Evaluating the Impact of Government Orientation on the Adoption of AI in Business: A Causal Inference Study

Lea Anna Cozzucoli (), Michelangelo Misuraca () and Maria Spano ()
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Lea Anna Cozzucoli: University of Trieste
Michelangelo Misuraca: University of Salerno
Maria Spano: University of Naples Federico II

A chapter in Artificial Intelligence and Networks for a Sustainable Future, 2026, pp 369-377 from Springer

Abstract: Abstract The widespread development and subsequent adoption of Artificial Intelligence (AI) has reshaped global economies and businesses, with factors such as GDP and RD expenditures identified as important drivers of this transition. However, the influence of political orientation on AI adoption in the industrial sector remains quite unexplored. This study aims to address the causal relationship between government political ideology and AI adoption in European businesses. To this end, the study is constructed within the potential outcome framework, incorporating economic and geographical confounders to account for possible differences. The findings indicates that left-leaning governments exhibit higher AI adoption rates in comparison to right-leaning governments, suggesting that progressive political ideologies may prioritize digitalization and innovation. The estimated Average Treatment Effect (ATE), weighted with respect to the propensity score, shows that right-leaning governments reduce AI adoption. These results emphasize the central role of government policies in enabling technological transformation. Despite the study’s limitations, generally related to small sample size and potential bias due to the inability to achieve covariate balance, the study highlights the necessity for policymakers to promote collaborative digital transformation strategies that rise above ideological division, ensuring that the benefits from AI advancements are equitably distributed across Europe.

Keywords: Artificial Intelligence; Causal Inference; Political orientation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-032-13458-5_20

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DOI: 10.1007/978-3-032-13458-5_20

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