Key-factors of international technology transfer within a Triple Helix framework. The case of Enterprise Europe Network

Key-factors of international technology transfer within a Triple Helix framework The case of Enterprise Europe Network by Ana Carina Araújo Master Program in Innovation Economics and Management Supervised by Aurora A.C. Teixeira 2011/ 2012 Biographic Note Ana Carina Pena Araújo was born in 25th November 1983 in Vila do Conde, Portugal. In 2001, she starts her degree in Management at the University of Porto School of Economics and Business. During that period she worked in FEP Junior Consulting and in 2007 she do the internship at the Oporto business angels association. In the same year she moves to Spain to work in an international trade consulting company under the Leonardo da Vinci program. Since 2008, she is working at Agência de Inovação in international technology transfer projects. Currently, she is a master candidate in Innovation Economics and Management at the University of Porto School of Economics and Business. Acknowledgments I would like to express my sincere appreciation to my dissertation supervisor, Prof. Aurora Teixeira. Her enthusiasm, encouragement and patience have been a source of inspiration for me. The valuable support, the patient guidance and the stimulating discussions we had helped me to go forward and view different approaches. I wish to acknowledge the help provided by Mr. Gunnar Matthiesen from the Executive Agency for Competitiveness and Innovation in the promotion of our study among the network. My thanks are extended to the Enterprise Europe Network partners that collaborate in our research taking their precious time to fill the survey and provide information for our data collection. To my family who experienced all the ups and downs of my dissertation, I am particularly grateful for their unconditional support and encouragement and especially for their belief on me. I also want to express my thankfulness to my boyfriend for his willingness to read and reread countless pages on determinants of technology transfer. Abstract Being recognized that science and technology are inductors of economic development (Etzkowitz, 2003), the emergence of the knowledge-based economy creates an overlay of communications and expectations that caused an institutional restructure based on innovative capacities. Thereupon, the Triple Helix of university-industry-government interactions plays an increasing role in the economic development. While the literature tends to concentrate in the university-industry relation, we go forth with the attempt of operationalising the university-industry-government relation established in a technology transfer context. Based on Enterprise Europe Network, a European program that supports innovation and internationalization and links universities, companies and governments across Europe, this dissertation aims to study the key-factors that foster technology transfer among the triad university-industry-government in an international context. Contrary to the hypotheses put forward, ours results, based on 71 technological Partnership Agreements (PAs), indicate that EEN’s human capital endowments and absorptive capability act as barriers to the international technology transference. In contrast, successfully transfer technology at an international level, within a Triple Helix framework, is associated with network connectedness, trust and prior experience in international or technological projects. Interestingly, PAs associated to EEN partners that provide their collaborators adequate training in technology transference related issues, that present substantial past experience in international or technological projects, and that possess a wide networks are of the ones that achieve better performances in terms of international technology transfer. Keywords: International technology transfer; Triple Helix; university-industrygovernment relations; Enterprise Europe Network JEL-codes: O32; O33; O38 iv Index Biographic Note ............................................................................................................................ i Acknowledgments ....................................................................................................................... ii Abstract ....................................................................................................................................... iii Introduction ................................................................................................................................. 1 Chapter 1. Literature review on technology transfer within a Triple Helix framework ...... 5 1.1. Initial considerations .......................................................................................................... 5 1.2. International Technology transfer within a triple helix framework ................................... 5 1.2.1. Conceptualizing technology transfer ........................................................................... 5 1.2.2. The Triple Helix basic framework .............................................................................. 9 1.2.3. University, Industry and Government: Towards hybridization ................................ 11 1.2.4. The emergency of trilateral networks and the skills brokerage model ...................... 14 1.3. Key factors of international technology transfer and main hypothesis to be tested ......... 15 1.3.1. Human capital and absorptive capability .................................................................. 16 1.3.2. Connectedness and networking dynamics................................................................. 17 1.3.3. Trust and common objectives ................................................................................... 18 1.3.4. Prior experience in international or technological partnerships ................................ 19 1.3.5. Control Variables ...................................................................................................... 20 Chapter 2. Methodological underpins ..................................................................................... 21 2.1. Initial considerations ........................................................................................................ 21 2.2. Enterprise Europe Network (EEN) as relevant basis of study ......................................... 21 2.2.1. Genesis of EEN ......................................................................................................... 21 2.2.2. Mission and activity of EEN ..................................................................................... 23 2.2.3. Institutional framework of EEN ................................................................................ 24 2.2.4. Technology transfer within the EEN ......................................................................... 24 2.3. The questionnaires implemented and the operationalisation of the relevant variables of the model ................................................................................................................................. 26 2.3.1. Successful international technology transfer ............................................................. 26 v 2.3.2. Human capital and Absorptive capability ................................................................. 29 2.3.5. Network connectedness ............................................................................................. 31 2.3.3. Trust .......................................................................................................................... 32 2.3.6. Prior experience with partnerships and international experience .............................. 33 2.3.7. Control variables: size and sector ............................................................................. 34 2.4. The process of data gathering .......................................................................................... 34 Chapter 3. Determinants of International Technology Transfer. Empirical Results ......... 39 3.1. Initial Considerations ....................................................................................................... 39 3.2. Brief descriptive analyses ................................................................................................ 39 3.3. Key hypothesis of the ‘theoretical’ model ....................................................................... 41 3.4. Determinants of ITT through the lens of EEN partner. Estimation results ...................... 42 Conclusions ................................................................................................................................ 47 References .................................................................................................................................. 50 Appendixes ................................................................................................................................. 55 Appendix 1 - Enterprise Europe Network’s Sector groups ..................................................... 56 Appendix 2 – Questionnaire implemented to EEN partners ................................................... 57 Appendix 3 – Questionnaire implemented to EEN clients ..................................................... 59 vi Index of Tables: Table 1: Conceptualizing technology transfer .............................................................................. 8 Table 2: Determinants of technology transfer within a Triple Helix relation ............................. 16 Table 3: EIP main instruments and how they contribute to achieve the objectives (EC-EIP, 2010)................................................................................................................................... 22 Table 4: Determinants of transfer of technology and proxies ..................................................... 27 Table 5: Measure of successful international technology transfer between two EEN clients ..... 29 Table 6: Measure to estimate the human capital and the absorptive capability of EEN partners and EEN clients .................................................................................................................. 30 Table 7: Measure to estimate the network connectedness between EEN partners and clients ... 31 Table 8: Measure to estimate the trust relation between EEN partners and clients .................... 32 Table 9: Measures for prior experience in technological and international partnership ............. 33 Table 10: Results from the Kruskal-Wallis Test ......................................................................... 40 Table 11: Hypotheses proposed .................................................................................................. 42 Table 12: Correlation analysis for international technology transfer measures on EEN partners 1 ............................................................................................................................................ 44 Table 13: Regression models for international technology transfer on EEN partners ............... 46 vii Index of Figures Figure 1: The Triple Helix I (statist) and II (laissez-faire) models of university-industrygovernment relations .......................................................................................................... 12 Figure 2: The Triple Helix III model of university-industry-government relations .................... 