Less willing to pay but more willing to buy: How the elicitation method impacts the valuation of a promotion
Bibliographic record
Abstract
Abstract Willingness to pay (WTP—how much one is willing to pay for something) and willingness to buy (WTB—whether one is willing to buy something at a given price) are two common methods to elicit valuations and normatively should yield the same valuation order between two options. However, this research finds that WTP and WTB can yield opposite valuation orders between the regular offer and the promotional offer of a product. Specifically, it demonstrate that, (a) if the valuation of a product is only elicited with WTP, consumers value the product less when it is offered with a price promotion than when it is not; (b) if the valuation of a product is only elicited with WTB, consumers value the product more when it is offered with a price promotion than when it is not; and (c) if the valuation of a product is first elicited with WTP and then elicited with WTB, consumers always value the product less when it is offered with a price promotion than when it is not. A value‐inference account is proposed for the above findings, according to which, consumers infer the value of a promoted product differently when the valuation is elicited only with WTP or only with WTB. Theoretically, this research extends prior literature on sales promotion, showing that the valuation of a promotion is subject to the elicitation method. Practically, this research suggests how to help consumers manage their purchase intentions for promoted products.
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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 | 0.013 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".