Unravelling the impact: supervisor incivility on employee health and the role of affective rumination
Bibliographic record
Abstract
Purpose This study investigates the influence of supervisor incivility on two important employee health-related outcomes–somatic complaints and emotional exhaustion. Similarly, the study examines the role of affective rumination as a mediator between the supervisor incivility–somatic complaints and emotional exhaustion relationship. Design/methodology/approach We collected data in three phases, separated by an interval of four weeks. The final sample comprised 154 employees from diverse occupations and professions. Partial least squares–structural equation modelling was used to examine the research model. Findings Employees’ perceptions of supervisor incivility increased somatic complaints and emotional exhaustion experiences. Moreover, drawing on the conservation of resources and the effort-recovery theories, we found support for the mediating role of affective rumination for somatic complaints but not for emotional exhaustion. Practical implications To help protect organizations from financial and productivity losses related to supervisor incivility, we encouraged organizations to be aware of supervisors’ uncivil behaviours and provide training on how to deal with such behaviours. We further advise organizations to coach supervisors on uncivil prevention and the importance of modelling proper behaviours. Originality/value This study expands the limited knowledge of supervisor incivility and health outcomes. Specifically, using a time-lagged design, the findings show that affective rumination is an essential mechanism for understanding the impact of supervisor incivility on health outcomes. Moreover, understanding how supervisor incivility impacts employee health outcomes is vital for advancing theory and designing interventions to mitigate adverse effects.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".