{"title":"酒店服务质量的维度与属性:在线酒店顾客评论分析","authors":"Kanapot Kalnaovakul, P. Promsivapallop","doi":"10.1177/14673584221145819","DOIUrl":null,"url":null,"abstract":"This study examines service quality dimensions and attributes of the hotel industry in a famous beach resort destination of Phuket based on 25,267 online reviews from the TripAdvisor website collected for 56 hotels. Machine learning analysis using the KNIME analytics platform was employed to analyze four datasets, namely the total dataset, the couple dataset, the family dataset, and the friend dataset. The results reveal six dimensions of guest service quality in the hotel industry: leisure activities, tangibles and surroundings, reliability, responsiveness, service process, and food, with specific attributes identified in each dimension. The study was able to verify the robustness of HOLSERV Plus model as the dimensions developed by topic modelling of online reviews are found to correspond to the dimensions of HOLSERV framework, with some adaptation required. It is also confirmed by the current study that the same set of service quality dimensions and attributes is not applicable to all groups of customers, instead each group has its own unique requirements and expectations. In addition, service process is revealed in this study as the most sensitive dimension that determines customer dissatisfaction.","PeriodicalId":47333,"journal":{"name":"Tourism and Hospitality Research","volume":"23 1","pages":"420 - 440"},"PeriodicalIF":3.0000,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hotel service quality dimensions and attributes: An analysis of online hotel customer reviews\",\"authors\":\"Kanapot Kalnaovakul, P. Promsivapallop\",\"doi\":\"10.1177/14673584221145819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study examines service quality dimensions and attributes of the hotel industry in a famous beach resort destination of Phuket based on 25,267 online reviews from the TripAdvisor website collected for 56 hotels. Machine learning analysis using the KNIME analytics platform was employed to analyze four datasets, namely the total dataset, the couple dataset, the family dataset, and the friend dataset. The results reveal six dimensions of guest service quality in the hotel industry: leisure activities, tangibles and surroundings, reliability, responsiveness, service process, and food, with specific attributes identified in each dimension. The study was able to verify the robustness of HOLSERV Plus model as the dimensions developed by topic modelling of online reviews are found to correspond to the dimensions of HOLSERV framework, with some adaptation required. It is also confirmed by the current study that the same set of service quality dimensions and attributes is not applicable to all groups of customers, instead each group has its own unique requirements and expectations. In addition, service process is revealed in this study as the most sensitive dimension that determines customer dissatisfaction.\",\"PeriodicalId\":47333,\"journal\":{\"name\":\"Tourism and Hospitality Research\",\"volume\":\"23 1\",\"pages\":\"420 - 440\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2022-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tourism and Hospitality Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/14673584221145819\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HOSPITALITY, LEISURE, SPORT & TOURISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tourism and Hospitality Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/14673584221145819","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
Hotel service quality dimensions and attributes: An analysis of online hotel customer reviews
This study examines service quality dimensions and attributes of the hotel industry in a famous beach resort destination of Phuket based on 25,267 online reviews from the TripAdvisor website collected for 56 hotels. Machine learning analysis using the KNIME analytics platform was employed to analyze four datasets, namely the total dataset, the couple dataset, the family dataset, and the friend dataset. The results reveal six dimensions of guest service quality in the hotel industry: leisure activities, tangibles and surroundings, reliability, responsiveness, service process, and food, with specific attributes identified in each dimension. The study was able to verify the robustness of HOLSERV Plus model as the dimensions developed by topic modelling of online reviews are found to correspond to the dimensions of HOLSERV framework, with some adaptation required. It is also confirmed by the current study that the same set of service quality dimensions and attributes is not applicable to all groups of customers, instead each group has its own unique requirements and expectations. In addition, service process is revealed in this study as the most sensitive dimension that determines customer dissatisfaction.
期刊介绍:
Tourism and Hospitality Research is firmly established as a leading and authoritative, peer-reviewed journal for tourism and hospitality researchers and professionals. Tourism and Hospitality Research covers: • Hospitality and tourism operations • Marketing and consumer behaviour • HR management • Social Media and Marketing • Technology • Planning and development • Policy • Performance and financial management • Strategic implications • Environmental aspects • Forecasting and prediction • Revenue management • Impact assessment and mitigation • Globalisation • Research methodologies • Leisure and culture • Risk Management • Change Management