. IntroductionIn the last years, emerging countries have been encouraged to foster high-tech sectors, as they arepresented as possible avenues these countries should explore in order to diversify their patterns ofspecialization towards more value added and technologically complex activities.Accordingly, many of these countries are moving forward into activities such as biotechnology,nanotechnology and ICT. The aim of this paper is to contribute to expand the existent empiricalevidence regarding how high-tech sectors develop in emerging countries, and in particular, whatstrategies do firms display in these settings to enhance their technological and productivecapabilities in order to compete in a globalised world. We study the case of Argentinean biotechfirms, and in particular we focus on firms’ networks of collaborations.To our view the relevance of studying the biotech firmsnetwork relies on several factors. First,it is known that biotech is an activity which is knowledge-intensive and in which technicalchange takes place at a rapid peace. Thus, exploring how knowledge diffuses within and toArgentinean firms becomes meaningful to comprehend how firmsacquire and build theirtechnological capabilities. Second, it is a widely held view that the complex and broadknowledge bases of new technologies encourage firms to become `networked organizationslooking for complementary knowledge, skills and resources outside their boundaries (Powell,1992; Barley, 1992; Powell, 1996a; Powell, 2005). Third, networks have been found to be meansthat facilitate firmsgrow and innovation performance in leading regions (Powell, 1996; Uzzi,1996; Ahuja, 2000). Thus, we aim at exploring to what extent this is the case for the Argentineancase.We are further interested in addressing the composition of the network in terms of the agents withwhom local firms exchange and share knowledge, and to what extent the industry network relieson local and non-local collaborations. This intends to address the debate regarding the role thatgeographical proximity plays in the economics of knowledge transmission as the availableempirical evidence is not conclusive about this matter (Brink, 2007; Bathelt et al, 2004). Non-local collaborations can be crucial vectors to bring novelty and diversity, and sustain the processof learning and technical change in relatively laggard knowledge regions.
The study is based on original data on Argentinean biotech firms collected in 2008. The firmsconsidered for the analysis apply at least one modern biotechnology technique to produce goodsand services and/or perform biotechnology R&D (OECD, 2005). These firms are active indifferent biotechnology applications: human health, animal health, GM and non-GM agriculturalbiotech and industrial processing.Our study finds that Argentinean biotech firms are networked organizations. Thus, these firmsget actively involved in cooperations particularly with the purpose of sharing, exchanging andsourcing knowledge from outside the firm. This is a pattern that spans across all firms, regardlessof their main area of biotech application. As regards the knowledge network structure,knowledge collaborations with local public research organizations2 (PROs) and foreign partners(mostly located in leading regions) are the most relevant and frequent type of interactions, whichwe also find to be valuable to enhance biotech firms’ innovation performance.The results obtained suggest that the development and sustainability of high tech activities inemerging countries cannot be explained only focusing on local knowledge interactions.Collaborations at a distance are not only frequent but also seem to be valuable to improve theinnovation performance of high-tech firms located outside leading regions. In addition, thedevelopment of the biotech activity is highly grounded on the local scientific knowledge basedcontained in local PROs. This reveals the relevance of a strong local scientific base for high-tech activities to spring and further develop in a country.The paper is organized as follows. Section 2 reviews the literature on collaboration networks,geography and innovation, in order to address the current debate regarding the role ofgeographical proximity and local knowledge flows to enhance learning and innovation. Section 3describes the methodology and process of data collection used in this study. Section 4 isconcerned to depict the main characteristics of firms’ collaboration activity. In particular, itfocuses on Argentinean biotech firms’ knowledge network, unraveling its main characteristicsand providing plausible explanations for the observed patterns of collaboration. In Section 5 we PROs refer to universities, research institutions, laboratories and hospitals.
assess the value of non local R&D collaborations and cooperations with local PROs for firms’innovation performance. Finally, in Section 6 we present the conclusions of the study.2. Literature reviewNetworks of collaborations: the value of embeddedness In the last years we have witness an outstanding increase in firms’ engagement in strategicalliances (Hagedoorn, 2000). These are ‘voluntary arrangements between firms involvingexchange, sharing, or co-development of products, technologies, or services’ (Gulati 1998, page293). Collaborative arrangements are assumed to be driven by the asymmetric distribution oftechnological, organizational, commercial and financial resources within an industry (e.g.Andrews, 1971). In addition, the expanding knowledge base and complexity of manytechnologies further trigger firms to enter into cooperations. That seems to be the case ofbiotechnology. The evolution and development of this activity has been found to rely on adiverse and complex array of cooperations between firms, universities, public researchorganizations and venture capitalists (e.g. Bartley et al, 1992, Shan et al, 1994, Koput et al, 1997,Owen-Smith and Powell, 2004, Powell et al, 1996, Powell et al, 2005). The complexity of thetechnology, the high risk that the process of innovation entail as well as the speed at whichtechnical change takes place, encourage firms to interact and exchange knowledge and resourceswith other agents within and outside the industry (Hagedoorn, 1992, Eisenhardt andSchoonhoven 1996, Mowery et al, 1998).Social network theory has been applied to study firms’ voluntary cooperation agreements as itoffers a framework to understand how firms came across the opportunity to cooperate with otherorganizations, obtain information about potential partners and overcome the uncertainties thatcooperation with others entails. Social network analysis follows the studies of economicsociology that explain how economic actions can be influenced by the social structure of relationswithin which they are embedded (Granovetter, 1985). Thus, the way a firm is embedded in acollaborative network can provide it with both opportunities and constraints for its behaviour andperformance (Gulati, 1995, 1998; Gulati and Garigulo, 1999). A network of collaborations thatis highly clustered was claimed to positively affect firm’s performance through the nurturing of
social capital (Coleman, 1988). Clustering arises as firms keep cooperating with the samepartners over time (‘relational embeddedness’) or collaborations with their partners’ partners(‘structural embeddedness’). Particularly, firms’ structural embededdness prevents opportunisticbehaviours and enhances trustworthiness which, in turn, favours collaboration and exchange ofinformation (Coleman, 1988).Hence, the value of embeddedness was found empirically significant in the biotechnologyindustry in which network formation and industry growth are highly influenced by thedevelopment and preservation of social capital (Koput et al, 1997; Powell et al 1996). Also inother industries embeddedness was found to be significant for network formation3 and to fosterfirms’ learning and innovation.4However, as it was examined by the empirical study of Ahuja (2000), the degree ofembeddedness that can be beneficial to knowledge creation depends on the context and the kindof links that the network structure facilitates. For instance, a network structure in whichstructural embeddedness prevails restricts the potential partners and therefore, ‘put limits to theinflow of diverse and fresh insights’ (Ahuja, 2000). This can be especially problematic when thecollaborative network is mostly composed by partners that are far from the technological frontier,as a technological ‘lock-in’ may affect the firms that compose the network. As a matter of fact,the empirical evidence that supports the value of firms’ embeddedness in networks ofcollaborations has been mostly collected in developed countries. Studies are generally based onsamples of firms that are leading technological change in a certain industry, and most of the firmsare already in the frontier or are close to it. We know little if firms’ embeddedness is likely to bevaluable and beneficial for high-tech firms located in more knowledge scarce environments.Accordingly, firms that are themselves connected to organizations situated outside the localnetwork may able to diversify their sources of knowledge and also become a bridge for freshinsights to enter into the local network. Thus, actors that bridge ‘structural holes’ by forging non-3 In the automobiles industry (Dyer and Nobeoka, 2000) or in new materials and industrial automatation industries(Gulati and Garigulo, 1999).4 In textiles (Uzzi, 1996), biotech (Powell et al, 1999) and chemicals (Ahuja, 2000), personal computers (Hagerdoonand Duysters, 2000).
