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

IF 6.5 2区 管理学 Q1 MANAGEMENT Journal of Operations Management Pub Date : 2023-12-17 DOI:10.1002/joom.1282
Yisa Jiang, Taiwen Feng, Yufei Huang
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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.

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供应链复原力的前因配置:供应链整合与大数据分析能力的共同影响
许多被认为对供应链复原力(SCR)至关重要的先决条件往往被单独研究,而不考虑其协同效应。本文以案例研究和资源协调理论为基础,重点研究了供应链整合和大数据分析能力等不同前因的配置,以发展主动和被动的 SCR。利用 277 家中国制造企业的调查数据,从供应链整合的三个维度--信息整合、运营整合和关系整合,以及大数据分析能力的三个维度--技术技能、管理技能和数据驱动决策文化,进行模糊集定性比较分析(fsQCA),探索产生高主动和高被动 SCR 的前因配置。我们发现,多种前因配置可以实现高 SCR,而且高主动和高被动 SCR 的配置并不完全相同,这可能涉及不同前因的替代效应。我们进一步实施倾向得分匹配分析,发现采用这些高 SCR 配置的企业也具有更好的经济和运营绩效。此外,我们还利用二手数据和机器学习的属性分析,检验了研究结果的稳健性。本文从配置的角度对现有的 SCR 文献进行了补充和扩展,为管理者建立 SCR 提供了实用的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Operations Management
Journal of Operations Management 管理科学-运筹学与管理科学
CiteScore
11.00
自引率
15.40%
发文量
62
审稿时长
24 months
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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