Work–Life Balance Programs and Organizational Profitability: Existence, Availability and Usage
Bibliographic record
Abstract
This paper investigates the relationship between work–life balance programs (WLBPs) and organizational outcomes. First, to balance the human cost approach that emphasizes the costs associated with WLBPs and the human investment approach that supports the positive benefits associated with WLBPs, we examine the effect of WLBPs on organizational profitability (revenues minus expenditures per employee). Second, we extend the discussion on the job demands-resources model by considering how and why WLBPs, a type of organizational job resources, can mitigate non-job demands such as personal health and wellness, and family duties and responsibilities. Specifically, we test the moderating role of the availability and usage of WLBPs in the relationship between WLBPs and organization profitability. Our findings support that these two dimensions of organizational job resources (availability and usage) influence organizational profitability. The availability of WLBPs was positively associated with organizational profitability, while the usage of WLPBs was negatively associated with organizational profitability. The availability of WLBPs significantly moderate the WLBPs-profitability relationship, and the usage of WLBPs marginally significantly moderate the WLBPs-profitability relationship. We discuss the theoretical and practical implication of the findings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".