Investigating experienced supervisor incivility: Does presenteeism play a role?
Bibliographic record
Abstract
Abstract This study investigated the role employees may play in making themselves targets of supervisor incivility. Drawing from Victim Precipitation Theory, and Conservation of Resources Theory, I hypothesized that engaging in presenteeism will be positively associated with experienced supervisor incivility, and that presentees’ experienced productivity loss will mediate this relationship. Furthermore, I hypothesized that presentees’ self-efficacy and perceived control (personal and condition resources, respectively) will each operate as boundary conditions of the presenteeism–productivity loss relationship such that presentees high in each resource will be less likely to experience supervisor incivility. I found that experienced productivity loss mediates the positive relationship between presenteeism and experienced supervisor incivility. Additionally, self-efficacy was found to moderate the presenteeism–productivity loss relationship; however, the relationship was stronger for low self-efficacy presentees, which increased the likelihood of experiencing supervisor incivility. Perceived control did not moderate the presenteeism–productivity loss relationship. I discuss the study’s implications for theory and practice.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".