<![CDATA[The advancement of artificial intelligence (AI) has significantly transformed the financial services sector, particularly in banking. One of the most widely adopted applications is the chatbot, which enables banks to deliver faster, more efficient, and responsive customer service through automated interactions. Despite its growing implementation, public acceptance of chatbot technology remains varied. Users tend to evaluate these systems not only based on how useful or easy they are to use, but also on how much they trust the system and the organization behind it. This study explores the factors that drive users’ behavioral intention to use banking chatbots by modifying the Technology Acceptance Model (TAM) to incorporate trust in the chatbot system and corporate reputation. The model incorporates perceived ease of use, perceived usefulness, trust in chatbot, and corporate reputation. Data were gathered through an online survey involving 111 respondents who had prior experience using banking chatbot services. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), reveal that perceived ease of use significantly predicts perceived usefulness, while corporate reputation strongly influences trust. Furthermore, both perceived usefulness and trust are found to be significant predictors of behavioral intention to use banking chatbots. Trust also exerts a notable effect on perceived usefulness. These findings suggest that fostering user trust and managing institutional reputation are critical in promoting the sustainable adoption of AI-driven financial technologies. Banks are encouraged to focus not only on improving the technical features of chatbot systems but also on strengthening their credibility and transparency to enhance user confidence and perceived utility]]>
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