redundant ties between previously unconnected networks may have an information advantage anda strategic position compared to their local partners (Burt, 1992).Collaborations and geography: local and non-local collaborationsThe embeddedness of firms in dense local networks was also pointed out as being beneficial forfirms’ learning and innovation by the agglomeration and cluster literature. A cluster is a‘geographically proximate group of inter-connected companies and associated institutions in aparticular field, linked by commonalities and complementarities’ (Porter 2000, p 254). Firms’clustering and spatial proximity not only can provide advantages in terms of costs as economiesof scale and scope can be achieved, but also facilitates access and circulation of knowledge(Marshall, 1920). This is specially the case when the knowledge to be transferred is highly tacit,which requires face-to-face and interpersonal interactions for its better diffusion.5The benefits of clustering for fostering learning and innovation can be even more important inthose industries in which knowledge creation is the key (Audretsch et al, 1996). Success storiesof high-tech clusters, among which the Sillicon Valley is the most prominent example, fosteredand enhanced the value of clustering.6 Following these successful stories deliberate efforts havebeen made to promote the creation of clusters elsewhere. Firms’ were provided incentives andfacilities to locate close to each other, and also nearby universities and scientific institutions, withthe aim that geographical proximity would naturally create room for knowledge diffusion.5 See the cluster literature based on the seminal work of Marshall (1920). The value of clustering for thedissemination of ideas in a cluster is addressed in the European literature on industrial districts (e.g. Piore and Sabel,1984; Becattini, 1990; Schmitz, 1995), Innovative Milleus (e.g. Camagni, 1991), Regional Systems of Innovation(e.g. Lawson and Lorenz, 1999; Cooke, 2001) and others. Mechanisms highlighted in the literature that facilitateknowledge transfer among agglomerated organizations are user-producer relationships, formal-informalcollaborations, inter-firm mobility of workers and spin-offs of new firms.6 Successful clusters in developed countries are the Silicon Valley, Emilia Romana in Italy and Bade-Wuerttembengin Germany. Besides other well documented clusters in developing countries are located in Brasil (Schmitz, 1995),Mexico (Rabelotti 1995), Peru (Visser, 1996) and India (Cawthorne, 1995; Nadvi, 1996).
However, empirical evidence that has started to flourish cast doubt on the predominance oflocalized knowledge networking and on the idea that learning processes are exclusively local.Local and non-local knowledge cooperations have found of equal importance by certain studies(Coenen et al 2004, Lawton-Smith, 2004, Van Geenhuizen, 2007, Mc Kelvey et al 2003, Fontes2005) and authors started to claim that the value of local links has been very much exaggerated(Oinas, 1999; Coenen 2004). Coenen (2004) argues that the ‘argument of proximity makesinteraction better, faster, easier and smoother runs the risk of spatial fetichism’ (page 1005). Thespace as such may not be of great value if other factors contained in a physical space such ascertain actors, relations, institutions, and shared values are not taken into account.In a review of the cluster literature Breschi S. and Malerba F. (2001) highlight the importance ofexamining the openness of clusters to understand their productive and innovative dynamism.Explanations mostly based on the benefits of geographical agglomeration lead to a narrow viewof clusters in which they are treated as isolated and self-constrained entities. On the contrary,external linkages should start to be contemplated as they can be critical to foster and enhance thedynamism of dense and local network relationships. For instance, they can be very valuable toavoid technological lock-in and keep aware of technological changes and market opportunities.Regarding the studies of clusters in developing countries, Bell and Albu (1999) also stress that ananalytical shift towards a more open view of the clusters is needed in order to understand thebases of their technological dynamism and long-term competitiveness. In fact, externalcollaborations may bring novelty and diversity, and thus become a source of competitiveness forthe development of high-tech industries in relatively laggard regions. It has been shown thevalue of external alliances to access knowledge in distant contexts. See, among others, Rees(2005) that analyze the medical biotechnology cluster in Great Vancouver (Canada) and(Rosenkopf, 2003) who focus on the semiconductor industryTherefore, the question that underlies here is whether it is the place (the geographicalagglomeration per se) or the network (without any a priori consideration of geographicalboundaries) that matters for encouraging learning and innovation.
When thinking about technical change in developing countries - specially in knowledge intensiveindustries – a great deal of the sources of knowledge resides outside the local network, andtherefore local densely connected networks by themselves may not be a sufficient condition toboost learning and generate technical change. In this case, ‘close’, local learning relationshipsmay fall short to sustain innovation and keep track to the ever changing technological frontier.The case of biotechnologyIn the case of biotechnology the pattern of spatial concentration seems strong. At world level, wecan identify a small number of `nodes of excellence’ constituted by clustered firms andinstitutions that lead the industry and the research in the area. Thus, the world-leading biotechregions are located in two main areas in the US (California and the north-eastern area that goesfrom Maasachusetts to North Carolina), in two areas in the UK (Oxford and Cambridge) and ascatter of small clusters elsewhere in Europe (Carlsson, 2001). However, recent evidence hasshown the emergence and importance of ‘newcomers’ into biotechnology (Heimeriks andBoschma, 2011). Many developing countries are also trying to move forward into thedevelopment of biotech as it can become a window of opportunity to generate a‘decommoditization’ in their patterns of specialization. We got intrigued by the followingquestions: How does biotech develop outside the world main hubs? Can the emergence andfurther development of biotech activities in emergent regions be explained solely based on localinteractions and local knowledge flows?The evidence from the development of biotechnology activities outside the `nodes of excellence’shows that even though local collaborations are important, non-local cooperations are morefrequent than expected. As a matter of fact, empirical studies of the biotechnology industry innon-leading biotech regions reveal that biotech firms in that localities tend to earlyinternationalize their cooperations (e.g. Fontes, 2005; Rees, 2005; McKelvey, 2003; Gilding,2008; Belussi, 2008). The internationalism of partnerships in non-leading regions is such that insome cases non-local partnerships even surpass the rate of local networking activity. For instance,R&D projects carried on by DBFs in Portugal with foreign partners were more frequent thanthose with local partners (Fontes, 2003; 2005). The biomedical firms in the region of Greater
Vancouver, a peripheral region of Canada, show to heavily rely upon non-local links as 77% ofthe collaborations reported were non-local (Rees, 2005). Similar evidence was found for Swedishbiotech firms specialized in bioscience (McKelvey et al, 2003) and for the Melbourne biomedicalcluster in Australia (Gilding, 2008).However, non-local collaborations are not exclusive for more laggard or peripheral biotechregions. On the contrary, Boston Biotechnology Cluster which is a benchmark in thebiotechnology area and it is one of the largest biotechnology clusters in the world, showed a highlocal density of connections along with out-of-the-cluster collaborations with organizations inother US regions and even in other countries (Owen-Smith, 2004). Thus, it seems that the waythe biotechnology activity develops can hardly be explained by closed local interactions. As amatter of fact, in the biotechnology and network literature the empirical observation that biotechfirms engage in R&D collaborations with foreign organizations is understood as a way firms canaccess knowledge, resources and expertise that are not available in their locality (Fontes, 2003;Rees, 2005; McKelvey, 2005). Thus, a mix between local and non-local knowledge flows canbe ideal to promote firms’ innovation performance particularly in regions that do not lead theindustry.Accordingly, Gertler and Levitte (2003) in a study of 359 Canadian biotech DBFs show thatthose firms that innovated had a more outward looking portfolio of collaborations. In addition,Cassiman (2006) using data from the Community Innovation Survey on Belgian manufacturingfirms provide econometric evidence showing that those firms that combine internal and externalR&D strategies introduce more and substantially improved products to the market. To ourknowledge there are not much more studies that address the relevance of non-local cooperationsfor firms’ learning and innovation in biotech.In the following sections we address the case of Argentinean biotech firms. Firstly, we providenew empirical evidence regarding the extent to these firms engage in collaborations for R&D,manufacturing and marketing purposes. We particularly explore the geographical scope as wellas the organizational composition of their networks of collaborations. Secondly, we assess the
value of local and non-local collaborations to enhance firms’ innovation performance. In the lastsection, we provide some conclusions.3. Data and MethodologyThis methodological section is organized as follows. First, we present the definitions ofbiotechnology and biotechnological firm used in this study. Then, the process of data collectionis described as long as the main characteristics of the data obtained.3.1. DefinitionsAs biotechnology is neither an industry in itself nor represents a natural grouping of processes orproducts (Miller, 2007) its definition is neither simple nor straightforward. As a matter of fact,biotechnology embraces several different technologies which can be used for different purposesin diverse economic activities. For instance, the technology of recombinant DNA can be used toproduce large molecule medicines by the pharmaceutical sector, create new crop varieties by theagricultural sector, or modify micro-organisms to produce industrial enzymes by the chemicalsector (OECD, 2005). A further concern associated with the term biotechnology is that, apartfrom being used to encompass a wide range of technologies and applications, it has been definedin many different ways (Kennedy, 1991).In this paper we follow the OECD’s definition of biotechnology as it is broadly accepted bymany countries which follow it to compile statistics on biotechnology activity (see Annex A).Thus, our study is focused on those firms that apply at least one modern biotechnology techniqueto produce goods and services and/or to perform biotechnology R&D.7 Therefore, those firmsthat just trade biotechnology products, or use biotechnology inputs without further modifications,are not subjects of our study.7 We adopt the OECD definition of biotechnology firm (OECD, 2005; Beuzekom, 2009) in order to obtainconsistent and international comparable data.
