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Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity, and the mediating role of virtual enterprise

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  • Sheng, Margaret L.
  • Saide, Saide

Abstract

In the context of the survivability of a firm’s supply chain during the worldwide COVID-19 crisis, there is still much to be explored. This study employs the viable system model to conceptualize the virtual enterprise, big data, and knowledge ambidexterity capability that can facilitate supply chain survivability. Empirical data have been collected from various industries in Indonesia (the largest labor force in Southeast Asia). SEM-SmartPLS and SPSS were used to validate the model. The results suggest that firms need to realize that the explorative and exploitative of knowledge have different effects and are integrated in different ways into the survivability of a supply chain. The findings also revealed two roles for mediation, whereby revolutionizing the explorative of knowledge and the analytics of big data through virtual enterprise are crucial strategies to achieves the survivability of the supply chain. A novel value added is the inclusion of a systems-environment perspective and knowledge-technology matrix, especially in times of crisis.

Suggested Citation

  • Sheng, Margaret L. & Saide, Saide, 2021. "Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity, and the mediating role of virtual enterprise," Journal of Business Research, Elsevier, vol. 137(C), pages 567-578.
    • Handle: RePEc:eee:jbrese:v:137:y:2021:i:c:p:567-578
    DOI: 10.1016/j.jbusres.2021.08.041
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    Cited by:

    1. Ali Zackery & Joseph Amankwah-Amoah & Zahra Heidari Darani & Shiva Ghasemi, 2022. "COVID-19 Research in Business and Management: A Review and Future Research Agenda," Sustainability, MDPI, vol. 14(16), pages 1-32, August.
    2. Samia Zaoui & Clovis Foguem & Dieudonné Tchuente & Samuel Fosso-Wamba & Bernard Kamsu-Foguem, 2023. "The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 633-657, December.
    3. Kumar Mangla, Sachin & Chauhan, Ankur & Kundu, Tanmoy & Mardani, Abbas, 2023. "Emergency order allocation of e-medical supplies due to the disruptive events of the healthcare crisis," Journal of Business Research, Elsevier, vol. 155(PA).
    4. Suqin Liao & Qianying Hu & Jingjing Wei, 2023. "How to Leverage Big Data Analytic Capabilities for Innovation Ambidexterity: A Mediated Moderation Model," Sustainability, MDPI, vol. 15(5), pages 1-19, February.
    5. Christopher M. Durugbo & Zainab Al-Balushi, 2023. "Supply chain management in times of crisis: a systematic review," Management Review Quarterly, Springer, vol. 73(3), pages 1179-1235, September.
    6. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    7. Efpraxia D. Zamani & Conn Smyth & Samrat Gupta & Denis Dennehy, 2023. "Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review," Annals of Operations Research, Springer, vol. 327(2), pages 605-632, August.
    8. Zhuoyue Zhu & Hongming Xie, 2022. "What Do We Know and What Do We Need to Know about COVID-19’s Implications on Business Economics? From Bibliometric Analysis to a Conceptual Framework," Sustainability, MDPI, vol. 14(11), pages 1-16, May.

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