Disrespectful Promotions: The Negative Impact of Price Promotions on Products Symbolically Linked to Stigmatized Identities
Bibliographic record
Abstract
To reach a more diverse consumer base, companies have begun to offer products symbolically linked to stigmatized identities, and these products are often promoted by price discounts. Despite past work finding that linking consumer identities to products is generally appealing and that price promotions benefit consumers, the current research finds that offering discounts on products symbolically linked to stigmatized identities may backfire. Across eight studies, which include a variety of stigmatized groups in U.S. society, we find that when a company offers a discount on a product symbolically linked to a stigmatized identity, members of the stigmatized group react negatively toward the company (i.e., they hold less favorable attitudes, have lower purchase intentions, and choose the company's competitor). These negative reactions, which do not arise for nonstigmatized consumers, occur because stigmatized consumers perceive the company's action of offering a discount as disrespectful toward their social group. The effect is contingent on whether the company is an ingroup or outgroup member, the selection of other discounted products, and the type of sales promotions employed. This research enriches our understanding of stigmatized consumers and offers insights into the nature of disrespectful cues in the marketplace and the social cost of price promotions.
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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 | 0.001 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".