Manfred Paier

Determinants of Collaboration in European R&D Networks: Empirical Evidence from a Discrete Choice Model

This paper focuses on inter-organizational R&D collaborations as captured by joint research projects funded within the European Framework Programmes for Research and Technological Development (EU-FPs). We identify determinants of collaboration, including actor characteristics, relational and network effects as well as geographical effects by means of a discrete choice model. Using data on EU-FP projects from the EUPRO database and from a representative survey of participants, we produce statistically significant evidence that collaboration choices in EU-FPs are primarily facilitated by prior acquaintance, thematic proximity and geographical proximity. Also, network effects are significantly related to collaboration choice. Moreover, the study shows that the impact of geographical effects increases for more intensive collaboration. The results are promising since a deeper understanding of these collaboration processes is needed for future governance of research policies in the EU.

Determinants of Collaboration in European R&D Networks: Empirical Evidence from a Binary Choice Model Perspective

R&D collaboration networks stimulated by the European Union in its Framework Programs (FP) on Research and Technological Development exhibit - like many other large social networks - characteristic features of complex networks. Existing analyzes of these networks, however, are mostly built on strongly simplifying assumptions for network construction, like fully connected project graphs or star-graphs. Still, apart from qualitative evidence, little is known about the determinants that affect partner choice and network dynamics. It is the aim of this paper to provide empirical evidence for a more differentiated picture of partner selection - and thus network formation - in the European FPs. We adopt an econometric perspective to identify determinants of link formation, including various actor characteristics, relational and network effects as well as geographical effects. We employ a binary choice model estimated by means of logistic regressions, with a dependent variable that represents the establishment of a formal cooperation between two organizations in FP5. We use data on EU FP projects from the sysres EUPRO database and from a representative survey of FP 5 participants. The study produces statistically significant evidence that R&D collaboration choices of organizations participating in European FPs are affected by geography, FP experience and relational factors including network characteristics. Thematic proximity matters more than geographic proximity, while most influential for collaboration appears to be prior acquaintance of the actors. Also, network effects significantly determine the collaboration choice, but to a slightly smaller extent than geographical effects.

R&D NETWORKS AND REGIONAL KNOWLEDGE PRODUCTION: AN AGENT-BASED SIMULATION OF THE AUSTRIAN COMPETENCE CENTRES PROGRAMME

Publicly funded competence centres have gained high recognition for improving science-industry collaboration. With the requirement for long-term and geographically concentrated R&D, competence centres provide an environment for joint learning and transfer of “sticky” knowledge. The objective of this paper is to investigate how a competence centres programme affects knowledge production in the regional innovation system. In order to address this issue, we draw on a simulation approach and develop an agent-based model of the Vienna Life Sciences innovation system. Companies, research organisations and universities are heterogeneous agents that create scientific publications, patents, as well as high-tech jobs. Simulation runs refer to long-term scenarios regarding the level and duration of public funding. By addressing the complexities of knowledge interaction in the context of the “local buzz” versus “global pipelines” discussion, the results show the potential of empirically calibrated simulation models for ex-ante impact assessment in R&D policy.

Joint knowledge generation in European R&D networks: Results from a discrete choice modelling perspective

