Present Bias and Quality Reduction on Daily Deal Platforms
Bibliographic record
Abstract
We study daily deal markets, i.e. platforms where sellers, called merchants, offer coupons for their products at a heavily discounted price for a short time window. Inspired by the evidence that both merchants and customers are often unsatisfied with their experiences with daily deals, we setup a two-period model that reconciles such evidence. In the first period, the merchant sells the coupons at the (low) price imposed by the platform and can choose the quality of its product, which is unobserved by the consumers. In the second period, when the deal period has expired and the merchant is free to set the selling price, first-period customers purchase again only if they were satisfied with the quality of the product. Our crucial result is that, if the merchant has present biased preferences, the daily deal market exacerbates the risk that the merchant provides a low quality product, even though, at the beginning of the daily deal campaign, the merchant was fully aware that only a high quality product would have made the campaign profitable. We also show that it might be in the interest of the platform to set a higher price for the coupons, as this would reduce the risk of having low quality products sold on the platform, avoiding negative reputational effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".