Information and selling mode strategies in a supply chain with an outsourced private label product
Bibliographic record
Abstract
Abstract This paper examines the interplay between the information strategy of an e‐commerce platform and the selling mode strategy of a manufacturer within a co‐opetitive supply chain, as well as the identification of the optimal supply chain strategy. We develop a supply chain model where a platform outsources production of its private label product to a manufacturer, who also sells its national brand product through the platform. The platform must decide whether to acquire consumer quality preference information at a cost and share it with the manufacturer, while the manufacturer needs to choose between the reselling mode or the agency selling mode for its national brand product. The two driving effects (competition‐intensification effect and mode differentiation effect) are identified. Our findings show that the platform will acquire and share information when the acquisition cost is sufficiently low, leading to the “competition‐intensification effect.” Additionally, the manufacturer prefers the agency selling mode when cost‐quality efficiency is low, and the reselling mode otherwise, driven by the “mode differentiation effect.” In cases where information sharing is absent, the manufacturer is more likely to choose the agency selling mode. Interestingly, when the cost‐quality efficiency of the manufacturer's product is moderate and the information acquisition cost is low, the “competition‐intensification effect” and the “mode differentiation effect” offset each other, resulting in the expansion of the region where the manufacturer chooses the reselling mode due to the platform's information‐sharing strategy. As a result, this enhances a cooperative relationship between the manufacturer and the platform. We also derive the optimal supply chain strategy, providing insights into both the manufacturer's selling mode and the platform's information strategies in online retailing.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".