The Mediation Effect of Customer Satisfaction on the Relationship Between Service Quality and Customer Loyalty
Bibliographic record
Abstract
The aim of the study was to investigate customer satisfaction’s mediation role in the relationship between service quality and customer loyalty within Ghana’s telecommunication sector. The report followed an approach to quantitative analysis and questionnaires to collect data from 105 respondents. The statistical analysis was performed using descriptive methods and inferential statistical techniques in IBM SPSS. The mediation analysis was made using Hayes' PROCESS macro model 4. Results of the analysis showed that quality of service is a substantially positive indicator of customer loyalty. It was also evident that Service Quality had a significant and positive influence on customer satisfaction. The direction between the mediator (Customer Satisfaction) and Customer Loyalty was positive but not significant. Again, it became evident that customer satisfaction partially balances the relationship between service quality and customer loyalty. It was evident that telecommunications companies' service quality cannot be the only predictor of customer loyalty, but customer satisfaction should be considered. The study recommends that telecommunications companies in Ghana should lose the script or the strict customer interaction code, review and analyze customer interactions, make multifunctional supports available and reconnect with disgruntled customers.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".