<![CDATA[Expectation-Confirmation Theory (ECT) and its information systems adaptation, the Expectation-Confirmation Model (ECM), have become important theoretical frameworks for explaining why users continue using digital technologies after initial adoption, a stage that is considered more critical for long-term success than initial acceptance. This scoping review mapped how ECT has been applied to emerging digital consumer technologies, focusing on applications, theoretical extensions, continuance outcomes, and methodological patterns. Given the rapid proliferation of these technologies, a synthesized understanding of ECT/ECM extensions remains limited. Following established scoping review guidelines, this review examined a curated body of ECT/ECM literature across mobile payment, mobile banking, e-commerce, food delivery applications, travel applications, social commerce, mHealth, video-on-demand services, e-learning platforms, and AI-enabled chatbots. The findings showed that ECT has expanded beyond its original confirmation–satisfaction–continuance pathway and is now frequently integrated with constructs such as trust, service quality, system quality, perceived risk, privacy, enjoyment, flow, engagement, affordance, self-determination, UTAUT, TAM, IS success models, and AI-specific factors, indicating a shift toward more integrative rather than standalone applications. The most frequently examined outcomes included continuance intention, reuse intention, repurchase intention, loyalty, satisfaction, recommendation intention, and electronic word-of-mouth. However, these outcomes were often treated interchangeably despite representing distinct behavioral and attitudinal concepts. Methodologically, the field was dominated by cross-sectional surveys and structural equation modeling, with increasing applications of meta-analysis, SEM-ANN, moderated mediation analysis, and cross-country comparisons. This review contributed by clarifying the evidence landscape of ECT-based digital consumer technology research and identifying research gaps related to conceptual fragmentation, limited longitudinal evidence, insufficient use of actual behavioral data, and underdeveloped theoretical explanations of AI-mediated continuance behavior, thereby providing a foundation for future research.]]>