The study is grounded both on Dedicated Biotechnology Firms (DBFs)8 and on firms involved inbiotechnology activities but which main activity is not the production of biotechnologicalproducts and process. Many empirical studies, particularly in leading biotechnology countries,analyzed the development of the biotech industry by only focusing on the study of DBFs.9However; we do not to restrict the study only to Argentinean DBFs as they fall short to representall the private efforts that take place in the area of biotech in the country.10Biotechnology can be applied in many fields such as health (human and animal), agriculture,food and beverages processing, natural resources, environment and industrial processing(Orsenigo, 2006). The area of life science, particularly human therapeutics and diagnostic, hasbeen chosen by many empirical studies to do research about (e.g. Powell et al, 2005; Powell et al,1999; Deeds and Rothaermel, 2004; Powell et al, 1996; McKelvey et al, 2003). However, ourstudy covers a larger scope of biotechnology applications as the aim of the study is to picturebiotechnology activity taking place in Argentina, regardless of the area of application.Therefore, we base our analysis on an empirical material that contemplates an expanding field ofknowledge with multiple application areas with the aim of enriching the empirical evidence andthe analysis of how high-tech activities develop in emerging countries.8 DBFs are defined by the OECD (OECD,2005) as biotechnology active firms whose predominant activity involvesthe application of biotechnology techniques to produce goods or services and/or the performance of biotechnologyR&D.9 See among others, for the US (Powell et al, 2005; Powell et al, 1996; Deeds and Rothaermel, 2004; Powell et al,1999; Koput et al, 1997), Australia (Gilding, 2008) and Canada (Niosi, 2003).10 The same criteria was followed by McKelvey et al (2003), Brink et al (2007), and Dahlander and Mc Kelvey(2005).
3.2. DatabaseThe fieldwork for data collection took place in Argentina between January 2009 and July 2010.We had the unique opportunity to participate and cooperate in the design of the questionnaire andin the process of data collection with the United Nations Economic Commission of LatinAmerica and the Caribbean (ECLAC) - Buenos Aires office - .We identified and surveyed those Argentinean firms that suited the adopted definition ofbiotechnology firm. The lack of an official and updated database of biotechnology firms inArgentina led us to the need of building up one. This was not a straightforward task due to thefact that firms performing biotechnology activities are widespread throughout the productivespectrum and their products are not easily identified as biotechnologicals at first glance. Thus, wecould not single out those firms solely based on traditional definitions of sectors or even of firmscompeting in a certain market. We created a database by searching in secondary sources.11 Thedatabase contained 142 firms which we presumed were active in biotechnology activities inArgentina. All these firms were approached and invited to participate in the survey.The main procedure to collect data was to survey firms by sending them a questionnaire12 by postor by email. Additionally, we further interviewed 33 of these firms. The interviews were semi-structured and had the purpose of checking and complementing the information given in thewritten questionnaire.In all, out of the 142 firms that composed the original database, 102 enterprises turned out to beeffectively involved in biotechnology activities. 40 companies were discarded as they weremainly dedicated to market biotechnological products developed by third parties such as11 The secondary sources consulted were lists of government grantsbeneficiaries, membership lists of TechnologicalPoles, firms incubated in universities, Internet searches on companies` websites, interviews with knowledgeablepeople, the business press and published reports on the matter.12 The questionnaire was pre-tested to control both for the length and the quality of the information gathered.Accordingly, the pilot survey was performed in the Santa Fe province from December 2008 to February 2009. Thepilot was run in this province due to the existence of a critical mass of active firms in the area of biotechnology aswell as scientific and technological infrastructure dedicated to that scientific field (e.g. two universities that offerdegrees in biotechnology, specialized research institutes, two technological poles with firmsincubators).
medicines, vaccines or genetically modified seed varieties. We achieved 59 responses which gavea response rate of 57, 84%.13 The firms surveyed use biotechnology tools for differentapplications such as human health, GM agriculture biotechnology, Non-GM agriculturalbiotechnology, veterinary health and industrial processing (see definitions in Annex A).The number of firms in each of the biotechnology applications considered varies. The largestshare corresponds to those active in health care applications (see Table 1). Thus, firms involvedin all health applications (including human and animal health care) made up 57.84% of the totalfirms surveyed.14 The second most important area of application of biotechnology in Argentinais agriculture representing 34.31% of the firms.15 Then, a smaller number of firms have to dowith industrial processing activities.As regards the extent to which our sample is representative of the population under study, itsomehow overestimates to a certain extent those firms to do with biotechnology in the humanhealth activity and underestimates those firms engage in non-GM agriculture biotechnology.However, the sample bias is small enough to have a trustable and representative sample tounderstand and comprehend the development and characteristics of the biotechnology activity inArgentina.13 Studies that focus on analyzing the economic dynamics and network structure of biotechnology in the world mainhubs, such as the US or the UK, are based on around 300 firms or so (e.g. Rothaermel et al, 2004; Powell et al, 2005;Niosi, 2003). However, studies for less advanced regions are generally grounded in a more limited number of firms.For instance, Gilding (2008) study the biotechnology network in Australia based on 50 DBFs, (Fontes, 2005)anchored the study of the Portuguese biotechnology network on 33 firms and Galhardi (1994) examined the patternof biotechnology development in Brazil out of the study of 12 representative firms. Our empirical evidence is in linewith studies that show a reduced number of firms involved in modern biotechnology in comparison with places thatlead the frontier of the field.14 The prevalence of firms dedicated to health care was also observed other countries such as Poland (100%),Sweden (89%), Austria (80%), Canada (58%) and Belgium (53%) (van Beuzekom and Arundel, 2009).15 This figure is high compared to the share of firmsdedicated to agriculture biotechnology in countries such asGermany (5%), Sweden (5%), Austria (4%) and Brazil (23%), but it is similar to other countries such as Philippines(38%) and South Africa (37%) (van Beuzekom and Arundel, 2009).
Table 1: Surveyed firms, by biotechnology application Biotech firms Biotechnology Application Surveyed firms in the dataset Human health 42 27 Veterinary health 17 11 GM Agricultural biotechnology 6 6 Non-GM Agricultural biotechnology 29 11 Industrial processing 8 43.3. Network dataData about the formal collaborations in which Argentinean firms were involved during the period2003-2008 was collected in order to unravel how the network of biotech firmsstrategic allianceswas constituted.In the absence of archival records of strategic alliances in the country we gathered data on firmscollaboration activity by introducing specific questions in the questionnaire used to surveybiotech firms in Argentina.16 Network data were collected using the egocentric network method,which focuses on a focal actor or object and the relationships in its locality. Thus, the wholenetwork is discomposed into each objectsegocentric network, so that based on the egocentricnetwork data the complete network can be built up (Marsden, 2005).The focal nodes of the network are those firms located in Argentina involved in biotechnologyactivities, and the ties of the network are contractual arrangements in which nodes participate topool or exchange resources or knowledge. Three types of collaboration ties were considered:16 Network studies draw extensively on survey and questionnaire data (Knoke and Yang, 2008; Marsden, 1990;Marsden, 2005). Recent studies that used surveys to collect network data are, among others, (Giuliani and Bell,2005; Gilding, 2008).
knowledge, manufacturing and marketing. We treated each formal agreement as a tie. Thus, anArgentinean firm is connected to a partner when one more ties exist between them.To elicit the ties of each focal node we used the name generator method which consists inasking each ego respondent to name the contacts to whom it has a specific kind of relationship(e.g. R&D contractual arrangement).17 Therefore, each firm freely generated a list of alters bywriting down the name of the partners with whom it had collaborated during the period 2003-2008. As the aim of the survey was to unravel both the organizational diversity and thegeographical scope of the network of collaborations, the type of partners to which Argentineanfirms collaborate was not restricted beforehand (see Annex B for details).As the goal was to picture the formal collaboration network in which Argentinean biotechnologyfirms participated in the period 2003-2008, and, in particular, unravel how organizationallydiverse and geographically dispersed the emergent network was, we coded partners by locationand type. Thus partners were classified into locals, when they were located in Argentina, andexternal, if they were located in regions outside Argentina. Foreign partners were classifiedaccording to their geographical location into Latin American, European, American and others.As regards the type of organization, we classified partner into biotechnology firms, other firmsand PROs.One limitation of our approach is that we do not end up having a complete network, as we lackcollaboration data between actors that are not Argentinean firms engaged in biotechnology. Weignore if two Argentinean biotechnology firms are indirectly connected through collaborationpartners which are themselves connected. We lack this information as it is hard to collect,particularly for international partners18. However, we are still able to picture and to explore17 The name generator method differs from the roster method as the former consists of contactsrecalling whereasthe latter is based on contactsrecognition. The roster method is recommended when the total number of possiblealters is known beforehand, while the name generator method is more appropriate when that it is not the case. Aswe mostly ignored all the possible nodes of the network in advanced, and hence one of the main objects of the studywas to unravel which were the nodes that compose the network, we followed the name generator proceudure.18 Other studies that faced the same difficulty are Powell et al (2005), Powell et al (1996), Koput et al (1997), Gilding(2008) and McKelvey et al (2003).
