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Blockchain-enabled traceability and producer’s incentive to outsource delivery

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  • Liu Yang
  • Yaodong Ni
  • Chi-To Ng

Abstract

Applications of the blockchain technology in supply chains have attracted extensive attention in both academia and industries. However, little research has investigated the effects of the blockchain technology on firms’ operational strategies. In this paper, we investigate the impacts of the traceability enabled by the blockchain technology on a producer’s decision whether to outsource delivery to a third-party logistics firm. We find that without the blockchain technology, the logistics firm has a moral hazard—an incentive to set the delivery quality at the lowest level even though improving the delivery quality is for free. The traceability enabled by the blockchain technology can resolve the logistics firm’s moral hazard and encourage the producer to improve the production quality. Furthermore, when the delivery is cost-efficient for the producer, the traceability enabled by the blockchain technology motivates the producer to outsource the delivery to the logistics firm; otherwise, the producer’s outsourcing decision is not affected. When quality cost is low, the blockchain technology makes the logistics firm improve its delivery quality significantly and encourages the producer to improve the production quality. When the quality cost is moderate, only under certain conditions, the blockchain technology has effects on both parties’ decisions.

Suggested Citation

  • Liu Yang & Yaodong Ni & Chi-To Ng, 2023. "Blockchain-enabled traceability and producer’s incentive to outsource delivery," International Journal of Production Research, Taylor & Francis Journals, vol. 61(11), pages 3811-3828, June.
  • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:11:p:3811-3828
    DOI: 10.1080/00207543.2022.2072785
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    Cited by:

    1. Li, Zhiwen & Xu, Xianhao & Bai, Qingguo & Chen, Cheng & Wang, Hongwei & Xia, Peng, 2023. "Implications of information sharing on blockchain adoption in reducing carbon emissions: A mean–variance analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).

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