Exploring Consumers’ Negative Electronic Word-of-Mouth of 5 Military Hospitals in Taiwan Through SERVQUAL and Flower of Services: Web Scraping Analysis
Notice bibliographique
Résumé
BACKGROUND: In recent years, with the widespread use of the internet, the influence of electronic word-of-mouth (eWOM) has been increasingly recognized, particularly the significance of negative eWOM, which has surpassed positive eWOM in importance. Such reviews play a pivotal role in research related to service industry management, particularly in intangible service sectors such as hospitals, where they have become a reference point for improving service quality. OBJECTIVE: This study comprehensively collected negative eWOM from 5 military hospitals in Taiwan that were at or above the level of regional teaching hospitals. It aimed to investigate service quality issues before and after the pandemic. The findings provide important references for formulating strategies to improve service quality. METHODS: In this study, we used web scraping techniques to gather 1259 valid negative eWOM, covering the period from the inception of the first review to December 31, 2022. These reviews were categorized using content analysis based on the modified Parasuraman, Zeithaml, and Berry service quality (PZB SERVQUAL) scale and Flower of Services. Statistical data analysis was conducted to investigate the performance of service quality. RESULTS: The annual count of negative reviews for each hospital has exhibited a consistent upward trajectory over the years, with a more pronounced increase following the onset of the pandemic. In the analysis, among the 5 dimensions of PZB SERVQUAL framework, the "Assurance" dimension yielded the least favorable results, registering a negative review rate as high as 58.3%. Closely trailing, the "Responsiveness" dimension recorded a negative review rate of 34.2%. When evaluating the service process, the subitem "In Service: Diagnosis/Examination/Medical/Hospitalization" exhibited the least satisfactory performance, with a negative review rate of 46.2%. This was followed by the subitem "In Service: Pre-diagnosis Waiting," which had a negative review rate of 20.2%. To evaluate the average scores of negative reviews before and during the onset of the COVID-19 pandemic, independent sample t tests (2-tailed) were used. The analysis revealed statistically significant differences (P<.001). Furthermore, an ANOVA was conducted to investigate whether the length of the negative reviews impacted their ratings, which also showed significant differences (P=.01). CONCLUSIONS: Before and during the pandemic, there were significant differences in evaluating hospital services, and a higher word count in negative reviews indicated greater dissatisfaction with the service. Therefore, it is recommended that hospitals establish more comprehensive service quality management mechanisms, carefully respond to negative reviews, and categorize significant service deficiencies as critical events to prevent a decrease in overall service quality. Furthermore, during the service process, customers are particularly concerned about the attitude and responsiveness of health care personnel in the treatment process. Therefore, hospitals should enhance training and management in this area.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».