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

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.