{"title":"Antecedent configurations toward supply chain resilience: The joint impact of supply chain integration and big data analytics capability","authors":"Yisa Jiang, Taiwen Feng, Yufei Huang","doi":"10.1002/joom.1282","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":51097,"journal":{"name":"Journal of Operations Management","volume":"70 2","pages":"257-284"},"PeriodicalIF":6.5000,"publicationDate":"2023-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Operations Management","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/joom.1282","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
The Journal of Operations Management (JOM) is a leading academic publication dedicated to advancing the field of operations management (OM) through rigorous and original research. The journal's primary audience is the academic community, although it also values contributions that attract the interest of practitioners. However, it does not publish articles that are primarily aimed at practitioners, as academic relevance is a fundamental requirement.
JOM focuses on the management aspects of various types of operations, including manufacturing, service, and supply chain operations. The journal's scope is broad, covering both profit-oriented and non-profit organizations. The core criterion for publication is that the research question must be centered around operations management, rather than merely using operations as a context. For instance, a study on charismatic leadership in a manufacturing setting would only be within JOM's scope if it directly relates to the management of operations; the mere setting of the study is not enough.
Published papers in JOM are expected to address real-world operational questions and challenges. While not all research must be driven by practical concerns, there must be a credible link to practice that is considered from the outset of the research, not as an afterthought. Authors are cautioned against assuming that academic knowledge can be easily translated into practical applications without proper justification.
JOM's articles are abstracted and indexed by several prestigious databases and services, including Engineering Information, Inc.; Executive Sciences Institute; INSPEC; International Abstracts in Operations Research; Cambridge Scientific Abstracts; SciSearch/Science Citation Index; CompuMath Citation Index; Current Contents/Engineering, Computing & Technology; Information Access Company; and Social Sciences Citation Index. This ensures that the journal's research is widely accessible and recognized within the academic and professional communities.
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