Bibliographic record
Abstract
The benefits of supply chain innovations such as quick response (QR) have been extensively investigated. This paper highlights a potentially damaging impact of QR on retailer effort. By lowering downstream inventories, QR may compromise retailer incentives to exert sales effort on a manufacturer's product and may lead instead to greater sales effort on a competing product. Manufacturer-initiated quick response can therefore backfire, leading to lower sales of the manufacturer's product and, in some cases, to higher sales of a competing product. Evidence from case studies and interviews shows that some manufacturers view high retailer inventory as a means of increasing retailer commitment (“a loaded customer is a loyal customer”). By implication, manufacturers should recognize the effect we highlight in this paper: the potential of QR to lessen retailer sales effort. We show that relatively simple distribution contracts such as minimum-take contracts, advance-purchase discounts, and exclusive dealing, when adopted in conjunction with QR, can remedy the distortionary impact of QR on retailers' incentives. In two recent antitrust cases we find evidence that, consistent with our theory, manufacturers adopted exclusive dealing at almost the same time that they were making QR-type supply chain improvements.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".