Study on the Perishable Product’s Pricing Decision With Overconfident Consumers in the Dual-Channel Setting
Bibliographic record
Abstract
With the development of internet, the online shopping mode has become more popular among consumers, and the online direct selling becomes more common. Besides buying products from traditional stores, consumers could get the product directly from the manufacturer online. In the dual channel setting, the competition becomes fiercer. Retailer should focus more on the price decision and take suitable pricing strategy to increase its profit. In this paper, consumer’s overconfidence behavior is incorporated into perishable products’ pricing decision in the partially integrated dual channel setting. Through the analysis of consumer’s decision making process, this paper constructs the model for partially integrated manufacturer and retailer under the mean and precision overconfidence scenarios, conducts the optimal analysis, and analyzes the effect of consumer’s overconfidence level on the optimal wholesale, retail and direct selling prices. We conclude that, no matter consumers are mean-overconfident or precision-overconfident; there are optimal wholesale price, direct sale price and retail price. Business enterprises should enhance their information collection capability and adopt some marketing measures to influence consumer’s overconfidence level in order to increase the sales revenue.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".