Expression Modalities: How Speaking Versus Writing Shapes Word of Mouth
Bibliographic record
Abstract
Abstract Consumers often communicate their attitudes and opinions with others, and such word of mouth has an important impact on what others think, buy, and do. But might the way consumers communicate their attitudes (i.e., through speaking or writing) shape the attitudes they express? And, as a result, the impact of what they share? While a great deal of research has begun to examine drivers of word of mouth, there has been less attention to how communication modality might shape sharing. Six studies, conducted in the laboratory and field, demonstrate that compared to speaking, writing leads consumers to express less emotional attitudes. The effect is driven by deliberation. Writing offers more time to deliberate about what to say, which reduces emotionality. The studies also demonstrate a downstream consequence of this effect: by shaping the attitudes expressed, the modality consumers communicate through can influence the impact of their communication. This work sheds light on word of mouth, effects of communication modality, and the role of language in communication.
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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 | 0.002 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".