On the nature, consequences and remedies of workplace incivility: No time for “nice”? Think again
Bibliographic record
Abstract
Executive Overview Incivility, or employees' lack of regard for one another, is costly to organizations in subtle and pervasive ways. Although uncivil behaviors occur commonly, many organizations fail to recognize them, few understand their harmful effects, and most managers and executives are ill-equipped to deal with them. Over the past eight years, as we have learned about this phenomenon through interviews, focus groups, questionnaires, experiments, and executive forums with more than 2,400 people across the U.S. and Canada, we have found that incivility causes its targets, witnesses, and additional stakeholders to act in ways that erode organizational values and deplete organizational resources. Because of their experiences of workplace incivility, employees decrease work effort, time on the job, productivity, and performance. Where incivility is not curtailed, job satisfaction and organizational loyalty diminish as well. Some employees leave their jobs solely because of the impact of this subtle form of deviance. Most of these consequences occur without organizational awareness. In addition to detailing the nature of incivility and its consequences, we provide keys to recognizing and dealing with habitual instigators, and remedies that are being used effectively by organizations to curtail and correct employee-to-employee incivility.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".