Open or Modular? The Influence of AIGC Interactive Interface on User Platform Engagement
Zhiyong Yang, Lianlian Song, Mengnan Qiu · Journal of theoretical and applied electronic commerce research · 2025
How human–AI interactive interfaces affect user engagement with AIGC platforms is a critical underexplored issue. Grounded in signaling theory, this paper constructs a theoretical model to examine the differential effects of two interactive interfaces—modular versus open—on user platform engagement, the mediating role of AIGC quality, and the moderating role of user type. Through two scenario experiments, our findings reveal that both AI interactive interfaces (open and modular) significantly enhance user platform engagement, with the open interface exhibiting a stronger effect. Specifically, AIGC accuracy serves as a mediator in the relationship between modular interfaces and user engagement, and innovativeness plays a mediating role in the relationship between the open interface and users’ engagement. Furthermore, novice users strengthen the effect of open AIGC interfaces on AIGC innovation and subsequent engagement, Non-novice users amplify the positive impact of modular AIGC interfaces on both AIGC accuracy; however, there is no significant difference for user engagement. These findings theoretically enrich the customer response model in the literature on human–computer interactions and provide actionable insights for AIGC platform enterprises. By designing tailored interactive interfaces, platforms can generate high-quality, user-perceived AIGC for diverse customer segments. This study offers both theoretical contributions and practical implications for the development of AI-driven user engagement strategies.