1. Exploring Consumers’ Negative Electronic Word-of-Mouth of 5 Military Hospitals in Taiwan Through SERVQUAL and Flower of Services: Web Scraping Analysis
- Author
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Ching-Yuan Huang, Po-Chun Lee, and Long-Hui Chen
- Subjects
Medicine - Abstract
BackgroundIn 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. ObjectiveThis 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. MethodsIn 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. ResultsThe 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
- Published
- 2024
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