Personalized Tourism Recommendations and the E-Tourism User Experience

Abstract

Previous research indicates that personalized tourism recommendation (PTR) is becoming increasingly important in tourism marketing. However, many areas of PTR remain unexplored. This study is based on Stimulus-Organism-Response theory; integrated constructs from PTR, big data, and artificial intelligence; and the technology acceptance model. The quantitative approach was conducted through an online survey from 496 users of Ctrip. PLS-SEM was used to test the collected data. Three factors were found to stimulate consumers’ perceptions of PTR: perceived personalization, visual appearance, and information quality. Consumers’ reactions to PTR can be divided into an internal processing organism, which includes the perception of the technology as “technology trust” and the perception of the recommended content as “PTR attitude.” This study contributes to the literature on smart tourism and marketing by developing and empirically testing an integrated model and providing a guide to determine users’ trust and attitudes toward PTR or other personalized e-services.