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Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting

Rameshwar Dubey, Angappa Gunasekaran, David J. Bryde, Yogesh K. Dwivedi and Thanos Papadopoulos

International Journal of Production Research, 2020, vol. 58, issue 11, 3381-3398

Abstract: There has been tremendous interest in blockchain technology (BT) (also known as distributed ledger technology) around the globe and across sectors. Following significant success in the financial sector, other sectors, such as humanitarian sector, have started deploying BT at various levels. Although the use of BT in the humanitarian sector is in its infancy, donors and government agencies are increasingly calling for building BT-enabled swift-trust (ST) and more collaborative relationships among various humanitarian actors in order to improve the transparency and traceability of disaster relief materials, information exchanges and flow of funds in disaster relief supply chains. Our study, which is informed by organisational information processing theory and relational view, proposes a theoretical model to understand how BT can influence operational supply chain transparency (OSTC) and ST among actors engaged in disaster relief operations. Our model also shows how BT-enabled ST can further improve collaboration (CO) among actors engaged in disaster relief operations and enhance supply chain resilience (SCR). We formulated and tested six research hypotheses, using data gathered from international non-governmental organisations with the help of the Coordinator for Humanitarian Affairs (OCHA) database. We received 256 usable responses using a pre-tested survey-based instrument designed for key informants. Our results confirm that our six hypotheses were supported. Our study offers significant and valid contributions to the literature on ST, CO and SCR and BT/distributed ledger technology. We have also noted the limitations of our study and have offered future research directions.

Date: 2020
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Citations: View citations in EconPapers (63)

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DOI: 10.1080/00207543.2020.1722860

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