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The Geography of Networks and R&D Collaborations by Thomas Scherngell (auth.), Thomas Scherngell (eds.)

By Thomas Scherngell (auth.), Thomas Scherngell (eds.)

The geography of networks and R&D collaborations, particularly the spatial size of interactions among businesses appearing joint R&D, have attracted a burst of awareness within the final decade, either within the clinical learn of the networks and within the coverage quarter. the quantity is meant to compile a variety of articles supplying novel theoretical and empirical insights into the geographical dynamics of such networks and R&D collaborations, utilizing new, systematic info resources and applying state-of-the-art spatial research and spatial econometric thoughts. It contains a piece on analytic advances and technique and thematic sections on constitution and spatial features of R&D networks and the influence of R&D networks and coverage implications. The edited quantity presents a suite of high-level study contributions with an goal to give a contribution to the hot debate in fiscal geography and local technological know-how on how the constitution of formal and casual networks modifies and impacts the spatial and temporal diffusion of knowledge.

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Hazir illustrate how a model explaining the effect of geography on micro processes can also explain for these global properties. On the other hand, CNA adopts a macro perspective to study the spatial dimension of such properties. The study by Barber and Scherngell in Chap. 10 illustrates studying the heterogeneity in the spatial configuration of communities in an R&D network. Whereas, the study by De Montis et al. in Chap. 3 illustrates the use of CNA to investigate whether similar geographical contexts give rise to similar global network properties or not.

The ability of this tool to handle tie dependence might be useful for better treatment of network effects and demarcate the role of geography more properly. In addition to that, Steglich et al. (2010) extended Stochastic Actor-based Models to distinguish partner selection from social influence in a dynamic network. This extension basis on the idea that two actors showing the same behaviour might be collaborating due to similarity in their behaviour, or one gets similar to the other as a result of being connected.

Small world phenomena) was carried out by the psychologist Stanley Milgram (1967) using graph theory. After Milgram’s work, many authors further investigated collaborative social systems (for a review of methods and applications, see Wasserman and Faust 1994). In these studies scientists, engineers, or inventors are modelled as vertices and the links are collaborative ties between them. Inter alia, Bloch (2005) scrutinised individual behaviours of agents and collective dynamics of wide organizations.

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