Compensatory knowledge signaling in consumer word‐of‐mouth
Bibliographic record
Abstract
Abstract This paper extends prior research on consumer knowledge beliefs and word‐of‐mouth transmission. Findings from four studies suggest that people compensate for unfavorable discrepancies between their actual and ideal consumer knowledge with heightened efforts to signal knowledgeability through the content and volume of their word‐of‐mouth transmissions. This compensatory knowledge signaling effect is moderated by the self‐concept relevance (psychological closeness) of the word‐of‐mouth target and lay beliefs in the self‐enhancement benefits of transmitting product knowledge. Content analysis of participants' product communications further supports our knowledge signaling account. The relationship between actual:ideal knowledge discrepancies and heightened word‐of‐mouth intentions is mediated by the specific negative emotion associated with actual:ideal self‐discrepancies. Overall, the findings suggest that the relationship between consumer knowledge and word‐of‐mouth transmission depends not only on what you think you know, but also on what you wish you knew.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".