Santini, Fernando de Oliveira, Bui, Hien ORCID: https://orcid.org/0000-0002-3146-7098, Perin, Marcelo Gattermann, Ladeira, Wagner Junior, Trez, Guilherme and Rasul, Tareq ORCID: https://orcid.org/0000-0002-1274-7000 (2026) Unveiling the drivers of ChatGPT adoption: a meta-analysis of factors influencing user acceptance across diverse contexts. EuroMed Journal of Business. pp. 1-19.

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Abstract

Purpose This study investigates the primary factors influencing ChatGPT adoption by synthesizing findings from 30 empirical studies. It aims to develop a generalized framework explaining user acceptance across global contexts, offering insights for businesses and researchers concerned with AI technology integration, consumer behavior, and cross-cultural adoption trends. Design/methodology/approach A meta-analytical approach combining correlation meta-analysis, meta-analytic structural equation modeling and hierarchical meta-regression was employed. Data from over 14,000 respondents across 16 countries were analyzed to test relationships grounded in the Technology Acceptance Model and to examine the moderating effects of demographic, cultural, and national innovation indicators. Findings The results confirm significant positive effects of hedonic motivation, device credibility, and social influence on perceived usefulness and perceived ease of use. These perceptions are positively associated with user attitudes and behavioral intentions. Cultural dimensions, gender, and national innovation levels significantly moderate these relationships, offering actionable insights for tailoring AI adoption strategies across global markets. Originality/value This study presents the first comprehensive meta-analysis to propose a generalized, cross-cultural model of ChatGPT adoption. By integrating TAM with cultural and demographic moderators, it resolves inconsistencies in prior research and bridges theory and practice. The findings provide novel strategic implications for AI deployment across diverse industries and international consumer markets.

Item Type:	Article
Status:	Published
DOI:	10.1108/emjb-01-2026-0024
School/Department:	York Business School
URI:	https://ray.yorksj.ac.uk/id/eprint/14785

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