Expectations and Sharing: Research on the Spread of AI-Generated Images on Social Media

In recent years, the application of artificial intelligence (AI)-generated images on social media has become increasingly widespread, profoundly influencing users’ content creation and sharing behaviors. This study, based on the Stimulus–Organism–Response (S–O–R) theory, explores how social media users’ interest in and engagement with AI-generated images influence their sharing intention through expectation confirmation, and examines the moderating role of perceived playfulness in this process. Through an online questionnaire survey, 589 valid responses were collected, and the data were analyzed using the partial least squares structural equation modeling (PLS-SEM) method. The results show that users’ engagement with AI has a significant positive effect on expectation confirmation, which in turn significantly promotes sharing intention. However, users’ mere interest does not have a significant impact on expectation confirmation or sharing intention. In addition, perceived playfulness has a significant positive moderating effect between expectation confirmation and sharing intention. This study reveals the key roles of user engagement and expectation confirmation in promoting AI-generated image sharing behavior, providing theoretical foundations and practical implications for AI content platforms to optimize user experience and communication strategies.