Exploring the attributes of hotel service quality in Florianópolis-SC, Brazil: An analysis of tripAdvisor reviews

Abstract

This study explores user-generated content (UGC) of the hotel sector in the city of Florianópolis-SC, Brazil, to identify the quality attributes of services and determine the polarity of the expressed feelings in the reviews of each attribute. The analysis is based on the latent topics and the polarity of feelings expressed in the reviews. UGC was collected using a crawler, resulting in a text corpus comprising 68,558 reviews. The polarity of feelings, positive or negative, was identified using sentiment analysis techniques and Latent Dirichlet Allocation (LDA) was used to identify latent topics in the corpus associated with the attributes of hotel service quality. This study found that "room," "location," "ambience," "staff," "breakfast," "parking," "reservation," and "cost-benefit" were the attributes most frequently assessed by consumers in their reviews. The attributes that generate the most negative reviews were "room," "parking," and "reservation." The attributes "location," "ambience," "staff," "breakfast," and "cost-benefit" were the attributes that generated most of the positive reviews. When comparing the results of this study to those of previous studies, two attributes demonstrated greater prominence: the attribute "room" that attracted a high number of negative comments and the attribute "parking" that had not presented itself with the same level of relevance in other studies.

Hotel service quality dimensions and attributes: An analysis of online hotel customer reviews

“…Online reviews have increasingly been recognized as an important reflection and source of customer satisfaction and service quality insights to the hospitality and tourism industry. As indicated by prior literature, online reviews permit an understanding of customer satisfaction and service quality in the hotel industry through data mining (Peres and Paladini, 2021; Thu, 2020). This current study provides insights into service quality dimensions and attributes based on online reviews from the TripAdvisor platform, such that determine guest satisfaction in the hotel industry of Phuket island, Thailand; and may be applied to other tropical beach destinations.…”

“…Furthermore, online reviews offer great help to customers in the decision process by reducing confusion and time in the selection process (Tan et al, 2018) and provide more influence on customers’ trust from word-of-mouth communication than direct communications from hotel companies (Chittiprolu et al, 2021). Due to these reasons, scholars have demonstrated the importance of online reviews, in their direct impact on the popularity of hospitality businesses such as restaurants (Tian et al, 2021; Gan et al, 2017) and hotels (Ban et al, 2019; Peres and Paladini, 2021).…”

“…In addition, hotel service quality studies using online reviews have recently emerged in the literature. These studies have been conducted to examine service quality dimensions of hotels in different parts of the world such as Malaysia (Padma and Ahn, 2020), India (Chittiprolu et al, 2021), Brazil (Peres and Paladini, 2021), USA (Kim et al, 2016), China (He et al, 2017), and Vietnam (Thu, 2020). These studies used TripAdvisor as the data source platform as this platform has been claimed by researchers to provide richer and more reliable reviews than the other platforms (Chittiprolu et al, 2021; Taecharungroj and Mathayomchan, 2019).…”

“…These factors relate directly to room, facilities, employees and reliability of the HOLSERV model. In addition, hotel location has been discovered in the majority of these studies (such as Peres and Paladini, 2021; Suvachart, 2021; Thu, 2020, He et al, 2017; Kim et al, 2016) as a key service quality component. This dimension is connected with the surroundings dimension of the HOLSERV model.…”

“…Secondly, they tend to analyze the data using an induction approach with a lack of theoretical foundation in their studies. Thirdly, most online review studies analyze aggregate data of all types of customers and base conclusions regarding the service quality on hotel customers in general (Chittiprolu et al, 2021; Peres and Paladini, 2021). Even if these studies offer useful insights into service quality dimensions and attributes, it is still uncertain whether the conclusions from these studies can be applied to specific groups of customers who may have different service behaviors and requirements during their hotel visits.…”