AI-powered travel recommendations and decision-making: The role of spatio-temporal efficiency, destination type, and travel party composition

This study examined the influence of AI-driven recommendation systems on perceived helpfulness in travel decision-making, emphasizing spatio-temporal efficiency, destination type, and travel party composition. Although AI-driven travel recommendations are becoming more common, current research has mainly concentrated on text-based suggestions, neglecting the advantages of real-time, location-aware AI recommendations. Our study posited that integrating spatio-temporal efficiency into AI suggestions enhance travel itineraries and diminish cognitive burden, thus facilitating improved user decision-making. The study employed a scenario-based experimental approach to gather data from 320 South Korean passengers assessing AI recommendations for trip. The results indicate that spatio-temporal AI recommendations are regarded as more beneficial than non-spatio-temporal alternatives. Furthermore, individuals seeking tourist attractions find spatio-temporal recommendations considerably more advantageous, but those searching for restaurants do not observe a substantial distinction between the two recommendation modes. Moreover, the travel party composition affects perceived helpfulness, as companion travelers recognize greater advantages from spatio-temporal AI recommendations, whereas sole travelers demonstrate no distinct preference. These findings facilitate both theoretical progress and practical implementations in AI-driven tourism.