Negativity bias in the diagnosticity of online review content: the effects of consumers’ prior experience and need for cognition
Bibliographic record
Abstract
The importance of online review valence is a subject of debate among scholars. Prior studies mostly assumed valence as a “peripheral” cue derived from online review surface features (e.g., star ratings). This assumption has important implications as it restricts the negativity bias effects to a certain group of consumers who lack pertinent prior experience with the product/service domain and the motivation to assess the product/service. Focusing on online service context and drawing on an adaptational view to negative information, we investigate the negativity bias in the effects of the valence of the “content” of online reviews on consumers’ attitudes and show that it can be attributed to the higher perceived diagnosticity of negative reviews. This is determined by consumers’ in-depth elaborations of reviews’ contents, which are contingent on their prior experience with the domain of online service and need for cognition. Our findings provide a new perspective to negativity bias by showing that more experienced and thoughtful consumers are also influenced by negativity bias when the content of online reviews is considered. This is a novel account of negativity bias in the effects of online reviews that underscores the importance of response strategies for reducing their adverse effects.
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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 | 0.009 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".