What are the mechanisms through which inter-organizational relationships contribute to supply chain resilience?
Bibliographic record
Abstract
Purpose Our study advances theory in supply chain resilience (SCRes) by identifying and describing the mechanisms through which interorganizational relationships (IORs) contribute to SCRes. Design/methodology/approach We employ a multi-method conceptual development design combining structured and narrative review of the literature, supported by illustrative case studies. A four-stage refinement process was used for data reduction, and analysis was informed by complex adaptive systems (CAS) theory. Findings Our findings identify connectivity, collectivity and scalability as key mechanisms through which relationships between organizations contribute to SCRes. These mechanisms draw on IOR elements of information sharing, decision synchronization and incentive alignment to augment self-organization and emergence, and adaptation and coevolution via modifying/advancing resilience strategies and practices. Originality/value Our study advances theory and practice of SCRes by expounding on how connectivity, collectivity and scalability act as mechanisms that drive and diffuse the contribution of resilient strategies/practices to resilience capability. This is significant for strategic alignment between IORs and SCRes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".