A bibliometric analysis of work-life balance policies on employee performance
Bibliographic record
Abstract
Work-Life Balance (WLB) Policies aim to balance employees' work and family life is a growing concern in developing nations compared to developed economies. This paper aims to provide a clear view of current dynamics and research diversification of literature in the field of work-life balance (WBL) policies on employee performance by using bibliometric analysis. Scopus database has been used for carrying out the bibliometric analysis based on 100 research papers published from 2000 to 2023 to see the trends of publications. The prominence of research is evaluated by publication trend, publisher, country, universities/research institution, frequency of citation and different keywords related to work-life balance, policies, and employee performance. The number of publications continues to increase after the year 2008. The region of reviewed papers is Nigeria, Kenya, the USA, the Philippines, India, Spain, South Africa, Romania, Jordan, Australia, Netherlands, Sweden, Sri Lanka, Bangladesh, Canada, Pakistan, and Malaysia. The United States, Spain and Nigeria have the highest rate of research publications, and the USA, Kenya, Nigeria Spain Netherlands Sweden, Bangladesh, and Romania countries focused on variables work-life balance policies, flexible working arrangements, workplace support, employee performance, employee satisfaction, retention, and organizational performance. The most common term used in the research is work-life balance policies. The novelty of this research paper is on the subject of WLB policies on employee performance using varied bibliographic approaches. Future studies avenues and recommendations and the study's limitations were also examined.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.132 | 0.228 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".