Bibliographic record
Abstract
Although several brand equity measures have been proposed in the literature, a comparative assessment of their characteristics and performances is lacking. This paper attempts to fill that gap. Combining survey data with real market data, it assesses two types of brand equity measure: customer mind-set measures (brand knowledge) and product-market performance measures (revenue premium). The results confirm that the customer mind-set measure captures cumulative brand-building effects better and offers diagnostic information. However, the revenue premium is found as a better choice for continuous tracking of brand equity because (a) it could reveal the true changes in brand equity; (b) it is a practical and convenient measure since its data requirements are readily available; and (c) it flags any change in brand-equity before the customer mind-set measure. Furthermore, the product-market performance measure is found to precede the customer mind-set. This study also conducts the first empirical test of the well-known brand value chain model on real market data. Finally, operationalising the customer mind-set measure on real market data for the first time, this study confirms that advertising and distribution are positively associated with brand-equity, while price promotion is negatively associated. By considering multiple measures, this study improves the robustness of the findings as well as addressing marketing accountability issues.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".