Negativity bias in the diagnosticity of online review content: the effects of consumers’ prior experience and need for cognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it