Failing to Be Family-Supportive: Implications for Supervisors
Bibliographic record
Abstract
Family-supportive supervision benefits employees in many ways. But what are the implications for the supervisors themselves, particularly when this support is not extended? Drawing on social exchange theory, we frame family-supportive supervision as a desirable resource that when withheld may trigger negative social responses from employees. We hypothesize that workplace ostracism is a mechanism through which employees sanction supervisors who fail to be family-supportive, thereby harming supervisor well-being. Study 1 captured the employee perspective and utilized an experimental design to understand whether employees engage in ostracism in response to a lack of family-supportive supervision. In Study 2, we captured the supervisor perspective with multisource data to examine whether supervisors report ostracism and in turn lower subjective well-being when employees report a lack of family-supportive supervision. Consistent findings were observed across studies, suggesting negative outcomes for supervisors who fail to be family-supportive. In Study 2, we also examined moderators of the relationship between failing to be family-supportive and workplace ostracism and potential conditional indirect effects. However, we did not find evidence of such effects. Theoretical implications for the study of family-supportive supervision and workplace ostracism are discussed.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".