The Impact of Brand Equity Drivers on Consumer-based Brand Equity in the Sport Service Setting
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".