When and why Language Assertiveness Affects Online Review Persuasion
Bibliographic record
Abstract
Recent research suggests that not only what is said (i.e., opinions) but also how it is said (i.e., language styles) can affect persuasion. Extending this stream of work, the current research aims to understand how language assertiveness affects online review persuasion. Study 1 explores consumers’ general perceptions of assertive versus nonassertive language and opinions about their relative persuasiveness in online reviews. Study 2 utilizes an experimental design to examine the congruency effects between language assertiveness and temporal distance on consumer responses. We find that online reviews containing assertive (vs. nonassertive) language engender higher perceived review helpfulness and more favorable attitudes toward the reviewed business for consumers whose travel time is in the distant future, whereas nonassertive (vs. assertive) language is more effective for consumers whose travel time is in the near future. Furthermore, mediation analysis results suggest that psychological comfort is the underlying mechanism explaining such effects.
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How this classification was reachedexpand
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 | 0.016 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".