The Effect of Consumer Attitudinal Disposition in Online Review Knowledge Transfer
Bibliographic record
Abstract
Online reviews, which are consumer-generated messages, play a vital role in the consumer decision making process especially prior to their purchase adoption (i.e., pre-usage). The objective of this research is to investigate the effects of two-sided online reviews’ contents affecting the consumers' attitudes at the pre-usage stage of a focal experience service. Contrary to one-sided reviews (i.e., only positive or negative information), two-sided reviews contain both positive and negative information about a product/service: Two-sided reviews are considered more informative. Extant studies make an important assumption that there is no information asymmetry between writer/source of two-sided reviews and consumers that read/receive it. Their implicit assumption is that the attitude of the writer/source of the two-sided review is completely transferred to the reader/receiver of the review. Given the subjective nature of two-sided online reviews for experience goods, we contend that such an assumption is flawed because transfer of personal experience in form of attitude towards a focal object/service to others is fraught with ambiguity and uncertainty that can mitigate the transfer. Drawing on ambivalence and prospect theories, our hypothesis states that: the anticipatory ambivalence of the receiver/reader based on a two-sided review content for a focal service is higher than the ambivalent attitude of the source/writer of the review who has already experienced the focal service. Our empirical study, consisting of 1492 subjects from Canada and the United States, supports our stated hypothesis. The implication of our finding is profound. It shows that the extant literature had underestimated the negative attitude of the receiver/reader of the online reviews in their investigation, which confound their findings. To that end, we provide future research direction and implications of our findings in practice.
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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 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".