Impacts of Work–Family Culture on Employee Job Performance in Achieving Sustainable Development Goals
Bibliographic record
Abstract
The harmonized balance between work and family is tough to reach in modern industrial societies because of increasing demands at work and in family settings. Previous studies analyzed work–family culture and its relationship with employees’ job performance without focusing on specific industries. Paralleled with the Sustainable Developments Goals (SDGs), this study aims to analyze the relationship between work–family culture and employees’ job performance in selected industries in Malaysia. This study surveyed 336 employees working in four industries in Peninsular Malaysia, and data were collected using a self-reported questionnaire. The collected data were analyzed using two statistical analyses, i.e., descriptive and inferential statistic multiple linear regression by assisting statistical software, namely SPSS version 26. This study found that organizational time demands, and managerial support have a significant positive relationship with employees’ job performance. Moreover, the career consequences variable does not have a significant effect on employees’ job performance. Therefore, the results provided valuable insights into the work–family culture among employees with family responsibilities. Applied implications for managers are suggested to give full support to the employees while juggling their family matters. This study also contributes to the existing work–family culture literature. After a thorough analysis of this field, the researcher believed that other factors might significantly affect employees’ job performance, such as religiosity, working environment, and integrity in the organizations.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".