Advertising strategies of one e-commerce platform and competitive sellers in a supply chain under agency mode
Bibliographic record
Abstract
As the internet traffic dividend disappears, the growth of e-commerce platforms slows and competition among sellers increases. We examine whether the sellers choose to cooperate with the e-commerce platform in advertising. This paper considers advertising strategies in a supply chain with one e-commerce platform and two competitive sellers, where each seller can choose either cooperative advertising or independent advertising in a strategic game. This paper shows that the cooperative advertising strategy is a unique Nash equilibrium. Only when a seller has a relatively high revenue share ratio in the cooperative venture, and competition with the other seller is low, will the seller obtain higher profits with cooperative advertising than independent advertising. After competition increases to a certain level, the sellers fall into the prisoner’s dilemma. Cooperative advertising can increase the profits of the platform. It is better for the platform to cooperate with two sellers than with only one seller. Besides, a seller’s market share can also impact its advertising strategy. Previous studies were mainly based on traditional supply chains in the wholesale mode where the platform acts as a retailer. Our paper considers the supply chains under the agency mode where sellers compete on price and advertising.
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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 | 0.001 | 0.003 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".