The Effects of Involuntary Working From Home on Work-Life Balance, Work-Life Conflict, and Employees’ Burnout
Bibliographic record
Abstract
Before the COVID-19 pandemic, working from home (WFH) was supposed to be an HR practice to help employees attain a higher work-life balance. WFH has also been shown in previous research to reduce work-life conflicts. We suggest that these benefits of WFH are specific to voluntary WFH, and cannot be generalized to involuntary WFH, when it is not a choice, but a requirement. Unfortunately, research on involuntary WFH is extremely scarce. Using a time-lagged design, we collected data in three waves during the COVID-19 pandemic to test the effects of WFH on work life balance (WLB), work life conflict (WLC) and burnout. Results show that working from home directly and indirectly affects work-life balance, work-life conflict and burnout. The results also show that emotional exhaustion is the burnout dimension, which is most strongly influenced. Finally, results show that effects of WFH on burnout are mediated through WLB and WLC. These results significantly contribute to the research on working from home and burnout and present important directions for future research. In addition, the results help policy makers and managers in designing better WFH schemes and to develop conditions in which harmful effects of WFH are minimized.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".