How design features influence users’ recommendation intention in digital museums: a stimulus-organism-response model approach

Despite sustained investment and policy support, digital museums show limited public participation and dissemination. Users’ recommendation intention is a key driver of information diffusion and social communication for digital museums. Prior studies have examined its formation in mobile application contexts from multiple perspectives. However, in the context of digital museums, there is still a lack of systematic theoretical modeling and in-depth analysis of how design features influence recommendation intention through users’ psychological mechanisms. This study applies the Stimulus-Organism-Response (SOR) model to examine four design features. These features include visual appeal, scene authenticity, ease of operability, and interactivity. The research investigates how these factors affect user recommendation intention. An online questionnaire collected 347 valid responses from users of the “Seeking Dunhuang” interactive exhibition in the “Cloud Tour in Dunhuang” mini-program. Partial Least Squares Structural Equation Modeling (PLS-SEM) tested 14 research hypotheses. The analysis indicates that visual appeal and scene authenticity directly influence recommendation intention. In contrast, ease of operability and interactivity do not produce direct effects. Nevertheless, ease of operability influences user attitude. Interactivity affects both perceived value and user attitude. Furthermore, both perceived value and user attitude significantly predict recommendation intention. This study constructs a theoretical framework based on aesthetic characteristics and functional characteristics. This framework explains the driving mechanisms behind the recommendation intention of digital museum users. On a practical level, the study suggests that digital museums should prioritize visual appeal and scene authenticity. At the same time, ease of operability and interactivity serve as important foundations for satisfying user attitude. These optimization measures help improve user experience and enhance user engagement with digital platforms. • A SOR model explains digital museum recommendation intention • Visual appeal and scene authenticity directly increases recommendation intention • Ease of operability and interactivity indirectly influence recommendation intention • Perceived value and attitude mediate design effects