A study on relationship of brand characteristics and customer satisfaction with brand loyalty
Bibliographic record
Abstract
Brands are considered as valuable assets of a company both economically and strategically. Thus, having customers who are loyal to a brand is one of the main goals of businesses companies. Identifying and anticipating the customers’ needs are vital to the enterprises gaining competitive advantage and market segmentation. Maintaining and enforcing customers’ loyalty is a strategic challenge for companies seeking to keep and promote their competitive status in the market. High brand loyalty of the customers generates competitive advantage for the company and it increases the income and decreases the costs of marketing. In addition to increasing the market share, loyalty allows to ask for higher prices compared with its competitors. This study seeks to investigate the factors influencing customer loyalty to sport brands. The statistical population of this research was Tehran city. Using simple cluster sampling, 502 customers of four known brand sports, i.e. Nike, Puma, Adidas, Reebok, and Fila were chosen. Furthermore, a questionnaire, which assessed seven variables including reputations, brand name, brand image, brand loyalty, customer satisfaction, promotion and the price was used and the hypotheses were analyzed using statistical tests such as Kolmogorov-Smirnov test (K-S test), Spearman correlation, simultaneous linear regression and binomial test. The findings suggested that the brand name has a strong correlation with brand loyalty. Moreover, variables such as reputation, brand image, customer satisfaction, price and promotions also have positive and significant effect on the brand loyalty.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".