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Record W4224717519 · doi:10.1002/cb.2057

Cultural congruity and extensions of corporate heritage brands: An empirical analysis of <scp>time‐honored</scp> brands in China

2022· article· en· W4224717519 on OpenAlexafffund
Luyang Zhou, Michael K. Hui, Lianxi Zhou, Shengxiao Li

Bibliographic record

VenueJournal of Consumer Behaviour · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of CanadaNational Office for Philosophy and Social Sciences
KeywordsCultural heritageChinaProduct (mathematics)Extension (predicate logic)Intangible cultural heritageBusinessMarketingAdvertisingSociologyPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Abstract This article furthers the understanding of cultural congruity in relation to extension evaluations of corporate heritage brands. The extension of corporate heritage brands remains challenging, due to their unique identities around heritage—typically fostering a narrow spectrum of product‐cultural domain. Drawing on the concept of cultural congruity, this paper puts forward the notion that one effective way to break the deadlock of a brand's heritage is to expand into product categories that share the unique cultural heritage in ways that are meaningful, not merely symbolic. Based on data from a large survey on time‐honored brands in China, this paper demonstrates that cultural congruity does not only influence extension evaluations directly, but also indirectly through perceived fit. The findings further indicate that brand cultural heritage positively moderates both direct and indirect effects. This study enhances the concept of product fit from the lens of cultural congruity and opens up a new avenue for managing corporate heritage brands in the marketplace.

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)0.002
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 score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.045
GPT teacher head0.293
Teacher spread0.248 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

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