Does Family Life Help to be a Better Leader? A Closer Look at Crossover Processes From Leaders to Followers
Bibliographic record
Abstract
Although research on family‐to‐work processes is accumulating, not many studies have looked at how the leader's family issues spillover to work and what the consequences are for their followers. We investigate whether leaders’ family‐to‐work conflict (FWC) and enrichment (FWE) influence first their own well‐being at work (i.e., job burnout and work engagement) and consequently the well‐being of their followers due to crossover processes. We test whether crossover is due to the transfer of emotions from the leader to followers (affective crossover) or due to diminished or enhanced support from the leader (behavioral crossover). Using a sample of 199 leaders and 456 followers, we found that leader FWC (Time 1) was positively related to leader feelings of burnout 4 weeks later (Time 2), consequently enhancing follower feelings of burnout 5 weeks after Time 1 (Time 3). Similarly, leader FWE had a positive relationship with follower engagement, through leader enhanced engagement. Our findings fully supported the affective crossover mechanism. In addition, leader burnout was negatively related to leader supportive behavior, indirectly increasing burnout among followers. Our results underscore that leaders’ family life matters at work, influencing not only their own well‐being but also how they motivate and support their followers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".