Optimal trade-in delegation strategy considering store brand introduction and different power structures
Bibliographic record
Abstract
In recent years, retailers have increasingly introduced their store brands, which have had a huge impact on the OM of incumbent-brand manufacturers. Given that some manufacturers in practice provide trade-in services themselves while others delegate trade-in services to retailers, incumbent-brand manufacturers, who are suppliers to retailers with store brands, face the challenge of determining optimal trade-in delegation strategy. To address this challenge, our paper develops theoretical models to explore optimal trade-in delegation strategies under different power structures (i.e. manufacturer-leading, retailer-leading, and vertical Nash). The results show that the optimal trade-in delegation strategy of the manufacturer and the optimal delegation acceptance strategy of the retailer mainly depend on the fixed costs of providing trade-in services. Moreover, as the leadership power of the manufacturer increases, the manufacturer’s willingness to delegate trade-ins will increase, but the retailer’s willingness to accept trade-in delegation will decrease. Improving the quality of the store brand will reduce the trade-in delegation willingness of the manufacturer but will improve the delegation acceptance willingness of the retailer. In the extended cases, the optimal trade-in delegation strategy still holds considering the cap-and-trade policy, but it reduces the retailer’s willingness to accept trade-in delegation considering store brands participating in trade-ins.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".