The mediating role of e-behavior in the relationship between the electronic word of mouth and electron-ic decision of purchas
Bibliographic record
Abstract
The current research aims to recognize the mediating role effect of the e-behavior (henceforth e-behavior) on the relationship between the electronic word of mouth (henceforth e-WOM) and the electronic decision of purchase (henceforth e-DOP) among the students of Jadara University/ Jordan. The research adopted a descriptive analytical methodology in data collection and analyses; and developed a questionnaire to measure the variables of e-behavior, e-WOM, and e-POD. Students using the university Facebook website completed the questionnaire, 400 retrieved questionnaires were valid for statistical analysis. Smart PLS software was used to analyze the collected data. The study found statistical differences of e-WOM on the e-DOP, significant differences were also found of e-behavior in the relationship between e-WOM and e-DOP. The study recommends companies to take interest in e-WOM and to add it to its marketing strategies, because e-WOM effects taking an e-DOP by potential customers and enhances positive purchase behavior.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.008 | 0.001 |
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".