Preventing supplier non-conformance: extending the agency theory perspective
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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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".