Attributions and Appraisals of Workplace Incivility: Finding Light on the Dark Side?
Bibliographic record
Abstract
Ample research demonstrates that workplace incivility has individual and organisational costs, but an important question remains unanswered: might it have benefits as well? We investigate this possibility by focusing on incivility appraisals—both negative and challenge appraisals (i.e. as an opportunity for learning, growth)—and their correlates. To explain this diversity of appraisals, we examine whether attributions (i.e. perceived intent to harm, perceived perpetrator control) predict perceptions. We conducted two multi‐method (quantitative and qualitative) surveys, one of which was multi‐source, of employees across a range of occupations. In Study 1, attributions that perpetrators acted with control and malicious intent fuelled negative appraisals of incivility, which undermined job satisfaction. Study 2 added to these findings by demonstrating that some targets formed challenge appraisals of uncivil encounters, especially when they attributed low malicious intent to perpetrators; challenge appraisal related to boosts in job satisfaction and thriving. These attitudinal outcomes then positively related to organisational citizenship behaviour, as reported by targets' coworkers. Showing paths to incivility harm (and potential benefit), our findings can inform interventions to alter the impact of workplace 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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".