Manufacturer-Retailer Supply Chain Cooperation Through Franchising: A Chance Constrained Game Approach
Bibliographic record
Abstract
In the literature of manufacturer-retailer supply chains, the focus of research is on a relationship in which a manufacturer is the leader and retailers are followers. This relationship implies a dominance of the manufacturer over retailers. Recent studies in marketing have shown a shift of retailing power from manufacturers to retailers. Retailers have equal or even greater power than a manufacturer when it comes to retailing. Based on this new market phenomenon, we intend to investigate a special manufacturer-retailer supply chain, i.e., the franchisor-franchisee supply chain. Utilizing chance constrained game theory, we explore the role of franchising efficiency with respect to transactions between a franchisor and a franchisee through fixed lump-sum fees, royalties, wholesale prices and retail prices. Two franchising game models are discussed. In a leader-follower non-cooperative game, the franchisor is assumed to be a leader who first specifies the fixed lump-sum fee, the royalty payment, and the wholesale price. The retailer, as a follower, then decides on the retail price. We then relax the assumption of the retailer’s inability to influence the manufacturer’s decisions and discuss a cooperative and partnership situation between the franchisor and the franchisee. The Nash (1950) bargaining model is utilized to implement profit sharing for the franchisor and the franchisee to achieve their cooperation.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".