Service Problems and Recovery Stratégies: An Experiment
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
Abstract This experiment examines the effectiveness of recovery stratégies after a service failure on customer loyalty and complaint intentions. Respondents encountered different core failures in ternis of problem severity (denial or delay) and criticality levels (high or low). The results suggest the effectiveness of service recovery strategies—assistance (fixing the problem) and/or compensation (defraying the costs incurred)—varied depending on the txpe of service, problem severity, and criticality levels. The implication is that recovery strategies need to be matched to the specific incident. Service firms should focus on avoiding or reducing core failures. Getting it right the first time is the best strategy. Résumé La présente recherche examine au moyen d'une expérience l'efficacité de différentes stratégies de récupération sur la fidélité et les intentions de porter plainte de la clientèle à la suite d'une défaillance de service. Les participants ont été confrontés à différentes défaillances de service en termes de gravité (interruption du service ou délai) et de niveau critique (élevé ou faible). Les résultats indiquent que l'efficacité des stratégies de récupération—aide technique (résolution du problème) et/ou compensation financière (défraiement des coǔts encourus)—varie en fonction du genre de service, de la gravité du problème et des niveaux critiques. Les résultats de l'étude laissent supposer que les stratégies de récupération doivent ětre associées à un incident spécifique. Les entreprises de services doivent mettre l'accent sur l'évitement ou la réduction des défaillances. La meilleure stratégie consiste encore à donner le service correctement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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