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Data governance and industry 4.0 for optimizing supply chain resilience

Mustafa Khan et al.

Industrial Management & Data Systems2026https://doi.org/10.1108/imds-04-2025-0534article
AJG 2ABDC A
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0.50

Abstract

Purpose This study aims to examine the impact of data governance (DG) on supply chain (SC) resilience, focusing on the mediating effect of IR4.0 and the moderating impact of environmental uncertainty in the context of Malaysia. Design/methodology/approach In this study, data were collected from managers of the Electrical and Electronics industry in Malaysia using a questionnaire survey approach. Researchers applied Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze 204 valid responses. Findings The findings from PLS-SEM indicate that DG significantly improve both IR4.0 and SC resilience in Malaysia. Besides, IR4.0 has a mediating effect on the relationship between DG and SC resilience, whereas environmental uncertainty has no moderating impact on the relationship between DG and SC resilience in Malaysia. Research limitations/implications The policymakers and practitioners should focus on data management in terms of structural, procedural and relational practices to align organizational resources with SC networks. Managers in IR4.0 should also prioritize the integration of robust DG frameworks into IR4.0 technologies to ensure the improvement in organizational resilience and enhancement in response to disruptions. Originality/value The originality of this research lies on the significance of DG framework and SC resilience, applying technological aspects and using analytical models. Moreover, this is the early study that examined the underexplored impact of DG on enhancing SC resilience. Moreover, this research adds the originality by identifying DG practices and the mediating role of IR4.0 that assist in achieving SC resilience in Malaysia.

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https://doi.org/https://doi.org/10.1108/imds-04-2025-0534

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@article{mustafa2026,
  title        = {{Data governance and industry 4.0 for optimizing supply chain resilience}},
  author       = {Mustafa Khan et al.},
  journal      = {Industrial Management & Data Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/imds-04-2025-0534},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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