The Role of Humor in the Relationship Between Transactional Leadership Behavior, Perceived Supervisor Support, and Citizenship Behavior
Bibliographic record
Abstract
This study, building on uncertainty management theory, examines the role of humor use by the supervisor and team members in the relationship between leader behaviors, perceived supervisor support, and citizenship behavior. Data were collected from a sample of 284 employees working in nine small organizations. The results show that weak contingent reward leaders are viewed as more supportive when they use constructive and self-defeating humor styles extensively as opposed to aggressive humor, whereas skillful contingent reward leaders are perceived as less supportive when they use constructive and self-defeating humor extensively, and more supportive when they favor an aggressive humor style. Laissez-faire leaders are viewed as less supportive when they use aggressive humor extensively. The results provide only partial support for the buffer effect of constructive humor and the undermining influence of aggressive humor style. Finally, whereas offensive coworker humor is negatively related to organizational citizenship behavior, the results do not provide significant evidence that coworker humor moderates the influence of perceived supervisor support on organizational citizenship behavior. We conclude by discussing the theoretical contributions and practical implications of our findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".