Incivility toward managers: gender differences in well-being outcomes
Bibliographic record
Abstract
Purpose Drawing on selective incivility theory (Cortina, 2008) and the literature on gender and leadership (e.g. Vialet al., 2016), the purpose of this paper is to investigate well-being outcomes of often neglected targets of incivility – those who manage or lead the work of others. The authors examined links between managers’ experiences of incivility from those to whom they report and five well-being outcomes, controlling for co-worker and subordinate incivility. Design/methodology/approach The authors used a cross-sectional correlational design to test the hypotheses, with a sample of 50 employees (28 females, 22 males) who supervise, manage or lead the work of others. Findings Male and female managers reported similar levels of incivility from subordinates and higher-ups; males reported greater incivility from co-workers. Significant interactions were also found: the relationship between incivility from those higher up and positive affect (high and low intensity) and perceived impact were significantly stronger for female (vs male) managers. Research limitations/implications Women did not experience greater workplace incivility than men, albeit the two-week timeframe of measurement may be too short to capture differences. The authors did, however, find evidence that well-being implications of experienced incivility from those higher up are generally stronger for female leaders. Originality/value The study investigates multi-source incivility directed at those in leadership/managerial positions and contributes to a growing literature seeking to understand the experiences of women in these roles. Although women in management roles may experience similar levels of incivility as men, they may interpret the behavior in a more negative light, in line with the persistence of sexism in the workplace.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".