Artificial Intelligence Adoption in Fishing SMEs: A Conceptual Framework Linking Perceived Relative Advantage, Compatibility, and Business Performance in Malaysia
Keywords:
Artificial intelligence adoption, Fishing SMEs, Relative advantage, Compatibility, Business performance, Diffusion of InnovationAbstract
This paper develops an integrative framework to explain how artificial intelligence (AI) adoption influences business performance in fishing small and medium-sized enterprises (SMEs) in Malaysia. Drawing on Diffusion of Innovation (DOI) theory, the study conceptually examines the roles of perceived relative advantage and compatibility as key innovation attributes shaping AI adoption decisions in a traditional, resource-constrained industry context. The framework positions AI adoption as a mediating mechanism through which innovation perceptions are translated into performance outcomes, including operational efficiency, market responsiveness, and competitive positioning. By situating AI adoption within the realities of fishing SMEs—characterised by limited technological readiness, strong reliance on established routines, and sensitivity to cost and risk—the paper extends AI adoption research beyond high-technology sectors. The study further discusses managerial and policy implications related to strategic AI investment, digital inclusion, and capacity-building initiatives, and outlines directions for future empirical research using structural equation modelling, mixed-method approaches, and longitudinal designs. Overall, the paper contributes to AI adoption and SME literature by offering a context-sensitive perspective on digital transformation in Malaysia’s fishing industry.
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