Culturally Contingent Electronic Word-of-Mouth Signaling and Screening: A Comparative Study of Product Reviews in the United States and Japan
Bibliographic record
Abstract
Electronic word of mouth (eWOM) is an important source of influence on consumer decision making, yet little is known about cross-cultural differences in both the occurrence of eWOM and the relationship between eWOM and sales. The authors draw on signaling theory to develop a conceptual model and assess the relationships between country and the occurrence of eWOM, as well as between online ratings and relative product sales according to country. Online reviews and sales rank data for books, CDs, and DVDs were collected from Amazon U.S. and Amazon Japan in 2009 and 2017. Results suggest cross-national differences in both the occurrence of eWOM (eWOM signaling) and the relationship between eWOM and relative product sales (eWOM screening). These national differences appear to change over time: some remain stable, some disappear, and others emerge. The proposed culturally contingent signaling and screening model may be adopted as a framework for future research on cross-cultural eWOM. The results also inform the literature on cultural change by suggesting that cultural differences in eWOM change in nuanced patterns over time.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".