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Big data and big disaster: a mechanism of supply chain risk management in global logistics industry

Lixu Li (School of Economics and Management, Xi'an University of Technology, Xi'an, China)
Yeming Gong (Artificial Intelligence in Management Institute, Emlyon business School, Ecully, France)
Zhiqiang Wang (School of Business Administration, South China University of Technology, Guangzhou, China)
Shan Liu (School of Management, Xi'an Jiaotong University, Xi'an, China) (System Behavior and Management Laboratory, Xi’an Jiaotong University, Xi'an, China) (Shaanxi Logistics Group, Logistics Science and Technology Innovation Integrated Development Research Center, Xi'an Jiaotong University, Xi'an, China)

International Journal of Operations & Production Management

ISSN: 0144-3577

Article publication date: 24 October 2022

Issue publication date: 7 February 2023

2823

Abstract

Purpose

Although big data may enhance the visibility, transparency, and responsiveness of supply chains, whether it is effective for improving supply chain performance in a turbulent environment, especially in mitigating the impact of COVID-19, is unclear. The research question the authors addressed is: How do logistics firms improve the supply chain performance in COVID-19 through big data and supply chain integration (SCI)?

Design/methodology/approach

The authors used a mixed-method approach with four rounds of data collection. A three-round survey of 323 logistics firms in 26 countries in Europe, America, and Asia was first conducted. The authors then conducted in-depth interviews with 55 logistics firms.

Findings

In the first quantitative study, the authors find mediational mechanisms through which big data analytics technology capability (BDATC) and SCI influence supply chain performance. In particular, BDATC and SCI are two second-order capabilities that help firms develop three first-order capabilities (i.e. proactive capabilities, reactive capabilities, and resource reconfiguration) and eventually lead to innovation capability and disaster immunity that allow firms to survive in COVID-19 and improve supply chain performance. The results of the follow-up qualitative analysis not only confirm the inferences from the quantitative analysis but also provide complementary insights into organizational culture and the institutional environment.

Originality/value

The authors contribute to supply chain risk management by developing a three-level hierarchy of capabilities framework and finding a mechanism with the links between big data and big disaster. The authors also provide managerial implications for logistics firms to address the new management challenges posed by COVID-19.

Keywords

Acknowledgements

Yeming Gong thanks the support of AIM Institute and BIC Center at emlyon. Lixu Li appreciates the support of grants under Shaanxi Provincial Department of Education [22JK0120]. Zhiqiang Wang appreciates the support of grants under National Natural Science Foundation Program of China [U1901222] and [72034002]. Shan Liu appreciates the support of grants under National Natural Science Foundation Program of China [71722014], [72032006], and [72011540408], and the support from the Youth Innovation Team of Shanxi Universities “Big data and Business Intelligent Innovation Team.”

Citation

Li, L., Gong, Y., Wang, Z. and Liu, S. (2023), "Big data and big disaster: a mechanism of supply chain risk management in global logistics industry", International Journal of Operations & Production Management, Vol. 43 No. 2, pp. 274-307. https://doi.org/10.1108/IJOPM-04-2022-0266

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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