Quality at the Source or at the End? Managing Supplier Quality Under Information Asymmetry
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
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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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