Business Performance in Fishing SMEs through Artificial Intelligence Adoption: The Mediating Roles of Relative Advantage and Compatibility
Keywords:
Artificial intelligence adoption, Fishing SMEs, Relative advantage, Digital transformation, Diffusion of Innovation, Compatibility, Business performanceAbstract
This study develops a conceptual framework to explain how artificial intelligence (AI) adoption influences business performance in fishing small and medium-sized enterprises (SMEs) through the mediating roles of perceived relative advantage and compatibility. Drawing primarily on Diffusion of Innovation (DOI) theory and complementary perspectives from digital transformation and SME performance literature, the paper argues that AI does not directly enhance performance unless it is perceived by owner-managers as both beneficial and compatible with existing operational routines, resource constraints, and skill bases. By focusing on fishing SMEs as a traditional and under-researched industry context, the study extends AI adoption research beyond technology-intensive sectors and highlights the importance of adoption readiness and fit in realizing performance outcomes. The paper further discusses theoretical, managerial, and policy implications, emphasizing strategic AI decision-making, phased investment approaches, and the role of digital inclusion and capacity-building initiatives. Finally, directions for future research are proposed, including the use of structural equation modeling, mixed-method designs, and longitudinal studies to empirically validate the proposed mediation framework. Overall, the study contributes to AI adoption and SME performance literature by offering a context-sensitive explanation of sustainable digital transformation in fishing SMEs.
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