Abstract
In recent decades economic theory has highlighted the benefits produced by networks of organizations in fostering innovation. A number of public policies were put in place to favor these innovation networks throughout Europe. The top-down institution of a number of specialized technological districts in Italy has been one of the main outcomes of this new wave of policies, in mid-2000. The aim of this paper is to explore what impact the institution of technological districts had on collaborative patterns over time. Using a pre-specified blockmodeling, observed network configurations obtained by the co-participation to R&D projects undertaken by organizations involved in a technological district are compared with a theoretical core-periphery structure in a 8-years time interval. The analyses of networks over time show that collaborative patterns have evolved from a core-periphery structure towards a complete network in which each research group is connected with the others.
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Notes
- 1.
In this study we use a direct approach consisting of analyzing the original data without transforming them into dissimilarities/similarities measures.
- 2.
We thank the IMAST’s administrative staff who helped us updating the data to July 2012. The network at time 1 (2005) has not been included in the further analysis, given that only one project has been started.
- 3.
In our case it is possible to know the exact year when a link is created/terminated and when a member is entered or withdrawn from the network according to the time duration of each research project.
- 4.
A penalty of 100 was imposed on this block to force the model to be as consistent as possible with a theoretically defined core-periphery configuration. Results both with and without this penalty are not too different.
References
Antonioli, D., & Marzucchi, A. (2010). The behavioural additionlity dimension in innovation policies: a review. Working Papers 201010, University of Ferrara, Department of Economics.
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 P. Giudici, S. Ingrassia, & M. Vichi (Eds.), Statistical models for data analysis (pp. 81–88). Heidelberg: Springer.
Choi, S. (2012). Core-periphery, new clusters, or rising stars?: International scientific collaboration among ‘advanced’ countries in the era of globalization. Scientometrics, 90, 25–41.
de Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory network analysis with Pajek. Cambridge: Cambridge University Press.
Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling. Cambridge: Cambridge University Press.
Everett, M. G., & Borgatti, S. P. (2013). The dual-projection approach for two-mode networks. Social Networks, 35, 204–210.
Gok, A., & Edler J. (2011). The use of behavioural additionality in innovation policy-making. Working paper University of Manchester.
Goyal, S. (2011). Social networks in economics. In P. J. Carrington & J. Scott (Eds.), The SAGE handbook of social networks (pp. 67–79). New York: Sage.
Keynes, J. M. (1936). The general theory of employment, interest and money. London: Macmillan.
Kronegger, L., Ferligoj, A., & Doreian, P. (2011). On the dynamics of national scientific systems. Quality & Quantity, 45, 989–1015.
Acknowledgement
Work supported by REPOS project “Reti, Politiche pubbliche e Sviluppo”. POR Campania FSE 2007–2013 (manager: M.R. D’Esposito). The authors would like to thanks the IMAST technological district for data availability.
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Prota, L., Vitale, M.P. (2014). A Pre-specified Blockmodeling to Analyze Structural Dynamics in Innovation Networks. In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_24
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DOI: https://doi.org/10.1007/978-3-319-06692-9_24
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