The Influence of Brand Loyalty on Cosmetics Buying Behavior of UAE Female Consumers
Bibliographic record
Abstract
The worldwide annual expenditures for cosmetics is estimated at U.S. $18 billion, and many players in the fieldare competing aggressively to capture more and more markets. The purpose of this article is to investigate theinfluence of brand loyalty on cosmetics buying behavior of female consumers in the Emirate of Abu Dhabi in theUAE. The seven factors of brand loyalty are brand name, product quality, price, design, promotion, servicequality and store environment. Questionnaires were distributed and self-administered to 382 respondents.Descriptive analysis, one-way ANOVA and Pearson Correlation were used in this study. The findings of thisstudy indicated that brand name has shown strong correlation with brand loyalty. The research results showedthat there is positive and significant relationship between factors of brand loyalty (brand name, product quality,price, design, promotion, service quality and store environment) with cosmetics brand loyalty.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".