Impact of information asymmetry and logistics service quality on sales mode selection in dual-channel supply chains: A game-theoretic analysis
Bibliographic record
Abstract
This paper explores sales mode choice under asymmetric information and varying logistics service quality. A dual-channel supply chain model is examined, comprising a manufacturer, a retail platform, and two heterogeneous logistics service providers, where the retail platform possesses private information regarding channel competition through a game-theoretical analysis. It is shown that under conditions of information symmetry, when the market size is small, the manufacturer can maximize profits under the FA scenario (Agency selling under complete information); otherwise, the retail platform tends to prefer the opposite strategy. When the market size is moderate, the reselling dual-channel strategy emerges as the optimal choice for maximizing the overall profitability of the supply chain. In such cases, all supply chain participants achieve a win-win outcome under the same strategy. Under asymmetric information, the manufacturer predominantly opts for scenario AR (Reselling under asymmetric information), while the manufacturer may achieve profitability under scenario AA (Agency selling under asymmetric information).
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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 | 0.004 | 0.014 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".