生成式人工智能个性化推荐对旅游信息决策的影响OACHSSCD

Generative artificial intelligence(GAI)personalized recommendations are reshaping tourists'decision-making.Based on the 5A model,this study divides the pre-travel stage into three phases:travel need recognition,itinerary planning,and booking decision-making.It examines how different types of GAI personalized recommendations influence tourists'information search behaviors and decision-making across these phases.The research comprises three studies.First,drawing on information processing theory and the Heuristic-Systematic Model,it examines the mechanisms and boundary conditions through which proactive(vs.Reactive)GAI-driven personalized recommendations during the travel need recognition stage affect tourists'continuance intention to use GAI.Second,from a trust perspective,it explores how personalized recommendation with high(vs.low)adaptive algorithms during the itinerary planning stage influence tourist satisfaction.Third,from the perspectives of human-AI collaboration and cue filtering theory,it investigates how personalized recommendation provided by GAI(vs.human-GAI collaboration)during the booking decision stage affect tourists'acceptance of GAI recommendations.The findings provide practical theoretical foundations and managerial implications for optimizing GAI-driven tourism information search and personalized recommendation systems.