12 Figure 3: Main services and instruments of Enterprise Europe Network ................................... 23 Figure 4: A typical support process in Enterprise Europe Network............................................ 25 Figure 5: Schematic overview of data gathering process ............................................................ 36 1 Introduction During the last years the world moved towards a knowledge-based economy (Bommer et al., 1991; Bessant and Rush, 1993; Sung et al., 2003; Arvanitis and Woerter, 2009; Lai, 2011) on which knowledge and technology became the most important resource (Sung et al., 2003; Wang et al.., 2004; Arvanitis et al., 2005; Lee et al., 2007) to the endowment of companies and to the growth of industries (Bessant and Rush, 1993; Soete and Weel, 1999; Sung et al., 2003; Arvanitis et al., 2005; Laroche and Amara, 2011). Studies conducted in sociology, economy and management confirmed the central role of technology in productivity change and economic development (Reddy and Zhao, 1990). Simultaneously, strategic theorists recommended a competitive strategy based on the rising of technology as a competitive force (Reddy and Zhao, 1990). The intensive global competition and the fast technological development (Santoro and Gopalakrishnan, 2000) create new challenges to organizations and more often they are lacking of resources and time to keep the leading edge (Sherwood and Covin, 2008) which impels them to go outside their boundaries and look for external sources of knowledge (Bessant and Rush, 1993; Zahra and Nielsen, 2002; Gopalakrishnan and Santoro, 2004; Sherwood and Covin, 2008; Arvanitis and Woerter, 2009). This new technological settings brought up new linkages between industry (suppliers, customers, competitors) and public organizations like research institutions (Arvanitis and Woerter, 2009) and universities (Santoro and Bierly, 2006; Sherwood and Covin, 2008; Lai, 2011). Universities realized the commercial value of their researches and they are now focused on the ‘capitalization of knowledge’ (Etzkowitz, 1998). Likewise, industry recognized the positive impact of the knowledge produced in the university (Laroche and Amara, 2011) in their innovation and economic performance (Arvanitis and Woerter, 2009). Increasingly, science and business institutions espouse strategies in order to improve their performance through cooperation with other organizations (Arvanitis and Woerter, 2009; Teixeira and Mota, 2012). In such scenario, technology transfer is of major importance (Arvanitis and Woerter, 2009; Duan et al.., 2010; Lai, 2011). The process from which technology is acquired from external sources has drawn the attention of a large number of researchers during the last years (Bessant and Rush, 1993). 8 Table 1: Conceptualizing technology transfer Scope Study Aim of study Definition Key dimension National Gibson and Smilor (1991) Understand technology transfer in an R&D consortium and its members companies Movement of technology (knowledge, ideas or physical products) across some type of channel (person-to-person, person-to-person, group-to-group, or organization-toorganization) Movement of knowledge, ideas or products Arvanitis and Woerter (2009) Factors that encourage the Swiss science institutions to engage in knowledge and technology transfer along with private entities Any transfer of knowledge and technology that enhance the activities of a company or university and/or research centre Enhancement of the receptor International Kohler et al. (1973) Study the source and receiver of technology transfer between German and American industrial firms Processes by which a country reproduces or replicates, profitably or usefully, a technical performance that had been achieved by another country. Profitability and usefulness Teece (1977) Study the level and determinants of the cost involved in an international technology transfer process Capability to transfer the manufacturing of a product or process between firms located in different countries Capability to transfer Madeuf (1984) Record and measure transfers of technology by technological balances of payments Should concern to a process owned and used by a firm during its production activities, and the technology includes the technology process and the know-how application Distinguish between technology transfer and technology flow Glass and Saggi (1999) Oligopoly model with a multinational firm with a superior technology in a host country with the aim of determinate whether a technological transfer occur or not Process by which a technical information is transfer from one party in one country to other party in a foreign country and the last one take it in into its products process Transference of knowledge and skills to the home country that had been acquire during a temporary movement of professionals or services suppliers in a developed country Absorptive capability EC (2007: 6) Alert researchers and business about the advantages of a close work in the R&D field “Processes for capturing, collecting and sharing explicit and tacit knowledge, including skills and competence. It includes both commercial and non-commercial activities such as research collaborations, consultancy, licensing, spin-off creation, researcher mobility, publication, etc. While the emphasis is on scientific and technological knowledge other forms such as technology-enabled business processes are also concerned.” Explicit and tacit knowledge Edler et al. (2011:793) Study the impact of temporary international mobility of scientists in their propensity to knowledge and technology transfer activities “Knowledge and technology transfer in a broad understanding that refers to knowledge embodied in technological artefacts, codified and non-codified knowledge, as well as knowledge that is co-produced in various forms, e.g. in collaborative projects” Knowledge embodied or not in an object/ technology 9 Although not being widely referred in the literature, in the context of this dissertation the distinction between technology transfer and technology flow proposed by Madeuf (1973) is of great relevance. Technological flows, such as consultancy services, are excluded of his definition of technology transfer, as they do not imply the transfer of a process owned and used by the supplier. Unless they are selling or leasing the knowledge to produce technical studies, consulting firms produce and sell technical services as an output. Within the context of the case study, the Enterprise Europe Network technological flows are not considered as technology transfer and for that reason they will not be object of study. 1.2.2. The Triple Helix basic framework Etzkowitz and Leydesdorff (1995, 2000) conceptualized the Triple Helix model of relations between university, industry and government with the aim to explain the increasing interactions between these three spheres and the innovation strategies and practices that result from that cooperation (Etzkowitz, 2003). According to Leydesdorff and Meyer (2003), besides the Triple Helix model, the Mode 2 (knowledge production) and Mode 1 (disciplinary knowledge production) distinction, and the National Systems of Innovation (NSI), in evolutionary economics, were also proposed to study the innovation system in a knowledge-based society. Nevertheless, their differences in conceptualizing the system integration and differentiation among spheres, led us to select Etzkowitz and Leydesdorff’s model (1995, 2000) as the main theoretical approach of this dissertation. Technological and academic knowledge has become a valuable resource in the economy as its application grew in the industrial production and social development (Leydesdorff and Etzkowitz, 2001). The competition for innovative products and cutting edge technologies transformed innovation from an internal process within individual companies to an external process embracing companies and universities (Santoro and Bierly, 2006), the traditional producers of knowledge (Etzkowitz, 2003). In this context, a new economic structure emerged based on the knowledge in which the university plays the most important role as a source of innovation (Leydesdorff, 2011). In this knowledge-based economy, apart from the two sub dynamics prevailing in a political economy - market equilibrium and normative control mechanisms – the production of knowledge has to be considered as a third transformation dynamics 10 (Leydesdorff, 2011). The institutional infrastructure provided by a political economy is then its substitute by the complex dynamic of an economy based on knowledge built over communication flows through networks (Leydesdorff and Meyer, 2003). As result, the technological and social context of science originates a continuous transformation in the society structure (Leydesdorff, 2011) and the overlay of communication reshaped the relation between universities, industries and government (Etzkowitz and Leydesdorff, 2000). The model proposed by Etzkowitz and Leydesdorff (1995, 2000) takes into consideration three main elements (Papagiannidis at al., 2009): (1) the prominent role of the university together with industry and government; (2) the interaction between university, industry and government as a key to innovation, and (3) the multiple functions of the three spheres. Opposite to the National System of Innovation where the firm has the leading role in innovation (Meyer et al., 2003), playing university and government supporting roles (Etzkowitz, 2003), the Triple Helix promotes the importance of the university (Etzkowitz and Leydesdorff, 2000). Another essential characteristic in the Triple Helix model is the interaction between university, industry and government (Etzkowitz, 2003). In a knowledge-based society, this interaction is the key to innovation (Etzkowitz, 2003). The innovation policy is no longer a prescription from the government but a result of the collaborative relation between the three spheres (Papagiannidis at al., 2009). In addition to the increasing interaction between spheres, the Triple Helix model also postulates the internal transformation of the three dimensions (Leydesdorff and Etzkowitz, 2001). Beyond their traditional functions, each one of the helices can assume the role of the other (Leydesdorff and Etzkowitz, 2001) into a reciprocal relationship of performance increasing (Etzkowitz, 2003). Traditional models such as NSI define institutions according with their traditional functions (Etzkowitz, 2003) but, since the innovation process went out of the internal boundaries of companies involving universities and government (Sherwood and Covin, 2008 ), each sphere no longer plays only their traditional role but also new roles. Not surprisingly, concepts such as ‘academic entrepreneurs’ and ‘entrepreneurial university’ emerge (Meyer et al., 2003) stumbling the traditional boundaries between the three dimensions (Etzkowitz, 2003). 11 To sum up, the helices present an internal development while interacting in the goods and services exchanging and overlaying functions (Leydesdorff and Meyer, 2003). 