SERVICE QUALITY IMPACTS CUSTOMER SATISFACTION AND CUSTOMER LOYALTY (EMPIRICAL EVIDENCE FROM APPAREL INDUSTRY OF PAKISTAN)
Bibliographic record
Abstract
As customers are getting prone to technology and have easy access to information, creating loyal customers is becoming a challenge for service providers. Therefore, it becomes important for companies to take corresponding actions proactively. The study has been conducted within the apparel industry of Karachi. Data has been collected from the young Apparel Brand users within Karachi and analyzed through SPSS and PLS-SEM. It has been analyzed that customer satisfaction and rapport have a significant impact on customer loyalty and the repurchase intention of the customer. The quality of the service offered to the customer plays a significant role in achieving customer satisfaction and enhancing their loyalty which helps in achieving the repurchase intention of the customer. The present study highlights the measures that managers need to take for strengthening customer loyalty which results in repurchase behavior and positive word of mouth. It will develop an understanding of how the better allocation of the available organizational resources can be carried out which will help in increasing the repurchase intention of the customer. For the success of any organization, a greater proportion of satisfied customers is required and the organization needs to enhance its satisfaction and loyalty level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".