Argentinean biotech firmsdirect partners which allows us to have an accurate approximation ofthe structure, the geographical extension and the organizational diversity of the knowledgenetwork. We acknowledge that both direct and indirect ties can affect firmsknowledgeacquisition and performance (Ahuja, 2000). However, the impact of indirect ties is ultimatelydetermined by the firmslevel of direct ties, which are the ones we were able to trace.4. The Argentinean biotech network: exploration and analysisThis section explores the extent to which Argentinean biotech firms participated in R&D,manufacturing and marketing collaborations during the years 2003-2008. Then, the analysis isnarrowed to the R&D network. We explore its organizational composition and geographicalscope. The possible explanations for the main features of the knowledge network structure arefurther discussed at the end of the section.4.1. The knowledge, manufacturing and marketing networksThe network of collaborations in which Argentinean firms got engaged in the period 2003-2008can be visualized in Figure 1. The network representation contains all cooperations in whichthese firms have participated in that period. Nodes are differentiated by their location (shape) andby type of organization (color), so that agents located in Argentina are represented by circles, andagents located somewhere else are represented by squares. Then, Argentinean biotech firms arewhite circles whereas Argentinean PROs are depicted as black circles, and foreign partners as redsquares. A glimpse to Figure 1 reveals that the Argentinean biotech network is bothorganizational diverse and geographically dispersed. Partnerships have been forged with agentslocated within and outside the business sphere, and located both in Argentina and abroad.In addition, ties are differentiated by types of collaborations (color). Knowledge-related ties areblack, manufacturing agreements are shown in red and marketing deals are colored in green.Directed ties, which represent transfers of technology (e.g. licensing agreements), have arrowspointed to the agent that receives the technology. The distinct colors of the ties that connect nodes
in the graph reveal the differing motives that aimed biotech firms to enter into strategic allianceswith third parties.The high degree of connectedness that can be observed in the graph is a reflection of a highdegree of firmsparticipation in cooperations. Accordingly, 51 out of the 59 enterprises surveyedactive in biotechnology in Argentina had engaged in collaborations with other partners either forR&D, manufacturing or marketing purposes. Thus, we found evidence aligned with the patternobserved for the development of biotechnology in other regions: biotech firms tend to benetworked organizations.Although different motives triggered firms to engage in collaborations, the overwhelminglysuperiority of black ties in the graph indicates the predominance of knowledge-related reasons.We found that 238 out of the 275 cooperations recorded (86%) had to do with knowledge flowsboth in the form of R&D collaborations and technology transfers (e.g. licensing). On thecontrary, the number of deals related to manufacturing and marketing are much scarcer as firmsset up 21 and 15 deals of these types of collaborations, respectively.Some network statistics depicted in Table 2 help to understand further the network structurepictured in Figure 1. The table shows the firmsaverage degree and standard deviation, themaximum and minimum number of cooperations forged by firms, and the number of isolates.Calculations are shown for R&D, manufacturing and marketing separately, and for allcollaborations together.Based on the average degree we can state that, on average, each Argentinean firm engaged inbiotechnology had set up around 5 collaborations, the majority of which have been related toR&D activities. Accordingly, the average degree for the knowledge network is 3.71 whereas themanufacturing and the marketing ones are 0.15 and 0.03, respectively. The different degree offirmsparticipation in each network is also illustrated by the reduced number of isolates in theknowledge network (11) in comparison to the much larger number of non-connected nodes in the
Figure 1 – The complete network of Argentinean biotech firms. Nodes: Argentinean biotechfirms (white circles), Argentinean PROs (black circles), foreign organizations (red squares).Ties: R&D cooperations and technology transfers (black), manufacturing agreements (red) andmarketing agreements (green).manufacturing (51) and marketing network (57). Taken all ties together the rate of dispersion ofcollaborations is 5.04, as it is indicated by the standard deviation measure. Although theknowledge network is on average less connected than the complete network, it is morehomogeneous in terms of the number of connections held by each of the firms.
Table 2 – Knowledge network statistics, by type of cooperation Type of Av. St. Max Min Isolates cooperation Degree Dev. R&D 3.71 4.21 23 1 11 Manufacturing 0.15 0.41 2 1 51 Marketing 0.03 0.18 2 1 57 All 4.61 5.04 27 1 8The analysis so far has not distinguish among different biotech application, as we groupedtogether those firms engaged in human health, veterinary health, agriculture and industrialprocessing applications. When we do this distinction, we see that for each of the biotechnologyapplications, the same pattern than for the aggregate network is observed (see Table 3):knowledge-related deals account for the bulk of collaborations, whereas manufacturing andmarketing ones are very limited. The area of human health is the one that shows the greatestnumber of R&D cooperations (93), accounting for 40% of the total number of R&Dcollaborations in the period analyzed. Then, also firms applying biotechnology to human healthare the ones that make most use of manufacturing deals19 whereas firms in veterinary health tendto engage relatively more than the rest in marketing-related cooperations.On the whole, even when we distinguish firms by main area of biotech application, we found thatthe frequency and patterns of interaction for business purposes (marketing and manufacturing)largely differ from those that involve knowledge flows.20 These results indicate that Argentineanbiotech firms make more use of strategic alliances to gather knowledge, expertise andtechnology, than to manufacture or commercialize goods. Thus, our tentative hypothesis is thatour results support the idea that firms become networked organizations as all the necessary skills19 This finding is alike the one presented by Thorsteinsdottir (2010) . They find that for health biotech firms locatedin developing countries both end-stage commercialization and manufacturing activities are highly importantpurposes that trigger collaboration.20 Similar results were also found by Giuliani (2006, 2007).
