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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.