Factors Affecting Work-Family Conflict: A Quantitative Approach
Bibliographic record
Abstract
Work-family conflict (WFC) has become a critical issue in modern organizational settings, affecting individuals' psychological well-being, job satisfaction, and family life. This study investigates the key factors contributing to WFC by integrating theoretical frameworks such as Role Theory, Conservation of Resources Theory, Social Support Theory, and Border Theory. Ten key variables were examined, including family demand, longer working hours, commitment to family, work schedules, high work demands, individual perception, traditional gender roles, unsupportive family members, demand for leisure time, and personal problems. Using a quantitative approach, data were collected from 100 participants across various industries in Bangladesh. The findings reveal that family demands, irregular work schedules, high work demands, and unsupportive family members significantly contribute to WFC, while commitment to family and positive perceptions of work-family balance reduce conflict. These insights provide actionable recommendations for organizations and policymakers to develop flexible work arrangements, supportive workplace environments, and strategies to mitigate WFC, fostering a better work-life balance for employees. This study contributes to both theoretical understanding and practical applications in managing WFC effectively.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".