Citation

Yasmeen, Ghazala and Anthonysamy, Lilian and Ojo, Adedapo Oluwaseyi (2025) Resource-Governed BDA Adoption for Resilient Supply-Chain Operations: Qualitative Evidence from Malaysian Manufacturing Industry. Sustainability, 17 (21). p. 9620. ISSN 2071-1050

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Abstract

Research on big data analytics (BDA) and supply chains often inventories “capabilities” but rarely explains how firms progress through adoption—or how governance over data and related resources shapes resilience outcomes. Drawing on 16 semi-structured interviews with senior managers in the manufacturing sector, we analyze organizational practices around data, analytics, and decision-making and synthesize a governed-adoption process framework. The framework specifies how five governance levers—ownership, standards, stewardship, access, lineage—operate differentially across four adoption gates (data plumbing—descriptive monitoring—predictive alerting—prescriptive decisioning). To move beyond staged descriptions, we make the underlying generative mechanisms explicit—Comparability, Explainability, Authorization, Fidelity, Executability—and link them to dynamic-capability micro foundations (sensing, seizing, reconfiguring) via decision-latency outcomes (“resilience timers”: Time-to-Detect, Time-to-Decide, Time-to-Reconfigure, Time-to-Recover). Brief deviant-case contrasts (e.g., notification without action; dashboards without owners) clarify boundary conditions under which governance enables or impedes resilient action. We also state concise, testable propositions (e.g., standards+lineage as a necessary condition for improving Time-to-Detect; ownership+access as necessary for improving Time-to-Decide) and provide gate exit-criteria to support evaluation and future comparative tests. Claims are bounded to analytic generalization from a single-country, manufacturing-sector qualitative sample; we make no assertion of statistical validation. Practically, the framework prioritizes governance work ahead of tool spend, helping organizations convert dashboards into repeatable decisions at speed.

Item Type:	Article
Uncontrolled Keywords:	big data analytics adoption, supply chain resilience, resource governance, manufacturing industry, qualitative, benefits, barriers
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management
Divisions:	Faculty of Management (FOM)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	10 Dec 2025 07:58
Last Modified:	10 Dec 2025 07:58
URII:	http://shdl.mmu.edu.my/id/eprint/15045

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