A survey on important factors influencing brand equity: A case study of banking industry
Bibliographic record
Abstract
One of the most important issues in increasing customers' needs is to increase the quality of services through providing better quality services.Customer satisfaction is one of the primary requirements to meet people's needs and to have an efficient customer relationship management (CRM) we need to detect the most important factors influencing efficiency and effectiveness in banking industry.In this paper, we present an empirical study to detect these factors in one of private banks in Iran.The proposed study of this paper tries to reach three objectives.We first detect important factors, which build customers' perception towards CRM, then we detect all influencing factors, which impact CRM, and finally, we evaluate the impact of CRM towards brand equity.The proposed study first designs a questionnaire and distributes it among 386 customers.Using structural equation modeling and certified factor analysis, we analyze the results.The results indicate that three factors including information, employee job behavior and suggestions and other factor have meaningful impact on customer brand equity.However, the impact of equipment on customer brand equity was not meaningful.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".