Table 3 - Number of cooperation agreements by Argentinean biotechnology firms, by typesof cooperation (2003-2008) Biotechnology Application Number of cooperations Knowledge Manufacturing Marketing Human health 93 10 2 Veterinary health 47 2 6 GM Agricultural biotechnology 43 2 3 Non-GM Agricultural biotechnology 41 6 4 Industrial processing 14 1 0and organizational capabilities needed to compete in biotechnology are not readily found under asingle roof (Powell and Brantley 1992). In addition, the complexity of the biotechnologyknowledge-base and the rapid evolution of technical change in this area further trigger firms tobecome relatively more active in creating knowledge-related alliances with third parties.Given the relevance of knowledge-related collaborations, and the importance of knowledge flowsto understand the development and evolution of the Argentinean biotechnology industry (Belland Albu, 1999), in the next section we will focus on studying the knowledge network in moredetail.4.2. The knowledge networkThe knowledge network is composed by all Argentinean biotech firms that engaged in R&Dcooperations and licensing agreements in the period 2003-2008. We found that all firmsdedicated to veterinary health and GM agricultural biotechnology and the vast majority of firmsinvolved in the other biotechnology applications considered are actively involved in theknowledge network. But, with whom do these firms exchange and share knowledge with? Is thenetwork composed only by local interactions? In order to answer these questions thecomposition of the knowledge network, in terms of the type of actors that participate, and their
geographical location is pictured in Table 4. For each biotech application it is shown the numberof collaborations that firms forged locally with other biotech firms, local PROs and other firms(e.g. suppliers), and the number of non-local collaborations with foreign firms and PROs locatedin other countries.We observe that Argentinean biotech firms forged cooperations both with local and non-localpartners. 146 cooperations have been forged with local partners whereas 90 collaborations tookplace with foreign organizations. Thus, although, on the whole, biotech firms cooperated morelocally than internationally, non-local cooperations still represent a large share of the total R&Dagreements (40%). These results suggest that when trying to understand how technical changetakes place non-local knowledge flows cannot be ignored.Table 4 – Number of local and non-local collaborations to firms and PROs, bybiotechnology application area. Number Number non-local Biotechnology Local collaborations to collaborations to application biotech local other Total firms PROs Total firms PROs firms Human health 6 55 0 61 13 19 32 Veterinary health 3 19 1 23 11 12 23 GM Agricultural biotechnology 0 22 0 22 19 1 20 Non-GM Agricultural biotechnology 0 28 2 30 3 8 11 Industrial Processing 0 10 0 10 4 0 4 Total 9 134 3 146 50 40 90
At local level, the degree to which biotech firms collaborate with peers and with PROs largelydiffer. There is an outstanding predominance of collaborations with local PROs and very scarceinter-firm collaboration. Accordingly, 91% of all local collaborations forged by biotech firmshave been set up with universities and research institutions located in Argentina. And, only 9 outof 146 local R&D-related collaborations were forged with local firms engaged in biotechnologyactivities. In fact, cooperations among biotech firms only occurred between firms engaged inhealth applications (human and animal) (see Figure 2).Figure 2 - The inter-firm R&D collaboration network. Nodes: Argentinean Biotech Firms(ABF) active in human health (blue), ABF active in veterinary health (yellow), ABF active inGM agricultural Biotech (grey), ABF active in non-GM agricultural biotech (green), ABF activein industrial processing (brown), Argentinean PROs (black), foreign organizations (red). Ties:R&D agreements
As regards non-local collaborations, Argentinean biotech firms set up R&D collaborations bothwith foreign firms (50) than with foreign PROs (40). The geography of non-local collaborationscan provide a hint of the sort of knowledge firms source and in particular whether non-localcollaborations may be a vehicle to access world-leading research. With that purpose weclassified foreign collaborations according to the region/country in which partners were located.Thus, we grouped firms’ partners into the following categories: Europe, the US, Latin Americaand others.We found out that North-South collaborations predominate as 54 partnerships (59%) have beenforged with European and American partners. This evidence is coherent with that obtained byother studies on the biotechnology knowledge network which also makes it visible that foreigncollaborations are not randomly distributed but very much oriented towards the world hubs ofbiotechnology. Thus, for the Swedish (McKelvey, 2003) and Australian (Gilding, 2008) casespartners were first drawn from the US, then the UK, then everywhere else. Assuming that agentslocated in the US and Europe possess more advanced knowledge and are closer to the frontier, wecan argue that more than half of the non-local collaborations forged by Argentinean biotech firmswere with agents at the cutting age. These non-local collaborations can actually become a sourceof novel and up-to-date technology.Discussion of resultsThe Argentinean biotech firms’ knowledge network encompasses both local and non-localcollaborations. On the one hand, knowledge flows at local level mainly through collaborationswith local PROs. Thus, we observe that firms seldom engage in joined R&D activities with theirco-located peers but the bulk of their local R&D cooperations take place with local PROs. Onthe other hand, a great deal of firms in all biotech applications actively gets involved inknowledge collaborations with partners located elsewhere. We analyze these results in detailbelow.The close and intense cooperation between Argentinean biotech firms and local PROs may notseem that surprising since the synergies between industry and science lye at the very core of the
birth and development of the biotechnology industry (e.g. Owen-Smith et al 2002, Zucker et al1998, Arora and Gambardella 1994). Firms are fed by scientific discoveries, which may befurther developed within the industry and applied to create new products and processes uponthem. Thus, the industry in itself mostly consists of the transformation of academic research intocommercial products. To our view, what this result highlights is the importance of a strong localscientific base for science-based firms to emerge and further develop.The very limited the degree of collaboration among Argentinean biotech firms requires someexplanation. The dense inter-firm local network that typically arises in leading regions wasneither observed in the Argentinean case nor in other countries that do not lead the industry.21One plausible explanation for the scarce local inter-firm interaction relies on firms’ knowledgespecialization. Biotech firms typically manage a reduced number of technologies whichconstitute their technological platform, out of which they develop their product portfolio. We canexpect that biotech firms in not leading regions cater specific market segments, and hence, arespecialized in different set of technologies. The heterogeneity of local firms’ knowledge basesmay be an important factor to explain the likelihood of local inter-firm synergies. Thus,empirical evidence is quite conclusive on the fact that some middle ground between diversity andsimilarity in firmsknowledge bases fosters R&D cooperation agreements as firms are moreprone to cooperate with partners who provide them with learning opportunities but with whomthey share some common knowledge so that mutual understanding is possible (Ahuja and Katila,2001; Mowery et al, 1996; Gulati and Gargiulo, 1999; Duysters and Shoenmakers, 2006).Therefore, following this argument, it may well be the case that local firms do not share with co-located peers enough knowledge or research interests so that engaging in partnerships amongthem become attractive. Probably, this is accentuated by the fact that the number of firmsengaged in biotech in these regions tends to be relatively reduced.Although we do not disregard the fact that knowledge diversity within the local industry may bean important factor preventing local cooperations, it seems that this is not an explanation that21 See, among others, Fontes (2003) and Fontes (2005) for the Portuguese case, Rees (2005) for the Canadian case,Gilding (2008) for Australia, McKelvey et al (2003) for Sweeden and Belussi (2008) for Italy.
explains it all. Other factors such as the building of trust, reputation as well as competitionshould be further studied. Indeed, for the case of Argentinean biotech firms’ we found that somefirms’ knowledge bases are similar enough for potential collaborations to take place.Accordingly, Argentinean biotech firmsmanagers provided other reasons, beyond ‘knowledgefit’, to explain the scarce inter firm collaborations among firms. Many of them claimed that theyacknowledged local peers with whom it could be fruitful to cooperate. Nonetheless, cooperationsdid not arise. The most frequent explanation given was related to market rivalry. Thus, localmarket competition may be a force that prevents potential cooperations in the Argentinean case,and may inhibit the possibilities of local cooperations when they do exist.As regards non-local collaborations, our study finds evidence aligned to other studies of biotechindustries in non-leading regions. Collaborations with foreign partners are frequent, relevant andnot random. In fact, firms mostly cooperate with partners in leading regions. The value of R&Dcooperations with geographically distant partners can be of great importance for high-techindustries as innovation requires knowledge that is both best global and diverse (Dahlander andMcKelvey, 2005). In particular, non-local collaborations can be a vehicle through which firmsupgrade their technological competences and overcome the relative knowledge disadvantages oftheir location (Rees, 2005). The interviews with managers of Argentinean biotech firms rovidedempirical evidence that supports the idea that foreign partners can provide local firms withknowledge and developments that are not available locally, and also sometimes cheaper.Accordingly, in the following section we provide empirical evidence regarding the value ofcollaborations both with foreign partners and local PROs to enhance Argentinean biotech firms’innovation performance.5. R&D alliances and innovation outputThis section intends to assess the value of R&D collaborations to enhance firms’ innovationperformance. We focus on local collaborations between firms and PROs, and non-localcollaborations. Both type of collaborations can be valuable to provide novelty and diversity tothe local industry sphere, and in turn, positively contribute to firms’ innovation performance.
