Journal of Theoretical and Applied Electronic Commerce Research

Conventional research on digital business development offers a limited view, overwhelmingly concerned with the isolated effects of individual variables while overlooking their synergistic relationships. This study challenges this reductive perspective by applying fuzzy set Qualitative Comparative Analysis (fsQCA) to Chinese city-level data. We specifically investigate how elements from the socio-technical framework interact synergistically to shape the urban digital business ecosystem. The results demonstrate that no single factor is sufficient as a determinant. Instead, we observe equifinality, meaning multiple distinct configurations can lead to equally high performance. Furthermore, the causal configurations for failure are not mirror images of those for success but instead exhibit a distinctive pattern. The influence of government size exemplifies this asymmetry. For policymakers, the implication is that effective strategies for urban digital business must be holistic and context sensitive, moving beyond universal prescriptions.

As artificial intelligence (AI) agents become deeply integrated into fitness systems, trustworthy human–AI agent interaction has become pivotal for user engagement in smart home fitness (SHF) e-commerce platforms. Grounded in the Computers Are Social Actors (CASA) framework, this study empirically investigates how, acting as AI fitness coaches, AI agents’ technical and social features shape users’ active engagement in the in-home social e-commerce context. A mixed-method approach was employed, combining computational text mining of 17,582 user reviews from fitness e-commerce platforms with a survey (N = 599) of Chinese consumers. The results show that (1) the technical–social features of AI agents serving as AI fitness coaches include visibility, gamification, interactivity, humanness, and sociability; (2) these five technical–social features of AI agents positively influence user compliance via both cognitive and emotional trust in AI agents; (3) these five technical–social features of AI agents serving as AI fitness coaches positively impact active engagement via both cognitive and emotional trust in AI agents. This study extends the CASA framework to the domain of AI coaching by demonstrating the parallel roles of cognitive and emotional trust in AI agents. For designers and managers in the fitness e-commerce industries, this study offers actionable insights for designing AI agents integrating functional and social features that foster trust and drive behavioral outcomes.

Mobile payment platforms not only streamline users’ financial transactions but also encourage their participation in investment activities and additional services. To deliver personalized financial services, it is essential to collect users’ personal information. This study aims to investigate the factors influencing users’ willingness to engage in self-disclosure within mobile payment platforms, thereby assisting practitioners in efficiently allocating resources and maximizing returns on investments dedicated to promoting user self-disclosure. Consequently, this study focuses on examining how institutional mechanisms influence users’ self-disclosure behavior within these platforms. The authors developed a comprehensive framework that elucidates the influence of institutional mechanisms on users’ self-disclosure, mediated by trust and privacy concerns. To empirically validate our research model, we administered an online survey targeting Alipay users in China. Subsequently, we analyzed 559 valid survey responses utilizing partial least squares structural equation modeling (PLS-SEM). The results indicate that trust and privacy concerns jointly influence users’ self-disclosure behavior when utilizing mobile payment platforms. Moreover, key institutional mechanisms can effectively foster trust and alleviate privacy concerns, ultimately facilitating users’ willingness to self-disclose. Our research shifts scholarly focus from conventional adoption to users’ self-disclosure in the mobile payment field and enhances the existing self-disclosure research by identifying the impact of institutional mechanisms on users’ self-disclosure behavior.