Service Problems and Recovery Stratégies: An Experiment
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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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".