1.2.3. University, Industry and Government: Towards hybridization The Triple Helix model proposals by Etzkowitz and Leydesdorff (1995, 2000) capture the recent transformation of roles and interaction among the triad universityindustrygovernment (Etzkowitz, 2003). Nonetheless, their paths began on the second half of the 19 th century (Leydesdorff 2000) from two opposite models (cf. Figure 1): (1) a statistic model with government driving industry and academia; (2) and the laissez-faire model where the three spheres are separate with strong boundaries and interactions are few (Etzkowitz, 2003). In Triple Helix I, the statist model, the government incorporates academia and industry and mediates the relations between them (Etzkowitz and Leydesdorff, 2000). This type of system is found in countries where the dominant institution is the government being industry and university are part of it (Etzkowitz, 2003). As an example, we could look at the 1970s and early 1980s Science & Technology policies that had had been undertaken by Brazilian government, which supported large-scale technology projects to leverage the universities research level and, consequently, stimulate new technology industries and affect the regional development (Etzkowitz, 2003). Other examples are the former Soviet Union and the Eastern European countries, and, its weaker version can be seen in some countries in Latin America and in some European countries such as Norway (Etzkowitz and Leydesdorff, 2000). Key features of this version are the university function, as a source of qualified human resources, and the separation between the local technology industry and the rest of the world (Etzkowitz, 2003). The laissez-faire Triple Helix separates the institutional spheres creating strong boundaries between them (Etzkowitz and Leydesdorff, 2000). In this version it is expected institutions to compete among them rather than to cooperate (Etzkowitz, 2003). The communication between university, government and industry is limited and, when happening, it is usually through intermediaries (Etzkowitz, 2003). The leading role of the regime belongs to the industry, being the function of university the provision of knowledge through basic research and graduates (Etzkowitz, 2003). The intervention of the government in the industry is limited to regulation and public procurement (Etzkowitz, 2003). 12 Figure 1: The Triple Helix I (statist) and II (laissez-faire) models of university-industry-government relations Source: Adapted from Etzkowitz and Leydesdorff (2000) Whether a country started from a statist or a laissez-faire model, a new global movement of knowledge and technology management is emerging (Etzkowitz, 2003) on which the Triple Helix converges into a knowledge infrastructure where the three dimensions compete simultaneously and cooperate (Etzkowitz and Leydesdorff, 2000), maintaining their traditional roles but also playing the role of the other (Etzkowitz, 2003). The overlap of spheres and roles is the basis of the emergence of hybrid organizations and trilateral networks (Etzkowitz and Leydesdorff, 2000). Figure 2: The Triple Helix III model of university-industry-government relations Source. Adapted from Etzkowitz and Leydesdorff (2000) 13 As the interactions between the three dimensions are in the basis of economic development in a knowledge society, regions and countries are working towards this last form of the Triple Helix (Etzkowitz and Leydesdorff, 2000). Such transformation can be seen in the industry with the creation of start-ups and universities’ spin-offs, and with the large firm investments in the development of incubation facilities, with the aim to develop new business models and to promote the post-doctoral researcher (Etzkowitz, 2003). Also, at the policy level, differences can be pointed. Government lost its central function, although it still has an important role in the Triple Helix, providing incentives to promote the innovation (Leydesdorff and Etzkowitz, 2001). Its role is now develop funding programs (Santoro and Bierly, 2006) and providing tax incentives that incentivize the cooperation between industry and universities, and provide legal frameworks (Leydesdorff and Etzkowitz, 2001; Papagiannidis et al., 2009). However, the major transformation occurred in the university sphere (Etzkowitz and Leydesdorff, 2000). Building upon its previous role as innovation support (Etzkowitz, 2003), providing trained persons and basic knowledge, the university is now a source of economic and social development (Leydesdorff and Etzkowitz, 2001), emerging as a prominent player among the Triple Helix (Etzkowitz, 2003). The relation between university and industry is evolving over the last years (Santoro and Bierly, 2006). Nowadays, universities are more aware of the economic value the knowledge they produce and researchers are more interested in product commercialization (Santoro and Bierly, 2006). As a consequence, the new relation does not involve consultation fees or donations, but the participation in companies and real estate development (Etzkowitz, 1998). This transaction is revered by Etzkowitz (1998) as ‘capitalization of knowledge’. From the standpoint of several authors (e.g. Etzkowitz, 1998; Santoro and Bierly, 2006), this can be considered as an university ‘third mission’, and its addition to the first and second missions – teaching and research - a ‘second revolution’ in the academy is predictable (Etzkowitz, 1998). In short, the economic and social development is now motivated by an innovation model that undermines the triad university-industry-government driving them into a knowledge infrastructure explained by the autonomy but also by the interdependence between spheres (Leydesdorff and Etzkowitz, 2001). 14 Nevertheless, the hybridization among the triad is not only reflected in the transformation of institutional boundaries but also in the redesigning of the national boundaries (Leydesdorff and Etzkowitz, 2001). With economies and markets internationally connected, organizations assume a global posture and also the governments actuation goes beyond the local and national boundaries and act at international level (Etzkowitz and Leydesdorff, 2000; Leydesdorff and Meyer, 2003). 1.2.4. The emergency of trilateral networks and the skills brokerage model The new innovation model emerged from the Triple Helix assumes the theoretical and practical integration of resources among university, industry and government to promote the economic development (Papagiannidis et al., 2009). The integration among the three helices created tri-lateral networks and hybrid organizations (Etzkowitz and Leydesdorff, 2000). In this context, Etzkowitz and Leydesdorff (2001) refer the emergence of the knowledge brokers, which act as network coordinators and organizers with the task of link people from different spheres. This innovation professionals move up in a complex system of overlay networks and its interorganizational and interpersonal skills increasingly empower this emergence of interface organizations (Etzkowitz and Leydesdorff, 2000). Also Papagiannidis and Li (2005) presented the skills brokerage business model that was later linked to Triple Helix model. Together they explain the triad transformation towards innovation and the emergence of hybrid entities (Papagiannidis et al., 2009). The skills brokerage business model of Papagiannidis and Li (2005) explain the emergence of new entities that moves among the three helices. This new and young companies support start-ups and established business in a networked economy: “In the skills brokerage business model, an entrepreneur or an established company exchanges skills; resources; access to networks and, more generally, other forms of human and social capital with a skills provider, who in exchange receives equity or direct access to the venture’s returns or a combination of both” (Papagiannidis et al., 2009: 219). The skills brokerage act as a facilitator between the market actors forming a link between them with the aim of encompass the lack of skills and costs, identify as the two main challenges of the entrepreneurs (Papagiannidis and Li, 2005). Not directly related with Triple Helix but important in the context of the present dissertation, stands the focus on specialized skills in the brokerage model. Papagiannidis 15 and Li (2005) refer that generally support services are focus on a broad range of skills and services, nevertheless specialized services are thought to be more beneficial to entrepreneurs. In a reference to the research of Davidsson and Honig (2003), the authors take into account the need of national and regional governments in considering the creation of communities and networking activities that focus on individual business needs rather than in generic activities. 1.3. Key factors of international technology transfer and main hypothesis to be tested Existing literature on technology transfer tend to focus in university-industry relation and the role of technology transfer offices. The role of hybrid organizations that moves between university, industry and government is still little debated in literature. The choice of determinants to study was guided by previous empirical studies on university-industry technology transferences, and also based on theorical literature on the Triple Helix model and the role of intermediaries in the transfer process. The technology transfer process tend to be stimulated if certain key facilitators – e.g., social connectedness, trust, prior experience - are present (Santoro and Bierly, 2006). These facilitators are deeply related with: (1) hybrid organizations characteristics (2) client’s characteristics and (3) relation between the hybrid organizations and its clients within a technology transfer process. Among the many determinants of technology transfer proposed, same stand out (cf. Table 2): absorptive capacity, human capital, trust, social connectedness, prior experience with partnerships, international experience. Within a triple helix framework, technology transfer depends of industry characteristics, EEN characteristics and from the industry perception of EEN. 16 Table 2: Determinants of technology transfer within a Triple Helix relation Key dimension Main determinants Author (year) Human capital Technical capabilities Succar (1987) Training Reddy and Zhao (1990) Human Capital Kneller (2010); Keller (2004); Absorptive capability Absorptive capability Reddy and Zhao (1990); Cohen and Levinthen (1990) ; Gibson and Smilor (1991); Keller (2001); Gopalakrishnan and Santoro (2004); Santoro and Bierly (2006); Arvanitis and Woerter (2009) Kneller et al. (2010) Connectedness Relationship Reddy and Zhao (1990) Communication Gibson and Smilor (1995); Gopalakrishnan and Santoro, (2004) Social connectedness Santoro and Bierly (2006) Trust Trust Gopalakrishnan and Santoro (2004);Santoro and Bierly (2006); Sherwood and Covin (2008) Prior experience with partnerships Prior Experience Santoro and Bierly (2006) Alliance experience Sherwood and Covin (2008) Number of partners Arvanitis and Woerter (2009) Experience in foreign countries Reddy and Zhao (1990) Existence of contacts to foreign universities Arvanitis and Woerter (2009) Size Firm Size Gopalakrishnan and Santoro(2004); Santoro and Bierly (2006) Sector Sector Santoro and Bierly (2006) 1.3.1. Human capital and absorptive capability The determinants of a successful transference of technology are deeply related with the actors involved, in fact, they can be drivers or barriers (Duan et al., 2010). In a transfer process the capability of absorb and re-use that technology can either enhance or undermine the successfulness of the transfer (Duan et al., 2010). According with the empirical evidence, the adoption of a technology can be facilitated by certain skills rooted in the human capital of a closed economy or a country promoting the acceptance of new or external technologies (Keller, 2004). In other words, human capital facilitates the technology transfer between and beyond national boundaries (Keller, 2004; Kneller et al., 2010). Since the EEN highlight the importance of their human resources, we believe that the skills of the EEN consultants are determinant during an international technology transfer. H1: International technology transfer depends directly on organizations’ human capital endowment. 