The use of Social Network Analysis for S3 Monitoring
About this good practice
Smart specialization strategies can be notoriously difficult instruments to monitor, due to the complexity of their governance models, fragile or complex linkages between strategy and policy-mix, and the difficulty to capture, through quantitative data, effects such as spillovers and related variety.
The National Innovation Agency in Portugal developed a methodology based on social network analysis to map out an analyse study the linkages between S3 priorities, at a multi-level scale, using data from national projects funded by ERDF in the period 2014-2019. This analysis allowed us to study the correlation between national and regional priorities and identify overlaps between the national priorities through an evidence-based approach. The results showed significant overlaps between a group of core priorities and, together with insights from the entrepreneurial discovery process and a bottom-up stakeholder auscultation exercise, they have been key in informing the revision process of the national S3 and defining new and improved priorities for the 2021-2027 period.
Furthermore, we were able to use the same set of data to analyse cooperation networks between fund beneficiaries, which allowed us to furthen our knowledge, supported by quantitative data, on how the stakeholders interact, and identify key stakeholders involved in R&D&innovation processes, which improved the outreach of the entrepreneurial discovery process and its integration in the implementation of the strategy.
The analysis was done using Gephi, an open source software with force-based algorithms and specific metrics for social network analysis (betweenness centrality, closeness, clustering coefficient, modularity and diameter). Expertise in social network analysis is required.
Evidence of success
Through this methodology we were able to process a sample of over 10.000 projects, analysing the no. of projects & total investment to create a network map of priorities and beneficiaries and study the relationships between them. Results were key in the revision of S3 priorities in Portugal, which were reduced from 15 to 6. Furthermore, this methodology allowed policy makers to have a better understanding on how the national and regional S3 align and interact, supported by quantitative data.
Potential for learning or transfer
This practice concerns the use of data management & data visualization tools to monitor complex instruments. It can be easily transferred and applied to the monitorization of S3 in any region and/or country as long as implementation data is available and includes project-level data on alignment with S3 priorities, investment and beneficiaries.
Further developments of this practice may include the automation of data collection (when possible) and automation of data analysis to increase the periodicity of update of monitoring indicators & the network map.
One additional advantage of this practice is that is generates a clear and easy-to-read visualization of a complex system, also constituting a good tool for the communication of monitoring results to the broader public.