Data Quality and AI Capabilities as Catalysts for Firm Performance in the Tourism and Recreation Industry

AI adoption has reshaped industries worldwide, including the tourism and recreation sector. However, the effectiveness of AI in driving performance depends on critical factors such as data quality and organizational adaptability. This study examines the role of data quality – completeness, accuracy, and currency – as a strategic resource for enhancing AI capabilities. Drawing on the Resource-Based View, Dynamic Capabilities Theory, and Contingency Theory, the research explores how AI capabilities influence financial and market performance, the hypothesized mediating role of dynamic capabilities, and the moderating impact of technological turbulence. Covariance-Based Structural Equation Modeling (CB-SEM) confirms that data quality significantly bolsters AI capabilities, which directly improve firm performance. Contrary to expectations, dynamic capabilities do not mediate this relationship, suggesting AI exerts a more immediate impact on outcomes. Moreover, technological turbulence amplifies AI’s performance benefits, underscoring AI’s heightened value under volatile conditions. Fuzzy-set Qualitative Comparative Analysis (fsQCA) further reveals multiple sufficient configurations of data quality, AI capabilities, and dynamic capabilities, highlighting diverse pathways to superior performance. These findings advance theoretical understanding of AI adoption and offer practical insights for firms seeking to leverage data and AI amid rapidly evolving market environments.