17 Human capital is frequently included in the absorptive capability (Kneller et al., 2010). Although the term absorptive capability has been presented by Cohen and Levinthen (1990), the idea was before referred by Madeuf (1983). In his work about international technology transfer and international technology payments, the author state that a transfer can only be successful when the recipient, by itself, is able to use, reproduce and even improve the technology transfer. Cohen and Levinthen (1990) introduce the term absorptive capability as the ability to recognize the value of new external information and successfully adopt, assimilate and exploit it. It can be applied not only to companies but also to countries (Keller, 2001) and, in equal circumstances of access, determinates the ability of a company or country to benefit from the technology (Kneller et al., 2010). Not surprisingly, the absorptive capability is referred by several authors as a key determinant in transference of technology (Cohen and Levinthen, 1990; Keller, 2001; Gopalakrishnan and Santoro, 2004; Santoro and Bierly, 2006; Kneller et al., 2010). Despite the main studies focus on the relation between the technology transfer and capabilities between the actors involved in a two spheres perspective, it is expectable the same connection between the actors of the Triple Helix. In the context of your analysis, absorptive capability will not only determinate the capacity of a EEN partner to identify the value of a technological cooperation for its clients but also the capacity of its clients to internalize external knowledge and take advantage of it. Therefore, it is expected that the successful technology transfer mediated by the EEN depends on the absorptive capacity of the stakeholders. H2: The success of an international technology transfer involving a technology broker depends directly on the absorptive capacity of the stakeholders. 1.3.2. Connectedness and networking dynamics Also related with the technology transfers actors, and as important as the absorptive capability, is the connectedness between the partners. According with several authors (Gopalakrishnan and Santoro, 2004; Santoro and Bierly, 2006; Duan et al., 2010; Laroche and Amara, 2011), the connectedness between partners plays a crucial role in the transference of technology. Environments that foster interpersonal relationships can be conducts of knowledge flow (Santoro and Bierly, 2006) since acquaintances facilitate the working arrangement 24 2.2.3. Institutional framework of EEN Nowadays the network has 589 member organizations in 49 European and neighboring countries. 3 Beyond the EU 27 countries, the network extend its coverage to European Economic Area countries and other economic areas such as United States of America, Russia, South Korea, Japan and China (EC-CIP, 2010). The partners are connected by databases and communication tools and have been working together for several years 4 in the previous network Euro Info Centres and Innovation Relay Centres. The network is organized through consortiums of members representing a country or a region. The members are in general chambers of commerce and industry, technology centre, universities and development agencies (EC-CIP, 2010) well recognized by their work with the local business sector. 5 At the same time, members organize themselves in working groups to discuss and work about specific matters concerning the network and in sector groups to provide a more customised support to clients. Additionally, training actions are locally organized with the aim to disseminate through the network the knowledge acquires by a partner in a specific subject. For members, these activities are not exclusive, on the contrary; they are complementary and enrich the service they provide. In accordance with that, the proximity with local business and network connection between partners are the key elements that permit EEN providing its business support and technology transfer services over Europe and beyond. 2.2.4. Technology transfer within the EEN EEN provides integrated services towards business development and technology transfer (EC-EIP, 2010) to companies with strategic objectives of finding international business and/or technological partners. Concerning technology transfer, it is important to refer that the EEN services are extended to universities and other researcher centres with interest in establish a technological partnership whether for development or commercialization. A typical support of technology transfer in the EEN is similar to the process exemplify in the Figure 4. The client (as mentioned, a company, university and other researcher 3 In: http://www.enterprise-europe-network.ec.europa.eu/about/mission, accessed in 31 January 2012. 4 In: http://www.enterprise-europe-network.ec.europa.eu/about/mission, accessed in 31 January 2012. 5 In: http://www.adeuropa.org/informacion/een/newsletter/ mar11/anexos/NetWorth_BrochureA4 _1_2010.pdf, accessed in 31 January 2012. 25 centre) with a technological offer or request contacts the local EEN partner, which will meet him. According with the strategy outlined by the organization and the objectives traced during the meeting, the best set of instruments will be use to find the right partner. Once found, a Partnership Agreement (PA) is sign by the organizations involved and the EEN partners. The Partnership Agreement (PA) is an internal document with reference to the technology transferred, the organizations (‘Client’) and EEN partners involved. The technology transfer within the EEN might involve three sets of flows (between EEN’s clients – bold arrow in the Figure 4):  transfer between two companies.  transfer between a company and a university/ research center.  transfer between two universities/ research center. Figure 4: A typical support process in Enterprise Europe Network Source: own elaboration 26 2.3. The questionnaires implemented and the operationalisation of the relevant variables of the model The determinants studied under this research are briefly discussed in this section. The operationalization of the relevant variables was guided by the literature review on university-industry partnerships and technology transfer activities. The summary of the determinants of transfer of technology proposed by different studies are describe in Table 4, as well as the proxies used. The questionnaire to EEN partners has three groups of questions on general information, activities and technological partnership agreements (cf. Appendix 2). The questionnaire to EEN’s clients was formed by four parts on general information, relationship with EEN, relationship within the Triple Helix and technological partnership agreements (cf. Appendix 3). The questionnaires were personalized, and each Partnership Agreement (PA) was treated separately, so that, the respondents receive a questionnaire in which one group was related to each PA they were involved. 2.3.1. Successful international technology transfer Transfer technology between international partners is the depend variable of this study. The transference of technology is not just the flow between a sending and a receiving company. Its success depends on the effectiveness and control of the recipient to use, reproduce and even improve the technology (Madeuf, 1983). Although various approaches were used (Cumming and Teng, 2003) in the attempt to define successful transfer as a variable, we will follow the point of view of Madeuf (1983) and state that the impact in the recipient organization determinate the successfulness of the technology transfer. With that in mind, we adapted Santoro and Bierly’s (2006) measure of knowledge transference from the university research center to companies. To measure the successfulness of the international technology transfer we adopted the seven-point Likert-type scale (1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree) and inquiry the EEN clients (that is, firms, universities or research centres) about the value and utility of the technology transfer to the organization. 27 Table 4: Determinants of transfer of technology and proxies Determinants Proxy Variables Impact in TT National/ International TT Sample Study Absorptive capability Absorptive capability Frequency of R&D activities -/0 National level IndustryPublic research institutions Arvanitis and Woerter (2009) Share of employees with a tertiary education on total employees (in full-time equivalents) + Absorptive capability Investment in R&D 0 Internacional level Country and firm access to foreign technology Kneller et al. (2010) Provision of formal training 0 Workforce education 0 Absorptive capability R&D intensity (R&D investment divided by the firm’s sales revenues) + National level IndustryURC Santoro and Bierly (2006) Network connectedeness Networking dynamics Importance of universities and HEIs in accessing knowledge - Regional level Triple Helix collaboration Gkikas, 2011 Importance of government in accessing knowledge - Importance of universities and HEIs in building innovation - Importance of government in building innovation - Importance of government in commercializing innovation Social connectedness Number of contacts with universities + National level IndustryPublic research institutions Arvanitis and Woerter (2009) Knowledge and technology transfer with foreign universities + Sum of the scores for the individual evaluation of the importance of mediating institutions 1 + Social connectedness Evaluation of closeness of the interactions at individual level of the partnership + National level IndustryURC Santoro and Bierly (2006) Social relation Intensity of linkages with managers an/ or professionals from five types of organizations + National level Transfer activities among Canadian researchers in occupational safety and health Laroche and Amara (2011) Technological relatedness Impact of accessing the URC expertise National level IndustryURC Santoro and Bierly (2006) Impact of accessing the URC contact network Notes: 1 Mediating institutions: Technology Transfer offices, CTI (Innovation Promotion Agency), SNF/SNFS (Swiss National Science Foundation), EU Framework Programmes, Other European Programmes Legend: + Positively related; (-) negatively related; (0) no significant . 