Let's Laugh About It! Using Humor to Address Complainers’ Online Incivility
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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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 | 0.003 | 0.018 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".