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Assessing the role of technological districts in regional innovation policies: a network analysis of collaborative R&D projects

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Abstract

As highlighted in systemic approaches to innovation, regions play an increasingly important role in designing and implementing place-based innovation policies. A wide debate has emerged on the limits and validity of different policy models, for example, between “platform” and “district-based” approaches or between a “corporatist” and an “evolutionary” Triple Helix. Within the EU Cohesion Policy framework, a number of technological districts (TDs) have been established since 2005 in the Italian “Convergence” regions to foster competitiveness, innovation, and research industry linkages. TDs have become critical actors in knowledge and technology transfer processes, and a significant amount of funding has been devoted to their development in the National Operational Programme for Research and Competitiveness (PON-R&C). In this work, we use methods drawn from social network analysis to locate TDs within the wider collaboration networks established through the PON-R&C programme. We highlight the specificity of TDs within the general policy and assess their ability to promote organisational and sectoral heterogeneity among project participants. We find that different network architectures coexist under the same policy umbrella and relate this variety to the ideal models identified in the literature.

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Notes

  1. Member states and regions are required to produce a strategy according to the RIS3 guidelines before they can receive EU financial support through the Structural Funds (European Commission, 2012).

  2. On these aspects, see the contributions that appeared in the Special Section”University Technology Transfer, Regional Specializations, and Local Dynamics: Lessons from Italy”, The Journal of Technology Transfer, Volume 46, Issue 4 (2021).

  3. In 2000, the “Lisbon Strategy” highlighted the importance of an economy based on knowledge and innovation, followed by the “Europe 2020 Strategy”, the “Horizon 2020” and the recent “Horizon Europe” funding programme.

  4. The strategic sectors of intervention are grouped in three macro areas: (1) Environment, energy and transports; (2) food and agriculture, health; (3) System of manufacturing, biotechnology, new materials and nanotechnology, ICT, cultural assets.

  5. Before the National Research Programme 2005–2007, public–private laboratories were conceived only as projects within the technological districts, assuming later the shape of “organisational model” for the technological transfer.

  6. See Tables 3 and 4, pp. 48–49.

  7. OECD refers to them as “regions with persistent underdevelopment traps facing a process of deindustrialisation or experiencing structural inertia. They have considerably lower GDP per capita than other groups and the highest average unemployment rate. Values on S&T-related indicators are low” (OECD 2011).

  8. http://www.ponrec.it/

  9. On these points see for example the contributions in the Special Issue on Advances in Two-Mode Social Networks, Social Networks, Volume 35, Issue 2 (2013).

  10. For example, the time window 2006–2008 includes all projects that were started before 2008 and whose end date was not earlier than 2006.

  11. This amounts to adopting a relational definition of the boundaries of TDs, rather than a membership-based definition. District members are identified according to their participation in projects involving the TD, whether or not they are formally listed in the roster of district members. More formally, we can say that a TD, as defined here, is an edge-induced subgraph of the entire network. This choice is consistent with the fact that districts have a dual membership structure composed of members (internal members) and partners (external members) and that, for most districts, the membership structure is not fixed in time.

  12. The degree centralization of a network is measured on a range between zero and one; it is zero when the degree distribution is entirely egalitarian, and one for star-shaped networks in which one node has degree n − 1 and the remaining n − 1 nodes have degree equal to one.

  13. The clustering coefficient ranges between zero and one; it is zero when there is no clustering and one for maximal clustering which happens when the network consists of disjoint cliques.

  14. It is worth noticing that, although their active participation in projects has been shorter, both Dare and AgroBio were formally established at around the same time as Imast and Dhitech.

  15. We also explored other network statistics, including density. The results confirm the findings of the previous analyses: Imast and Dhitech exhibited relatively high density throughout the entire period, with a peak in 2011–13. On the other hand, Agrobio and Dare joined the network in 2012, maintaining a consistently low density of less than 0.3%.

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Acknowledgements

We would like to warmly thank Dr. Ivan Cucco for the stimulating discussions and the valuable technical assistance provided during the various phases of this study.

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Authors and Affiliations

  1. Department of Economics, University of Campania “L. Vanvitelli”, Capua, Italy

    Alessandro De Iudicibus

  2. The American University of Rome, Rome, Italy

    Laura Prota

  3. Department of Management, University of Bologna, Bologna, Italy

    Francesco Savoia

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  1. Alessandro De Iudicibus
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  2. Laura Prota
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  3. Francesco Savoia
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Contributions

Conceptualisation: Laura Prota and Francesco Savoia, Data curation: Alessandro de Iudicibus and Francesco Savoia, Methodology and formal analysis: Laura Prota, Writing–original draft: Laura Prota and Francesco Savoia, Writing–review and editing: Francesco Savoia.

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Correspondence to Francesco Savoia.

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Appendix

Appendix

The evolution of the two-mode network presented in Sect. 7 is reported below with full-page figures for each time slice between 2005 and 2015. Blue squares represent projects and red circles represent actors. Links are colour-coded to identify projects belonging to different TDs. Blue identifies the Dare district, green is the Dhitech, purple is the Imast and red is the AgroBio. Links painted orange correspond to projects that did not involve any of the analysed TDs.

See Figs. 11, 12, 13, 14, 15, 16, 17, 18, 19.

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2005–2007

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2006–2008

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2007–2009

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2008–2010

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2009–2011

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2010–2012

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2011–2013

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Fig. 18
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2012–2014

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Fig. 19
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2013–2015

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De Iudicibus, A., Prota, L. & Savoia, F. Assessing the role of technological districts in regional innovation policies: a network analysis of collaborative R&D projects. J Technol Transf (2024). https://doi.org/10.1007/s10961-024-10088-4

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