Artificial intelligence (AI) is widely regarded as a transformative technology in logistics; however, adoption among cold-chain logistics small and medium-sized enterprises (SMEs) remains limited despite the sector’s high operational demands and potential performance gains. Prior research has predominantly emphasised organisational readiness and environmental pressures, while giving comparatively less attention to the technological conditions shaping adoption decisions in SME contexts. This conceptual paper develops a focused model to explain AI adoption in cold-chain logistics SMEs by examining technical complexity and technical compatibility as key technological determinants, with perceived usefulness positioned as a mediating mechanism. Drawing on Diffusion of Innovations theory and the Technology Acceptance Model, the framework theorises how complexity and compatibility shape managers’ evaluations of AI usefulness, which in turn influence adoption intentions. Synthesising recent literature on AI-enabled cold-chain applications such as predictive temperature analytics and real-time anomaly detection, the paper demonstrates that technological determinants are particularly salient in data-intensive and risk-sensitive environments. By explicitly positioning technical complexity and compatibility as antecedents of perceived usefulness, the study offers a refined theoretical explanation of AI adoption and provides practical insights for improving AI uptake among cold-chain logistics SMEs.
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