The impact of platform fee scheme on manufacturer-E-tailer co-operative advertising: A game-theoretic analytical study
Bibliographic record
Abstract
This study analyzes how the new online platform-selling format affects the optimal co-operative advertising choices of both the manufacturer and the e-commerce platform (e-tailer), and explores the effects of the e-tailer’s contractual forms and commission fees on these choices. In the model, the manufacturer can decide its advertising expenditure as well as the level of participation in the e-tailer’s co-operative advertising program. Unlike the traditional reselling format, in platform selling the manufacturer can decide the retail price charged to the customer directly. The underlying forces of the profit corrosion effect of the commission fee and the demand expansion effect of co-operative advertising reshape the channel’s outcomes. Our findings show that a high commission fee can hinder or foster investment in the co-operative advertising program. Furthermore, the e-tailer always prefers the revenue-sharing scheme under which both e-tailer and manufacturer are better off in most cases, but it results in a lower participation rate and profitability for the manufacturer compared with the per-unit rent scheme. Moreover, the supply chain’s performance could be better when the relative efficiency of co-operative advertising is either sufficiently low or sufficiently high.
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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 | 0.019 |
| 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 | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".