Generative AI on Optimizing Travel Decisions: New Insights with Dual Moderation of Information Complexity and Cognitive Load

This study examines how generative artificial intelligence (GenAI) optimizes travel planning by moderating information complexity and cognitive load. A quantitative approach with a formative–reflective modeling framework was applied to data from 1,242 respondents who experienced e-travel services. Results show that high information complexity increases cognitive load, negatively affecting travel outcomes, including personalization, logistical efficiency, financial management, and sustainable decision-making. GenAI mitigates these cognitive challenges, enhancing user satisfaction, trust, and adoption while supporting operational efficiency and AI-driven marketing insights. By integrating cognitive, technological, and behavioral perspectives, this study offers theoretical and practical contributions, guiding the design of AI-enabled travel systems and advancing knowledge on smart travel decision-making. These empirical findings expand existing knowledge by highlighting the potential of GenAI to transform traveler experiences and optimize tourism operations.