Some Things are Better Left Unsaid: How Word of Mouth Influences the Storyteller
Bibliographic record
Abstract
Consumers frequently tell stories about consumption experiences through word of mouth (WOM). These WOM stories may be told traditionally, through spoken, face-to-face conversation, or non-traditionally, through written online reviews or other electronic channels. Past research has focused on how traditional and nontraditional WOM influences listeners and firms. This research instead addresses how specific linguistic content in nontraditional WOM influences the storyteller. The current article focuses on explaining language content, through which storytellers reason about why experiences happened or why experiences were liked or disliked. Four studies examine how and why explaining language influences storytellers’ evaluations of and intentions to repeat, recommend, and retell stories about their experiences. Compared to non-explaining language, explaining language influences storytellers by increasing their understanding of consumption experiences. Understanding dampens storytellers’ evaluations of and intentions toward positive and negative hedonic experiences but polarizes storytellers’ evaluations of and intentions toward positive and negative utilitarian experiences.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".