Modeling the blockchain enabled traceability in agriculture supply chain

Sachin S. Kamble, Angappa Gunasekaran, Rohit Sharma

Research output: Contribution to journalArticlepeer-review

120 Scopus citations

Abstract

Blockchain Technology (BT) has led to a disruption in the supply chain by removing the trust related issues. Studies are being conducted worldwide to leverage the benefits provided by BT in improving the performance of the supply chains. The literature reveals BT to offer various benefits leading to improvements in the sustainable performance of the agriculture supply chains (ASC). It is expected that BT will bring a paradigm shift in the way the transactions are carried in the ASC by reducing the high number of intermediaries, delayed payments and high transaction lead times. India, a developing economy, caters to the food security needs of an ever-growing population and faces many challenges affecting ASC sustainability. It is therefore essential to adopt BT in the ASC to leverage the various benefits. In this study, we identify and establish the relationships between the enablers of BT adoption in ASC. Thirteen enablers were identified from the literature and validated by the experts before applying a combined Interpretive Structural Modelling (ISM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology to envision the complex causal relationships between the identified BT enablers. The findings from the study suggest that, among the identified enablers, traceability was the most significant reason for BT implementation in ASC followed by auditability, immutability, and provenance. The findings of the study will help the practitioners to design the strategies for BT implementation in agriculture, creating a real-time data-driven ASC. The results will also help the policymakers in developing policies for faster implementation of BT ensuring food safety and sustainable ASCs.

Original languageEnglish (US)
Article number101967
JournalInternational Journal of Information Management
Volume52
DOIs
StatePublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Computer Networks and Communications
  • Marketing
  • Information Systems and Management
  • Library and Information Sciences
  • Artificial Intelligence

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