How tourist perception with short video platform algorithm drive tourists’ decisions through perceived authenticities: A study in hospitality and tourism industry with evidence from Vietnam

Abstract

This study examines how algorithmic features and content attributes on short-form video platforms influence destination authenticity perceptions and behavioral intentions across generational cohorts (Gen X, Y, Z). Using the Elaboration Likelihood Model (ELM), Stimulus-Organism-Response (SOR) framework, and Self-Determination Theory (SDT), data from 715 users in Vietnam were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study highlights the role of algorithmic diagnosticity and interpretability in shaping destination authenticity across generational preferences. By integrating SOR and ELM frameworks, it uncovers how algorithmic transparency, and motivational dynamics influence advocacy and visit intentions in AI-driven tourism. The findings provide actionable insights for tailoring algorithms to generational needs (e.g., existential authenticity for Gen Z, intrapersonal for Gen X) to enhance trust, advocacy, and engagement. Result: AI-driven recommendations significantly enhance perceptions of destination authenticity (brand, existential, and intrapersonal), which mediate advocacy and visit intentions. Algorithmic characteristics such as unbiasedness and diagnosticity positively influence all three dimensions of authenticity, while interpretability shows varied effects across generations. For instance, Gen Z prioritizes existential authenticity in fostering advocacy, Gen X emphasizes intrapersonal authenticity, and Gen Y exhibits a more neutral stance. Destination advocacy strongly predicts visit intentions across all cohorts, with personal innovativeness showing limited moderating effects.