The mediating effect of employee happiness on the relationship between quality of work-life and employee intention to quit: A study on fast-food restaurants in Jordan
Bibliographic record
Abstract
This study aims to investigate the mediating role of employee happiness in the effect of the quality of work-life on employee intention to quit. Data were collected using a questionnaire developed regarding prior related works. It was administered to a sample consisted of 122 employees selected from four fast-food restaurants in Jordan. Data analysis was conducted using structural equation modelling via Smart-PLS3; the results revealed that the quality of life had significant direct effects on both employee happiness and employee intention to quit. The first effect was positive, while the second was negative. Employee happiness had a significant impact on employee intention to quit. Consequently, the results indicated that employee happiness played a significant mediating role in the effect of the quality of work-life on employee intention to quit. It was concluded that the quality of work-life is not enough to reduce employee intention to quit, which means that organization should pay great attention to employee happiness to ensure the positive effect of the quality of life and to reduce employee intention to quit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".