A growing number of innovation policies rely on publicly-funded innovation intermediaries to provide knowledge-intensive services to firms, particularly small- and medium-sized ones. The performance of innovation intermediaries is often assessed using indicators that need to be closely aligned with policy objectives to be effective. However, this alignment is difficult to achieve and is often overlooked in practice. We analyse the relationship between performance indicators and the behaviour of intermediaries by examining a case study of innovation intermediaries funded with public resources in Tuscany, Italy. The intermediaries implemented actions that allowed them to achieve their performance targets rapidly. However, due to a misalignment between indicators and policy objectives, these actions were not entirely consistent with the latter. After reviewing the literature on this key issue, we build on our findings to suggest how to design performance indicators that can induce intermediaries to more effectively support the achievement of policy objectives.

Recent developments in innovation theory and policy have led policy makers to assign particular importance to supporting networks of cooperation among heterogeneous economic actors, especially in production systems composed of small and medium enterprises. Such innovative policies call for parallel innovations in policy analysis, monitoring and assessment. Our analysis of a policy experiment aimed at supporting innovation networks in the Italian region of Tuscany intends to address some issues connected with the design, monitoring and evaluation of such interventions. Combining tools from ethnographic research and social networks analysis, the article explores the structural elements of the policy programme, its macroscopic impact on the regional innovation system and the success of individual networks in attaining their specific objectives.This innovative approach allows us to derive some general methodological suggestions for the design and evaluation of similar programmes.

In the future of European Union (EU) Cohesion Policy, a critical feature is how to capitalize on the current Research and Innovation Strategy for Smart Specialisation (RIS3), introduced in the 2014–20 programming period as an ex-ante conditionality for accessing European Structural Investment Funds. As a contribution to the current debate, this paper frames the socioeconomic comparative analysis of EU regions, considering subnational structural similarities, and referring to population, labour market and the sectoral composition of the economy. Principal component analysis and cluster analysis are performed on 31 input variables, returning 19 different types of EU regions. With an application to the EU macro-regions, the paper supports policy planning in macro-regions as a meso-level of interventions for the future Cohesion Policy.