Does the Scene Presentation of AI-Generated Photos Affect Travel Intention? An Empirical Study Drawing on Construal Level Theory

The tourism industry is witnessing an increase in innovative AI-generated content (AIGC). AI-generated images are a key visual form of AIGC, and this study focuses on a specific subset of them: AI-generated images that realistically depict destination scenery, which we define as AI-generated photos (AIGP). Based on construal level theory, this study explored the visual cues of AIGP. Two empirical studies were conducted to examine the influence of scene presentation within AIGP on tourists' travel intention. The findings revealed that panoramic series in AIGP, as opposed to close-up series, significantly enhanced travel intention. The results further suggested an indirect association through destination images and a moderating role of AIGP source. Specifically, the effects of scene presentation were found to be diminished when an AIGP was sourced from user-generated content. This study makes several notable contributions. First, it expands the application of AIGP within the tourism sector. Second, it enriches the literature on visual cues and destination images by delineating the process through which AIGP scene presentation influences travel intention. Third, the research clarifies the moderating role of the AIGP source, expanding the boundary of AIGP effects. These insights add depth to the dual fields of social media and destination marketing, highlighting the contextual variability in the efficacy of AIGC-driven marketing strategies.