Exploring how interaction with ai content recommendation algorithms influences travel intentions and destination advocacy among tourists on short video platforms

Abstract

Driven by the desire to enhance tourist experience with AI-based recommendation algorithms on short video platforms in the tourism sector, this study explores the intricate sequential impacts of informativeness, information quantity, and perceived enjoyment in tourists’ interaction on destination image, destination brand love and destination brand resonance, as well as whether these relationships truly contribute to visit intention and destination advocacy intention through the creation of short videos. An exploratory sequential mixed-method design was employed, combining both qualitative and quantitative methods. Initially, the study utilized a quantitative approach, collecting 536 surveys from Vietnamese users, with the data analyzed using structural equation modeling in SmartPLS software. Subsequently, qualitative research, through 22 in-depth interviews, provided insights to verify the results of the relationships established through quantitative methods and offered more multidimensional perspectives. The results indicated that the hypothesized relationships between informativeness, information quantity, perceived enjoyment, destination brand love, and destination brand resonance positively influenced one another, highlighting the significant interplay between these factors in shaping visit intention and destination advocacy. Additionally, the differences observed between the quantitative and qualitative research findings will be discussed in greater depth in the article. This framework not only deepens scholars' understanding of users' emotional and behavioral responses to AI recommendation systems on short-video platforms but also equips practitioners and marketers with the insights needed to accurately target their audiences, minimizing costly missteps and boosting both visit intention and advocacy