Preventing supplier non-conformance: extending the agency theory perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The supply chain management literature and agency theory suggest that preventing supplier non-conformance—a supplier's failure to conform to the requirements of the buyer—requires monitoring supplier behavior. However, case studies collected to explore how buyers monitored suppliers revealed an unexpected empirical phenomenon. Some buyers believed they could prevent non-conformance by either trusting their suppliers or relying on a third party, without monitoring their behavior. The purpose of this article is to examine conditions when buyers should monitor supplier behavior to prevent non-conformance. Design/methodology/approach This article employs a mixed-method design by formulating an agent-based simulation grounded in the case-study findings and agency theory to reconcile observed unexpected behaviors with scholarly suggestions. Findings The simulation results indicate that buyers facing severe consequences from non-conformance should opt to monitor supplier behavior. Sourcing from trusted suppliers should only be reserved for buyers that lack competence and have a small number of carefully selected suppliers. Moreover, buyers facing minor consequences from non-conformance should generally favor sourcing from trusted suppliers over monitoring their behavior. The results also suggest that having a third-party involved in monitoring suppliers is an effective path to preventing non-conformance. Originality/value By combining a simulation with qualitative case studies, this article examines whether buyers were making appropriate decisions, thereby offering contributions to theory and practice that would not have been possible using either methodological approach alone.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it