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Record W2133989800 · doi:10.5539/ijms.v3n3p151

The Possible Effects of Need for Uniqueness’s Dimensions on Luxury Brands: Case of Iran and UAE

2011· article· en· W2133989800 on OpenAlexvenueno aff
Alireza Miremadi, Hiva Fotoohi, Farhad Sadeh, Farhad Tabrizi, Kasra Javidigholipourmashhad

Bibliographic record

VenueInternational Journal of Marketing Studies · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsSimilarity (geometry)UniquenessCreativityPerspective (graphical)MarketingQuality (philosophy)Relation (database)Order (exchange)AdvertisingPsychologyBusinessSocial psychologySociologyMathematicsComputer scienceEpistemology

Abstract

fetched live from OpenAlex

The current research aims to explore the possible effects of need for uniqueness’s dimensions on fashion luxury brands purchase intentions and to compare Kish and Dubai market in this perspective. The researchers consider three dimensions for uniqueness: creative choice, similarity avoidance and unpopular choice. In addition, the relations between those three dimensions were investigated.Findings indicate that consumers want to express their individuality, and they also want to maintain social norms. This findings support the idea that some consumers prefer expensive and high quality brands that are considered prestigious. This study reveals that there is a relation between creativity choice and unpopular choice among respondents of two markets and the relation between unpopular choice and similarity avoidance, creativity choice and similarity avoidance is only valid among respondents of Iran. The definition of uniqueness for both markets is the same due to order of importance for each constructs in mind of participants in the survey while this definition remain the same between genders and among respondents with different level of education.

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 score0.681
Threshold uncertainty score0.210

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 communication0.0000.000
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 head0.057
GPT teacher head0.311
Teacher spread0.254 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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