L’effet d’e-WOM sur l’image de marque, l’attitude et l’intention d’achat : une étude empirique sur l’industrie agroalimentaire en Tunisie
Bibliographic record
Abstract
L’émergence d’internet a permis au bouche à oreille électronique (e-WOM) à se développer. Ce moyen de communication facilite aux consommateurs en ligne d’accéder à plus d’informations d’une part et de faciliter la communication interpersonnelle via les commentaires en ligne, d’autre part. L’objectif de cet article est donc d’examiner dans quelle mesure l’e-WOM peut influencer l’image de marque, l’attitude et l’intention d’achat dans l’industrie agroalimentaire en prenant, comme champ d’étude, le contexte tunisien. Une enquête est réalisée auprès de 270 répondants. Les résultats montrent que l’e-WOM a des effets positifs sur l’image de marque, l’attitude et l’intention d’achat.
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.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".