Dr. İbrahim Tuğrul Çınar (p), Prof. Dr. Tüzin Baycan

The Smart Specialization Strategy (S3) has become a central pillar of European regional policy, encouraging regions to diversify into domains that are technologically related to their existing knowledge base and possess high complexity. However, while the theoretical importance of institutional quality is widely acknowledged, current analytical frameworks typically treat governance as an external context rather than an endogenous determinant of diversification. This study addresses this gap by integrating institutional capacity directly into the core of the complexity-relatedness framework. We propose that institutional quality is not merely a background condition but a decisive factor that defines the feasibility of translating technological opportunities into economic outcomes. To operationalize this, we construct a novel institution-conditioned composite index. This metric combines a region’s technological capability, calculated as the arithmetic mean of technological complexity and relatedness density derived from OECD REGPAT patent data, with governance scores from the European Quality of Government Index. A key methodological contribution is the use of a geometric mean for this aggregation, which penalizes imbalances between technological potential and institutional readiness, reflecting the theoretical reality that advanced innovation strategies cannot materialize in weak governance environments. The empirical analysis covers 235 NUTS-2 regions across 22 European Union member states, utilizing patent and economic data spanning 1996-2022. Using logit models to predict technological entry and twoway fixed effects panel regressions to assess economic performance, we demonstrate that the composite index significantly outperforms the two dimensional complexity-relatedness framework. The results indicate that institutional capacity magnifies the returns to technological capabilities. Regions scoring high on the composite index are nearly five times more likely to develop new technological specializations. Furthermore, this tripartite capability is a robust predictor of medium-term growth in GDP, Gross Value Added, and employment rates. The study also identifies distinct spatial dynamics, revealing positive spillovers from strong national institutional environments but competitive effects among immediate administrative neighbors.

Dr. Jianhua Zhang (p), Dr Iris Wanzenböck, Prof Koen Frenken, Prof Jarno Hoekman

Innovation in agrifood systems remains unevenly captured by conventional STI-based indicators. This bias stems from several structural characteristics of the sector. First, production relies on a fragmented base of adopters, where change emerges through the distributed actions of many small producers. Second, innovation in agrifood systems is diverse, ranging from high-tech food and agricultural technologies to more practice-based and experimental forms. Moreover, innovation confined to production alone is insufficient to generate systemic change, as outcomes must be translated across downstream stages and accepted by markets through intermediary actors such as retailers and distributors.

To address these limitations, this paper conceptualises agrifood innovation as a multi-stage process unfolding across interconnected supply-chain activities, including input supply, primary production, processing and manufacturing, logistics and distribution, retail, consumption, and disposal. Empirically, it integrates publications, patents, EU-funded research projects, trademarks, and standards for European countries over the period 1996–2024. Using text-based NLP methods, innovation activities are identified and positioned across supply-chain stages based on their semantic content. The geo-localised and organisation-based nature of the data further allows the identification of spatial patterns and regional heterogeneity in agrifood innovation across Europe, thereby extending measurement beyond upstream technological invention.

Dr. Miroslav Šipikal (p), Dr. Miroslav Špurek

Innovation plays a crucial role in the economic development and competitiveness of regions. The creation of innovations is increasingly understood as the result of collective efforts involving multiple institutions. Several studies have focused on identifying overall patterns of innovation activities and typologies of regions, but more detailed views are lacking that consider the analysis of the internal institutional structure of these regions and the different contributions of individual actors to the innovation process. One of the important actors is universities.
The aim of our article is to examine the typology of regions with an emphasis on the role of universities in regional innovation systems, focusing on their patent activity in relation to the overall patent activity and the activity of companies in a given region, and subsequently to create a typology of regions that will point out the different positions of individual innovation actors in the process of innovation creation measured by their patent activity.
To achieve the aim, the paper use OECD REGPAT and SCOPUS publication data, augmented with ETER and Eurostat, we identify university-owned and university-invented patents and geolocate inventors and owners at regional levels. To access the importance of different inovation actors, indicators of academic and non-academic patenting intensity, academic share, the share of university-invented patents, and multiple patent-quality metrics were used. A K-median cluster analysis (Manhattan distance) were conducted lead to four different types of regions in Europe.The study’s typology of regions highlights the diverse roles universities play in different innovation ecosystems, revealing that their contribution to regional innovation varies considerably depending on the regional context. The results show that in most RIS universities account for only a small share of innovation, but in a few RIS they account for an extremely large share. In regions where firms patent the most, the contribution of universities is lower and vice versa. The research shows the importance of universities especially in less developed and peripheral regions, where firms fail to innovate sufficiently.