Scanbacks and direct rebates: manufacturer's tools against forward buying
Bibliographic record
Abstract
Abstract This paper evaluates the role of trade incentives specifically designed to fight forward‐buying practices on the part of the retailers, by examining the use of scanbacks and direct rebates as manufacturers' tools for the prevention of these practices. Scanner data allows the manufacturer to keep track of the retailer's pricing policies at the point of sale and hence tie its discount policy to the magnitude of the retailer's pass‐through to the final customers. Trade incentives of this type are called scanbacks, whereby the determination of the retailer's compensation is based on actual performance normally measured by scanner data. Another incentive is the direct rebate, whereby the manufacturer passes on directly to the final customer some discount, normally in the form of a coupon, upon proof of purchase. Rebates are one of the oldest trade incentives and certainly predate the advent of electronic commerce. Their relevance is enhanced by the fact that they can be easily adapted to the modern B2B marketplace. The economic effects of these incentives are evaluated in terms of their effect on the three basic links of the supply chain in question, namely (i) the manufacturer that offers the incentive; (ii) the retailer that develops the optimal pricing and ordering policy for each manufacturer's incentive; and (iii) the final customer who is the ultimate purchaser of the merchandise.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".