Scholarlink

<p>Artificial intelligence (AI)–powered recommendation systems are increasingly shaping cultural tourism experiences by personalizing destination suggestions and enhancing informational richness. While AI offers practical benefits such as efficiency and convenience, cultural tourists often experience ambivalence driven by concerns over authenticity loss, privacy risks and algorithmic manipulation. This study investigates the perceptual heterogeneity of outbound cultural tourists toward AI-enabled cultural recommendations and identifies distinct psychological profiles that inform differentiated engagement behaviors. Using survey data from 358 respondents, k-means clustering based on perceived benefits (PB), perceived concerns (PC), engagement intention (ENG), trust and AI familiarity revealed three meaningful segments: AI-Enhanced Cultural Explorers, Selective Cultural Learners and Authenticity-Driven Skeptics. MANOVA results confirmed significant multivariate differences among clusters, while machine-learning validation using Random Forest, SVM, XGBoost and SHAP analysis demonstrated that emotional–cognitive variables—particularly trust and perceived concerns—are the strongest predictors of segment membership. These findings indicate that perceptions of AI in cultural tourism extend beyond functional utility and are strongly shaped by emotional interpretation and authenticity-related values. The results highlight the importance of targeted communication, transparency and culturally sensitive AI design strategies tailored to distinct tourist orientations. The study contributes to theory by advancing perception-based segmentation within AI tourism research and suggests practical pathways for responsible, human-centered AI implementation that supports cultural sustainability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-driven segmentation of outbound cultural tourists: clustering based on perceived benefits, concerns, engagement and trust toward AI cultural recommendations

  • 期刊论文

  • 发表日期：

    2026年6月2日

查看全文
  • Zhi Zheng,
  • Xin Yu Bie,
  • Jong Min Kim

摘要

Artificial intelligence (AI)–powered recommendation systems are increasingly shaping cultural tourism experiences by personalizing destination suggestions and enhancing informational richness. While AI offers practical benefits such as efficiency and convenience, cultural tourists often experience ambivalence driven by concerns over authenticity loss, privacy risks and algorithmic manipulation. This study investigates the perceptual heterogeneity of outbound cultural tourists toward AI-enabled cultural recommendations and identifies distinct psychological profiles that inform differentiated engagement behaviors. Using survey data from 358 respondents, k-means clustering based on perceived benefits (PB), perceived concerns (PC), engagement intention (ENG), trust and AI familiarity revealed three meaningful segments: AI-Enhanced Cultural Explorers, Selective Cultural Learners and Authenticity-Driven Skeptics. MANOVA results confirmed significant multivariate differences among clusters, while machine-learning validation using Random Forest, SVM, XGBoost and SHAP analysis demonstrated that emotional–cognitive variables—particularly trust and perceived concerns—are the strongest predictors of segment membership. These findings indicate that perceptions of AI in cultural tourism extend beyond functional utility and are strongly shaped by emotional interpretation and authenticity-related values. The results highlight the importance of targeted communication, transparency and culturally sensitive AI design strategies tailored to distinct tourist orientations. The study contributes to theory by advancing perception-based segmentation within AI tourism research and suggests practical pathways for responsible, human-centered AI implementation that supports cultural sustainability.