Bibliographic record
Abstract
As the use of Internet is getting more widespread and people are putting more trust on the Internet-based information, a new form of word of mouth termed as electronic word of mouth (E-WOM) has been developed. People receive E-WOM messages from social media, consumer review sites, discussion forums etc. Researches say that people tend to rely on E-WOM messages as much as they do on personal word of mouth. But what variables influence E-WOM credibility? After conducting an intensive background research on this topic this study has been able to identify certain variables such as E-WOM's quantity, polarity, logic and articulation, source and user's prior knowledge/expertise that affect E-WOM credibility. Based on the identified variables a survey was conducted on the students of 10 private and public universities of Bangladesh with a view to measure the effect of those variables on the E-WOM credibility. The regression analysis result indicates the quantity of E-WOM and the source of E-WOM has significant impact on E-WOM credibility. While, the designed model overall with all the included variables came strongly significant in explaining E-WOM credibility. In addition, to measure the internal consistency and correlation of the variables Cronbach's Alpha technique and correlation analysis are also conducted which have brought satisfactory outcome. From a strategic point of view, this study is useful for the modern marketers who want to use E-WOM to promote their products or services. By focusing on the predictor variables which have impact on E-WOM credibility, they can be able to enhance the effectiveness of their marketing strategy with a very cost efficient and a time savvy manner.
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How this classification was reachedexpand
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 | 0.006 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".