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Purpose As fintech continues to grow, understanding users’ inclination to continue using robo-advisory services is crucial for the survival of these platforms. This study aims to uncover the mechanisms driving continuance intention by extending the expectation-confirmation model (ECM) with contemporary constructs and examining their effects through sequential mediation analysis. Design/methodology/approach Based on a two-wave time-lagged study, data collected from 298 robo-advisor users were examined using a sequential mediation approach with partial least squares path modelling. Findings Users’ continuance intention is shaped by several interrelated mechanisms. The reputation of the robo-advisory platform, users’ expectations of performance and the perceived ease of use (effort expectancy) indirectly influence continuance intention through expectation confirmation and user satisfaction. Additionally, the perceived intelligence of the robo-advisor significantly predicts performance expectancy, which in turn drives continued usage. Research limitations/implications This study helps robo-advisory services better align with user needs and expectations, ultimately fostering stronger customer relationships and loyalty in the competitive fintech landscape. Practical implications The findings highlight the importance of enhancing user satisfaction, managing platform reputation and improving intelligent system design to encourage continued use. These insights offer valuable guidance for developing user-centric and sustainable robo-advisory services. Originality/value By integrating constructs such as perceived intelligence, reputation, effort expectancy, performance expectancy, perceived risk and trust into the ECM framework, this study offers a comprehensive understanding of continuance intention. The use of sequential mediation analysis reveals the complex relationships among these factors, delivering both theoretical insights and practical implications for fintech providers.