Promotion Architecture: A Deal Fairness Model of Restricted Price Promotions
Bibliographic record
Abstract
Abstract This research examines the effectiveness of two common types of restricted price promotions: threshold promotions (conditional on spending more than a threshold amount; e.g., “Get $5 off on orders of $10 or more”) and capped promotions (limited to a maximum dollar value; e.g., “Get 50% off, up to $5 per order”). Results from seven preregistered studies, including one field study, show that threshold promotions lead to higher purchase intentions and conversion rates (but potentially lower purchase amounts) than comparable capped promotions—even though capped promotions are equivalent in maximal economic savings for the consumer—when the trigger value (the spending amount at which the promotion activates or caps) is low. This effect occurs because consumers have higher expected promotion levels for capped promotions and lower expected spending levels for threshold promotions, leading them to perceive the threshold promotion as a fairer deal. However, this effect reverses when the trigger value is high, wherein consumers perceive capped promotions as a fairer deal and prefer them to threshold promotions. The implications of our results for the optimal management of price promotion architectures were discussed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".