The Effect of Firm's Brand Reputation on Customer Loyalty and Customer Word of Mouth: The Mediating Role of Customer Satisfaction and Customer Trust
Bibliographic record
Abstract
The purpose of this study is to explain the role of corporate brand reputation in building strong relationships with customers. This study aims to build and test a model that includes interrelationships among a corporate brand reputation and its possible consequences. A structured questionnaire was distributed to users of home appliances who are located in Egypt. Data were obtained from 357 respondents and were analyzed using statistical package for social science (SPSS) version.16 and analysis of moment structure package (AMOS) version.18.The results indicated that a firm's brand reputation has a significant impact on customer satisfaction which has a significant effect on customer trust and both customer satisfaction and customer trust have a significant effect on customer loyalty. The results of this research can be generalized to other industries and other countries. Firms can have a good reputation by offering high-quality and innovative products to the customers and considering the criteria that the customers use to evaluate home appliances before purchasing. This study is one of the few studies that investigate the interrelationships among a corporate brand reputation and its possible consequences. This study helps firms' managers to know how to create a positive corporate brand reputation and gain its benefits.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".