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Lookup NU author(s): Professor Ying YangORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Purpose – Increasingly studies are reporting supply chain analytical capabilities as a key enabler of supply chain agility (SCAG) and supply chain performance (SCP). This study investigates the impact of environmental dynamism and competitive pressures in a supply chain analytics setting, and how intangible supply chain analytical capabilities (ISCAC) moderate the relationship between big data characteristics (BDC’s) and SCAG in support of enhanced SCP. Design/methodology/approach – The study draws on the literature on big data, supply chain analytical capabilities, and dynamic capability theory to empirically develop and test a supply chain analytical capabilities model in support of SCAG and SCP. ISCAC was the moderated construct and was tested using two sub-dimensions, supply chain organisational learning and supply chain data driven culture. Findings – The results show that whilst environmental dynamism has a significant relationship on the three key BDC’s, only the volume and velocity dimensions are significant in relation to competitive pressures. Furthermore, only the velocity element of BDC’s has a significant positive impact on SCAG. In terms of moderation, the supply chain organisational learning dimension of ISCAC was shown to only moderate the velocity aspect of BDC’s on SCAG, whereas for the supply chain data driven culture dimension of ISCAC, only the variety aspect was shown to moderate of BDC on SCAG. SCAG had a significant impact on SCP. Originality/value – This study adds to the existing knowledge in the supply chain analytical capabilities domain by presenting a nuanced moderation model that includes external factors (environmental dynamism and competitive pressures), their relationships with BDC’s and how ISCAC (namely, supply chain organisational learning and supply chain data driven culture) moderates and strengthens aspects of BDC’s in support of SCAG and enhanced SCP.
Author(s): Cadden T, McIvor R, Cao G, Tracey R, Yang Y, Gupta M, Onofrei G
Publication type: Article
Publication status: Published
Journal: International Journal of Operations and Production Management
Year: 2022
Volume: 42
Issue: 9
Pages: 1329-1355
Print publication date: 12/08/2022
Online publication date: 13/07/2022
Acceptance date: 04/05/2022
Date deposited: 05/05/2022
ISSN (print): 0144-3577
ISSN (electronic): 1758-6593
Publisher: Emerald Publishing Limited
URL: https://doi.org/10.1108/IJOPM-06-2021-0383
DOI: 10.1108/IJOPM-06-2021-0383
ePrints DOI: 10.57711/6y64-8r79
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