How Do Observers React to Companies’ Humorous Responses to Online Public Complaints?
Why this work is in the frame
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Bibliographic record
Abstract
The current research examines the way that observing consumers react when companies use humor to address online public complaints on social media. Drawing on, first, a field study using companies’ humorous responses on social media and, second, on two main scenario-based experiments, we use benign violation theory to capture simultaneously the unfavorable effect (i.e., inferred negative motives) and the favorable effect (i.e., humor appreciation) of employing humor in a public complaining context. The results reveal that online observers respond more favorably (in terms of likes, retweets, and purchase intentions) when firms use affiliative humor (e.g., laughing with the complainer) rather than aggressive humor (e.g., laughing at the complainer). Also, affiliative humor and an accommodative recovery (e.g., apologies and compensation) provide equal results in terms of observers’ purchase intentions. Because observers infer more negative motives of companies, affiliative humor compensates over an accommodative recovery by being funnier. Finally, our last study presents a reversal effect depending on brand personality; while sincere brands should always favor affiliative humor, aggressive humor elicits higher purchase intentions when performed by exciting brands. This research gives managerial insights about observers’ reactions to humorous responses to online complaints and the importance for humor to fit with brand personality.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it