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Driven by the explosive growth of artificial intelligence, WealthTech has played a pivotal role in reshaping the wealth management industry in recent years. Within this context, this study aims to explain the antecedents of users’ continuance intention to use the WealthTech platform by integrating the technology continuance theory (TCT), task-technology fit (TTF) and digital nudging.

To empirically test the research model, an online survey was conducted among 337 investors who had previously used WealthTech platform. The authors used partial least squares structural equation modeling (PLS-SEM) to assess the research model and test the hypotheses.

PLS-SEM results show that the proposed model has moderate explanatory power in explaining WealthTech continuance intention. The results also found that attitude, digital nudging and satisfaction are important drivers in promoting WealthTech continuance intention. According to importance performance map analysis, digital nudging, expectation confirmation and satisfaction are critical factors in explaining continuance intention, which require managerial action.

To the best of the authors’ knowledge, this is one of the earliest studies that analyze the determinants of WealthTech continuance intention by integrating TCT with TTF and digital nudging. The study’s findings highlight the importance of fit factors and digital nudging in promoting successful WealthTech services.