Biotechnology R&D Collaborations Network in the FP6

Transcription

1 Biotechnology R&D Collaborations Network in the FP6 B. Moussa Ν. Βαρσακέλης

2 Aim To put into further scrutiny the effectiveness of the EU research policy,as has been applied by the Framework Programmes, using social network analysis on biotechnology data of FP6 extracted from the Community Research and Development Information Service (CORDIS) database. 2

3 Research question Whether the policy applied so far in the biotechnology research field has achieved the goal of cohesiveness. Is the knowledge transferred across the biotechnology research network of the EU? 3

4 Database on FP6 from CORDIS in the field of Biotechnology 673 projects were funded under seven sub-programs: Life health (45.52%) Food (9.32%) Nanotechnologies and Nanosciences (NMP) (7.72%) Information Society Technologies (IST) (7.27%) International cooperation (INCO) (3.27%) Joint Research Center (JRC) (2.27%) Sustainable development (SUST.DEV) (2.7%). 4

5 Geographical Distribution of the participating organizations in the field of Biotechnology PUBLIC SECTOR PRIVATE SECTOR UNIVERSITY TOTAL EUROPE OTHER OECD COUNTRIES OTHER EUROPEAN COUNTRIES AFRICA ASIA LATIN AMERICA TOTAL

6 1. Empirical Findings of FP6-BioNet Number of nodes is 3865 Number of links is FP6-BioNet consists of one giant component that includes the 91% of the nodes. Network is connected. FP6-BioNet graph density Network is very sparse. Diffusing knowledge is not present in biotechnology. Sparse neighborhoods or structural holes are environments not encouraging knowledge transfer 6

7 2. Empirical Findings of FP6-BioNet European biotechnology area consists of isolated neighborhoods and the final outcome, because of the low network effect, is just the sum of the individual attempts. 45 communities which are modular by

8 4. Empirical Findings of FP6-BioNet Average neighborhood density of the FP6-BioNet is FP6-BioNet consists of neighborhoods that are relatively less connected because there are agents that are not directly connected with other agents in the neighborhood 8

9 5. Empirical Findings of FP6- BioNet This lack of direct communication might be considered as a barrier in the communication of knowledge. Average path length equal to 2.93 which means that if the R&D collaboration alliances are also knowledge transfer mechanism between the participants, then the knowledge created by one agent will reach another agent of the network after 2.93 nodes on average. 9

10 6. Empirical Findings of FP6-BioNet Global efficiency of the FP6-BioNet has been estimated at 0.103, communication efficiency of the network is rather low. Transitivity or Clustering Coefficient equal to

11 7. Empirical Findings of FP6-BioNet The small world indicator of the FP6-BioNet is equal to 1.04 This finding is in accordance to the low density and low dense neighborhoods and is also another indication that the FP6-BioNet as evolved does not effectively facilitate the cohesion in the knowledge transfer. 11

12 Nodes 3865 Edges Average degree Clustering Coefficient Average Path Length Average Neighborhood Overlap Number of shortest paths Average Embeddedness 56,265 Graph Density Βetweenness centrality Modularity 0.43 Closeness centrality Number of 45 Degree centrality Communities Erdos Number Small world 1.04 Average Clustering Average Neighborhood Coefficient density Global communication Average Node Strength 43,259 efficiency

13 Conclusions Economic convergence one of the main objectives of the EU, could be facilitated by the convergence of the stock of knowledge of the EU countries and regions. Higher the knowledge diffusion the higher the impact on an agent s knowledge production. The FP6-BioNet is relatively large network. 13

14 Conclusions If research collaborations is a proxy of the knowledge transfer between the participants in the alliance then this characteristic of the network indicates that some of the knowledge created in an area of the network does not diffuse directly or indirectly to other areas of the network. Small world characteristic is not so clear. 14

15 Suggestions The semantic barriers could be decreased if, using policy instruments, the EU will provide incentives for participation in more alliances. The communication noise could be reduced by intervening into the network and through policy instruments decrease the communication paths. The greater the density, the more dominant the network effect. 15

16 Bachar Moussa & Nikos C. Varsakelis Aristotle University of Thessaloniki, Department of Economics Thessaloniki, Nov

17 The Authority of Public Research on R&D: The Case of Biotechnology R&D Collaborations Network in the FP6 National Innovation System Innovation and technology development are the result of a complex set of relationships among actors in the system, which includes enterprises, universities and government research institutes. Innovative performance of a country depends on how these actors relate to each other as elements of a collective system of knowledge creation and use, as well as the technologies they use. Thus network analysis help us to see the reaction between these actors. 17

