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Intelligent manufacturing for strengthening operational resilience during the COVID-19 pandemic: A dynamic capability theory perspective

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  • Xi, Mengjie
  • Liu, Yang
  • Fang, Wei
  • Feng, Taiwen

Abstract

The occurrence of unexpected crises, such as the COVID-19 pandemic, creates a serious disruption in business operations, causing many companies to reassess the way they operate and seek new ways to enhance efficiency and resilience. Intelligent manufacturing may provide greater flexibility and adaptability, but less is known about whether and how intelligent manufacturing improved operational resilience during the COVID-19 pandemic. The present study aims to address this gap in knowledge by comparing the operational resilience before and after the pandemic between companies that adopted intelligent manufacturing and those that did not. Drawing upon dynamic capability theory, this study explores the impact of intelligent manufacturing on operational resilience via ambidextrous capability during the COVID-19 pandemic, as well as the moderating role of managerial myopia. To test hypotheses, this study used the difference-in-differences (DID) approach by collecting 3960 observations from 1980 Chinese listed companies in 2019–2020. The findings suggest that intelligent manufacturing aids companies in developing operational resilience in times of crisis. It also enables companies to develop ambidextrous capability which further enhances operational resilience. However, managerial myopia tends to weaken the positive effect of intelligent manufacturing on operational resilience. The findings extend the current understanding of enhancing operational resilience and provide insights for companies to adopt intelligent manufacturing to improve their operational resilience during disruptive times.

Suggested Citation

  • Xi, Mengjie & Liu, Yang & Fang, Wei & Feng, Taiwen, 2024. "Intelligent manufacturing for strengthening operational resilience during the COVID-19 pandemic: A dynamic capability theory perspective," International Journal of Production Economics, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:proeco:v:267:y:2024:i:c:s0925527323003109
    DOI: 10.1016/j.ijpe.2023.109078
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