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Record W2578088830 · doi:10.5539/ijms.v9n1p91

Investigating the Effect of Selected Marketing Efforts in Brand Equity Creation and Its Cross-Cultural Invariance in Emerging Markets

2017· article· en· W2578088830 on OpenAlexvenueno aff
Isaac Asare, Shen Lei

Bibliographic record

VenueInternational Journal of Marketing Studies · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsBrand equityMarketingBrand managementBusinessConceptualizationBrand awarenessStructural equation modelingAdvertisingBrand extensionMathematicsComputer science

Abstract

fetched live from OpenAlex

In the field of brand management, numerous studies have been conducted on brand equity conceptualization, measurement and validation. Also, previous researchers have shown that consumer-based brand equity via its dimensions can be created and maintained through a company’s marketing mix activities. Brand equity according to Keller, is the differential effect of brand knowledge on consumer response to the marketing activities performed on the brand. Due to cultural differences, consumers’ reaction will differ and thus these marketing efforts will have varying results in different markets.Drawn from both Aaker’s & Keller’s conceptualizations of brand equity, the current study develops a brand equity creation process model similar to Yoo et al.’s and examines its cross-cultural invariance through a structural invariance test using data from two important growing markets—Ghana and China. Results prove that some marketing efforts and dimensions of brand equity have invariant effects on brand equity across the Ghana and Chinese samples. Specifically, the effect of price on perceived quality was not equivalent in both markets. Relationship among brand equity dimensions were also not equivalent, however these dimensions all show an equivalent, positive effect on brand equity. Managerial implications for international brands and limitations for future research are discussed.

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)0.010
metaresearch head score (Gemma)0.024
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 score0.065
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.040
GPT teacher head0.376
Teacher spread0.336 · 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.

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

Citations9
Published2017
Admission routes1
Has abstractyes

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