Big data analytics capability in building supply chain resilience: the moderating effect of innovation-focused complementary assets
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 29 January 2024
Issue publication date: 16 February 2024
Abstract
Purpose
This research investigates the mechanism by which big data capability enables superior supply chain resilience (SCRe) by empirically examining the links among big data analytics (BDA), supply chain flexibility (SCF) and SCRe, with innovation-focused complementary assets (CA-I) as the moderator.
Design/methodology/approach
Extensive surveys were conducted to gather 308 responses from Malaysian manufacturing firms in order to explore this framework. The structural and measurement models were examined and evaluated by using partial least squares structural equation modelling.
Findings
The findings revealed that BDA is linked to flexibilities in a manufacturing firm’s value chain, which in turn is related to the firm’s SCRe. However, the association between BDA and SCRe is surprisingly non-significant. Additionally, CA-I was discovered to moderate the connections between all of the constructs, except for the relationship between BDA and SCRe. Such findings imply that with the aim of enhancing resilience, a company should concentrate on SCF; and that BDA capability is a prerequisite for increasing these flexibilities.
Originality/value
This research extrapolates the findings of previous studies regarding BDA’s influence on SCRe by investigating the indirect effect of SCF, as well as the moderating influence of CA-I. This research is one of the first few studies to empirically examine the relationships between BDA, SCF and SCRe across manufacturing firms, with CA-I acting as a moderator.
Keywords
Citation
Lee, V.H., Foo, P.-Y., Cham, T.-H., Hew, T.-S., Tan, G.W.-H. and Ooi, K.-B. (2024), "Big data analytics capability in building supply chain resilience: the moderating effect of innovation-focused complementary assets", Industrial Management & Data Systems, Vol. 124 No. 3, pp. 1203-1233. https://doi.org/10.1108/IMDS-07-2022-0411
Publisher
:Emerald Publishing Limited
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