The Impact of Brand Identification, Brand Equity, Brand Reputation on Brand Loyalty: Mediating Role of Brand Affect in Pakistan
Bibliographic record
Abstract
Brand loyalty has been an important factor for quite a while in marketing structure of the organizations. Organizations tend to achieve brand loyalty through employing different strategies and by engaging customers to produce a sense of belongingness among them regarding the brand. One of the major sector that has shown a great trend and growth in recent era is the apparel industry of Pakistan. However, little studies have tried to conceptualize and check the influence of brand related concepts on the brand loyalty in Pakistan. Current study has tried to check the influence of Brand Reputation, Brand Identification, Brand Equity and Brand Effect on Brand Loyalty. An important contribution of the study was to consider the brand affect as the mediating variable among the Brand Reputation, Brand Identification, Brand Equity and Brand Loyalty. 180 questionnaires were distributed among the students of the universities and the 166 valid questionnaires were considered for the data analysis giving use response rate of 92%. Correlational and regression analysis was used to check the influence of the independent variables on the dependent variable while package of Preacher and Hayes 2016 was used for the mediation analysis. Limitations and Future Directions have been discussed in the study as well.
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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 | 0.001 | 0.002 |
| 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) | 0.006 | 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".