The Price Entitlement Effect: When and Why High Price Entitles Consumers to Purchase Socially Costly Products
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".