How AI-based recommendations on short video platforms drive tourists’ decisions: A study of destination authenticity with evidence from Vietnam

Abstract

This study investigates the factors influencing user perceptions and behavioral intentions toward AI-recommended destinations on short-form video platforms, focusing on how algorithmic characteristics and content attributes affect users’ perceptions of authenticity, trust, and engagement within the Vietnamese cultural context. Guided by the Elaboration Likelihood Model (ELM) and the Stimulus- Organism-Response (SOR) framework, data were collected from 729 online users in Vietnam through structured questionnaires. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to analyze the relationships among the variables. This research contributes originality by integrating under-examined dimensions, including algorithmic transparency and self-compatibility, within the Vietnamese context, thereby extending the ELM and SOR frameworks to incorporate AI personalization in tourism marketing. The study also offers novel theoretical insights into the interplay of technology, culture, and consumer behavior in AI-driven recommendation systems. Practically, these findings provide actionable strategies for local tourism marketers, platform developers, and policymakers to boost user engagement and trust. By customizing AI algorithms and content to align with Vietnamese cultural expectations, stakeholders can optimize AI-driven recommendation systems and foster authentic, meaningful travel experiences for users. Result: AI-driven recommendations have a significant impact on Vietnamese users' perceptions of destination authenticity (brand, existential, and intrapersonal authenticity), which mediates their trust, advocacy, and visit intentions. Algorithmic attributes such as unbiasedness and diagnosticity, as well as content attributes like relevance and diversity, enhance destination authenticity, fostering credibility and advocacy. However, attributes like entertainment and aesthetic quality do not directly influence authenticity. The findings indicate that personalized, culturally aligned AI recommendations indirectly drive trust and visit intentions by enhancing perceptions of authenticity.