The objective of this study is to explore the determinants of joint knowledge generation within European networks of R&D collaboration. This study distinguishes between two types of joint knowledge generation: scientific and commercially relevant knowledge generation. Joint generation of scientific knowledge is measured by co-authored scientific publications, while joint commercially relevant knowledge is measured by co-owned patents and artefacts. Unit of analysis are dyads of organisations jointly participating in projects of the 5th EU Framework Programme (FP5). The data for carrying out this study is taken from a survey among FP5 participants and the EUPRO database. 23 EU member countries (Bulgaria, Cyprus, Malta and Romania are excluded) plus Switzerland and Norway are included. Regression methods for discrete choice (logit and probit) are employed to meet the objective. The independent variables taken into consideration encompass the types of organisations involved in the dyad, geographical and cultural obstacles, relational factors and project characteristics. Results show that dyads involving universities have the highest probability not only to jointly generate scientific knowledge but also to jointly generate commercially relevant knowledge, whereas the involvement of an industry organisation results in a low probability for both types of knowledge generation. Perhaps, this can be attributed to the fact that joint knowledge generation entails disclosure of own knowledge, which is actually a task of universities but is problematic for industry organisations. Another important result is that crossing national border has a significant positive rather than negative effect on joint scientific knowledge generation, which is essentially a consequence of how the Framework Programmes had been set up. Similarly, crossing EU-15 external border has a positive effect on joint knowledge generation, indicating that the FPs work well in achieving their aim of supporting the catching up process of CEE countries. But, joint generation of commercially relevant knowledge is negatively influenced by language borders. This can be explained by the fact that the co-development of patentable knowledge or artefacts requires more intensive and complex interactions than to co-author a scientific publication where English is the lingua franca anyway. Results on relational factors and project characteristics satisfy expectations: Duration of collaboration and the existence of previous collaboration have a positive effect on joint knowledge generation, whereas the project size, measured by number of participants, affects joint knowledge generation negatively.

Ranking Lists and European Framework Programmes

The operational context for higher education institutions has become increasingly competitive: universities have to compete on national and international markets for students, staff, funding and prestige. The emergence of various markets, market mechanisms and competition in higher education have become a well-established and much discussed fact, and have shaped the dynamics of the higher education arena (Enders & Jongbloed 2007; Texeira et al. 2004) In a global competition of knowledge societies, higher education institutions have been vested with the task of economic and social change, and are expected to contribute to the competitiveness of nationstates as well as their local communities.

Simulating the Effects of Public Funding on Research in Life Sciences: Direct Research Funds Versus Tax Incentives

An on-going debate in the innovation policy arena revolves around the effects of public research funding. While government intervention is usually not questioned, appraising the role of direct research funds (government grants for research projects) versus tax incentives (tax exemption/deduction of research expenses) remains a core issue. In this chapter we make methodological contributions to ex-ante evaluation of these alternative government research funding instruments. Building on the SKIN model, we develop an agent-based simulation of a localized life sciences innovation system (Vienna, Austria). Companies, universities, public research and other relevant research organizations are modelled as heterogeneous agents that make investment decisions about conducting research, exchange assets with other agents and produce knowledge output. Simulation runs refer to a 30 year period, distinguishing three funding scenarios: Direct funding (no tax incentives), tax incentives (no direct funding) and the co-occurrence of both (direct funding and tax incentives). First simulation results for the Vienna life sciences innovation system suggest that the overall volume of required public funds could be lower for tax incentives than for direct funding. However, we find also indications that direct funding—in contrast to tax incentives—could have a decreasing effect on public investment per patent in the long run.

Knowledge Creation and Research Policy in Science-Based Industries: An Empirical Agent-Based Model

There is an increasing demand for ex-ante impact assessment of policy measures in the field of research. Existing methods to explore the effects of policy interventions in innovation systems often lack transparency or just extrapolate current trends, neglecting real-world complexities. Therefore, we propose a simulation approach and develop an empirical agent-based model (ABM) of knowledge creation in a localized system of researching firms in a science-based industry. With its strong emphasis on empirical calibration, the model represents the Austrian biotechnology industry. In our simulations, effects of different public research policies on the knowledge output—measured by the patent portfolio—are under scrutiny. By this, the study contributes to the development of ABMs in two main aspects: (1) Building on an existing concept of knowledge representation, we advance the model of individual and collective knowledge creation in firms by conceptualizing policy intervention and corresponding output indicators. (2) We go beyond symbolic ABMs of knowledge creation by using patent data as knowledge representations, adopting an elaborate empirical initialisation and calibration strategy using company data. We utilise econometric techniques to generate an industry-specific fitness function that determines the model output. The model allows for analysing the effect of different public research funding schemes on the technology profile of the Austrian biotechnology innovation system. The results demonstrate that an empirically calibrated and transparent model design increases credibility and robustness of the ABM approach in the context of ex-ante impact assessment of public research policy in an industry-specific and national context.

