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Record W2216284865 · doi:10.1509/jmr.12.0388

When Do Consumers Avoid Imperfections? Superficial Packaging Damage as a Contamination Cue

2015· article· en· W2216284865 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Marketing Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsProduct (mathematics)BusinessFunction (biology)Packaging and labelingMarketingAdvertisingWork (physics)PsychologyRisk analysis (engineering)EngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Across six experiments, the authors demonstrate that superficial imperfections in the form of packaging damage can engender negative consumer reactions that shape subsequent attitudes and behaviors in ways that are not always objectively justified. Their findings show that these reactions function in a relatively automatic fashion, even emerging under conditions in which the packaging damage does not convey information about a health and safety threat from the product. The authors extend work on contagion to show that superficial packaging damage can act as a contamination cue, automatically activating thoughts of contamination and health and safety concerns. This tendency to avoid superficial packaging damage can be eliminated by counteracting these thoughts of contamination. This can be done with positive brand associations (i.e., by branding the product as organic) or by creating a physical buffer between the packaging damage and the product itself. The authors close with a discussion of implications for marketers, consumers, and public policy makers.

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.

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)0.021
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.099
GPT teacher head0.358
Teacher spread0.259 · 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