Generative artificial intelligence and sustainable tourist technology behavior: A complexity and risk-benefit approach

This study investigates the intersection of Generative Artificial Intelligence (GAI) adoption and sustainable tourism. While GAI tools like ChatGPT offer significant functional utility for travel planning, they introduce novel risks regarding privacy and system reliability (e.g., AI hallucinations causing travel disruption), as well as environmental concerns related to their massive carbon footprint. However, how tourists navigate this paradox between functional utility and systemic/environmental risks is under-investigated. To address this gap, this research theoretically integrates sustainability commitments (climate change mitigation and sustainable tourism) as central boundary conditions that alter tourists’ benefit-risk evaluations. Grounded in Benefit-Risk Theory and Complexity Theory, the study employs a multi-analytical quantitative approach. Data collected via a web-based survey of 600 South Korean travelers were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The findings demonstrate how symmetric and asymmetric analyses complement each other. Symmetrically, perceived information and system benefits drive usage and willingness to recommend ChatGPT, effectively overshadowing privacy and conflict risks. Asymmetrically, a more nuanced configurational reality emerges. Although the absence of risk is generally required, highly eco-conscious tourists willingly override significant risk perceptions (e.g., data privacy, travel disruption risks) when provided with exceptionally high informational utility (e.g., hyper-personalized itineraries). The integrated findings offer actionable insights to tailor risk-mitigation strategies to specific tourist segments and program AI to nudge users toward sustainable travel choices.