28 Determinants Proxy Variables Impact in TT National/ International TT Sample Study Trust Trust Willing to share ideas, feelings and goals with the university center + National level IndustryURC Gopalakrishnan and Santoro, 2004 Confidence in the centre’s competence and abilities, and in its motives and fairness in sharing these abilities + Sharing of a set of principles that the company finds acceptable + Trust Firm willingness in sharing concerns and problems with the URC + National level IndustryURC Santoro and Bierly (2006) Firm awareness in URC capability in understand their needs + Firm willingness in sharing confidences with the URC + Sharing of common business values + Trust Willing to share ideas, feelings and goals with the university center + Nacional level Institualization of knowledge transfer within UniveristyIndustry Santoro and Gopalakrishnan, 2000 Confidence in the centre’s competence and abilities, and in its motives and fairness in sharing these abilities + Sharing of a set of principles that the company finds acceptable + Prior experience Prior experience with partnerships Relationships between the company and the URC prior to the partnership Control variable National level IndustryURC Santoro and Bierly (2006) Prior experience with partnerships Number of prior technology transfer agreements with the universities - National level University-Industry Sherwood and Covin (2008) Size Size Number of employees Control variable National level IndustryURC Santoro and Bierly (2006) Size Number of employees + National level IndustryURC Gopalakrishnan and Santoro (2004) 2 Sector High tech and capital intense 3 Control variable National level IndustryURC Santoro and Bierly (2006) Notes: 2 The authors use the 7-S Framework as a teorical ground to identify organizational characteristics that may influence the technology transfer activity. The 7-S Framework is a model of organizational effectiveness Developed by Tom Peters and Robert Waterman. The model is based on the assumption that for an organization to be successful, seven internal factors must be aligned (strategy, structure, systems, shared values, skills, style and staff); 3 High tech (biotechnology, electronics, pharmaceuticals, optical equipment, medical laboratories, and research and development services) and capital intense (primary metals, fabricated metal products, industrial machinery, plastic molding, and ceramics). Legend: + Positively related; (-) negatively related; (0) no significant 29 Depending on the function in the transfer, sender or receiver, we asked to evaluate, the degree of learning, assimilation and results occur from the concerned PA (Table 5). As referred before, within the EEN, only technological transference between international partners can be reported as a partnership agreement. Therefore, the PA in this study is, by definition, international. Table 5: Measure of successful international technology transfer between two EEN clients Proxies: Source Successful international technology transfer (average score of the following items): Sending organization: We learn a great deal from the company involved. Santoro and Bierly (2006) The technology held by my organization was assimilated by the other partner and contributed to development of products/services. The technology held by my organization directly resulted in new products and services offered to the other partner customers. Recipient organization : We learn a great deal from the company involved. Santoro and Bierly (2006) The technology held by the other partner was assimilated by us and contributed to development of products/services. The technology held by the other partner directly resulted in new products and services offered to our customers. 2.3.2. Human capital and Absorptive capability R&D activities, workforce education and training are point out by numerous authors as the main indicators of the firm absorptive capability (Santoro and Bierly, 2006; Arvanitis and Woerter, 2009; Kneller et al., 2010). As we can see in Table 4, although the authors are consensual about the importance of R&D, workforce and training, there is not uniformity among authors regarding the proxies to be used as reflecting the absorptive capacity of an organization. Education achievement of organisations’ labour force (Arvanitis and Woerter, 2009), R&D intensity (Kneller et al., 2010) or training are some of the different proxies used to analyze the absorptive capacity of an organization. Due the importance of capabilities and skills of the EEN consultants in the network strategy and activity (EC-EIP, 2010), we compute the proxy for absorptive capability of the EEN partners based on the education level of the consultancy staff and by the average of the EEN budget invested in training activities in technology related fields (Table 6 - EEN Partners). In the same way, the absorptive capability of the EEN clients 30 is measure by the education level of the employees involved in ITT and the average of the turnover invested in training activities in technology related fields. Additionally to these proxies, we follow Cohen and Levinthal’s (1990) study, and measure the R&D intensity of a firm by its share of investment on company’s sales revenue (Tables 6 - EEN Clients ). This measure helps us to understand the client’s technological capability and therefore its capacity of transfer technology. Table 6: Measure to estimate the human capital and the absorptive capability of EEN partners and EEN clients Proxies: Source Human capital of EEN partners % of EEN staff involved in ITT Human capital of EEN partners % of EEN staff involved in ITT Absorptive capability of EEN Partners (average score of the following items): % of EEN staff involved in ITT % of EEN staff involved in ITT with tertiary education degree Reddy and Zhao (1990); Cohen and Levinthen (1990) ; Gibson and Smilor (1991); Keller (2001); Gopalakrishnan and Santoro (2004); Santoro and Bierly (2006); Arvanitis and Woerter (2009) Kneller et al. (2010) % EEN budget invested in training activities ( average over the last three years ) Cohen and Levinthal (1990) Absorptive capability of EEN Clients: (average score of the following items): % of employees involved in ITT with tertiary education degree Reddy and Zhao (1990); Cohen and Levinthen (1990) ; Gibson and Smilor (1991); Keller (2001); Gopalakrishnan and Santoro (2004); Santoro and Bierly (2006); Arvanitis and Woerter (2009); Kneller et al. (2010) % of the turnover invested in R&D activities (average over the last three years ) Cohen and Levinthal (1990) % of the turnover invested in training activities (average over the last three years ) Level of absorptive capability: (average score of the following items): Absorptive capability of EEN Partners Absorptive capability of EEN Clients 31 2.3.5. Network connectedness As the unit of analysis of this dissertation is the international technology transfer involving a trilateral network, we want to understand whether the connectedness between those organizations and their clients is determinant to the successfulness of the transference. Hence, we follow the work of Santoro and Bierly (2006) to measure the interactions at the individual level of the partnership and to measure the networking dynamics between the EEN and its clients we follow the Triple Helix metrics proposed by Gkikas (2011). We asked to EEN partners, by reference to the last three years, the (1) number of technological offers (TO) and requests (TR) submitted; (2) number of expression of interest (EOI) and (3) the number of technological partnerships obtain. We also asked to the EEN partner their opinion regarding their role within the client’s strategy in the access new ideas, development and transference of new technologies (Table 7). On the other hand, EEN clients where asked how important is the EEN to access, building and transfer technology. Except the overall number of TO/TR, EOI and technological PA, connectedness and networking dynamics indicators were measured using a seven-point Likert-type scale (1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree). Table 7: Measure to estimate the network connectedness between EEN partners and clients Proxies: Source Connectedeness between EEN partner and client (average score of the following items): Overall number of TO/ TR submitted by the client Santoro and Bierly (2006) Overall number of EOI’s received/made by the client Overall number of Technological PA signed with the client Level of networking dynamics (average score of the following items): EEN partner networking dynamics (average score of the following items): The EEN is an important source of ideas and information for this client’s TT process. Gkikas (2011) The EEN had helped to develop new technologies that result in new or improved products and services for this client. The EEN had played a major role in helping this client transfer and/or acquire new technologies. EEN Client networking dynamics (average score of the following items): EEN is an important source of ideas and information in my TT process. Gkikas (2011) EEN had helped to develop new technologies that result in new or improved products and services. EEN had played a major role in helping transfer and/or acquire new technologies. Network connectedness (average score of the following items): Connectedeness between EEN partner and client Level of networking dynamics 32 2.3.3. Trust As referred earlier, EEN foster a proximity relationship between its consultants and its clients. For this reason, in this context, trust was measured blending the interorganizational and interpersonal trust. To measure the client’s trust in the EEN partner we blend the work of Mayer et al. (1995), about factors of trustworthiness, with the work of Zaheer et al. (1998) regarding interorganization trust on performance. These blended measures require that the EEN clients assess their trust in the EEN partner in terms of ability, goodwill and integrity of the partner. Table 8: Measure to estimate the trust relation between EEN partners and clients Proxies: Source Trust of EEN Partners (average score of the following items): Interorganizational trust: Based on past experience, she/he can with complete confidence rely on EEN. Zaheer (1998) My client considered me trustworthy. Interpersonal trust: She/he knows that I to look out for her/his interests. Zaheer (1998) My performance was above my client’s expectations. I was committed in the search of a technological partner. She/he was committed in the search of a technological partner. Trust: My client is perfectly aware and has confidence in my competences and abilities as well as my motives and fairness in sharing these abilities. Santoro and Bierly (2006) This client is confident in freely share ideas, feelings, and goals with EEN. We share a set of principles that we both find acceptable. Trust of EEN Clients (average score of the following items): Interorganizational trust Based on past experience, I can rely on my EEN with complete confidence. Zaheer (1998) Interpersonal trust (average score of the following items): She/he is trustworthy. Zaheer (1998) I have faith in her/him to look out for my interests. Her/his performance was not below my expectations. She/he has been committed in the search of a technological partner. Trust (average score of the following items): I can freely share ideas, feelings, and goals with my EEN. Santoro and Bierly (2006) We share a set of principles that I find acceptable. I have confidence in her/him competence and abilities as well as its motives and fairness in sharing these abilities. Level of trust between EEN partner and its client (average score of the following items): Trust of EEN Partner Trust of EEN Clients 33 However not all of the Zaheer et al.’s (1998) items are applicable to our research. As in these latter authors’ work, the items measuring the interorganizational trust were deeply related with a supply-costumes relation. Thus, we had had to adapted and completed it with the measures propose by Mayer (1995). To access the level of trust between the EEN partners and its clients we asked to EEN partners and clients using a seven-point Likert-type scale (1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree) the extent to which they agree with the statements presented in Table 8. 