Coordination of cooperative promotion efforts with competing retailers in a manufacturer-retailer supply chain
Bibliographic record
Abstract
In this paper, the issue of cooperative (co-op) promotion efforts is addressed in a two-stage supply chain (SC).The investigated SC includes one monopolistic manufacturer and two duopolistic retailers facing different market demands.The customers' demand is affected by both advertising efforts of the manufacturer and two retailers.Moreover, the retailers compete with each other on local advertising investments within the market.In order to boost the retailers' advertising level, it is assumed that the manufacturer pays a ratio of the retailers' advertising expenditures.We propose four non-cooperative game scenarios and one cooperative game.Non-cooperative models are established through both Stackelberg and Nash game between two echelons.Moreover, both Cournot and Collusion behaviors are assumed to be followed by two retailers.We develop a promotion cost sharing contract to achieve the channel coordination.Under cooperation model, all SC members seek to reach the highest profit for the entire SC by considering the bargaining power of the SC participants.In each game scenario the optimal solution and unique equilibrium are determined.In addition, a comparison on the advertising level of all SC members along with the value of participation rate are provided.In addition, the feasibility of the cooperative game is discussed and resulted.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".