AI-Driven Green Technology Adoption and Sustainable Tourism Behavior: An Extended Technology Acceptance Model - Zany Irayati Aunalal; Andrie Christina Salhuteru; Shella Kriekhoff; Jessy Juniu Hahury; Grace Fredriksz (Global Research and Collaboration, 2026)
This study examines the determinants of AI-driven green technology adoption and their implications for sustainable tourism behavior in environmentally sensitive destinations. Drawing on the Technology Acceptance Model, the study extends the framework by incorporating green technology trust and environmental concern to explain how users form attitudes toward AI-enabled green tourism technologies and how these attitudes translate into sustainable behavioral intention and actual behavior. Data were collected through a survey and analyzed using Partial Least Squares Structural Equation Modeling. The results show that perceived ease of use (β = 0.388; t = 10.819), green technology trust (β = 0.274; t = 7.670), and environmental concern (β = 0.194; t = 4.993) significantly influence attitudes toward green tourism technologies. In addition, attitude has a significant positive effect on sustainable behavioral intention (β = 0.545; t = 17.218), which subsequently exerts a strong effect on actual sustainable tourism behavior (β = 0.656; t = 24.675). These findings suggest that accessible, reliable, and environmentally credible AI-enabled tourism technologies can strengthen responsible tourism practices. The study contributes to the literature by demonstrating that technology-related perceptions and pro-environmental orientation jointly shape sustainable tourism behavior. It also offers practical implications for destination managers and tourism service providers seeking to design digital sustainability initiatives in ecologically fragile tourism contexts.