Juggling work and family responsibilities when involuntarily working more from home: A multiwave study of financial sales professionals
Bibliographic record
Abstract
Summary Using multiwave survey data collected among 251 financial sales professionals, we tested whether involuntarily working more from home (teleworking) was related to higher time‐based and strain‐based work‐to‐family conflict (WFC). Employees' boundary management strategy (integration vs. segmentation) and work–family balance self‐efficacy were considered as moderators of these relationships. Data were collected one month before, three months after, and 12 months after the implementation of a new cost‐saving policy that eliminated employees' access to office space in a centralized work location. The policy resulted in employees being forced to work more from home. A voluntary telework program had been in effect before the new policy, implying that working more from home as a result of the new policy was involuntary in nature. Results revealed that involuntarily working more from home was associated with higher strain‐based WFC but not higher time‐based WFC. However, moderator analyses revealed that the positive association between involuntarily working more from home and both types of WFC was significantly stronger among employees with weaker self‐efficacy in balancing work and family. Boundary management strategy had no detectable moderating effect. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".