Abstract

This study examines the factors influencing user satisfaction and continued use of e-government services in Vietnam, where digital transformation is rapidly progressing, yet post-adoption behavior remains under-researched. Using the DeLone & McLean IS Success Model combined with Technology Continuance Theory, the framework identifies system quality, user experience, and perceived benefits as precursors to satisfaction, which in turn drives continuance intention. Data from 400 respondents across various regions, mostly young users with an average age of 23.26, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model showdemonstrated good reliability (composite reliability: 0.851–0.936) and convergent validity (AVE > 0.5), confirming scalethe scale's robustness. Discriminant validity was verified via the Fornell-Larcker criterion. Results indicate that user satisfaction significantly impacts continuance intention (β = 0.796, p < 0.001), accounting for 63.2% of its variance. System quality emerged as the most critical predictor of satisfaction (β = 0.437, p < 0.001), followed by user experience (β = 0.206, p < 0.001) and perceived benefits (β = 0.194, p < 0.001). Collectively, these factors explain 41.2% of the variance in satisfaction. The prominence of system quality reflects Vietnamese users’ emphasis on reliable and accessible services over convenience, aligning with the nation’s digital infrastructure stage. Theoretically, the study extends the integrated D&M IS Success Model and TCT to the post-adoption phase in the context of e-government in a developing country. PracticallyIn practice, it recommends that policymakers focus on enhancing information security, system stability, user-friendly interfaces, and communicateing benefits effectively. Limitations include the a focus on young users in Southeast Vietnam, affectingwhich affects generalizability. Future research should include broader demographics, additional factors like trust and digital literacy, and longitudinal approaches to observe behavioral changes over time.