On Socio-Technical Concerns for Smart Grid Security and Resilience

The future smart grid will make use of ICT to support the inclusion of new renewable energy sources and advanced energy services. In some cases, the Internet will be used as part of this critical infrastructure, e.g., to enable communication with end-user services. Consequently, developing technologies for ensuring the security and resilience of smart grids and the Internet are of paramount importance. For these technologies to be widely adopted, important socio-economic aspects must be considered, which will be addressed in a new EU-funded project, called SPARKS. In this paper, two examples are given of how socio-economic aspects affect security and resilience of smart grid development. The first one outlines the chain-of-custody issue, which might influence the security architecture of a smart grid. The second one outlines a bottleneck in the institutional setting, which might restrict the flexibility and resilience at the distribution level of smart grids.

The Evolution of the Biotechnology Sector in Austria. Evidence Using Patents Over the Time Period 1990-2010

Today it is widely recognized that biotechnology is one of the key sectors of the emerging knowledge based economy (see, e.g., Trippl and Todtling 2007). Revenues and turnovers from biotechnology applications – here defined to include all applications of science and technology to living organisms, as well as parts, products and models thereof – have increased considerably, in particular over the last two decades. A specific property of the biotechnology industry is its knowledge-intensive character. Thus, the biotechnology sector is highly localized in geographical space, stressing the importance of knowledge interactions at the local level, most often within in local clusters. Until late 1990s, Austria has often been considered to be lagging concerning the evolution of the biotechnology sector and its contribution to overall macro-economic development. This has also been recognized by the Austrian economic policy as well as Science, Technology and Innovation (STI) policy that has put the commercialization and development of biotechnology on the top of their agenda after millennium. The initiative “LISA-Life Science Austria” is the most prominent example in this context, promoting the foundation of new companies in biotechnology and the formation of networks between the industry and the science sector in this field. Based on these policy efforts, several biotech clusters have appeared in the meantime, most prominently the Vienna biotech cluster mainly focusing on medical biotechnology (red biotechnology). The focus of this chapter is on the development of the Austrian biotechnology sector over the past two decades. The objective is to describe the development of the sector from a quantitative perspective, using patents as main indicator to track knowledge production activities. We identify relevant patents from the PATSTAT database of the OECD which enables us to systematically trace knowledge production in the Austrian biotechnology sector between 1990 and 2010, disaggregated by different, detailed technological fields and regions. Furthermore, determinants of knowledge production activities at the firm level are tested by means of a panel econometrics, relating knowledge production activities of Austrian biotechnology actors to relevant actor-specific characteristics, such as R&D expenditures. The results will provide a systematic insight into the evolution of the Austrian biotechnology sector, tracing the development across different technological fields and identifying determinants of the observed development.

Effects of Competence Centres on Regional Knowledge Production: An Agent-Based Simulation of the Vienna Life Sciences Innovation System

Competence centres have gained high recognition as a policy instrument for improving science-industry collaboration. With the requirement for longer-term, institutionalized and geographically concentrated R&D, competence centres provide an environment for joint learning processes and transfer of “sticky” knowledge. They can thus be interpreted as spatially focused R&D networks linking academia and industry. The objective of this chapter is to investigate in a dynamic perspective how a public competence centres programme affects knowledge production in its environment – the regional innovation system. In order to address this issue, we draw on a simulation approach and develop an agent-based model of the Vienna Life Sciences innovation system. Heterogeneous agents representing companies, research organisations and universities are endowed with knowledge and create output, thus generating system performance in terms of scientific publications, patents as well as high-tech jobs. Simulations refer to different long-term scenarios regarding public funds for competence centres. Thus, we explore agent-based simulation as a potential way to address the complexities of knowledge interaction in the context of the “local buzz” versus “global pipelines” discussion in the geography of innovation literature. First results with the empirically calibrated model, e.g. on long-term effects, indicate the potential of the approach for ex-ante impact assessment of network-related measures in R&D policy.