<![CDATA[This study investigates the factors influencing user satisfaction (US) and continuous usage intention (UI) of the digital banking platform in Indonesia. Utilizing a quantitative research approach, structural equation modeling (SEM) via SmartPLS was employed to analyze data from 376 users. The study integrates key constructs, including Task-Technology Fit (TTF), System Quality (SQ), Performance Expectancy (PE), US, and UI, into a comprehensive model. The findings confirm that TTF, SQ, PE, and US significantly influence UI. Specifically, higher TTF and SQ directly enhance PE (path coefficient = 0.871, t-value = 92.895) and US (path coefficient = 0.798, t-value = 47.957), positively impacting UI. Performance Expectancy emerged as a stronger predictor of UI (path coefficient = 0.559, t-value = 12.800) compared to the US (path coefficient = 0.245, t-value = 5.229), underscoring the critical role of perceived performance benefits in driving continuous usage. All five hypotheses were supported: TTF positively affects UI (path coefficient = 0.250, t-value = 7.154); SQ positively influences PE and US; PE positively impacts UI; and US positively affects UI. The Sobel test results indicated that PE significantly mediates the relationship between SQ and UI (Z = 12.60), and US also significantly mediates this (Z = 5.19). The R-squared values indicate the explanatory power of the model: PE (0.758), UI (0.956), and US (0.637), demonstrating that the model explains a substantial portion of the variance in these constructs. The study contributes to the literature by validating the integrated model, extending existing models such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), and highlighting the importance of technical and perceptual factors in technology adoption. Practically, the results offer actionable insights for digital banking providers. Enhancing TTF and maintaining high SQ are crucial for fostering positive user experiences and encouraging continuous usage. Providers should also emphasize the performance benefits of their platforms to improve PE and UI. Despite its contributions, the study has limitations, including sample size and reliance on self-reported data, which may affect generalizability. Future research could expand the sample size, incorporate objective usage data, and explore additional factors such as social influence and facilitating conditions. Overall, the study provides a robust framework for understanding user behavior in digital banking and offers practical strategies for improving user satisfaction and retention in the industry.]]>
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