The Mediating Effect of Organizational Commitment on the Relationship between Work-life Balance and Intention to Leave: Evidence from Working Women in Jordan
Bibliographic record
Abstract
The aim of this study is to examine the mediating effect of organizational commitment on the relationship between work-life balance and intention to leave. The study population consists of (800) working women in King Abdullah University Hospital (KAUH). To collect the primary data a questionnaire survey was distributed to (200) working women, from them (144) questionnaires were returned. The study used linear regression and path analyses to test hypotheses. The study findings revealed that; there is a significant relationship between work-life balance and intention to leave, there is a significant relationship between work-life balance and (affective, continuance and normative) commitment, but not with continuous commitment, there is a significant relationship between (affective, continuance and normative) commitment and intention to leave. Finally, affective commitment fully mediates the relationship between work-life balance and intention to leave, while normative commitment partially mediates the relation. In light of these findings, the study recommends; adoption of the work-life balance program in Jordanian hospitals to improve the commitment of working women, and encourages Jordanian organizations to enhance the participation of employee in decision making and locus of control to improve organizational commitment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".