We study the relation between R&D alliances and innovation output based on a descriptiveanalysis of the data collected.22Table 5 shows the number of firms which introduced new products and processes in the period2003-2008, given that they have engaged or not in strategic alliances with local PROs and foreignorganizations. Three degrees of innovation’s novelty are considered: those product/processwhich constitute an innovation for the firm but that already existed in the local and theinternational market; those products/process that are innovations for the local market (and also tothe firm) but that already existed in other foreign markets; and those product/process which arethemselves innovations for the international market as a whole. Clearly, a product that is`internationallynew is a more relevant innovation, that one that is just new for the firm.One of the main results displayed in Table 5 is that the majority of firms that introduced productsand processes new to the international market during the period 2003-2008 have also engaged instrategic alliances during that period. 17 out of the 21 firms that succeeded to produce a productinnovation that was new for the global market have collaborated with local PROs, whereas14 outof those 21 had set up R&D collaborations with foreign partners. In the case of the 17 firms thatachieved a process innovation at international level, 13 have engaged in joint R&D projects withlocal PROs and 10 with foreign partners. Therefore, these results suggest that there is a strongcorrelation between firms’ innovation output and the engagement in collaborations with localPROs23 and non-local partners. o explain firms’ innovation performance by firms’ engagement in R&D collaborations we face a possiblesimultaneity bias. Firms’ innovation performance could cause as well as be caused by R&D cooperations. Thus, inorder to correct for that we needed to build up an econometric model that accounts for it so to have meaningfulresults. Several attempts were pursued with that aim, but the limited number of observations and the cross-sectionnature of the data prove to be great limitations to achieve that goal. Still, we find strong and clear evidence of therelation between collaborations and innovation performance.23 This result is aligned to the study of Mohnen and Hoareau (2003) which shows firms that rely on PROs introducemore radical product innovations
Table 5 - Biotech firms’ innovation performance and R&D alliance activity Biotech firms that engaged in collaborations with Number of biotech firms that innovated in Local PROs Foreign organizations No Yes No Yes Products New only to the firm 1 2 1 2 New to the local market 4 15 10 9 New to the international market 4 17 7 14 Processes New only to the firm 2 8 4 6 New to the local market 4 7 5 6 New to the international market 4 13 7 10In addition, we also observe that the majority of firms that innovated in products and processes,whatever the degree of product innovation considered, engaged in collaborations with localPROs. This result provides further support to the idea that universities are one of the mostrelevant sources for innovation activity by firms (e.g., Cohen et al 2002; Arundel and Geuna2004; Kaufmann et al. 2001). Universities and research institutions may help to speed upinnovation (Mansfield, 1991; Klevorick et al 1995) and contribute to reinforce firm´s scientificcapabilities (Arora and Gambardella 1994) by providing knowledge which is not available, or atleast more difficult to obtain, within the industrial sphere. In addition, this result also contributesto highlight the important role that scientific and research institutions play in the development ofa knowledge-intensive industry.In the rest of the analysis we just focus on innovations that are new for the international market.We evaluate the extent that firms that innovated have also engaged in relatively more R&Dcollaborations. We consider four type of innovation indicators: whether surveyed firms declaredto have achieved a product innovation or a process innovation, applied for a patent in Argentinaand applied for a patent in the U.S. Table 6 shows the average number of collaborations with
local PROs and foreign organizations of firms that succeeded to innovate or not during the periodanalyzed.Table 6 – Biotech firms’ collaborations with local PROs and foreign firms and innovationperformance Firms that in the period 2003-2008 Average Applied for number of Innovated in Innovated in Applied patents patents in thecooperations products processes in Argentina US Yes No Yes No Yes No Yes Nowith localPROs 2.31 2.2 3.05 1.93 3.76 1.45 3.92 1.74 ** *with foreignorganizations 1.28 1.26 1.64 1.11 2.05 0.87 2.46 0.93 **, ** Significance at the 5% and 1% level, respectively.Having a look at Table 6 we observe that those firms that innovated in products and in processesnew to the international market tend to be on average relatively more involved in R&Dcooperations than firms that did not innovate. However, the differences observed are not largeenough to be statistically significant.However, firms that applied for patents in Argentina have forged more than the double of R&Dcollaborations with local PROs and foreign partners than the ones that did not apply for patents inthe period. These differences prove to be statistically significant. Also, firms that applied forpatents in the US have forged more collaborations with PROs and foreign organizations than theones that did not apply for patents in that country. The difference between the number ofcollaborations forged with local PROs by those firms that intend to patent in the US in
comparison to those that did not apply for intellectual property rights in this country is found tobe statistically significant.On the whole, this last set of results reveal that firm that collaborated relatively more with localPROs and foreign organizations show a higher propensity to innovate. These results arestatistically significant when we take patent applications as innovation indicators.6. ConclusionsArgentinean biotech firms are actively involved in alliances to exchange, share and sourceknowledge. Most of these firms are networked organizations, as are biotech firms located inother leading and non-leading regions. It seems that the characteristic of the industry, thecomplexity of the technology and the rapid pace of technical change drives firms to enter intocollaborations. Our results illustrate that even though firms engage in cooperations formanufacturing and marketing purposes, knowledge is the major factor that stimulates firms to getinvolved in collaborations with other partners.The knowledge network structure of the Argentinean biotech firm is different from the onesfound for the leading regions, but similar to those in other non-leading ones. The salient featuresare the scarcity of collaborations among co-located firms, the key role that PROs play in knittingthe local network together and the relevance of non-local partnerships predominantly forged withpartners in leading regions.The mix between local and non-local cooperation forged by Argentinean biotech firms revealsthat the sustainability and the development of the Argentinean biotech industry cannot beexplained focusing only on local knowledge interactions. Furthermore, we found that non-localcollaborations are valuable for firms’ innovation activity. Most of those firms that introducedinnovations new to the international market have entered into collaborations with foreignpartners. In addition, innovative firms also show a larger number of collaborations with externalorganizations than those firms that did not innovate.
Another result of our study is that the strength of the local scientific knowledge base, contained inlocal PROs, seems to be critical for the development of biotechnology in Argentina, as it is inevery region where a biotechnology industry emerges. We find evidence that suggests thatentering into R&D collaborations with local PROs may have a positive effect on firms’innovative performance.Even though we found collaborations with local PROs and non-local collaborations to bevaluable for firms’ innovation, stronger results claim for data available for more years, in order toapply econometric techniques that permit to address the potential simultaneity bias betweenknowledge cooperations and innovation performance.However, we can still draw some tentative implications of these results for policyrecommendation. The persistent choice of firms to exchange and source knowledge from thelocal scientific community and from foreign partners should be acknowledged by policy makers.In particular, because both types of collaborations seem to be valuable to enhance firms’innovation performance. Thus, non-local collaborations and cooperations with universities andscientific organizations should not be ignored but promoted and facilitated. These results alsoclaim for a more open view of clusters, and an abandonment of a close and geographicallybounded view of knowledge flows.Future research should address the reasons that lead firms to display the observed patterns ofR&D collaborations. In particular, which are the factors that prevent inter-firm synergies to takeplace. We suggest that not only absorptive capacity issues and knowledge related factors shouldbe addressed, but also factors such as the building of trust, reputation and competition should beconsider in the analysis.AcknowledgmentsResults presented in this article are based on a study joint with ECLAC, Buenos Aires office.The data collected were drawn from interviews and data from companies, we would like to thank.We also wish to thank Bernardo Kosacoff for facilitating field research and being supportive and
positive about this research, Roberto Bisang for his invaluable insights and guidance during thefieldwork study, and Robin Cowan for his useful comments and suggestions, and continuoussupport on this study.ReferencesAhuja, G. (2000) ‘Collaboration networks, structural holes and innovation: A longitudinal study’Administrative Science Quarterly 45:425-455.Ahuja, G. and R. Katila (2001) ‘Technological acquisitions and the innovation performance ofacquiring firms: A longitudinal study’ Strategic Management Journal 22:197-220Andrews, K. (1971) ‘The Concept of Corporate Strategy’ Irwin, Homewood, IL.Arora and A. Gambardella (1994) ‘Complementarity and external linkages: The strategies oflarge firms in biotechnology’ Journal of Industrial Economics 38:361-379Arundel A. and A.Geuna (2004) ‘Proximity and the use of public science by innovative Europeanfirms’ Economics of Innovation and New Technology 13 (6): 559-580Audretsch, D.B. and M.P. Feldman (1996) ‘R&D spillovers and the geography of innovation andproduction’ American Economic Review 86: 630–640Bartley, S, Freeman, J. and R. Hybels (1992) ‘Strategic Alliances in Commercial Biotechnology’in Networks and Organizations: Structure, Form and Action, pages 311-347, President andFellows of Harvard College.Bathelt, H., Malmberg, A. and P. Maskell (2004) ‘Clusters and knowledge: local buzz, global pipelinesand the process of knowledge creation’ Progress in Human Geography 28 (1):31-56Bell, M. and M. Albu (1999) ‘Knowledge systems and technological dynamism in industrialclusters in developing countries’ World Development 27 (9): 1715-1734Belussi, F., Sammarra, A. and S. Sedita (2008) ‘Managing long distance and localized learning inthe Emilia Romagna life science cluster’ European Planning Studies 16(5):665-692.
