With the rapid integration of generative artificial intelligence into social media platforms, lifestyle community platforms such as Xiaohongshu have become important channels for users to obtain information, refer to experiences, and support decision-making. However, whether in-platform AI functions can translate into users’ Continuance Intention remains a key issue in platform intelligence development. Based on expectation-confirmation theory and information systems post-adoption research, the study divides in-platform Generative AI Use into three dimensions: Use Intensity, Use Breadth, and Use Depth. It then constructs a relationship model among in-platform Generative AI Use, Personalized Experience, and users’ Continuance Intention, and tests the model using 500 valid survey responses. The results show that in-platform Generative AI Use significantly enhances users’ Continuance Intention, and that Use Intensity, Use Breadth, and Use Depth all exert significant positive effects. In-platform Generative AI Use also significantly improves users’ Personalized Experience, which mediates the relationship between in-platform Generative AI Use and users’ Continuance Intention. Theoretically, this study extends expectation-confirmation theory to the context of Generative AI embedded in social media platforms. Practically, it offers implications for lifestyle community platforms such as Xiaohongshu to optimize intelligent function design, improve personalized experience, and strengthen user retention.
Modern Management Science & Engineering
