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.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".