No One Wants to Look Cheap: Trade‐Offs Between Social Disincentives and the Economic and Psychological Incentives to Redeem Coupons
Bibliographic record
Abstract
Existing research on price deals has largely demonstrated positive financial and nonfinancial consequences of obtaining a deal. In contrast, the research reported here suggests that certain price deals—in this case, coupons—can also produce negative social consequences, such as creating an impression of cheapness or stinginess. Decisions to redeem coupons are shown to involve a trade‐off between the social incentives to avoid coupons and competing economic and psychological incentives to redeem coupons. Consumers strategically adjusted their decision in response to factors that changed the relative strength of these incentives; specifically, they avoided coupons when they were concerned that coupon use would lead to negative social consequences but redeemed coupons when the circumstances reduced these concerns. Although decisions to refuse a coupon might violate principles of economic rationality, it is argued that such decisions are nevertheless functional as they serve important social goals. In this sense, it can be smarter for consumers to forgo a deal rather than obtain one.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".