Markdown pricing strategy under a dual-channel supply chain with strategic consumers
Bibliographic record
Abstract
This study investigates the markdown pricing strategies for a manufacturer and a retailer in a two-period dual-channel supply chain, where the manufacturer sells its products via its own direct channel and an independent retail channel to strategic consumers who may wait for markdowns. A two-period game is developed to systematically study the optimal regular prices and markdown prices under four cases, i.e. , no markdown in both channels, markdown only in the direct channel, markdown only in the retail channel, and markdowns in both channels. By comparing the different cases, we find that the manufacturer benefits most from the case with markdowns in both channels, where the markdown rate of the retail channel is lower than that of the direct channel. On the other hand, the results indicate that the retailer may also profit most from the case with markdowns in both channels when the consumer acceptance of the direct channel is sufficiently high; otherwise, the retailer enjoys the highest profit under the case with markdown only in the retail channel. Finally, it is found that strategic consumer behavior has a positive impact on the retailer’s profit but a negative impact on the manufacturer’s profit.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".