Antecedent configurations toward supply chain resilience: The joint impact of supply chain integration and big data analytics capability

• Published in: Journal of operations management Vol. 70; no. 2; pp. 257 - 284
• Main Authors: Jiang, Yisa, Feng, Taiwen, Huang, Yufei
• Format: Journal Article
• Language: English
• Published: Boston, USA Wiley Periodicals, Inc 01.03.2024; Wiley Subscription Services, Inc
• Subjects: Big Data; big data analytics capability; Comparative analysis; Configurations; Data analysis; Decision analysis; Fuzzy sets; Machine learning; Managerial skills; Manufacturing; Mathematical analysis; Qualitative analysis; qualitative comparative analysis; Resilience; Skills; supply chain integration; supply chain resilience; Supply chains; Synergistic effect; China
• ISSN: 0272-6963; 1873-1317
• DOI: 10.1002/joom.1282

Many antecedents identified as essential to supply chain resilience (SCR) are often studied independently, without considering their synergistic effects. Based on a case study and resource orchestration theory, this article focuses on configurations of different antecedents regarding supply chain integration and big data analytics capability to develop proactive and reactive SCR. Using survey data from 277 Chinese manufacturing firms, we consider three dimensions of supply chain integration, information integration, operational integration and relational integration, and three dimensions of big data analytics capability, technical skills, managerial skills and data driven‐decision culture, and conduct fuzzy‐set qualitative comparative analysis (fsQCA) to explore antecedent configurations generating high proactive and reactive SCR. We find that multiple antecedent configurations can achieve high SCR and configurations for high proactive and reactive SCR are not identical, which may involve alternative effects across different antecedents. We further implement propensity score matching analysis and reveal that firms following these configurations for high SCR also have better economic and operational performance. Moreover, we check the robustness of findings by using secondary data and attributes analysis with machine learning. This article complements and extends existing SCR literature from the configurational perspective and provides practical insights for managers to build SCR. Highlights Firms should be aware that configurations of various antecedents rather than a single antecedent determine SCR. No single antecedent is necessary for high SCR and there are alternative configurations resulting in high proactive or reactive SCR; thus, when certain specific capabilities are lacking, firms should properly orchestrate other capabilities for achieving high SCR. Antecedent configurations in building proactive and reactive SCR are not identical; thus, firms sometimes need to adjust solutions for different SCR strategies.