Manufacturer’s agency channel encroachment on an online retail platform
Bibliographic record
Abstract
Channel encroachment intensifies competition among channels and changes the relationships within the supply chain. This study examines the manufacturer's agency channel encroachment decision and its impact when it has already operated a platform reselling channel and a retailer channel on the platform. Equilibrium results reveal that the manufacturer's agency channel encroachment triggers a competition effect, leading to a reduction in market demand for both the platform's reselling channel and the retailer's channel, as a larger share of the market shifts toward the manufacturer's agency channel. To compensate for the losses in sales experienced by the platform and retailer, the manufacturer lowers the wholesale price. The manufacturer consistently benefits from channel encroachment and a Pareto improvement region exists, allowing all supply chain participants to improve their profits. The model is extended to consider sequential decision-making and asymmetric substitution. In comparison, under sequential decision-making, the manufacturer tends to focus more on the competitive effects of channel encroachment, leading to a reduction in channel sales. However, this approach only enhances the manufacturer's agency profit when the retailer's substitution capability is relatively strong. The manufacturer faces greater competitive pressure from the retailer under asymmetric channel substitution. Although the manufacturer increases the wholesale price and adjusts sales across channels according to the competitive situation, its profits are always lower than in the symmetric substitution case. The presence of a Pareto improvement region in the extended model confirms the robustness of our findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".