Assessing a brand equity model for fast moving consumer goods in cosmetic and hygiene industry
Bibliographic record
Abstract
This paper presents an empirical investigation to study the effects of ten factors on brand equity.The study provides an assessment using a brand equity model for fast moving consumer goods in cosmetic and hygiene industry.The study has accomplished among people who purchase goods in six major cities of Iran based on an adapted questionnaire originally developed by Aaker (1992a) [Aaker, D. A. (1992a).The value of brand equity.Journal of Business Strategy, 13(4), 27-32.].Cronbach alpha has been calculated as 0.88, which is well above the minimum acceptable level of 0.7.In addition, Kaiser-Meyer-Olkin Measure of Sampling adequacy and Bartlett's test of Sphericity approximation Chi-Square are 0.878, 276628 with Sig.= 0.000, respectively.The proposed study of this paper uses structural equation modeling to test different hypotheses of the survey.The Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI) and Chi-Square/df are 0.067, 0.840 and 4.244 and they are within desirable levels.While the effects of seven factors on brand equity have been confirmed.However, the survey does not confirm the effects of perceived value, advertisement effectiveness and advertisement to brand on brand equity.In our survey, brand loyalty maintains the highest positive impact followed by having updated brand, trust to brand, perceived quality to brand, brand awareness, intensity of supply and perception to brand.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".