Leaders’ Emotional Intelligence and Employee Retention: Mediation of Job Satisfaction in the Hospitality Industry
Bibliographic record
Abstract
The present study has catered to one of the major issues faced by organizations across the globe, employee retention. Therein, the study attempted to examine the relationship of leaders’ emotional intelligence with employee retention. In parallel, the study also tested to underline if job satisfaction could mediate this relationship. Through targeting non-managerial employees working in the four- and five-star hotels in Jordan, the present study sampled 380 employees from the 56 hotels. Through using structural equation modelling approach via Smart PLS 2.0 M3, the present study found significant relationship between leaders’ emotional intelligence and employee retention hence suggesting that leaders’ emotional awareness and management of employees whilst enriching their emotional expression and skills can significantly help boost employee retention. Accordingly, the study also reported significant mediation of job satisfaction in the relationship between leaders’ emotional intelligence and employee retention. The study has reported that leaders’ emotional intelligence can induce sense of belongingness leading to enhance job satisfaction which further results in harnessing employee retention. The study forwards notable implications for practice and scope for future studies in light of the findings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".