Does gender matter? Acceptance and forwarding of electronic word of mouth: A moderated mediation analysis
Bibliographic record
Abstract
Presently, online users usually tend to use the Internet to get some details information and forward their purchasing experience on social media platforms. This activity helps other users create their own purchasing plans simpler. However, there are not many studies on accepting and forwarding e-WOM. This paper examines the effect of EWOM elements containing tie strength, message credibility, and source credibility on acceptance of e-WOM (AEWOM). The study also investigates the effects of other EWOM elements including customers' mood on forwarding e-WOM (FEWOM). Moreover, this study examines the mediator effect of (AEWOM) between (tie strength, message credibility, and source credibility) and FEWOM together with the moderator effect of gender differences between AEWOM and FEWOM. In the current paper a sample of 381 students is selected at the Jordanian university. The findings show positive relationships among all variables. The research also offers recommendations to marketers in order to improve the value of content which is generated by online users.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".