Workplace flexibility and its relationship with work-interferes-with-family
Bibliographic record
Abstract
Purpose Drawing from the workplace flexibility and coping literatures, the purpose of this paper is to re-conceptualize the workplace flexibility construct as a coping resource that may help prevent work-interferes-with-family (WIF) from arising and/or assist employees manage such interference when it has occurred. A measure capturing this re-conceptualized view of flexibility is developed and tested using two samples of dual-income employees with dependent care demands. Design/methodology/approach In Study 1, the authors use LISERL to develop and test a new multi-dimensional measure of workplace flexibility ( n 1 =6,659). In Study 2 ( n 2 =947), the authors use partial least squares, a component-based structural equation modeling technique, to test a model that posits workplace flexibility that helps employees cope with WIF. Findings This research provides support for the idea that workplace flexibility helps employees cope with WIF by: preventing interference (i.e. negatively moderating the relationship between work hours and WIF), and managing interference that has occurred (i.e. negatively moderating relationship between WIF and perceived stress). Originality/value This study highlights the complexity of the relationship between workplace flexibility and work-to-family interference and offers guidelines on how employers and employees can use the workplace flexibility measure developed in this study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".