Stratégies pour servir avec le sourire : effet des orientations clients et impacts sur la performance de service
Bibliographic record
Abstract
Dans les entreprises de service, on demande aux employés de servir les clients avec le sourire afin de les fidéliser. Les objectifs de la présente étude sont d’évaluer l’effet sur la performance des employés de trois stratégies pour servir avec le sourire et de déterminer si certaines prédispositions des employés (orientations vers la résolution des problèmes des clients et vers le développement de bonnes relations avec les clients) influencent les meilleures stratégies. L’article développe et valide les principales hypothèses d’un modèle auprès de 210 employés et de leur supérieur immédiat. Les résultats démontrent l’importance des orientations clients pour servir authentiquement les clients avec le sourire et démontrent l’importance de l’authenticité des employés dans la performance au travail.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".