Monika Kot (p), Katarzyna Kopczewska

Arian Osroosh

The modern economy relies heavily on innovation, especially in high-technology firms (medium-high-tech, high-tech and high-tech knowledge-intensive services). Understanding the socio-economic environment in which these firms operate is crucial to fostering innovation and creating a supportive business climate. While high-tech firms are typically thought to cluster in metropolitan areas, close to similar firms, recent trends suggest that many are also thriving in peripheral locations, outside densely populated business centres.

The aim of this research is to explore the spatial factors that support the establishment and growth of high-tech firms. Key questions include whether proximity to other technology firms, to firms in different sectors, or to urban centres influences innovation and business success. The study also seeks to identify which sectors foster collaboration and clustering with technology firms, and where these clusters come from. All spatial factors are examined in the context of the socio-economic drivers of entrepreneurship and innovation, including supply and demand factors.

Preliminary results indicate a significant increase in high-tech firms in Poland between 2012 and 2021, from 4.3% to 6.08% of all firms, with knowledge-intensive services experiencing the most pronounced growth. Notably, almost a third of these firms operate in low to medium density areas, challenging the assumption that high-tech firms only thrive in metropolitan clusters. Using innovative methods tailored to high-granularity data (point and grid data), this research captures spatial phenomena at a highly localised scale. We apply spatial statistics, spatial econometrics and spatial machine learning to quantify the spatial relationships and identify the drivers of high-tech business location.

This research contributes to a deeper understanding of the spatial dynamics of high-tech firms and provides insights into the development of innovation-driven business environments. The study provides insights to further understand the spatial dimension of entrepreneurial ecosystems.

Arian Osroosh (p), Jan Zenka

Diego Chavarro

Development studies have traditionally focused on urban firms and high-tech clusters, with agglomeration economies as a central theme in economic geography. While recent research acknowledges urban bias and examines low-tech firms in peripheral contexts, rural manufacturers—particularly those not classified as innovative—remain understudied. Existing literature has explored knowledge sourcing and customer-supplier linkages separately, yet their combined role in shaping rural firm competitiveness is unclear. This study addresses this gap by investigating how rural manufacturers integrate knowledge acquisition with supplier-customer relationships to enhance performance. We analyze whether constraints in one area affect the other and how firms adapt to their geographical limitations. By synthesizing insights from studies on rural firms, low- and medium-technology sectors, and peripheral contexts, this research contributes to economic geography and rural development policy. Our findings offer recommendations for policymakers on fostering knowledge networks and overcoming structural barriers in rural economies.

Diego Chavarro (p), Tommaso Ciarli, Bernardo Caldarola

Monika Kot

This study investigates the dynamics of economic convergence and divergence across European NUTS3 regions, emphasizing the role of innovation and rural-urban distinctions within innovation clubs. Building on prior research on regional inequalities and European convergence policies, the study identifies four distinct innovation clubs—Core-Core, Core-Periphery, Periphery-Core, and Periphery-Periphery—based on regional innovation and economic performance, as well as centrality in knowledge networks. Using network analysis, clustering techniques, and regression models, the research examines inter- and intra-club convergence patterns and the influence of rurality, urbanization, and geography on economic growth.

Key findings demonstrate that convergence is not uniform across regions but primarily occurs within similar clubs, such as rural-to-rural or urban-to-urban. Rural regions exhibit higher growth rates compared to urban areas but face stronger diminishing returns, reflecting faster convergence. However, these dynamics are more strongly influenced by core-periphery structures and geographic factors than rural-urban distinctions. Core regions, often concentrated in economically dominant countries like Germany, France, and Italy, exhibit high centrality in knowledge exchange, while peripheral clusters are slightly more balanced in their composition and reflect lower innovation capabilities. Rural regions are predominantly located in the least integrated and lowest-performing Periphery-Periphery club, while urban areas are concentrated in the Core-Core and Periphery-Core clusters.

Contrary to aggregate analyses, within-club regressions reveal that rurality has minimal influence on growth dynamics after accounting for club membership and geographic location. Geographic disparities are evident, with Eastern European regions experiencing stronger convergence and catch-up effects compared to their North-Western and Southern counterparts.

This research underscores the importance of tailored regional policies that address local conditions, spatial heterogeneity, and the complex interplay between innovation, geographic context, and economic growth. Future work aims to expand the analysis by incorporating additional science, technology, and innovation indicators to further explore the role of innovation in driving convergence and to refine the characterization of regional dynamics across the rural-urban and core-periphery spectra.