A Framework for Understanding Blockchain Adoption in Supply Chain Operations: A Multicase Study

Alok Raj et al.

IEEE Transactions on Engineering Management2026https://doi.org/10.1109/tem.2026.3653443article
AJG 3ABDC A
Weight
0.50

Abstract

Blockchain has gained significant attention, but operations literature lacks an integrated framework capturing its adoption dynamics. This paper addresses the gap through a multi-case study, proposing a framework that examines drivers, enablers, resistors, mechanisms, and outcomes of blockchain adoption in supply chains. The process is segmented into three phases—preadoption, early adoption, and full adoption—across technological, organizational, and environmental dimensions. Findings show organizational and environmental factors dominate preadoption, while enablers and resistors shape early adoption. In full adoption, mechanisms such as resilience, trust, visibility, information sharing, and cost reduction deliver operational benefits. The study offers valuable insights for researchers and practitioners to better understand and implement blockchain in supply chain operations.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1109/tem.2026.3653443

Or copy a formatted citation

@article{alok2026,
  title        = {{A Framework for Understanding Blockchain Adoption in Supply Chain Operations: A Multicase Study}},
  author       = {Alok Raj et al.},
  journal      = {IEEE Transactions on Engineering Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tem.2026.3653443},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

A Framework for Understanding Blockchain Adoption in Supply Chain Operations: A Multicase Study

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.