<![CDATA[The research aims to investigate the role of chatbot quality in influencing user satisfaction and continuance usage intention within the Indonesian banking industry. The research is among the first to apply Expectation Confirmation Theory (ECT) to chatbot usage in the Indonesian banking industry and offers a novel integration of chatbot quality dimensions within the framework. A quantitative explanatory method is adopted, and a purposive sampling method is used to collect 347 valid responses via an online structured questionnaire. Data analysis is conducted using Partial Least Squares-Structural Equation Modeling (PLSSEM) with a focus on reflective-formative evaluation, bootstrapping for hypothesis testing, PLS-Predict for out-of-sample predictive performance, and Importance-Performance Analysis (IPMA) for managerial insights. The results show that chatbot quality significantly enhances both perceived usefulness and confirmation to subsequently reinforce user satisfaction and continuance usage intention. Satisfaction is identified as the strongest predictor of continuance usage. Meanwhile, chatbot disclosure does not have a significant impact on perceived quality, and it reflects the gap between transparency efforts and user perception. The observations underline the importance of designing chatbots that are responsive, context-aware, and linguistically adaptive specifically in the diverse communication landscape of Indonesia. The research contributes to the growing body of knowledge on AI-driven customer service technologies in emerging markets by offering practical implications for chatbot implementation in the financial sector. The identification of critical determinants of chatbot success also leads to the provision of insights for banks to enhance digital engagement, foster trust, and ensure long-term usage through optimized conversational experiences.]]>
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