One Size Does Not Fit All: A Comparative Study of Success Factors for AI Adoption in SME vs. Large firms in Supply Chains

Sammanfattning

Although artificial intelligence (AI) is increasingly acknowledged as a strategic resource in supply chain management (SCM), adoption outcomes are still variable, with many organizations limited to pilot-stage implementation and just a small number converting early expenditures into quantifiable value. The way that adoption circumstances vary between small and medium-sized enterprises (SMEs) and large firms has been obscured by existing AI-SCM research, which has mostly regarded company size as a demographic variable rather than an analytical condition. This study addresses that gap by examining how firm size shapes the conditions under which AI is adopted in SCM, and which configurations of success factors emerge as most critical across organisational contexts. A qualitative comparative multiple-case study was conducted, drawing on seven semi-structured interviews and guided by the Technology-Organization-Environment (TOE) framework.Results indicate that adoption is not symmetrically impacted by the three TOE factors. Environmental conditions function as a ceiling on practical adoption, organizational conditions influence adoption results, and technology conditions serve as enabling preconditions. Firm size moderates which constraints become binding: SMEs are characterised by governance fragility and vendor dependency, while large firms face coordination complexity and legacy integration. Instead of focusing on technological access, the study reframes the digital divide as a matter of adoption resilience.