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Empirical evidence shows that knowledge transfer from research institutions and universities contributes to local innovation processes in a variety of ways. Several studies have also shown that the extent of innovation diffusion is greatly affected by the structure of the network in which innovation processes take place. In this contribution, we aim to identify the complex structure of relationships at the basis of knowledge and innovation diffusion among actors from various organizations (firms, academic and research institutions) in a given territory. A multiplex approach is proposed to explain co-authorship and co-inventions interactions among Author-Inventors community in a specific geographic area. To this end, we carry out a case study of the Trieste area (North-East Italy), characterized by a very high concentration of research organizations and by the emergence of a lively sector of small firms working in the field of R&D.

In this paper we discuss the use of Multiple Correspondence Analysis to analyze and graphically represent two-mode networks, and we propose to apply it in a Greenacre's doubling perspective. We discuss how Multiple Correspondence Analysis: (i) properly takes into account the nature of relational data and the intrinsic asymmetry of actors/events in two-mode networks; (ii) allows a proper graphical appraisal of the underlying relational structure of actors or events; (iii) makes it possible to add actor and event attributes to the analysis in order to improve results interpretation; and (iv) gives different results with respect to the usual Simple Correspondence Analysis. © 2014 Elsevier B.V.

The role of academics in patenting activity is not limited to patents owned by universities (university-owned patents) as academics often contribute to inventions patented by other organizations (university-invented patents). Moreover, it has been shown that academics play central roles inside the patenting network and that appropriability rules can interfere with the pattern of knowledge diffusion. In this study, we use social network analysis to analyze university-owned and university-invented patents in two Italian universities. We investigate the quality of the ties and the reasons why academics patent with their universities or with external organizations. We identify three subnetwork typologies. Two out of three subnetworks well exemplify the conditions for university ownership and for professor privilege, but the most complex structure, stemming from academic gatekeepers, need more flexible property attribution arrangements. In this contexts, aggressive policies toward university ownership can damage the networks with the highest grade of science-industry cross-fertilisation.

In this paper, we present the results of a network analysis applied to academic patent data in a subsector of the chemical field in Italy in the period 2000–2011. In particular, we analyse the micro-level interactions to point out the different network structures shaped by university-owned and university-invented patents. We detected three subnetwork typologies (labelled type A, B, and C) that exemplify different qualitative relational structures as well as different attributions to propriety rights. Type A (open science) exemplifies the typical owned patent; type B (multiple ties) represents the hybrid structure with multiple ties and involvement of academics as individuals and of universities as organisation; type C (disconnected subnetworks) represents the typical invented patent with no role of universities as organisation. The whole network seems to show a breaking point in terms of connectivity around 2005, a year that marks a change in policy rule and strategic orientation of Italian universities towards patenting. After 2005, the number of actors grew disproportionally and the network appears disconnected in several comparable components. Also, the composition in terms of subnetwork types changed. The overall picture seems to underline a big structural change dominated by the important increase of academic patenting both direct (university ownership) and indirect (increasing academic patenting).

Factorial techniques are widely used in Social Network Analysis to analyze and visualize networks. When the purpose is to represent the relational similarities, simple correspondence analysis is the most frequent used technique. However, in the case of affiliation networks, its use can be criticized because the involved χ 2 distance does not adequately reflect the actual relational patterns. In this paper we perform a simulation study to compare the metric involved in Correspondence Analysis with respect to the one in Multiple Correspondence Analysis. Analytical results and simulation outcomes show that Multiple Correspondence Analysis allows a proper graphical appraisal of the underlying two-mode relational structure.

This paper analyses the relationships between firms involved in a pilot project to promote the creation of a multifunctional local system, a case of collective action. The project area is a rural alpine space in the Friuli Venezia Giulia region (NE Italy). The analysis, which is part of the pilot project, used Social Network Analysis tools to explore the structure and dynamics of firm relationships in terms of mutual awareness, types and strength of business ties, and extension and location of economic and social interactions. These characteristics are described at both complete and ego network levels. The findings can be used to design policies and rural development agendas. The analysis also revealed the potentials of Social Network Analysis in assessing networking projects and enhancing rural actors’ awareness of their relational system.

Scientific collaboration is a complex phenomenon that improves the sharing of competences and the production of new scientific knowledge. Social Network Analysis is often used to describe the scientific collaboration patterns defined by co-authorship relationships. Different phases of the analysis of collaboration are related to: data collection, network boundary setting, relational data matrix definition, data analysis and interpretation of results. The aim of this paper is to point out some issues that arise in these different phases, highlighting: (i) the use of local archives versus international bibliographic databases; (ii) the use of different approaches for setting boundaries in a whole-network; (iii) the definition of a co-authorship data matrix (binary and weighted ties) and (iv) the analysis and the interpretation of network measures for co-authorship data. We discuss the different choices that can be made in these phases within an illustrative example on real data which is referred to scientific collaboration among researchers affiliated to an academic institution. In particular, we compare global and actor-level network measures computed from binary and weighted co-authorship networks in different disciplines.