Abstract
Evidence from economic literature suggests that innovative activities based on extensive interactions between industry, universities and local government can yield high levels of economic performance. In many countries, therefore, steps have been taken at an institutional level to set up innovation networks and, in particular, regional technological districts. Our paper deals with Italian Technological Districts: we aim to analyse the network additionality for territorial innovation determined by district policy. The analysis is based on a priori structural regional characteristics and on Social Network Analysis techniques.
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- 1.
Recently a list of technological clusters in Italy has been published (Intesa Sanpaolo - Servizio Studi e Ricerche 2010), which identifies four technological areas (pharmaceutical, biomedical, aeronautical and ICT) based on the ATECO 2007 classification. The conditions to identify a technological cluster in an area are: i) number of employees > 500; ii) share of employees > 5%; iii) number of firms > 20. If two of these conditions are satisfied, the area is considered a technological cluster.
- 2.
The online information provided by the TDs’ websites and the databases of the research projects have been integrated with information directly obtained from TD administrative staff.
- 3.
For TD 3 and TD 4, the district is considered as an actor in the network because it participates as a partner in some research projects. Furthermore, for TD 4 the different departments of the same institution are considered as single nodes in the network.
- 4.
Collaboration data are extracted from the set of research projects and from the set of organizations arranged in four affiliation matrices. A (n ×p) is the affiliation matrix with a ij (i = 1, …, n; j = 1, …, p) = 1 if the i-th member participates in the j-th research project, 0 otherwise. From A we derive an adjacency matrix G w of size (n ×n) that represents an undirected weighted adjacency matrix, whose entries are equal to 0 if two organizations have never collaborated in research projects, or to the number of research projects shared by pairs of organizations. The G w matrix can be analysed after removing the diagonal entries (which represent the total number of research projects for each member) and setting all entries greater than zero to “1”. The new G b matrix is an undirected binary adjacency matrix, where only the presence of ties is taken into account.
- 5.
In the following the network indices, at both global and actor level, are computed starting from the four G b matrices to explore the collaboration patterns among members in each district, while blockmodeling analysis is performed on the four G w matrices to identify the main characteristics of network structures.
- 6.
This index represents the average weight of activated links computed as the ratio of the sum of valued links compared to the number of activated links for each actor.
- 7.
Euclidean distance, Ward agglomerative method and blockmodeling analysis are performed on the valued adjacency matrix \(\mathbf{G}_{w}\) using the blockmodeling package of R software (Ziberna 2007).
References
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Acknowledgements
Work supported by PRIN 2008 Network Theory, Evaluation of the technological districts and of the public policies for innovation.
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Capuano, C., De Stefano, D., Del Monte, A., D’Esposito, M.R., Vitale, M.P. (2013). The Analysis of Network Additionality in the Context of Territorial Innovation Policy: The Case of Italian Technological Districts. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_10
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DOI: https://doi.org/10.1007/978-3-319-00032-9_10
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