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The rapid adoption of Artificial Intelligence (AI) in the tourism sector has prompted the need to evaluate its impact on managerial and destination performance. This study aims to investigate how the adoption of AI influences performance in tourist destinations by integrating the Human-Organization-Technology Fit (HOT-fit) model and the Technology-Organization-Environment (TOE) model. A quantitative approach was employed, collecting data from 305 tourist destination managers in Indonesia through a purposive sampling. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). The findings reveal that human, organisational, technological, and environmental factors significantly influence destination performance, while managerial performance is driven mainly by environmental and organisational support. Surprisingly, managerial performance negatively impacts destination performance, indicating potential overreliance on AI. This study’s key contribution lies in the theoretical integration of the HOT-fit and TOE frameworks to explain AI adoption and performance in the tourism industry. The novelty of this research is the comprehensive dual-model approach that offers a deeper understanding of the multidimensional drivers of AI-enabled performance improvements in tourism destinations. These insights guide destination managers, developers, and policymakers to align AI capabilities with organisational goals and environmental demands strategically.
Andrianto et al. (Mon,) studied this question.