This paper extends the Regional Knowledge Production Function framework by introducing an artificial intelligence dimension into the analysis of regional innovation across Europe. AI dimension is captured through firm-level adoption and a novel Regional AI Exposure (AIRE) indicator. To address spatial dependence and heterogeneity, a Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) model is used. Results show significant spatial heterogeneity in the AI-innovation nexus. Firm-level AI adoption is strongly linked to higher innovation, especially in Northern and Western Europe. AIRE plays a dual role, reinforcing innovation in technologically advanced regions while acting as a catalyst for catch-up in less developed areas, especially in Eastern Europe.

New Approaches to Measuring AI Contribution to Innovation Across European Regions

Bruno, Emma
;
Castellano, Rosalia;Punzo, Gennaro
2025-01-01

Abstract

This paper extends the Regional Knowledge Production Function framework by introducing an artificial intelligence dimension into the analysis of regional innovation across Europe. AI dimension is captured through firm-level adoption and a novel Regional AI Exposure (AIRE) indicator. To address spatial dependence and heterogeneity, a Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) model is used. Results show significant spatial heterogeneity in the AI-innovation nexus. Firm-level AI adoption is strongly linked to higher innovation, especially in Northern and Western Europe. AIRE plays a dual role, reinforcing innovation in technologically advanced regions while acting as a catalyst for catch-up in less developed areas, especially in Eastern Europe.
2025
978-3-031-96735-1
978-3-031-96736-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/147199
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