Anas Iftikhar, Laura Purvis, I. Giannoccaro, Yingli Wang
{"title":"The impact of supply chain complexities on supply chain resilience: the mediating effect of big data analytics","authors":"Anas Iftikhar, Laura Purvis, I. Giannoccaro, Yingli Wang","doi":"10.1080/09537287.2022.2032450","DOIUrl":null,"url":null,"abstract":"Supply chains (SC) are increasingly complex and if the resulting complexity is not managed effectively, it could lead to adverse consequences for the firm. The effect big data analytics (BDA) can have on managing distinct types of SC complexity is not well understood in the extant literature. Based on a sample of 166 firms from Pakistan, this study empirically investigates the effects of BDA, and of structural and dynamic SC complexities, on SC resilience. The study also investigates the role of BDA as a mediator between SC complexities and SC resilience. We find that structural SC complexity positively affects SC resilience, while there doesn’t seem to be a significant impact for dynamic SC complexity. We also find a mediating effect of BDA for structural and dynamic SC complexities on SC resilience. Our results contribute to the extant literature investigating BDA and SC resilience by offering a more nuanced understanding of distinct types of SC complexities. We establish a more critical understanding of the role of BDA in mediating the critical link between the two types of SC complexity and SC resilience. The proposed model highlights that there are both direct and indirect effects between structural SC complexity and SC resilience, however dynamic SC complexity only influences SC resilience via BDA. These findings provide strategic insights for SC executives as to where to invest in BDA to build much needed SC resilience.","PeriodicalId":20627,"journal":{"name":"Production Planning & Control","volume":"2016 1","pages":""},"PeriodicalIF":6.1000,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Production Planning & Control","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1080/09537287.2022.2032450","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 29
Abstract
Supply chains (SC) are increasingly complex and if the resulting complexity is not managed effectively, it could lead to adverse consequences for the firm. The effect big data analytics (BDA) can have on managing distinct types of SC complexity is not well understood in the extant literature. Based on a sample of 166 firms from Pakistan, this study empirically investigates the effects of BDA, and of structural and dynamic SC complexities, on SC resilience. The study also investigates the role of BDA as a mediator between SC complexities and SC resilience. We find that structural SC complexity positively affects SC resilience, while there doesn’t seem to be a significant impact for dynamic SC complexity. We also find a mediating effect of BDA for structural and dynamic SC complexities on SC resilience. Our results contribute to the extant literature investigating BDA and SC resilience by offering a more nuanced understanding of distinct types of SC complexities. We establish a more critical understanding of the role of BDA in mediating the critical link between the two types of SC complexity and SC resilience. The proposed model highlights that there are both direct and indirect effects between structural SC complexity and SC resilience, however dynamic SC complexity only influences SC resilience via BDA. These findings provide strategic insights for SC executives as to where to invest in BDA to build much needed SC resilience.
期刊介绍:
Production Planning & Control is an international journal that focuses on research papers concerning operations management across industries. It emphasizes research originating from industrial needs that can provide guidance to managers and future researchers. Papers accepted by "Production Planning & Control" should address emerging industrial needs, clearly outlining the nature of the industrial problem. Any suitable research methods may be employed, and each paper should justify the method used. Case studies illustrating international significance are encouraged. Authors are encouraged to relate their work to existing knowledge in the field, particularly regarding its implications for management practice and future research agendas.