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Record W2618095357 · doi:10.5430/ijba.v8n3p55

The Impact of Brand Equity Drivers on Consumer-based Brand Equity in the Sport Service Setting

2017· article· en· W2618095357 on OpenAlexvenueno aff
Brian S. Gordon, Jeffrey James

Bibliographic record

VenueInternational Journal of Business Administration · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsnot available
Fundersnot available
KeywordsBrand equityBrand awarenessBrand managementAdvertisingBusinessMarketingBrand extensionStructural equation modeling

Abstract

fetched live from OpenAlex

The importance of brand equity to a firm has been well documented by previous literature. Brands with high equity allow a firm to charge a premium price as well as garner a larger market share in relation to competitors (Simon & Sullivan, 1993). From the consumer’s perspective, previous research has failed to explain precisely how consumers perceive and become loyal to specific brands. Therefore, this study constructed and tested a consumer-based brand equity model based on Keller’s (2003a) brand equity pyramid that explains how consumer perceptions influence brand resonance. Data were collected from a general consumer sample (n = 787) in a mid-sized southeastern community in order to validate the consumer-based brand equity model. The results from an examination of the structural model confirmed a significant relationship between brand awareness and brand associations as posited by previous research. Brand associations were found to have a significant impact on a consumer’s cognitive evaluation (brand superiority) and affective response (brand affect) to a focal brand in the service realm. Further, this study highlighted the important role that emotions play in the process of building strong brand equity. Cumulatively, these findings revealed that two attitudinal constructs (brand superiority and brand affect) played a differential role in the brand association-brand resonance relationship in the services context.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.074
GPT teacher head0.394
Teacher spread0.320 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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