On Braggarts and Gossips: A Self-Enhancement Account of Word-of-Mouth Generation and Transmission
Bibliographic record
Abstract
Previous research on word of mouth (WOM) has presented inconsistent evidence on whether consumers are more inclined to share positive or negative information about products and services. Some findings suggest that consumers are more inclined to engage in positive WOM, whereas others suggest that consumers are more inclined to engage in negative WOM. The present research offers a theoretical perspective that provides a means to resolve these seemingly contradictory findings. Specifically, the authors compare the generation of WOM (i.e., consumers sharing information about their own experiences) with the transmission of WOM (i.e., consumers passing on information about experiences they heard occurred to others). They suggest that a basic human motive to self-enhance leads consumers to generate positive WOM (i.e., share information about their own positive consumption experiences) but transmit negative WOM (i.e., pass on information they heard about others' negative consumption experiences). The authors present evidence for self-enhancement motives playing out in opposite ways for WOM generation versus WOM transmission across four experiments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".