Keywords
information and communication technologies (ICT)
Spatial Data Envelopment Analysis with Independent Neighbour Inputs (SDEA- INI)
regional development
How to Cite
Abstract
The aim of this article is to assess the digital-innovation efficiency of European NUTS 2 regions in 2025, taking into account inputs related to information and communication technologies (ICT), artificial intelligence (AI), research and development (R&D), and employment in high-technology sectors. The study applies the SDEA-INI model (Spatial Data Envelopment Analysis with Independent Neighbour Inputs), which incorporates spatial dependencies and the non-controllability of spatially related inputs into the efficiency assessment framework. Based on the model results, the Regional Digital-Innovation Efficiency Classification (RDIEC) was developed to identify regions that most effectively transform digital and innovation-related resources into economic outcomes.
Furthermore, the RDIEC results were compared with the European Commission’s Regional Innovation Scoreboard (RIS) classification, allowing an assessment of the relationship between the level of regional innovativeness and the efficiency with which innovation potential is utilised.
The findings reveal substantial spatial disparities in digital-innovation efficiency across European regions. Only 6% of the analysed regions achieve full efficiency, indicating that high levels of digital and innovation-related inputs do not automatically translate into comparable economic outcomes. Moreover, the coexistence of highly inefficient regions in close proximity to digital-innovation leaders and strong performers demonstrates that spatial proximity alone does not guarantee the diffusion of benefits. The results highlight that regions differ considerably in their capacity to absorb knowledge, leverage available resources, and translate digital capabilities into measurable economic outcomes. These findings emphasise the importance of considering local institutional, organisational, and economic conditions when analysing the effectiveness of digital transformation and innovation-driven regional development.
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