Attitude Predictability and Helpfulness in Online Reviews: The Role of Explained Actions and Reactions
Bibliographic record
Abstract
This article examines explanation type in online word of mouth (WOM), focusing on what individuals explain: their actions (“I chose this product because...”) or their reactions (“I love this product because...”). Results show that review writers explain their actions more than their reactions for utilitarian products, but they ex-plain their reactions more than their actions for hedonic products. They do so to be helpful to review readers, who find explained actions more helpful for utilitarian products and explained reactions more helpful for hedonic products. Explained ac-tions and reactions are differentially helpful across product type because they increase readers ’ ability to predict their attitude toward the reviewed product: ex-plained actions increase attitude predictability for utilitarian products, whereas explained reactions increase attitude predictability for hedonic products. These increases in attitude predictability and review helpfulness ultimately increase read-ers ’ choice of the product in question. This article contributes to the explaining and the WOM literatures by focusing on what individuals explain, rather than on how they explain, by identifying product type as a novel moderator of what review writers explain (actions or reactions), and by examining when and why review readers prefer different types of explanations.
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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 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".