Bridging the Affordance-Actualization Gap in User Preferences for AI-Assisted Trip Planning
Richard Hrankai, Barry Mak
Abstract
This study explores the adoption of artificial intelligence (AI)-enabled trip-planning assistants, focusing on user interaction dynamics and preferences. Although users appreciate the quick access to basic information offered by AI-powered travel platforms, challenges such as information accuracy and adaptability create a gap between the potential and realized advantages of these technologies. Drawing on affordance-actualization theory, this research analyzes user preferences and their drivers regarding the characteristics of AI-enabled trip planners. The data collected through a stated-choice experiment were analyzed with discrete choice modeling to measure users’ preferences and heterogeneity in their interactions with AI-assisted trip-planning platforms. Findings reveal that preferences are influenced by contextual elements, such as trip purpose, activity type, and the perceived importance of information quality. The systematic investigation of user preferences contributes significantly to the understanding of AI adoption and interaction dynamics in the digital age, offering insights for improving platform design and user satisfaction.