The Influences of Procedural Justice on Turnover Intention and Social Loafing Behavior among Hotel Employees
Bibliographic record
Abstract
This study examines the influences of procedural justice on the turnover intention and social loafing behavior among employees in the hotel industry. Despite a growing body of literature regarding the relationship between organizational justice, turnover intention and social loafing, there is limited published research on the influence of procedural justice on social loafing behavior among hotel employees with the mediating effect of turnover intention. For this purpose, a questionnaire was self-administered to employees working at different hotels in Saudi Arabia. AMOS software was employed for structural equation modeling (SEM) data analysis. The results show that procedural justice significantly and negatively influences social loafing behavior. Furthermore, procedural justice significantly and negatively influences turnover intention, whereas the turnover intention significantly and positively influences social loafing behavior. Turnover intention partially mediates the link between procedural justice and social loafing. The study outcomes confirm that procedural justice is important for any organization; nevertheless, it is not enough to decrease social loafing behavior among hotel employees, especially when turnover intention exists. The results have implications for hotel practitioners and scholars in relation to reducing turnover intentions and social loafing behavior among employees.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".