Cooperative advertising to induce strategic customers for purchase at the full price
Bibliographic record
Abstract
Abstract In this paper, cooperative advertising in a manufacturer–retailer supply chain in the presence of the strategic customer is studied. It is assumed that a proportion of the customers strategically postpone their purchase in order to achieve a higher surplus. Advertising can enhance the willingness to pay (WTP) of the customers and, as a result, their surplus. In this paper, the effect of cooperative advertising in order to induce customers for early purchase is analyzed. While customer purchasing in the full price leads to higher income for the retailer, it requires spending more on advertising. This trade‐off between the benefits of inducing customers to early purchases and the advertising expenditure is a key to understanding the retailer's optimal advertising decision. On the other hand, it is interesting for the manufacturer to find out when supporting the retailer for its advertising expenditure is beneficial. Nash and Stackelberg (manufacturer as a leader) game‐theoretic models are established to investigate whether, in the channel, it is optimal to induce customers by increasing advertising expenditure or not. Results of the Stackelberg game show that the manufacturer participates in the retailer's advertising expenditure only if it leads to early purchasing of the strategic customers and the participation rate depends on the wholesale price. From the retailer's point of view, when the impact of advertising on the WTP of customers is not significant, the more the proportion of strategic customers, the more inducing customers for early purchase is beneficial. When advertising effectively increases WTP, regardless of the fraction of strategic customers, it is beneficial for the retailer to enhance its advertising in order to dissuade customers from postponing their purchase.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".