Integration Processes in European R&D: A comparative spatial interaction approach using project based R&D networks, co-patent networks and co-publication networks

Rafael Lata, Thomas Scherngell, Thomas Brenner

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

Abstract

This study focuses on integration processes in European R&D by analyzing the spatio-temporal dimension of three different R&D collaboration networks across Europe. The studied networks cover different types of knowledge creation, namely project based R&D networks within the EU Framework Programmes (FPs), co-patent networks and co-publication networks. Integration in European R&D - one of the main pillars of the EU Science Technology and Innovation (STI) policy - refers to the harmonization of fragmented national research systems across Europe and to the free movement of knowledge and researchers. The objective is to describe and compare spatio-temporal patterns at a regional level, and to estimate the evolution of separation effects over the time period 1999-2006 that influence the probability of cross-region collaborations in the distinct networks under consideration. The study adopts a spatial interaction modeling perspective, econometrically specifying a panel generalized linear model relationship, taking into account spatial autocorrelation among flows by using Eigenfunction spatial filtering methods. The results show that geographical factors are a lower hurdle for R&D collaborations in FP networks than in co-patent networks and co-publication networks. Further it is shown that the geographical dynamics of progress towards more integration is higher in the FP network.
OriginalspracheEnglisch
Seiten (von - bis)349-375
Seitenumfang27
FachzeitschriftGeographical Analysis
Volume47
Issue2
DOIs
PublikationsstatusVeröffentlicht - 2015

Research Field

  • Ehemaliges Research Field - Innovation Systems and Policy

Schlagwörter

  • R&D networks
  • European Framework Programme
  • European Research Area
  • Spatial interaction modelling
  • Eigenvector spatial filtering

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