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Record W4220668519 · doi:10.1177/00222437221094301

The Price Entitlement Effect: When and Why High Price Entitles Consumers to Purchase Socially Costly Products

2022· article· en· W4220668519 on OpenAlexaff
Saerom Lee, Karen Page Winterich

Bibliographic record

VenueJournal of Marketing Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEntitlement (fair division)Product (mathematics)EconomicsConsumption (sociology)HarmMicroeconomicsClass (philosophy)BusinessPsychologySocial psychology

Abstract

fetched live from OpenAlex

This research investigates when and why consumers purchase products with social costs (e.g., environmental harm). Six studies demonstrate that upper-class consumers are more likely to purchase a product with social costs when it has a higher price because they experience greater entitlement, which the authors term the “price entitlement effect,” allowing for purchase justification. In contrast, lower-class consumers do not feel entitled to purchase a product with social costs when it is higher-priced. This effect occurs because upper-class consumers tend to have a greater self-focus, with a higher price entitling them to more resources than others. Consistent with the entitlement mechanism, when egalitarian values are made salient, the price entitlement effect is mitigated, reducing upper-class consumers’ purchase of socially costly products. Notably, the price entitlement effect occurs only when products have social costs rather than for all higher-priced products. However, when the social costs of a product are severe, price entitlement does not sufficiently justify product purchase. This research provides theoretical and practical insights regarding when and why higher price entitles purchase of socially costly products, contributing to research on social class and socially responsible (vs. costly) consumption as well as choice justification.

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)0.033
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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 designNot applicable
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

Citations20
Published2022
Admission routes1
Has abstractyes

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