Bibliographic record
Abstract
Purpose Mail‐in rebates are an oft used, but poorly understood mechanism to promote the purchase of a product. In particular, prior research suggests that a significant percentage of consumers who purchase a product intending to redeem an accompanying rebate, fail to do so – a phenomenon known as “slippage.” To date, however, there has been very little research designed to understand why this takes place. The authors here aim to propose that the presence of a rebate provides a consumer with the means to justify a preferred course of action. Specifically, when considering the purchase of a desired product that carries a rebate, consumers tend to generate scenarios of successful rebate redemption and fail to adequately account for things that can go wrong in the redemption process. As a result, they systematically overestimate their likelihood of rebate redemption. Design/methodology/approach The authors conduct three laboratory experiments to test the proposed framework. Findings Study 1 shows that consumers overestimate their redemption likelihood because they tend to generate scenarios of successful rebate redemption and fail to adequately account for things that can go wrong in the redemption process. In studies 2 and 3, it is found that this effect is moderated by the valence and strength of one's motivation to purchase the promoted product. Originality/value The authors propose a new psychological account to explain consumer responses to rebate offers, and in particular study the role of motivation and elaboration. The results suggest that managers could use rebates in situations where customers need a reason to purchase, and that rebates for hedonic products are best delivered at the point‐of‐purchase.
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".