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Beyond algorithms: How socio-technical antecedents drive social-exchange outcomes in AI travel planning personalization

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  • Liu, Gus Guanrong
  • Lv, Linxiang
  • Meng, Lucia Lu
  • Tao, Jinyan

Abstract

Generative AI is transforming tourism personalization through conversational and creative capabilities that differ from traditional recommendation systems, yet little is known about how travelers perceive and respond to AI-enabled personalization during the emotionally-intensive trip planning stage. Guided by socio-technical systems and social exchange theories, this study employs a sequential explanatory mixed-methods approach to explore the antecedents and outcomes of AI travel planning personalization. Quantitative findings reveal four key determinants of perceived personalization: two technical (connectivity and ubiquity) and two social (dynamism and empathy), which enhance hedonic well-being through dual psychological pathways: inferred AI benevolent and unselfish intentions. Follow-up qualitative interviews with participants engaging in hands-on AI travel planning reveal that connectivity reduces information overload and ubiquity provides always-on support, while AI's perceived commercial neutrality differentiates it from human agents. Travelers particularly value AI's ability to recommend free activities without commission-driven motives. These findings provide actionable guidelines for AI developers and tourism service providers to design systems that balance technical efficiency with social intelligence, while highlighting AI's perceived commercial neutrality as a strategic advantage in digital tourism services.

Suggested Citation

  • Liu, Gus Guanrong & Lv, Linxiang & Meng, Lucia Lu & Tao, Jinyan, 2026. "Beyond algorithms: How socio-technical antecedents drive social-exchange outcomes in AI travel planning personalization," Journal of Retailing and Consumer Services, Elsevier, vol. 89(PA).
    • Handle: RePEc:eee:joreco:v:89:y:2026:i:pa:s0969698925003868
    DOI: 10.1016/j.jretconser.2025.104607
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