Organisational and Environmental Determinants of AI Adoption in Logistics SMEs: A Conceptual Framework Integrating Readiness, Support, and Employee Adaptability

HRMARS - Artificial intelligence (AI) is increasingly recognised as an important enabler of efficiency, responsiveness, and resilience in logistics operations; however, AI adoption among logistics small and medium-sized enterprises (SMEs) remains uneven and limited due to organisational capability constraints and insufficient external support. Existing technology adoption studies, particularly those grounded in the Technology–Organization–Environment (TOE) framework, have primarily emphasised structural and contextual determinants while offering limited explanation of the behavioural mechanisms through which these conditions translate into effective adoption. Addressing this gap, this conceptual (non-empirical) paper develops an integrative framework that combines the Technology–Organization–Environment framework with organisational capability and behavioural adaptation perspectives to explain AI adoption in logistics SMEs. The proposed model positions organisational readiness and external support as key structural antecedents and introduces employee adaptability as a mediating behavioural mechanism linking organisational and environmental conditions to AI adoption outcomes, from which six theoretically grounded hypotheses are derived for future empirical testing. The study contributes to the literature by clarifying the multidimensional nature of external support, highlighting employee adaptability as a critical behavioural pathway, and extending existing adoption theories to better reflect the constraints and realities faced by logistics SMEs, while also offering managerial and policy implications for promoting effective AI adoption in resource-constrained logistics contexts.