Research Information System
IEEE Transactions on Engineering Management, 2025 (SCI-Expanded, SSCI, Scopus)
The integration of Blockchain Technology (BT) in Supply Chains (SCs) is advancing rapidly due to its benefits in transparency, traceability, security, and decentralization. However, SC managers face challenges in evaluating diverse strategies for effective BT adoption. This study proposes a comprehensive framework combining criteria weighting and evaluation models that prioritize simplicity, idealism, structure, and efficiency. It integrates five decision-making techniques; i.e., Simple Additive Weighted (SAW), Entropy Method (EM), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Super-Efficiency Data Envelopment Analysis (SEDEA), and Network Data Envelopment Analysis (NDEA), to provide a dynamic, adaptable evaluation process. Additionally, a novel risk- and scenario-based optimization model and a predictive comparison approach are introduced to enhance decision-making. The framework is applied to the Norwegian Oil & Gas (O&G) sector, analyzing four BT adoption strategies. It incorporates Machine Learning (ML) for similarity analysis and dual-perspective clustering to better understand inter-strategy and method relationships. Findings show that a focused, single-purpose blockchain use, especially under high-risk conditions and transformational strategies, is most effective. Key adoption drivers include market and customer pressures, collaboration needs, and limited IT infrastructure. Simplicity and idealism/non-idealism-based models aligned most closely with existing literature, while network structure-based models varied more. The results demonstrate the importance of consensus-based evaluation when selecting blockchain strategies in SCs.