2.3.6. Prior experience with partnerships and international experience It would be expected that companies or other entities that request EEN services, would have more probability of successfully transfer technology at an international level if they have been already involved in other partnerships or if they have already contact with foreign entities both at commercial or technological level. Concerning the EEN partners, it is assumed that the entities have prior experience in partnerships as the EEN project is by itself an international partnership. Nonetheless, EEN partners were asked to provide an approximate number of international projects related with technology or technology transfer, in which the host organization was involved in the last three years of activity (Table 9). With the aim of simultaneously measure the entity experience in national and international partnerships, it was asked to EEN clients to estimate the number of alliances and the number of technological agreements, in which they were involved during the last three years, at both national and international level (Table 10). Table 9: Measures for prior experience in technological and international partnership Proxies: Prior experience of EEN Partners in international or technological projects: Approximate number of international projects related with technology or technology transfer, in which the host organization was involved, during the last three years of activity. Prior experience of EEN Partners in international or technological projects: Approximate number of partnerships established, during the last three years of activity, and relatively to international organizations (e.g., firms, universities, business associations, government organizations). Approximate number of agreements for technology transfer, during the last three years of activity, and relatively to international organizations (e.g., firms, universities, business associations, government organizations). 40 that these entities reckon that international technology transfer was quite successful, resulting in new or improved products and services for this client and helping this client transfer and/or acquire new technologies. The view point of the clients is, however, much more disappointing (scoring below 4), revealing that international technology transfer on clients’ perspective was not highly successful. Kruskal Wallis test confirms that such differences are indeed statistically significant (for a level of significance below 1%). 16 Table 10: Results from the Kruskal-Wallis Test Variable Mean value of the Variable Kruskal-Wallis Test EENs Clients p-value International Technology Transfer (IIT) 5.821 3.786 0.003 *** Absorptive capability (AC) Human capital (HC) 1.000 0.919 0.007 *** % staff involved in ITT 0.152 0.356 0.569 % budget invested in training activities 0.094 0.054 0.015 ** Absorptive capacity 0.416 0.485 0.134 Network Connectedness (NC) Network dynamics 5.571 3.500 0.003 *** Connectedness Trust 6.102 5.120 0.017 *** Prior experience in international partnerships (PE) 9.643 5.885 0.016 *** Size 51.214 69.500 0.190 Sector diversity (SDIV) 14.857 1.429 0.000 *** Note: ***,**,* denote statistical significance at the 1%, 5% and 10%, respectively. Analysing the variables that were thought relevant for international technology transfer (cf. Chapter 1) - absorptive capability, including human capital, network dynamics, trust, and prior experience in international partnerships – the evidence shows that partners and clients significantly differ on certain dimensions. Specifically, the human capital endowment (i.e., the percentage of personnel with the tertiary education first cycle) is higher in the case of EEN partners (100%) than clients (91.9%). Budget devoted to training (other item of absorptive capacity) also differ 16 Kruskal-Wallis test, a nonparametric analysis of variance test that compares the median of two independent samples (For p-values not higher than 10%, the null hypothesis is rejected, i.e., differences exist between the population means):    clients and partners EENbetween econsistencnot are ITT of tsdeterminan : clients and partners EENbetween econsistenc are ITT of tsdeterminan : 1 0 H H 41 significantly with EEN partners devoting a larger share of their budget (almost 10%) to these activities as compared to clients (5%, approximately). The entities do not differ, however, in the absorptive capacity as a whole or in the proportion of staff involved in IT. The perception regarding the importance of EEN partners as a source of ideas and information for clients’s TT process (i.e, network dynamics) is much more positive for EEN partners than for its clients (5.571 versus 3.500). The same happens regarding trust – although trust levels are relatively high (over 5 out of 7), EEN partners tend to perceive higher trust levels in TT relations that their clients do (6.102 versus 5.120). These organizations also differ in prior experience, that is, the number of projects they had participated in the past: on average, approximately 10 in the case of EEN and 6 in clients. As expected, given their nature, EEN partner and clients strongly differ on the number of sectors where they are present in terms of activity, approximately 15 for partner and 2 for clients. 3.3. Key hypothesis of the ‘theoretical’ model The key hypothesis of our theoretical model of ITT is that certain factors are determinant to the successfulness of the international technology transference within a Triple Helix collaboration (Table 11). Following the literature review in Chapter 1, successful international technology transfer is influenced by: human capital (HC), absorptive capability (AC), network connectedness (NC), trust (Trust), prior experience in international or technological partnership (PE). Moreover, size (Size) and sector diversity (SDIV) also matter (control variables). In algebraic terms, we have: iiiiiiiiii eLnSDIVLnSizeLnPELnTrustLnNCLnNetACHCITT ˆ ˆˆˆˆˆˆˆˆˆ ln 987654321 +++++++++= βββββββββ where e i is the estimate of the error term. 42 Table 11: Hypotheses proposed Hypotheses description Determinants H1 International technology transfer depends directly on organizations’ human capital endowment. Human capital (HC) H2 The success of an international technology transfer involving a technology broker depends directly on the absorptive capability of the stakeholders. Absorptive capacity (excl. human capital) (AC) H3 International technology transfer is facilitated if network connectedness is encouraged. Network connectedness (NC) H4 International technology transfer success is positively related with the relation of trust between the technology sender/ recipient and the intermediary hybrid network. Trust H5 International technology transfer depends on the prior experience in international or technological partnership. Prior experience (PE) Consistent with the results of other studies, a positive relationship is expected between international technology transfer and the relevant variables proposed. 3.4. Determinants of ITT through the lens of EEN partner. Estimation results The technological PA is an internal document that describes the transference of technology between two EEN clients from different countries and assisted by two EEN partners. In line with this, the model proposed in Section 2.3 encompasses the perspectives from two EEN partners and two EEN clients. Nevertheless, the small sample obtained did not allow the operacionalization of the model as initially proposed. Notwithstanding, we were able to use the EEN partners’ questionnaires (N=71) because, as a trilateral network, the perceptions of EEN partners can reflect the determinants of ITT in a Triple Helix framework. With this in mind, we proceed with a correlation analysis to describe the linear relationship between the model variables relatively to the EEN partners’ perception of the determinants that boost the ITT. However, as refered in Section 2.3.1, and by the project definition, the existence of a technological PA implies technology transference between international clients. Therefore, the EEN partner’s survey did not included the variables related with successful international transfer of technology. This implies that we device an alternative approach for the dependent variable measurement. Consistent with the technology transfer definition, we adapted the proxy “Networking dynamics” proposed by Gkikas (2011) and use it as a proxy for the successful international transfer of technology. Henceforth, the dependent variable is a measure taking into account EEN 43 partner perception of its role in the clients’ process of building and transfer technology. 17 In the same line of reasoning, the proxy “Networking dynamics” is measured by the EEN perception of its role in the client’s access to ideas and information. 18 At a first glance, the correlation matrix shows that human capital, network connectedness, trust and size are positively and significant correlated with international technology transfer as predicted in the theoretically model. Contrary to our expectations, absorptive capability in negative correlated with our dependent variable. In a bivariate perspective, the majority of the correlations among independent variables are not considered high, nevertheless significant correlations (estimates of the Pearson correlation coefficient>0.70) are found between trust and network dynamics and between prior experience and size which might put potential problems of multicollinearity if we use these variables in simultaneous in the models estimations. To avoid the multicollinearity problems in the regression analysis, we use eight models alternating between each of the correlated variable. Additionally, the proposed models also capture the effects of the variables that compose the proxies for absorptive capability and network connectedness (Table 12). Table 13 presents the estimation results for the models. The results show that the explanatory variables included in the model tend to significantly explain (for p-value < 10%) the successfulness of the international technology transfer in a Triple Helix context. Furthermore, the R 2 adjusted of the models varies between 0.544 and 0.687 which means that between 54.4% and 68.7% of the amount of variance in the successfulness of the international technology transfer is explained by the independent variables considered. Contrary to our expectations, both human capital and absorptive capability are negatively correlated with international technology transfer. Awkwardly, the estimations suggests that EEN partners with less human resources dedicated to ITT achieve higher results in terms of PAs, which contradicts Hypothesis 1. 17 The variables are “The EEN had helped to develop new technologies that result in new or improved products and services for this client” and “The EEN had played a major role in helping this client transfer and/or acquire new technologies”. 18 “Networking dynamics” will be measure by the variable “The EEN is an important source of ideas and information for this client’s TT process”. 