Participative pricing and donation programs in a socially concerned supply chain
Bibliographic record
Abstract
Abstract This study analyzes a participative pricing scheme, name‐your‐own‐price, as a practical marketing mechanism for a socially sustainable supply chain. There is an ongoing argument on the profitability of participative pricing. This study introduces a different approach in capturing optimal decisions of a corporate social responsibility practice. In the proposed approach, the manufacturer is willing to donate as long as, (I) business image is improving; thus, the potential market size is expanding and, (II) consumers are also donating through the proposed donation scheme. Results indicate that not only the total earnings are increasing, the market participation is also boosted. Further, operational decisions are coordinated successfully with a revenue‐sharing contract. Findings imply that there is a minimum threshold for manufacturer's participation ratio to ensure the profitability of the proposed scheme, not to mention consumer's donation size. Sensitivity analysis is conducted to prove the applicability of the proposed mechanism in real‐world applications.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".