From human-like AI to user adoption: the role of trust, attitude, and social influence in shaping behavioral intention

Abstract

The adoption of artificial intelligence (AI) in digital banking continues to increase as financial institutions seek to improve service efficiency, personalization, and customer experience. However, user acceptance of AI-enabled banking services remains a significant challenge, particularly in high-risk financial environments where trust, security, and perceived reliability are critical considerations. Unlike conventional technology adoption studies that primarily emphasize utilitarian evaluations and social influence, this study examines how psychological and relational factors shape AI adoption in high-risk digital banking contexts. Specifically, this study investigates the influence of customer trust and anthropomorphic characteristics of AI on Behavioral Intention to Use AI-enabled digital banking services, with customer attitude acting as a mediating variable and social influence serving as a moderating variable. A quantitative approach was employed using survey data collected from 350 users of digital banking services. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that customer trust and anthropomorphic characteristics of AI positively and significantly influence both customer attitude and Behavioral Intention to Use AI-enabled banking services. Customer attitude was also found to be the strongest predictor of behavioral intention. However, the moderating effect of social influence on the relationship between customer attitude and behavioral intention was not significant. The findings suggest that AI adoption in digital banking is influenced not only by technological functionality, but also by psychological and relational factors such as trust, emotional comfort, and institutional credibility. In high-risk financial contexts, users appear to rely more heavily on personal trust evaluations and perceptions of security than on social pressure when deciding whether to adopt AI-enabled banking services. This study contributes to the AI adoption literature by demonstrating that technology adoption mechanisms in high-risk financial environments differ from conventional consumer technology contexts, where social influence is often assumed to be a dominant predictor of behavioral intention. The findings further highlight the importance of trust and human-like AI interaction in reducing uncertainty and strengthening users’ acceptance of AI-enabled banking services. Practical implications are also provided for financial institutions seeking to improve customer acceptance through transparent AI governance, trust-building strategies, and user-centered AI interaction design.