44 Table 12: Correlation analysis for international technology transfer measures on EEN partners 1 1 2 3 4 5 6 7 8 9 10 11 12 1. International Technology Transfer (IIT) (ln) 1 0,048 -0,240 ** -0,149 -0,161 0,803 *** 0,116 0,733 *** 0,656 *** 0,049 -0,290 ** -0,101 Absorptive capability (AC) 2. HC 1 0,106 -0,084 0,641 *** 0,079 0,022 0,040 0,219 -0,314 *** -0,617 *** -0,124 3. Proportion of staff involved in ITT with TE 1 0,435 *** 0,818 *** -0,231 * 0,348 *** -0,003 0,100 0,306 *** 0,111 0,388 *** 4. Proportion of the budget invested in training 1 0,407 *** -0,318 ** 0,095 -0,131 -0,141 0,083 -0,093 -0,264 ** 5. Absorptive capability 1 -0,163 0,267 0,001 0,174 0,042 -0,297 ** 0,154 Network Connectedness (NC) 6. Network Dynamics 1 0,177 0,734 *** 0,771 *** 0,019 -0,238 ** -0,092 7. Connectedness 1 0,724 *** 0,166 0,111 0,062 0,317 *** 8. Network Connectedness 1 0,571 *** 0,072 -0,136 0,124 9. Trust (ln) 1 0,266 ** -0,208 * -0,033 10. Prior experience in international partnerships (PE) (ln) 1 0,641 *** 0,101 11. Size (ln) 1 0,324 *** 12. Sector diversity (SDIV) (ln) 1 1 N= 71. ***,**,* denote statistical significance at the 1%, 5% and 10% test level, respectively 45 Regarding absorptive capability, the regression models where the proxy was scrutinized (Models 2, 4, 5 and 8) suggest surprising patterns. As a whole, absorptive capability is negatively and significantly related with the success of PAs leading us to reject for this sample the Hypothesis 2. Nonetheless, the variables that constitute the proxy for absorptive capability, apart from human resources, reflect different trends. On one hand, the proportion of staff involved in ITT with tertiary education is surprisingly negative and significant. On the other hand, proportion of the budget invested in training is positive and significant. This means that, on average and ceteris paribus, PAs associated to EEN partners with small teams and higher investments in training tend to reflect more successful ITT. The estimation coefficients of the regression models presented evidence that the impact of network connectedness in the international transference of technology is positive and significant, corroborating the Hypothesis 3. Globally, network connectedness is positively and highly significant (p-value < 0.001 in Models 1 and 5) but we can further add that network dynamics is the variable that most contribute to this result. In the same way, the variable trust is positively related with international technology transfer success which supports Hypothesis 4. In the models where trust was included, the corresponding estimated coefficient emerged as positive and highly significant (pvalue< 0.001). The results for the variable prior experience in international or technological partnership are not clear cut. In the models where the variable trust is included (model 3 and 4), the prior experience has a negative and significant estimate. In models without the trust variables (Models 1, 2, 5 and 6), prior experience evidences a positive and significant estimate coefficient in the two most robust models (Models 5 and 6). Hence, given these latter remarks, we might consider that the results supports the Hypothesis 5, being more successful ITT associated with EEN partners with more experience in international or technology partnerships. Regarding the control variable size, the models present a negatively and significant estimate coefficient with the dependent variable, which indicates that PAs associated with smaller EEN partners are more successful in terms of ITT. For the sector diversity, the results are ambiguous. Nevertheless, in the more robust model (Model 6) the results point out that the sector specialization is an advantage in terms of ITT. 46 Table 13: Regression models for international technology transfer on EEN partners Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Absorptive capability (AC) HC -0,298 * 0,105 -0,618 ** -0,088 -0,746 *** -0,091 -0,818 *** -0,260 * Proportion of staff involved in ITT with TE -0,270 ** -0,434 ** -0,292 ** -0,388 ** Proportion of the budget invested in training 1,284 ** 0,795 0,923 * 0,499 Network Connectedness (NC) Network Dynamics 0,622 *** 0,744 *** 0,522 *** 0,650 *** Connectedness -0,015 -0,005 Trust (ln) 0,757 *** 0,778 *** 0,679 *** 0,717 *** Prior experience in international partnerships (PE) (ln) 0,004 0,021 -0,028 -0,016 0,075 ** 0,061 * Size (ln) -0,106 *** -0,072 * -0,056 ** -0,053 ** Sector diversity (SDIV) (ln) -0,048 ** 0,043 -0,005 0,035 0,003 0,057 * 0,019 0,042 Constant 1,15 0,443 0,698 0,658 1,619 0,857 1,025 0,937 N 71 71 71 71 71 71 71 71 R 2 adjusted 0,569 0,652 0,497 0,509 0,668 0,687 0,544 0,545 F-Test (p-value) 24,09 (0,000) 19,752 (0,000) 18,270 (0,000) 13,098 (0,000) 29,197 (0,000) 20,249 (0,000) 21,896 (0,000) 14,989 (0,000) ***,**,* denote statistical significance at the 1%, 5% and 10% test level, respectively 47 Conclusions The fundamental theme of the research model in this dissertation is that there are keyfactors that can facilitate the international technology transfer in a Triple Helix framework. With a literature review on technological collaboration as our conceptual grounding, we identify the relevant determinants and put forward our hypothesis in a context of international technology transfer. The empirical results obtained from the analyses of technological partnerships agreements signed with the EEN support, showed that international technology transfer in Triple Helix collaboration is related with human capital, absorptive capability, network connectedness, trust and prior experience. Our first and second hypothesis postulated that human capital and absorptive capability had a positive impact in the successfulness of ITT under the EEN project. Notwithstanding, the results of our empirical model showed the opposite: both human capital and absorptive capability emerged as negatively associated with the ITT. Thus, apparently, a high proportion of staff with tertiary degrees involved in ITT hampers the successful transference of technology across borders. The negative impact of absorptive capability can be explain by the fact that human capital has also a negative tendency, nevertheless the results for human capital are ambiguous (regression results with positive and negative signs) or without statistical significance. In fact, in a close examination to the absorptive capability variables, we can verify that, apart from the human capital, the other two variables have different tendencies. From one side, on average, every other factors remaining constant, the higher the proportion of staff with tertiary degree working with ITT the lower the success associated to the international technology transference. On the other side, higher levels of investment in training seem to be translated into higher propensity for successfully international transfer of technology. Thus, our results underline that high levels of formal schooling per se is not a key determinant of ITT, the critical factor is to have highly educated human resources who complementary to the formal education receive adequate training in TT related issues. In line with other studies (Santoro and Bierly, 2006; Arvanitis and Woerter, 2009; Laroche and Amara, 2011) the result of our research shows that ITT can be enhanced by network connectedness. A detailed analysis of the variables that constitute the proxy demonstrate that network dynamics, measure by the perception of EEN partner being a source of ideas and information for its client’s TT process, is positively and significant 48 related with improved ITT. In contrast, the connectedness variable, measured by the number of formal outputs between EEN partner and client 19 reflects a negative impact in ITT, although is not significant. This difference in signs and significance can be justified by the impact of more formal or informal contacts in technology transference. The literature refers that informal contacts are the most frequent form of transference (Arvanitis and Woerter, 2009). Indeed, in terms of formality, the exchange of ideas and information is a less formal and tacit process than the creation of documentation. We also find that ITT process is strongly influenced by the relation of trust built between the EEN partner and its client. This results are in line with previous studies (Gopalakrishnan and Santoro, 2004; Santoro and Bierly, 2006) that describe trust as the glue that foster university and industry alliances. It is interesting to note, that in the EEN partners sample, trust and network connectedness are significantly correlated (p<0.10), as such trust may be a path to connectedness. This not implies that trust necessarily conduct to a network connectedness, but being the last one measured by the perception of EEN partner as a source of ideas and information for its client’s TT process, an enlightenment for the association can be found. A high level of trust between organizations, in our case between EEN partners and clients, can enrich their interaction (Santoro and Bierly, 2006), being the client more willing to share their ideas and requirements (Santoro and Bierly, 2006). Our results also show that the capability of EEN partner of successfully transfer technology among their clients is influenced by its prior experience in international projects related with technology. This can be justified not only by the accumulation of relevant knowledge regarding the appropriate alliances approaches, but also by the capability of more easily identify the collaborative possibilities (Sherwood and Covin, 2008). Regarding the control variables, size and sector, our research shows that both are negatively correlated with ITT, this means that EEN partners with reduce teams and working in less sectors of activity presents a higher propensity to successfully transfer technology. Considering the results of the variables organization size and proportion of EEN staff dedicated to technology transfer (human capital), the negative impact of both 19 Technological profiles, expressions of interest and partnership agreements. 49 variables in ITT may be interpreted as a hint that the overcrowding in an organization are more likely to hinder the international technology transfer than to boost it. Our results also shows that EEN partners specialized in specific sectors of activities tend to be associated with more successful technology transfer. Although studying the competence specialization in other context (absorptive capability), our result is corroborated by the Santoro and Bierly’s (2006) study. In an attempt to clarify the definition of absorptive capability, they refer that, although the importance of R&D intensity measure proposed by Cohen and Levinthal, other authors (Lane and Lubatkin and Mowery et al. in Santoro and Bierly, 2006) argue that only the technological competence of the firm in the specific area of transfer could affect the absorptive capability. In their research results, Santoro and Bierly (2006) found that not only the technological capability measure by the R&D intensity but also the technological relatedness measure by the competence in the area of transference facilitates the knowledge transfer. This can also explains the positive effect of training in absorptive capability. Summing up, most of the results of the present research met our (and the existing literature) expectations regarding the determinants of international technology transfer within a Triple Helix context. Network connectedness, trust and prior experience are critical for boost the international transfer of technology. Overall, we conclude that training, international experience and network are the basis for a trilateral network broker of international technology transfer in a Triple Helix environment. While our empirical operationalisation of the Triple Helix framework provides strong support for some ITT determinants that are backed by a solid theoretical background, it nevertheless suffers from methodological limitations. Since the PA involve two EEN partners and their respective clients, our focus in just one side of the PA limits the scope of our model. Collecting data from different intervenients in the PA could have enhanced the data’s and results’ richness. Also the focus on determinants which props the technological partnership agreements in a Triple Helix scheme barred the study of possible outcomes of the transference. Future studies should attempt to measures the outcomes of technology transfer. Finally we must emphasize that this study has merely provided a description of a very complex dynamics. Therefore, further qualitative and quantitative research capturing the determinants of international technology transfer within the Triple Helix is on demand. 