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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".