Artificial Intelligence Analysis of Tourist Behavior for Designing Personalized Nudge Strategies

Tourism concentration at popular destinations creates economic imbalances, with limited spillover benefits reaching nearby regions. The study addresses this challenge by developing artificial intelligence (AI)-driven personalized nudge strategies to redistribute tourist flows, using Naoshima Island and Tamano City, Japan, as case studies. The study employed self-organizing maps (SOMs) combined with k-means clustering to analyze the behavioral patterns of 55 international tourists during summer 2023. The analysis identified three distinct segments: young art-seeking tourists (33%), characterized by high social media engagement and spontaneous decision-making; middle-aged convenience-oriented tourists (36%), who prioritize structured experiences and are highly sensitivity to language barriers; and older recreation-focused tourists (31%), who demonstrate a strong interest in cultural authenticity and the highest spending levels. Based on these behavioral profiles, the study proposes cluster-specific nudge interventions: social proof and gamification for young tourists, default options and simplified information for convenience-oriented visitors, and value-added cultural experiences for recreation-focused travelers. The framework integrates machine learning analytics with behavioral economics to provide tourism authorities with actionable, evidence-based strategies projected to increase multi-destination visits by 25%, while respecting tourist autonomy, promoting sustainable destination management, and ensuring equitable economic distribution.