The Effectiveness of Humor in Persuasion: The Case of Business Ethics Training
Bibliographic record
Abstract
In this study, persuasion theory was used to develop the following predictions about use of humor in persuasive messages for business ethics training: (a) cartoon drawings will enhance persuasion by creating liking for the source, (b) ironic wisecracks will enhance persuasion by serving as a distraction from counterarguments, and (c) self-effacing humor will enhance persuasion by improving source credibility. Canadian business students (N = 148) participated in 1 of 4 versions of "The Ethics Challenge," a training exercise used by the Lockheed Martin Corporation. Three versions were modified by adding or removing cartoon drawings (of cartoon characters Dilbert and Dogbert) and humorous responses (Dogbert's wisecracks). Removing the cartoon drawings had little effect on persuasiveness. Removing ironic wisecracks had more effect, and interfering with the self-effacing combination of cartoons and wisecracks had the strongest effect. The results suggest that researchers should ground their predictions in existing theory and that practitioners should differentiate among humor types.
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".