Becattini, G. (1990): ‘The Marshallian industrial district as a socio-economic notion’ in Pyke, F.,Becattini G. and W. Sengenberger (eds.) Industrial districts and inter-firm cooperation in Italy.International Institute for Labour Studies, Geneva, 37-51.Breschi, S. and F. Malerba (2001) ‘The Geography of Innovation and Economic Clustering: someIntroductory Notes’ Industrial and Corporate Change 4 (10):817-833Breschi, S. and F. Lissonni (2001) ‘Knowledge Spillovers and Local Innovation Systems: ACritical Survey’ Industrial and Corporate Change 10(4):2001Brink, J., Dahlander, L. and M McKelvey (2007) ‘Developing capabilities: An analysis ofbiotechnology in two regions in Australia and Sweden’ European Planning Studies 15(6):727-751Burt, R. S. (1992) ‘Structural Holes: The Social Structure of Competition’ Harvard UniversityPress, Cambridge, Mass.Camagni, R. (1991) ‘Introduction: from the local ‘milieu’ to innovation through cooperationnetworks’ in Camagni, R. (ed.) Innovation networks: spatial perspectives, Belhaven Press,London, 1-9.Carlsson, B. (2001) ‘Technological systems in the bio industries. An international study’ KluwerAcademic Publishers.Cassiman, R. and B.Veugelers (2006) ‘In search of complementarity in the innovation strategy:Internal R&D and external knowledge acquisition’ Management Science 52:68-82.Cawthorne, P.M. (1995) ‘Of Networks and Markets : The Rise and Rise of a South Indian Town,the Example of Tiruppur’s Cotton Knitwear Industry’ World Development 23(1): 43-56
Coenen, L., Moodysson, J. and B. Asheim (2004); ‘Nodes, networks and proximities: on theknowledge dynamics of the medicon valley biotech cluster’ European Planning Studies12(7):1003-1018.Cohen W.M., R.R. Nelson and J.P. Walsh (2002) ‘Links and Impacts: The Influence of PublicResearch on Industrial R&D’ Management Science 48 (1): 1-23Coleman, J.A. (1988) ‘Social Capital in the Creation of Human Capital’ The American Journal ofSociology 94:95-120.Cooke P. (2001) Regional innovation systems, clusters and the knowledge economy, Industrial &Corporate Change, 10, 945-974Dahlander, L. and M. McKelvey (2005) ‘The occurrence and spatial distribution ofcollaboration: biotech firms in Gothenburg, Sweden’ Technology Analysis & StrategicManagement, 17(4):409-431.Deeds, D. and F. Rothaermel (2004) ‘Exploration and exploitation alliances in biotechnology: asystem of new product development’ Strategic Management Journal 25(3):201-221.Duysters, G. and W. Schoenmakers (2006) ‘Learning in Strategic Technology Alliances’Technology Analysis & Strategic Management 18(2):245–264Dyer, J.H. and K. Nobeoka (2000) ‘Creating and Managing a High Performance KnowledgeSharing Network: the Toyota Case’ Strategic Management Journal 21:345-367Eisenhardt, K.M. and C.B. Schoonhoven (1996) ‘Resource-based view of strategic allianceformation: Strategic and social effects in entrepreneurial firms’ Organizational Science 7:136-150.Fontes, M. (2003) ‘Distant networking? The out-cluster strategies of new biotechnology firms’Paper presented at ASEAT Conference on Knowledge and Economic and Social Change: NewChallenges to Innovation Studies, Manchester, 79 April., 2003.
Fontes, M. (2005) ‘Distant networking: The knowledge acquisition strategies of out-clusterbiotechnology firms’ European Planning Studies 13(6):899-920.Galhardi, R. (1994) ‘Small high technology firm in developing countries. The case ofbiotechnology’, Aldershot, Averbury Press.Gertler, M. and Levitte, Y. (2003) ‘Local nodes in global networks: The geography of knowledgeflows in biotechnology innovation’ Paper presented at DRUID Summer 2003 Conference, sHelsingore.Gilding, M. (2008) ‘The tyranny of distance: Biotechnology networks and clusters in theantipodes’ Research Policy 37(6/7):1132-1144.Giuliani, E. and M. Bell (2005) ‘The micro-determinants of meso-level learning and innovation:Evidence from a Chilean wine cluster’ Research Policy 34:47–68Giuliani E. (2006) ‘The uneven and selective nature of cluster knowledge networks: evidencefrom the wine cluster’ CIRCLE Electronic Working Paper Series 2006-11 - CIRCLE (Centre forInnovation, Research and Competence in the Learning Economy) - Lund University.Giuliani, E. (2007) ‘Towards an understanding of knowledge spillovers in industrial clusters’Applied Economics Letters 14:87–90Granovetter, M. (1985) ‘Economic action and social structure: The problem of embeddedness’American Journal of Sociology 91:481–510Gulati, R. (1995) ‘Does familiarity breed trust? The implications of repeated ties for contractualchoice in alliances’ Academy of Management Journal 38: 85–112.Gulati, R. (1998) ‘Alliances and networks’ Strategic Management Journal 19: 293–317Gulati R. and M. Gargiulo (1999) ‘Where Do Inter-Organizational Networks Come from?’American Journal of Sociology 104:1439-1493.
Hagedoorn, J. (1992) ‘Strategic Alliances in Information Technology among Firms in WesternIndustrialized Nations’ in L.S Peters (ed.) International Issues in the Management of Technology(10) JAI Series on International Business and Finance.Hagedoorn, J. and G. Duysters (2000) ‘The effects of Mergers and Acquisitions on theTechnological Performance of companies in High Tech Environment’ Mimeo. University ofMaastrichtHeimeriks, G. and R. Boschma (2011) ‘The Geography of Biotechnology’ Paper presented at thefinal DIME Conference, Maastricht 6-8 April.Kauffman A. and F. Todtling (2001) ‘Science-industry interaction in the process of innovation:the importance of boundary-crossing between systems’ Research Policy 30: 791-804Hagedoorn, J. and N. Roijakkers (2006) ‘Inter-firm R&D partnering in pharmaceuticalbiotechnology since 1975: trends, patterns, and networks’ Research Policy 35:431-446.Kennedy, M.J. (1991) ‘The evolution of the word biotechnology’ TRENDS in Biotechnology9:218-220.Klevorick A.K., R.C. Levin, R.R. Nelson and S.G. Winter (1993) ‘On the sources andsignificance of inter-industry differences in technological opportunities’ Research Policy 24:185-205Knoke, D. and Yang, S. (2008) ‘Social Network Analysis’ SAGE Publications.Kogut, B. (1989) ‘The stability of joint ventures: Reciprocity and competitive rivalry’ Journal ofIndustrial Economics 38(2): 183-198Koput, K.W. and Smith-Doerr, L. and W. W. Powell (1997) ‘Learning and IndustryStructure: The Evolution of Networks in Biotechnology’ in Advances in Strategic ManagementResearch pages 229-254 Greenwich, CT: JAI Press.
