Influence of Work Life Balance Policies on Employee Job Satisfaction In Kenya’s Banking Sector; A Case of Commercial Banks in Nairobi Central Business District
Bibliographic record
Abstract
The aim of this research was to analyze the relationship between work life balance policies and employee job satisfaction.Work life balance entails attaining equilibrium between professional work and other activities, so that it reduces friction between official and domestic life.Job satisfaction refers to the attitude people have towards their job and the organisations they work for.The quality of work life policies is increasingly becoming part of the business strategy and the focus is on the potential of these policies to influence employee's quality of working life and more importantly to help them maintain work-life balance with equal attention on performance, commitment at work and job satisfaction.This study proves to be a milestone for the researchers, policy makers and students to properly understand the concepts of employee job satisfaction, work life balance and their relationship.The study employed descriptive research design.The target population was Two hundred and Forty Respondents.Data was collected using questionnaires, and analysed using Statistical Package for Social Sciences.The findings of this study emphasized that each of the work life balance policies on its own is a predictor of job satisfaction.The goodness of fit, R= 0.618 showed a good strength of the relationships between independent variables and the dependent variable.The result of this study makes the recommendation that managers in banks should improve the work life balance policies offered to employees in order to increase their job satisfaction, to improve staff commitment and productivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".