Mapping AI Adoption across Europe: A Cluster Analysis of National Responsibility

Manta Eduard-Mihai (), Geambasu Maria Cristina () and Birlan Ioana ()
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Manta Eduard-Mihai: Bucharest University of Economic Studies, Bucharest, Romania
Geambasu Maria Cristina: Bucharest University of Economic Studies, Bucharest, Romania
Birlan Ioana: Bucharest University of Economic Studies, Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2025, vol. 19, issue 1, 1532-1545

Abstract: This paper explores the responsibility of European countries in adopting artificial intelligence (AI) through a cluster analysis approach. Using hierarchical clustering (Ward’s method) and K-means clustering, distinct groupings of nations are identified based on their level of AI adoption. The analysis reveals key performance poles, highlighting countries leading to AI adoption and those facing significant challenges due to digital infrastructure, public trust, and policy frameworks. The study relies on data from Eurobarometer 95.2 (2021), incorporating variables related to digital literacy, AI perception, internet usage, and technological infrastructure. Results show clear regional disparities: Northern and Western European countries demonstrate higher AI adoption responsibility, benefiting from strong policies and digital ecosystems. In contrast, Southern and Eastern European nations face obstacles such as limited infrastructure and weaker regulatory frameworks. The findings contribute to a deeper understanding of AI adoption dynamics, revealing structural differences between clusters and offering insights into the key drivers of AI integration. By applying unsupervised machine learning techniques, this research underscores the need for tailored policy interventions to bridge the AI adoption gap. The results provide a foundation for designing targeted strategies that promote AI integration across different national contexts, ensuring more inclusive and sustainable technological development.

Keywords: Cluster Analysis; Artificial Intelligence Adoption; Hierarchical Clustering; K-Means Clustering; European Countries (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:19:y:2025:i:1:p:1532-1545:n:1016

DOI: 10.2478/picbe-2025-0119

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