Lawson C. and E. Lorenz (1999) ‘Collective Learning, Tacit Knowledge and Regional InnovativeCapacity’ Regional Studies 33(4)305-317Lawton Smith, H. (2004) ‘The Biotechnology Industry in Oxfordshire: Enterprise andInnovation’ European Planning Studies 12:985-1001.Mansfield E. (1991) ‘Academic research and industrial innovation’ Research Policy 20: 1-12Marsden, P.V. (1990) ‘Network data and measurement’ Annual Review of Sociology 16:435-463.Marsden, P.V. (2005) ‘Recent Developments in Network Measurements’ in Models and Methodsin Social Network Analysis, pages 8-30 Cambridge University Press.Marshall, A. (1920) ‘Principles of Economics’, MacMillan, LondonMcKelvey, M., Alm, H. and M. Riccaboni (2003) ‘Does co-location matter for formal knowledgecollaboration in the Swedish biotechnology pharmaceutical sector?’ Research Policy 32(3):483-2003.Miller, H.I. (2007) ‘Biotech defining moments’ TRENDS in Biotechnology 25:56-59. sMohnen, P. and Hoareau, C. (2003) ‘What type of enterprise forges close links with universitiesand government labs? Evidence from CIS 2’ Managerial and Decision Economics 24:133-145.Mowery, D.C., Oxley, J.E. and B.S. Silverman (1996), ‘Strategic alliances and Inter-firmknowledge Transfers’ Strategic Management Journal 17:77-91.Mowery, D.C., Oxley, J.E. and B.S. Silverman (1998) ‘Technological Overlap and Inter-firmCooperation: Implications for the Resource-based View of the Firm’ Research Policy 27:507-523.Nadvi, K. (1996) ‘Small firms industrial districts in Pakistan’, IDS D.Phill thesis, University ofSussex
Niosi, J. (2003) ‘Alliances are not enough explaining rapid growth in biotechnology firms’Research Policy 32:737-750.OECD (2005) ‘A framework for biotechnology statistics’ OECD, ParisOinas, P. (1999) ‘Activity-specificity in organizational learning: implications for analyzing therole of proximity’ GeoJournal 49:363–72.Orsenigo, L. and M. McKelvey (2006) ‘The economics of biotechnology’ Edward ElgarPublishers.Owen-Smith J., M. Riccaboni, F. Pammolli and W.W. Powell (2002) ‘A comparison of US andEuropean university-industry relations in the life sciences’ Management Science 48 (1): 24-43Owen-Smith, D. and W.W. Powell (2004) ‘Knowledge networks as channels and conduits: Theeffects of spillovers in the Boston biotechnology community’ Organizational Science 15(1) 5-21Piore, M. J. and C. F. Sabel (1984) ‘The Second Industrial Divide: Possibilities for Prosperity’New York, Basic Book.Porter, M. (2000) ‘Locations, clusters and company strategy’ in The Oxford Handbook ofEconomic Geography pages 253-274 Oxford University Press.Powell, W.W. and P. Brantley (1992) ‘Competitive Cooperation in Biotechnology:Learning Through Networks?’ in Nohria, N. and R. Eccles (Eds.) Networks and organizations,366–394. Boston, MA: Harvard Business Press.Powell, W. W., Koput, K. W. and L. Smith-Doerr (1996) ‘Inter-Organizational Collaboration andthe Locus of Innovation: Networks of Learning in Biotechnology’ Administrative ScienceQuarterly 41:116-145.Powell, W. W., Koput K.W., Smith-Doerr L. and J. Owen-Smith (1999) ‘Network Position andFirm Performance: Organizational Returns to Collaboration in the Biotechnology Industry’ pp.
129-59 in Networks In and Around Organizations. (Special Issue in the series Research in theSociology of Organizations), edited by S. Andrews and D. Knoke (eds.) Vol. 16, JAI Press.Powell W.W., D.R. White, K.W. Koput and J. Owen-Smith (2005) ‘Network Dynamics and FieldEvolution: The Growth of Inter-organizational Collaboration in the Life Science’ AmericanJournal of Sociology 110:1132-1205Rabelotti, R. (1995) ‘External economies and cooperation in industrial districts: A comparison ofItaly and Mexico’, IDS D.Phil thesis, University of SussexRees, K (2005) ‘Interregional collaboration and innovation in Vancouver emerging high-tech scluster’ Tijdschrift voor Economische en Sociale Geografie 96(3):298-312.Rosenkopf, L. and P. Almeida (2003) ‘Overcoming local search through alliance and mobility’Management Science 49:751-766.Rothaermel, F.,Deeds, D. and T. Frank (2004) ‘Exploration and exploitation alliances inbiotechnology: a system of new product development’ Strategic Management Journal 25:201-221.Shan W., G. Walker and B. Kogut (1994) ‘Inter-firm cooperation and start-up innovation in thebiotechnology industry’ Strategic Management Journal 15 387–394.Schmitz, H. (1995) ‘Small Shoemakers and Fordist Giants: Tale of a Super Cluster’ WorldDevelopment 23(1): 9-28Thorsteinsdottir, H., Melon, C., Ray, M., Chakkackal, S., Li, M., Cooper, J., Chadder, J., Saenz,T., De Souza, M., Ke, W., Li, L, Madkour, M., Aly, S., El-Nikhely, N., Chatuverdi, S., Konde,V., Daar, A. and P. Singer (2010) ‘South-south entrepreneurial collaboration in health biotech’Nature Biotechnology 28:407-416.
Uzzi, B. (1996) ‘The Sources and Consequences of Embeddedness for the EconomicPerformance of Organizations: The Network Effect’ American Sociological Review 61(4):674-698.van Beuzekom, B. and A. Arundel (2009) ‘OECD biotechnology statistics 2009’ OECD ParisVan Geenhuizen, M. (2007) ‘Modeling dynamics of knowledge networks and localconnectedness: a case study of urban high-tech companies in The Netherlands’ Annual RegionalScience 41: 813-833Visser, E.J. (1996) ‘Local Sources of Competitiveness. Spatial Clustering and OrganizationalDynamics in Small-scale clothing in Lima, Peru’, PhD Thesis, University of Amsterdam.Zucker L. G.., M. R. Darby and M. B. Brewer (1998) Intellectual Human Capital and the Birth ofUS Biotechnology Enterprises American Economic Review 88:290-306Annex A – MethodologyThe OECD definition of biotechnology encompasses both a single definition and a list-baseddefinition. The single definition defines biotechnology as:The application of science and technology to living organisms, as well as parts, products andmodels thereof, to alter living or non-living materials for the production of knowledge, goods andservices.This single definition is intentionally broad as it covers not only biotechnological techniquesindentified as traditional biotechnologies but also those labeled as modern biotechnologies. Thesingle-OECD biotechnology definition is of minimal value to distinguish those firms engaged inmodern biotechnology from those that are only focused on traditional ones (OECD, 2005;Arundel, 2007; Miller, 2007). The list-based definition functions as an interpretative guide ofthe single definition, as it encompasses all technologies that are identified as modern
biotechnologies. Thus, the list-based definition narrows the single definition only to (modernbiotechnology) methods as it includes the following biotechnology techniques: • [DNA/RNA] genomics, pharmacogenomics, gene probes, genetic engineering, DNA/RNA sequencing/synthesis/amplification, gene expression profiling, and use of antisense technology. • [Proteins and other molecules] sequencing/synthesis/engineering of proteins and peptides (including large molecule hormones); improved delivery methods for large molecule drugs; proteomics, protein isolation and purification, signaling, identification of cell receptors. • [Cell and tissue culture and engineering] cell/tissue culture, tissue engineering (including tissue scaffolds and biomedical engineering), cellular fusion, vaccine/immune stimulants, embryo manipulation. • [Process biotechnology techniques] fermentation using bioreactors, bioprocessing, bioleaching, biopulping, biobleaching, biodesulphurisation, bioremediation, biofiltration and phytoremediation. • [Gene and RNA vectors] gene therapy, viral vectors. • [Bioinformatics] construction of databases on genomes, protein sequences; modeling complex biological processes, including systems biology. • [Nanotechnology] applies the tools and processes of nano/microfabrication to build devices for studying bio systems and applications in drug delivery, diagnostics, etc.Biotechnology areas definitions: • Human health: firms active in the following biotech applications: large molecule therapeutics and monoclonal antibodies produced using rDNA technology, other therapeutics, artificial substrates, diagnostics and drug delivery technology. • GM agriculture biotechnology: firms involved in the production of new varieties of genetically modified plants, animals and microorganisms for use in agriculture, aquaculture, silviculuture. • Non-GM agricultural biotechnology: firms that develop new varieties of non-GM plants, animals and microorganisms for use in agriculture, aquaculture, silviculuture, biopest
control and diagnostics developed using biotechnology techniques (DNA markers, tissue culture,etc. • Veterinary health: firms active in all health applications for animals. • Industrial processing: firms that develop bioreactors to produce new products (chemicals, food, ethanol, plastics, etc.), biotechnologies to transform inputs (bioleaching, biopulping, etc.).Annex BQuestions 13 and 17 elicit data about unidirectional knowledge flows. Respondents are asked toname those agents from whom they have obtained and transferred biotechnology-relatedtechnologies in the period 2003-2008.Q 13 - From which firms/institutions did your firm acquire technology (e.g. R&D services,patent rights) during the period 2003-2008?Local organizations Foreign organizationsQ 17 - Please specify the name of firms/institutions that your firm has licensed technology toduring the period 2003-2008Local organizations Foreign organizations
Question 20 aims at eliciting collaborative ties for R&D, manufacturing and marketing purposes.Respondents are asked to name those alliance partners with whom they have set up these types ofcooperations and also provide the number of collaborations forged with each of the namedpartners in the period 2003-2008.Q 20 - With which institutions did your firm set up R&D, manufacturing and marketingcollaboration/cooperation alliances in the period 2003-2008? For each type of alliance, pleasespecify the names of the partners in Argentina and abroad. Please consider all possible sorts ofpartners such as other firms, universities, research institutes, and others. Purpose of collaborationsName of partners R&D Manufacturing MarketingArgentinean PartnersForeign Partners