56 Appendix 1 - Enterprise Europe Network’s Sector groups (in www.enterprise-europe-network.ec.europa.eu.) • Agrofood • Automotive, Transport and Logistics • Biotech, Pharma and Cosmetics • Chemicals • Creative industries • Environment • Healthcare • ICT Industry and Services • Intelligent Energy • Maritime Industry and Services • Materials • Nanoand Microtechnologies • Services and Retail • Space and Aerospace • Sustainable Construction • Textile & Fashion • Tourism and Cultural Heritage 57 Appendix 2 – Questionnaire implemented to EEN partners 1. GENERAL INFORMATION Regarding your host organization, please provide the following information: Type of organization: Private Company, Public company, University, Business/industry Association, Governmental organization Sector of activity: • Agrofood • Maritime Industry and Services • Automotive, Transport and Logistics • Materials • Biotech, Pharma and Cosmetics • Nano and Microtechnologies • Chemicals • Services and Retail • Creative industries • Space and Aerospace • Environment • Sustainable Construction • Healthcare • Textile & Fashion • ICT Industry and Services • Tourism and Cultural Heritage • Intelligent Energy • All Number of employees (total): Number of employees involved in ITT: Distribution (%) by education level of employees involved in ITT: • Basic ( < 6 years of schooling ) • Secondary ( 7-12 years of schooling ) • University ( > 12 years of schooling ) Considering the last three years of activity, please provide an approximate number of international projects related with technology or technology tranfer, in which your host organization was involved: 2. ENTERPRISE EUROPE NETWORK ACTIVITIES Considering the last three years of activity, please indicate the % average of the EEN budget: • Invested in training activities • Invested in network promotion Regarding the network promotion and dissemination of technological profiles, please provide: • Human resources allocated • Hours by week allocated Please estimate the overall number of: TO/ TR (Technological of fers and technological requests) submitted, EOI’s (Expression of Interest ) , Technological PA (Partnership agreements ) : Please provide the importance of the services provided by EEN for fostering international technology partnerships among your clients (1= unimportant; 7 = very important) and order them (writing 1 for the most important, 2 for the following and so on) • Direct contacts • Brokerage events • Company Missions • Fair • First Class • BBS Profiles • Other: 58 Please provide the importance of the following organizations for your activities in international technology transfer support? Evaluate (1= unimportant; 7 = very important) and order them (writing 1 for the most important, 2 for the following and so on) Frequency of the contacts between you and … (weekly, monthly, yearly, other) • Other companies (clients, suppliers, …) • Government (government departments, agencies, …) • Universities and research centers • Business and industry associations • Technology brokers * • Other: * Technology brokers: intermediaries between technology suppliers and offers that support companies and other organizations to transfer technology. They act as a network coordinator between industry, university and government. Which sectors of activity are you and your EEN team dedicated? (you can choose more than one option) • Agrofood • Maritime Industry and Services • Automotive, Transport and Logistics • Materials • Biotech, Pharma and Cosmetics • Nano and Microtechnologies • Chemicals • Services and Retail • Creative industries • Space and Aerospace • Environment • Sustainable Construction • Healthcare • Textile & Fashion • ICT Industry and Services • Tourism and Cultural Heritage • Intelligent Energy • All 3. TECHNOLOGICAL PARTNERSHIP AGREEMENTS Concerning each technological partnership agreement brokered by you as Enterprise Europe Network, please indicate your degree of agreement with the following sentences (1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree) Title of the Partnership agreement: Client involved in the PA: Contact Person, Email Other EEN partner involved • My client considered me trustworthy. • My client is perfectly aware and has confidence in my competences and abilities as well as my motives and fairness in sharing these abilities. • She/he knows that I to look out for her/his interests. • My performance was above my client’s expectations. • I was committed in the search of a technological partner. • She/he was committed in the search of a technological partner. • This client is confident in freely share ideas, feelings, and goals with EEN. • Based on past experience, she/he can with complete confidence rely on EEN. • We share a set of principles that we both find acceptable. • The EEN is an important source of ideas and information for this client’s TT process. • The EEN had helped to develop new technologies that result in new or improved products and services for this client. • The EEN had played a major role in helping this client transfer and/or acquire new technologies. • Access to the expertise of the EEN has strengthened the client’s core area of business. • Access to the EEN network contacts has strengthened the client’s core area of business. Regarding this specific client, please provide the number of: TO/ TR published, EOIs generated, Other PA (commercial or research) 59 Appendix 3 – Questionnaire implemented to EEN clients 1. GENERAL INFORMATION Regarding your organization, please provide the following information: Type of organization: Private Company, Public company, University, Business/industry Association, Governmental organization Sector of activity: Agrofood Maritime Industry and Services Automotive, Transport and Logistics Materials Biotech, Pharma and Cosmetics Nano and Microtechnologies Chemicals Services and Retail Creative industries Space and Aerospace Environment Sustainable Construction Healthcare Textile & Fashion ICT Industry and Services Tourism and Cultural Heritage Intelligent Energy All Number of employees (total): Number of employees involved in ITT: Distribution (%) by education level of employees involved in ITT: • Basic ( < 6 years of schooling ) • Secondary ( 7-12 years of schooling ) • University ( > 12 years of schooling ) Considering the last three years of activity, please indicate the average % of the turnover: • Invested in R&D activities • Invested in training activities Considering the last three years of activity, and relatively to international organizations (e.g., firms, universities, business associations, government organizations), please provide an approximate number of: • Partnerships established • Agreements for technology transfer 2. RELATIONSHIP WITH ENTERPRISE EUROPE NETWORK (EEN) Please evaluate the following sentences according with your experience during the last three years with the EEN organization that provides technological and innovation support to your company: (1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree) • I can freely share ideas, feelings, and goals with my EEN. • We share a set of principles that I find acceptable. • Based on past experience, I cannot rely on my EEN with complete confidence. • EEN is an important source of ideas and information in my TT process. • EEN had helped to develop new technologies that result in new or improved products and services. • EEN had played a major role in helping transfer and/or acquire new technologies. • Access to the expertise from the EEN has strengthened my organization’s core area of business. • Access to the EEN network contacts has strengthened my organization’s core area of business 60 3. RELATIONSHIP WITH INDUSTRY, UNIVERSITY AND GOVERMENT Please evaluate and rate the importance level of the contacts with the following organizations for your technology transfer activities (1= unimportant; 7 = very important) and rate (1= less important; 5= more important) . Considering issues related with technology transfer, how frequent are the contacts between your organization and the following organizations? (Weekly, monthly, yearly, other) • Other companies (clients, suppliers, …) • Government (government departments, agencies, …) • Universities and research centers • Business and industry associations • Technology brokers* • Other: * Technology brokers: intermediaries between technology suppliers and offers that support companies and other organizations to transfer technology. They act as a network coordinator between industry, university and government. 4. TECNOLOGICAL PARTNERSHIP AGREEMENTS Concerning each technological partnership agreement brokered by Enterprise Europe Network and signed by you, please answer the following questions. PARTNERSHIP AGREEMENT “Title of the partnership agreement” Evaluate the following statements regarding the EEN consultant involved in the partnership agreement (1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree) • She/he is trustworthy. • I have faith in her/him to look out for my interests. • Her/his performance was below my expectations. • She/he has been committed in the search of a technological partner. • I have confidence in her/him competence and abilities as well as its motives and fairness in sharing these abilities According with your role in the partnership, evaluate the following statements (1 = strongly disagree, 4 = neither agree nor disagree, 7 = strongly agree) Sending organization: • We learn a great deal from the company involved. • The technology held by my organization was assimilated by the other partner and contributed to development of products/services. • The technology held by my organization directly resulted in new products and services offered to the other partner customers. Recipient organization: • We learn a great deal from the company involved. • The technology held by the other partner was assimilated by us and contributed to development of products/services. • The technology held by the other partner directly resulted in new products and services offered to our customers. Please indicate the frequency of contacts with the EEN consultant involved in these partnership agreements: (Weekly, monthly, yearly, other)