Quality at the Source or at the End? Managing Supplier Quality Under Information Asymmetry
Bibliographic record
Abstract
Despite the many benefits of outsourcing, firms are still concerned about the lack of critical information regarding both the risk levels and actions of their suppliers, who are usually just a few links away. Usually, companies manage supply chain risks by deferring payments to suppliers until after the delivery has been made. Even though the deferred payment approach shunts the risk from the buyer to the supplier, recent supply chain failures suggest that it does not necessarily eliminate the risk completely. Hence, many companies offer incentives and conduct inspections of the actions taken at the source rather than waiting for the end delivery. In this paper, we study the effectiveness of such incentive and inspection mechanisms undertaken by manufacturers to manage the quality of suppliers who are “privately” aware of the risk of failure. By comparing the agency costs associated with each contractual setting, we characterize the value of output- and action-based incentive mechanisms from the perspective of the manufacturer. We find that employing action-based incentives is effective for the manufacturer, specifically when working with a supplier that faces high costs of production and quality improvement. However, if the manufacturer faces high inspection costs or a low degree of information asymmetry, employing an output-based contract that results in differentiated quality improvement efforts becomes more effective. Finally, we analyze the marginal value of the combined contracting strategy and characterize when it strictly dominates over output- and effort-based contracts. The online appendix is available at https://doi.org/10.1287/msom.2017.0652 .
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".