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The increasingly widespread availability of data on characteristics and funding related to joint R&D programmes between European countries represents a key element to shed light on the process of Europeanization of research activities. While there is a growing literature on the national funding schemes devoted to transnational research (Reale et al, 2013), there are few contributions in this field using approaches focusing on interactions and relationships between agents, such as social network analysis (SNA) and spatial analysis. These approaches are likely to provide important insights into communication and knowledge flows, in addition to the effects of proximity and similarity between the units of analysis (Reale et al., 2017). However, the contribution based on the reciprocal interaction in the process of Europeanization of research activities at the level of political decision-making process (policy design and implementation) remains little debated. In fact, most of the studies have been focused at performer level related to research programmes/projects, (Barber et al, 2011; Hoekman et al. 2013; Scherngell and Barber 2009; Zinilli, 2016). By changing the level of analysis from the performer to the policy level, we expect to achieve an understanding of the decision-making network and its evolution over time. The aim of this paper is to discuss the contribution and the benefits of using this different application of methodological approaches to the transnational research programmes.
RISIS-Jorep dataset, complexity, network analysis
RISIS-Jorep dataset, complexity, network analysis
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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