The Determinants of eWoM in Social Commerce
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The influence of eWoM use for s-commerce in the context of Arab region remains unexplored. To bridge this gap, this study develops a model for eWoM use for s-commerce post adoption. This model link three antecedents-factors (trust towards other people, trust of internet/Instagram, and perceived risks) to eWoM use for s-commerce through the mediation of perceived enjoyment, perceived value, and customer satisfaction. The model is validated with a large sample of 843 Instagram users using LISREL tool. Research findings revealed that propensity to trust, trust of internet, perceived risk, and perceived value affect use of eWoM through the indirect effect of perceived enjoyment and satisfaction, while perceived value has no direct effect on eWoM. Customer satisfaction was a predominant predictor of eWoM use. The model has relevant contributions and implications for both research and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it