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Record W4403386933 · doi:10.1177/00222437241293065

Disrespectful Promotions: The Negative Impact of Price Promotions on Products Symbolically Linked to Stigmatized Identities

2024· article· en· W4403386933 on OpenAlexaff
Guanzhong Du, Kobe Millet, Aylin Aydinli, Jennifer Argo

Bibliographic record

VenueJournal of Marketing Research · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdvertisingBusinessMarketingSocial psychologyEconomicsMicroeconomicsPsychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.396
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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