18 Literature RJV private and public Research Joint Ventures among enterprises and public sector institutions, aims at the promotion of research and advanced technology partnerships. RJV is a mechanism of knowledge sharing and transfer between the participants in the RJVs. 18

19 Research Hypothesis Public Research Institutions and Universities are the most important agents in Biotechnology Network in Europe FP6? 19

20 Network of participant organization in the health thematic area FP6 20

21 Empirical Findings Rank ORGANIZATION HUBS 1 UNIVERSITY COLLEGE LONDON (UNITED KINGDOM) 0, UNIVERSITY OF OXFORD (UNITED KINGDOM) 0, UPPSALA UNIVERSITY (SWEDEN) 0,0071 Rank ORGANIZATION BC 1 INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE (FRANCE) 1 2 KAROLINSKA INSTITUTET (SWEDEN) FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FOR SCHUNGEV (DEUTSCHLAND) 0.68 Rank ORGANIZATION Eigenvector 1 UPPSALA UNIVERSITY (SWEDEN) 1, UNIVERSITAT MARBURG (DEUTSCHLAND) 0, UNIVERSITY OF NEWCASTLE UPON TYNE (UNITED KINGDOM) 0,7376 Rank ORGANIZATION Preferential attachment 1 INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE (FRANCE) 0, KAROLINSKA INSTITUTET (SWEDEN) 0, CENTRENATIONAL DE LA RECHERCHE SCIENTIFIQUE (FRANCE) 0,

22 Rank ORGANIZATION Eigenvector 1 UPPSALA UNIVERSITY (SWEDEN) 1, UNIVERSITAT MARBURG (DEUTSCHLAND) 0, UNIVERSITY OF NEWCASTLE UPON TYNE (UNITED KINGDOM) 0, UNIVERSITY OF YORK (UNITED KINGDOM) 0, UNIVERSITY OF OXFORD (UNITED KINGDOM) 0, UNIVERSITY OF TARTU (ESTONIA) 0, UNIVERSITAT ZU KOLN (DEUTSCHLAND) 0, WESTPFALZ KLINIKUM GMBH (DEUTSCHLAND) 0, UNIVERSITY OF SOUTHAMPTON (UNITED KINGDOM) 0, VUACADEMIC MEDICAL CENTER (NEDERLAND) 0, UNIVERSITA DEGLI STUDI DI MILANO (ITALY) 0, UNIVERSITY OF LONDON (UNITED KINGDOM) 0, VILNIUS UNIVERSITY HOSPITAL SANTARISKIU CLINICS (LIETUVA) 0, UNIWERSYTET MEDYCZNYW LODZI (POLAND) 0, USTAV HEMATOLOGIE A KREVNI TRANSFUZE (CZECH REPUBLIC) 0, UNIVERSITY COLLEGE LONDON (UNITED KINGDOM) 0, UNIVERSITATS KLINIKUM HEIDELBERG (DEUTSCHLAND) 0, UNIVERSITY OF MILANO BICOCCA (ITALY) 0, UNIVERSITY OF BASEL (SWITZERLAND) 0, UNIVERSITY OF BIRMINGHAM (UNITED KINGDOM) 0, UNIVERSITY OF DUNDEE (UNITED KINGDOM) 0, UNIVERSITY OF MODENA AND REGGIO EMILIA (ITALY) 0, UNIVERSITEIT VAN AMSTERDAM (NEDERLAND) 0, UNIVERSITY OF HELSINKI (FINLAND) 0, UNIVERSITY OF SALAMANCA (SPAIN) 0, UNIVERSITY OF LIVERPOOL (UNITED KINGDOM) 0, UNIVERSITY OF MAINZ (DEUTSCHLAND) 0, UNIVERSITY OF COPENHAGEN (DENMARK) 0, UNIVERSITY OF CAMBRIDGE (UNITED KINGDOM) 0, UNIVERSITY OF CRETE (GREECE) 0,

23 Conclusions 27 Universities and 3 Public Research Institutes (knowledge multipliers) have the highest eigenvector values. When Universities and Public Research Institutes are connecting to other central nodes will have a multiplier effect on absorptive capacity by increasing the capacity for acquiring new knowledge and developing innovations. Capacity for innovation and productivity is increased by connecting to more functional areas with high eigenvector centrality. 23

24 Conclusions Low state finance on EU universities entails lower multiplier effect on velocity of knowledge, preventing to offer this gauge of economic growth. low velocity of knowledge (driven principally by the lack of connecting and state financial policy), has and continues to keep downward pressure on both development and knowledge diffusion. Contradiction with the official EU policy to become a knowledge driven economy. 24