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Record W2113001274 · doi:10.1509/jm.10.0400

Brand Concepts as Representations of Human Values: Do Cultural Congruity and Compatibility between values Matter?

2012· article· en· W2113001274 on OpenAlexafffund
Carlos J. Torelli, Ayşegül Özsomer, Sergio W. Carvalho, Hean Tat Keh, Natalia Mæhle

Bibliographic record

VenueJournal of Marketing · 2012
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of ChinaUniversity of Minnesota
KeywordsAdvertisingCompatibility (geochemistry)Function (biology)PsychologyCultural valuesMarketingSociologyBusinessSocial psychologySocial science

Abstract

fetched live from OpenAlex

Global brands are faced with the challenge of conveying concepts that not only are consistent across borders but also resonate with consumers of different cultures. Building on prior research indicating that abstract brand concepts induce more favorable consumer responses than functional attributes, the authors introduce a generalizable and robust structure of abstract brand concepts as representations of human values. Using three empirical studies conducted with respondents from eight countries, they demonstrate that this proposed structure is particularly useful for predicting (1) brand meanings that are compatible (vs. incompatible) with each other and, consequently, more (less) favorably accepted by consumers when added to an already established brand concept; (2) brand concepts that are more likely to resonate with consumers with differing cultural orientations; and (3) consumers’ responses to attempts to imbue an established brand concept with new, (in)compatible abstract meanings as a function of their own cultural orientations.

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)0.005
metaresearch head score (Gemma)0.029
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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.005
Scholarly communication0.0050.005
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.109
GPT teacher head0.456
Teacher spread0.347 · 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

Citations201
Published2012
Admission routes2
Has abstractyes

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