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Record W1987412795 · doi:10.1108/10610420010351402

Do counterfeits devalue the ownership of luxury brands?

2000· article· en· W1987412795 on OpenAlexaff
Arghavan Nia, Judith Lynne Zaichkowsky

Bibliographic record

VenueJournal of Product & Brand Management · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCounterfeitLuxury goodsAdvertisingBusinessMarketingPerceptionBrand namesValue (mathematics)CommercePsychology

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the perceptions and attitudes of original luxury brand owners towards counterfeit luxury goods. The results indicated that all respondents found luxury products fun and worth the price they paid for them, whether they were original or counterfeit. Almost 30 percent of respondents owned no counterfeits and only original goods. These respondents believed that counterfeits were inferior products and believed that ownership of original luxury products was more prestigious compared to counterfeit luxury goods. Conversely, those who owned more counterfeits had a positive image of them and did not believe these products were inferior. Overall, 70 percent of respondents indicated that the value, satisfaction, and status of original luxury brand names were not decreased by the wide availability of counterfeits. Further, the majority of respondents disagreed that the availability of counterfeits negatively affects their purchase intentions of original luxury brands.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.252
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations625
Published2000
Admission routes1
Has abstractyes

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