Artificial Intelligence Technology in Banking: Green Light to Move Ahead … but with a Twist! Insights from Stakeholders’ Perspectives

The potential of Artificial Intelligence to transform banking practices has been extensively documented, yet restrained acceptance can hinder its envisioned practical success. The paper employs empirical investigation developing and examining the modelled representation of users’ intention and interaction with AI in the Greek systemic banking system. The study employs TAM and UTAUT-2 and data analysis through PLS-SEM. The findings confirmed the strong theoretical relevance of constructs like Performance Expectancy, Effort Expectancy and Hedonic Motivation, while Social Influence was deemed not significant, indicating a practical perspective towards AI. Different stakeholders’ attributes were evaluated via the Control Variables, highlighting that demographic variables of gender and age are not significant moderators, challenging presumed stereotypes for related divides. Interestingly, occupation and education were significant, suggesting differences of attitudes among professions and education levels. The paper reenforces previous research, adds data on emerging markets but also provides practical implementation path for banking institutions.