An Empirical Study on the effect of Brand Equity of Mobile Phones on Customer Satisfaction
Bibliographic record
Abstract
In a competitive and brand conscious market like India, building brand equity of mobile phones is a challenging task. The companies need to assess the prior experiences and future aspirations of consumers to possess these brands. In this context, present study examines the relationship between brand equity dimensions, overall brand equity and customer satisfaction. Data was collected from 245 university students in New Delhi and adjoining cities of India through self-administered questionnaires using convenience sampling technique. Out of these, 205 were used for the purpose of study, which provided a response rate of 83.5 percent. Multiple regressions confirmed a significant relationship between the dimensions of brand equity and overall brand equity; and also between overall brand equity and customer satisfaction. Marketing managers often have limited resources in terms of money, time, and manpower to implement branding strategies, so these findings can help them to prioritize and allocate resources across important dimensions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".