Perceived regional economic benefits of smart agriculture: a dynamic capability and leadership perspective on AI adoption

Introduction Firms and regions increasingly adopt artificial intelligence (AI) in agriculture, yet the mechanisms through which AI adoption translates into broader economic outcomes remain underexplored. Drawing on dynamic capability theory, this study examines how AI adoption and transformational leadership are associated with perceived regional economic benefits through smart agriculture, while investigating the moderating role of workforce digital skills. Methods A time-lagged survey was conducted over eight months in China, collecting data from 452 respondents working in agricultural and agri-food organizations. The proposed relationships were examined using partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA). Results The findings indicate that AI adoption and transformational leadership are positively associated with smart agriculture and perceived regional economic benefits. Smart agriculture partially mediates the relationships between AI adoption, transformational leadership, and perceived regional economic benefits. Furthermore, workforce digital skills significantly strengthen the relationship between AI adoption and smart agriculture. The necessity analysis identifies AI adoption and transformational leadership as key bottleneck conditions for achieving higher levels of perceived regional economic benefits. Discussion This study advances the literature on agricultural digitalization by conceptualizing smart agriculture as an organizational digital-agriculture capability and integrating technological adoption, transformational leadership, and workforce digital skills into a unified theoretical framework. The findings provide practical insights for agricultural organizations and policymakers seeking to strengthen smart agricultural transformation and promote perceived regional economic benefits in China.