Multi-Level Determinants of Sustainable Blockchain Technology Adoption in SCM: Individual, Organisational, and Societal Perspectives

Abstract

This study examines how individual, organisational, and societal factors influence blockchain technology (BCT) adoption in supply chain management (SCM). Using Partial Least Squares Artificial Neural Networks (PLS-ANNs) and Necessary Condition Analysis (NCA), it identifies key determinants of sustainable BCT adoption among small- and medium-sized enterprises (SMEs). The results show that compatibility, top management support, and relative advantage are critical for adoption. This study focuses on SMEs, and further research is needed to assess whether these findings apply to larger organisations. Insights from this research provide a foundation for improving BCT adoption in high-impact sectors and inform strategic adoption practices. By analysing multi-level factors, the study enhances understanding and guides policy development for equitable and sustainable supply chain innovations. Additionally, the findings refine existing BCT adoption models by introducing and validating new determinants, contributing to both theory and practice in SCM. This comprehensive approach bridges research gaps and offers actionable insights for improving BCT adoption, supporting broader economic and social benefits.