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Developing capabilities for supply chain resilience in a post-COVID world: A machine learning-based thematic analysis

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  • Dun Li
  • Bangdong Zhi
  • Tobias Schoenherr
  • Xiaojun Wang

Abstract

This study examines the past, present, and future of Supply Chain Resilience (SCR) research in the context of COVID-19. Specifically, a total of 1717 papers in the SCR field are classified into 11 thematic clusters, which are subsequently verified by a supervised machine learning approach. Each cluster is then analyzed within the context of COVID-19, leading to the identification of three associated capabilities (i.e., interconnectedness, transformability, and sharing) on which firms should focus to build a more resilient supply chain in the post-COVID world. The derived insights offer invaluable guidance not only for practicing managers, but also for scholars as they design their future research projects related to SCR for greatest impact.

Suggested Citation

  • Dun Li & Bangdong Zhi & Tobias Schoenherr & Xiaojun Wang, 2023. "Developing capabilities for supply chain resilience in a post-COVID world: A machine learning-based thematic analysis," IISE Transactions, Taylor & Francis Journals, vol. 55(12), pages 1256-1276, December.
    • Handle: RePEc:taf:uiiexx:v:55:y:2023:i:12:p:1256-1276
    DOI: 10.1080/24725854.2023.2176951
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    Cited by:

    1. Essuman, Dominic & Owusu-Yirenkyi, Diana & Afloe, William Tsiatey & Donbesuur, Francis, 2023. "Leveraging foreign diversification to build firm resilience: A conditional process perspective," Journal of International Management, Elsevier, vol. 29(6).

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