Let's Laugh About It! Using Humor to Address Complainers’ Online Incivility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research investigates whether companies’ use of humor is an effective strategy to address complainers’ incivility on social media. Using three main experiments, the authors examine observers’ evaluation of companies’ humorous responses on social media in relation to the degree of incivility of the complaints. The authors find, first, that observers develop greater purchase intentions toward companies that use humor to respond to uncivil complaints. Drawing on benign violation theory, they explain that observers are less committed to uncivil complainers, which makes the use of humor more benign and thus more amusing. Second, they compare the effectiveness of humor with an accommodative recovery (e.g., apologies). When the complaint is civil, an accommodative recovery is a more effective strategy than affiliative humor. However, when the complaint is uncivil, affiliative humor is more interesting than an accommodative recovery because of greater engagement with the post (i.e., likes and shares) and similar purchase intentions. Theoretical and managerial implications of these results are then discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it