Drivers of AI-Powered Digital Banking Adoption: Corporate Reputation, Customer Trust, and Anthropomorphic Interface Design

Abstract

Artificial intelligence (AI) has transformed digital banking by enhancing efficiency, personalization, and automation, yet customer adoption of advanced AI features remains limited due to concerns regarding trust, perceived value, and institutional credibility. Understanding the determinants that influence customer behavioral intention is essential in high-involvement financial contexts. This study applied a quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the influence of corporate reputation, customer trust, and feature usefulness on behavioral intention to use AI-enabled digital banking, with customer attitude as a mediating variable. Data were collected from 350 users of digital banking services through a structured online questionnaire. The findings indicate that corporate reputation, customer trust, and feature usefulness significantly and positively influence customer attitude. Feature usefulness and corporate reputation also directly affect behavioral intention, while customer attitude strongly mediates the relationship between antecedents and behavioral intention to use AI-enabled digital banking. The results highlight the importance of enhancing credible brand image, strengthening digital trust signals, and developing features that demonstrate functional value to reinforce customer acceptance and continuous usage intent. Customer attitude plays a central mediating role in shaping the behavioral intention to adopt AI-based digital banking. The study is limited to digital banking users within a single national context and may require cross-market validation.