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Record W2081489660 · doi:10.1016/j.jcps.2013.07.003

The flip side of vanity sizing: How consumers respond to and compensate for larger than expected clothing sizes

2013· article· en· W2081489660 on OpenAlexaff
JoAndrea Hoegg, Maura L. Scott, Andrea C. Morales, Darren W. Dahl

Bibliographic record

VenueJournal of Consumer Psychology · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClothingSizingConsumption (sociology)MarketingAdvertisingBusinessWork (physics)SociologyLawEngineering

Abstract

fetched live from OpenAlex

Abstract Vanity sizing has become a popular retail trend and recent work shows it has a favorable impact on consumers. However, as the current research demonstrates, significant variations in sizing standards across retailers mean that consumers are as likely to encounter larger sizes as they are smaller, “vanity” sizes when shopping, highlighting the importance of understanding how consumers react to this potential threat in the marketplace. Across five studies we demonstrate that larger sizes result in negative evaluations of clothing and show that these effects are driven by consumers' appearance self‐esteem. Importantly, we also find that instead of unilaterally lowering purchase intent as one might assume, larger sizes can actually increase spending, as consumers engage in compensatory consumption to help repair their damaged self‐esteem. In so doing, this research reveals a dynamic and complex relationship between consumers and sizing labels, where shopping can serve to build, strengthen, threaten, and/or repair appearance self‐esteem.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.305
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations50
Published2013
Admission routes1
Has abstractyes

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