Microfoundations of Adaptive Decision‐Making Capability: How Chief Supply Chain Officers Convert Technology‐Enabled Visibility Into Coordinated Action
ABSTRACT Supply chain leaders increasingly operate with extensive technology‐enabled visibility, yet many firms still struggle to convert shared signals into timely, coordinated action. We frame this tension as the visibility‐to‐action problem in supply chain management (SCM): executives observe more than they can process and often fail to route, prioritize, and execute quickly enough for information to translate into coordinated outcomes. We examine how Chief Supply Chain Officers (CSCOs) close this gap. Drawing on dynamic capabilities and microfoundations research, we adopt a theory‐elaborating qualitative design that pairs a theories‐in‐use approach with the Gioia methodology, analyzing 25 CSCO interviews spanning manufacturing, retail, logistics, and technology sectors. Informants theorize that technology‐enabled advantage depends less on tool acquisition than on the intentional design of interoperable data architectures, conversion routines that filter and route exceptions to accountable owners, and learning stabilizers that preserve reliable adjustment over time. We conceptualize these as the microfoundations of adaptive decision‐making capability (ADMC), a higher‐order managerial capability linking information, judgment, and action into self‐reinforcing learning cycles. As a theorized downstream implication, participants further suggest that ADMC supports the development of adaptive supply chain capability (ASCC), reflected in agility, resilience, alignment, and learning velocity. The study extends microfoundations and dynamic capabilities research by specifying the cognitive, governance, and exception‐management mechanisms through which technology‐enabled visibility becomes coordinated action in data‐intensive SCM settings.
Authors
- Kerry T. Manis (ORCID: https://orcid.org/0000-0001-7895-8946)
- Sreedhar Madhavaram (ORCID: https://orcid.org/0000-0002-2252-3449)
- R. Glenn Richey (ORCID: https://orcid.org/0000-0002-7620-4435)
- Matthew Belford
Institutions
- New Mexico State University (US)
- Texas Tech University (US)
- Auburn University (US)
Publication Details
- Journal
- Journal of Business Logistics
- Published
- 2026-09-16
- DOI
- https://doi.org/10.1111/jbl.70096
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00