Resource-Governed BDA Adoption for Resilient Supply-Chain Operations: Qualitative Evidence from Malaysian Manufacturing Industry

Ghazala Yasmeen, Lilian Anthonysamy () and Adedapo Oluwaseyi Ojo
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Ghazala Yasmeen: Faculty of Management, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia
Lilian Anthonysamy: Faculty of Management, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia
Adedapo Oluwaseyi Ojo: School of Management, College of Business & Law, Coventry University, Coventry CV1 5DL, England, UK

Sustainability, 2025, vol. 17, issue 21, 1-51

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.

Keywords: big data analytics adoption; supply chain resilience; resource governance; manufacturing industry; qualitative; benefits; barriers (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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