How Retailer Co-Opetition Impacts Pricing, Collecting and Coordination in a Closed-Loop Supply Chain
Bibliographic record
Abstract
The cooperative and competitive (i.e., co-opetition) behavior between retailers plays a significant role in the development of operations and marketing strategies in a supply chain. Specifically, retailers’ co-opetition relationship pivotally influences the sustainable performance in a closed-loop supply chain. This study examines the impact of retailer co-opetition on pricing, collection decisions and coordination in a closed-loop supply chain with one manufacturer and two competing retailers. Based on observations in some industries (e.g., electronic manufacturing, fabric and textile, etc.), the cooperative and competitive relationships between retailers can be classified into the following three different modes: Bertrand competition, Stackelberg competition, and Collusion. In this paper, we establish a centralized and three decentralized game-theoretic models under these three co-opetition modes and characterize the corresponding equilibrium outcomes. The results indicate that the Bertrand competition mode yields the highest return rate, which is also superior to the other two modes for both the manufacturer and the supply chain system in terms of profitability. However, it can be shown that which mode benefits the retailers would depend on the degree of competition between the retailers and the relative remanufacturing efficiency. Interestingly, we find that the retailer’s first-move advantage does not necessarily lead to higher profits. In addition, we design a modified two-part tariff contract to coordinate the decentralized closed-loop supply chains under three different retailer co-opetition modes, and the results suggest that the optimal contractual parameters in the contracts highly rely on the remanufacturing efficiency and the competition degree between the two retailers. Several managerial insights for firms, consumers and policy makers are provided through numerical analysis.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".