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

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  • Sunyoung Hlee

    (Beijing Institute of Technology)

  • Zhijun Yan

    (Beijing Institute of Technology)

  • Ping Li

    (Beijing Institute of Technology)

Abstract

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.

Suggested Citation

  • Sunyoung Hlee & Zhijun Yan & Ping Li, 2025. "AI-powered travel recommendations and decision-making: The role of spatio-temporal efficiency, destination type, and travel party composition," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-18, December.
    • Handle: RePEc:spr:elmark:v:35:y:2025:i:1:d:10.1007_s12525-025-00825-4
    DOI: 10.1007/s12525-025-00825-4
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    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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