The Effect of Service and Food Quality on Customer Satisfaction and Hence Customer Retention
Bibliographic record
Abstract
The aim of this study was to investigate the relationships between service quality, food quality, customer satisfaction and customer retention in limited service restaurants in Jordan. A questionnaire-based survey was distributed to 400 students served at 10 limited service restaurants in the neighbourhood of universities in Amman, the capital city of Jordan. Service quality was measured in terms of SERVQUAL attributes. The key dimensions of food quality, customer satisfaction and customer retention were identified through literature. The data collected (283 valid questionnaires) were analysed using SPSS 20.0. The findings showed that service quality and food quality have a positive influence on customer satisfaction. In addition, service quality dimensions besides customer satisfaction have a positive influence on customer retention. Finally, the results confirmed that customer satisfaction mediates the relationship between service quality and customer retention. The small size of the sample is the main limitation of this study. The practical implications of this study are founded on the fact that limited service restaurants in the neighbourhood of universities should realize the critical role of service and food quality in satisfying their customers as an antecedent of their retention. This study is original as it examines the relationships between service and food quality and customer satisfaction and retention in a specific type of restaurants in Jordan.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".