The impact of supply chain complexities on supply chain resilience: the mediating effect of big data analytics

Iftikhar, Anas, Purvis, Laura ORCID: https://orcid.org/0000-0002-1425-5894, Giannoccaro, Ilaria and Wang, Yingli ORCID: https://orcid.org/0000-0001-5630-9558 2023. The impact of supply chain complexities on supply chain resilience: the mediating effect of big data analytics. Production Planning and Control 34 (16) , pp. 1562-1582. 10.1080/09537287.2022.2032450

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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 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Taylor and Francis
ISSN:	0953-7287
Date of First Compliant Deposit:	24 January 2022
Date of Acceptance:	18 January 2022
Last Modified:	11 Dec 2023 23:27
URI:	https://orca.cardiff.ac.uk/id/eprint/146867

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