Behavioral Drivers of AI Adoption in Banking in a Semi-Mature Digital Economy: A TAM and UTAUT-2 Analysis of Stakeholder Perspectives

The transformative potential of artificial intelligence (AI) in banking is widely acknowledged, yet its practical adoption often faces resistance from users. This study investigates the factors influencing AI adoption behavior among various stakeholders in the Greek semi-mature systemic banking ecosystem, addressing a critical gap in the relevant research. By utilizing the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2), and Partial Least Squares Structural Equation Modelling (PLS-SEM) models, data from 297 respondents (bank employees, digital professionals, and the general public) were analyzed. The results highlight the strong relevance of constructs such as Performance Expectancy, Effort Expectancy, and Hedonic Motivation, whereas Social Influence was deemed non-significant, reflecting a pragmatic stance toward AI. Demographic factors like gender and age were found to have no significant moderating effect, challenging traditional stereotypes. However, occupation and education emerged as significant moderators, indicating varying attitudes among professions and educational levels. This study is the first to develop a theoretical framework for AI adoption by Greek banking institutions, offering Greek banking practitioners actionable insights. The findings also hold relevance for countries with similar digital maturity levels, aiding broader AI integration in banking.