AI-Enabled Super Apps as Complex Socio-Technical Ecosystems: A Systemic View of User Continuance

Super apps have emerged as complex digital service ecosystems that integrate multiple heterogeneous services within a unified platform architecture. As artificial intelligence (AI) capabilities become increasingly embedded into these platforms, understanding how AI-enabled features influence user evaluations has become an important research issue. This study develops a new research model by extending the stimulus–organism–response (SOR) framework to examine the determinants of users’ continuance intention toward super apps. Specifically, performance efficacy, service efficiency, and perceived security are conceptualized as stimulus factors. Satisfaction is modeled as the organism variable; and continuance intention represents the behavioral response. In addition, this study conceptualizes AI system capability as a platform-level capability that enables the integration, adaptation, and personalization of heterogeneous services. It examines both its direct effect on user satisfaction and its moderating role in the relationships between functional affordances and satisfaction. Based on survey data collected from 614 super-app users in South Korea, the research model was analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal that performance efficacy and perceived security significantly influence user satisfaction, whereas service efficiency does not have a significant effect. Furthermore, AI system capability not only directly enhances user satisfaction but also strengthens the relationships between functional affordances and satisfaction. A multi-group analysis comparing financial and non-financial super apps shows that these effects vary depending on the service context. These findings contribute to the literature by conceptualizing AI as a system-level capability that both enables and enhances the realization of functional affordances in complex digital ecosystems.