Individual and Situational Factors Influencing Negative Word‐of‐Mouth Behaviour
Bibliographic record
Abstract
Abstract Negative word‐of‐mouth is one form of consumer response to dissatisfaction that has received Utile attention from business firms, yet it is a silent and potent force that is capable of wreaking havoc on a firrn's bottomline. This study examines the influence of some individual and situational factors affecting negative word‐ofmouth behaviour. The results reveal that product involvement, purchase decision involvement, self‐confidence, perceived worthiness of complaining, and proximity of others ajfect negative word‐of‐mouth behaviour in both Singapore and Canada. Two additional factors, attitudes towards business in generai and the perceived reputation of the firrn, affect negative word‐of‐mouth behaviour in the Singapore sample, while an additional factor, sociability, affects negative word‐of‐mouth behaviour in the Canadian sample. Résumé Le bouche à oreille négatif est une forme de réponse des consommateurs à leur propre insatisfaction qui a reçu peu d'attention de la part des entreprises, et qui cependant, représente une force silencieuse et puissante, capable de nuire aux succès de l'entreprise. Cette étude examine l'influence de facteurs individuels et situationnels sur les comportements de bouche à oreille négatif. Les résultats révèlent que le degré de contact avec le produit, le degré d'implication dans la décision d'achat, la confiance en soi, la perception qu'a le consommateur du suivi donné à une éventuelle plainte, et l'influence d'autrui affectent les comportements de bouche à oreille négatif, aussi bien à Singapour qu'au Canada. Deux facteurs supplémentaires, l'attitude vis‐à‐vis du monde des affaires en général et la réputation de l'entreprise, affectent le bouche à oreille négatif dans l'échantillon singapourien, alors qu'un facteur supplémentaire, la sociabilité, influence le bouche à oreille négatif dans l'échantillon canadien.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".