Explanatory models of regional innovation performance in Europe: policy implications for regions

Abstract

Research and Innovation Strategies for Smart Specialization (RIS3) was integrated as a key piece of the cohesion policy for the European Union (EU) for 2014–2020. During the recent years, more than 120 RIS3 have been developed, being a large-scale EU experience aimed to develop innovation-driven economic transformations at national and regional levels. The objective of this article is to explore the models that best explain innovative employment and the emergence of new markets in Europe’s regions. For that purpose, the latest dataset of the Regional Innovation Scoreboard 2019 was used, and regressions performed to identify the main factors behind the impact of regional innovation. The results unveil the ‘double edge role’ that some variables have on regional innovation, indicating the difficulties of managing different trade-offs and of a standalone innovation policy strategy. Policy measures are discussed to best manage these critical compromises and increase the impact of RIS3.

Keywords:

• Innovation
• smart specialization strategies
• RIS3
• regional innovation scoreboard
• Europe
• policy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Compared to the Regional Innovation Scoreboard 2016 that includes 12 variables (European Commission 2016), the Regional Innovation Scoreboard 2019 comprises 17 variables (European Commission 2019c).

2 In addition to information on innovation performance, the Regional Innovation Scoreboard also classifies regions as: innovation leaders; strong innovators; moderate innovations; and modest innovators (see https://ec.europa.eu/growth/industry/policy/innovation/regional_enforanoverviewofregionsclassification).

3 An attempt to overcome this limitation is the explanation on the existing associations between the four Regional Innovation Scoreboard dimensions in the theoretical section of the paper.

4 Correlations among variables are provided in the Appendix (Cohen et al. 2013).

5 For example, Romer (1990) endogenous growth theory poses that economic growth is strongly influenced by human capital and the rate of technological innovation; Innovation is also considered one of the key factors behind regional growth (Garcia-Bernabeu, Cabello, and Ruiz 2020).

6 See Nelson (2009) for details on publications impact on innovation diffusion.

Additional information

Funding

This research was developed under the support of the Research Program ‘CeNTER Community-led Networks for Territorial Innovation’ (CENTRO-01-0145-FEDER-000002), funded by Programa Operacional Regional do Centro (CENTRO 2020), PT2020. This work also had the support of national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., Project UIDB/05037/2020.