The Influence of Person–Job Fit, Work–Life Balance, and Work Conditions on Organizational Commitment: Investigating the Mediation of Job Satisfaction in the Private Sector of the Emerging Market
Bibliographic record
Abstract
This study aims to provide critical managerial implications for human resource (HR) practitioners at private-sector organizations from an emerging economy perspective. The study helps to optimize organizational commitment in the assessment of work–life balance, person–job fit, work condition, and the mediation of job satisfaction. It also investigates the influence of certain demographic variables on organizational commitment. The population comprises employees working in private sector organizations across Pakistan. A total of 1100 survey questionnaires were sent to potential respondents; 843 responded, giving a response rate of 77%. SmartPLS 3 software and SPSS were used to perform structural equation modeling. The study revealed that work–life balance, person–job fit, and job satisfaction have a positive influence on organizational commitment. Job satisfaction intervenes complementarily with the relationship of work–life balance and person–job fit with organizational commitment, while full mediation of job satisfaction was found for work conditions. Age, female gender, experience with current employee, and total industry experience were positively related to organizational commitment. HR managers at private-sector organizations must strive to provide work–life balance, person–job fit, and better work conditions so that employees are optimally satisfied on the job and exercise strong affective 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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".