Work-Life Balance Initiatives and Organizational Performance
Bibliographic record
Abstract
We investigate the influence of work-life balance (WLB) initiatives on organizational performance based on open role-systems theory, extending the existing strategic HRM perspectives on WLB initiatives. While the open-systems approach to organizational roles has largely been applied to organizational roles (e.g., in-role behaviors and extra-role behaviors), we apply the open role-system theory in explaining the interface between work roles and family roles. We differentiate work schedule control from WLB benefits and examine whether and how work schedule control provides an additional/separate path to enhance organizational productivity beyond the level of contribution of WLB benefits to organizational productivity. While work schedule control may help employees to reduce paid overtime work by adjusting their schedules to best accomodate work and family issues, WLB benefits can lead employees to work more hours even though this extra work is not directly compensated by their employers. In addition, based on the systems hierarchy concept, we consider WLB initiatives as a mediator linking business strategies to organizational outcomes. We also introduce the argument that reduced employee turnover can be a key mechanism linking WLB initiatives to organizational productivity. This study contributes to the literature by demonstrating the value of WLB initiatives from the open-role systems approach in explaining how and why the two types of WLB initiatives can enhance organizational effectiveness.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".