1. Introduction
Nowadays, reviewing a product or service online is common for customers. In the online review, customer leave text comments with a numeric rating to briefly indicate their evaluation of the product or service [
1]. The review can be called word of mouth (WOM). In the Internet era, the effect of WOM has been further enhanced in the form of electronic word of mouth (eWOM), and it has developed substantially [
2]. The eWOM is a new method to identify the main attributes of service quality from a customer’s perspective. The eWOM is the result of a summary of the customer’s experience and is usually written voluntarily without any economic cost or external stimulus [
3]. This eWOM is used by customers who have experienced a particular service to help other customers make the right choice. Therefore, the experience of the services mentioned in the eWOM implies the main attributes and quality levels of the product or service that the customer considers [
4]. Accordingly, online review mining research is actively under way to extract customer information needed to develop new products or improve existing products [
5] from online review in various service industries such as medical service, airline [
6], dining [
7], and hotel [
8].
Previously, many studies have focused on identifying key attributes that investigate customer satisfaction with the survey method. Cheng et al. [
9] investigated the impact of service recovery dimensions on customer satisfaction and subsequently on customer loyalty in the context of the hotel industry. Nysveen et al. [
10] examined the influence of a brand’s innovativeness and green image with hotel brand satisfaction. Pizam et al. [
11] discussed customer satisfaction and its application to the hospitality and tourism industries. However, the majority of prior studies using the survey method were done a few years ago [
12,
13,
14,
15,
16]. Nowadays researchers are using the text mining method for analyzing eWOM [
4,
8,
16,
17,
18,
19].
A hotel is an industry in which consumers themselves are the property of the enterprise and engage in production activities through direct contact with the customer. That is why understanding the opinions of customers and their experiences is significant. In addition, the hotel is a special product that combines the tangible product of the facility with the intangible product of human service [
20]. Therefore, it is difficult to identify the main attributes because there are so many service attributes and the customer’s needs vary depending on the type, purpose of visit, and local characteristics. In view of these characteristics, this study focuses on understanding the customer through online review mining and examines customer experience and satisfaction to find out what key attributes were affecting the customer intention.
5. Discussion
This study was conducted to enhance the customer’s experience and satisfaction using online hotel reviews. For the online hotel review data analysis, the first process was extracting keywords by text mining and the second was calculating the frequency of words used by customers. Based on the frequency analysis, the degree and eigenvector centrality of top 99 frequent words were analyzed to search their connection and the most affected keywords among them. The CONCOR analysis was performed for grouping them. As a result, the top 99 keywords were divided into four groups, namely, “Intangible Service”, “Physical Environment”, “Purpose”, and “Location”. Moreover, they were visualized by drawing networks and nodes using NetDraw in UCINET 6.0. In addition, the study conducted factor analysis and linear regression analysis to understand the relationship between extracted factors and customer satisfaction. The factor analysis reduced the dimension of the original 64 keywords to 22 keywords and grouped them into five factors, which are “Access”, “F&B”, “Purpose”, “Tangibles”, and “Empathy”. The clusters can be related between CONCOR analysis and factor analysis, such as “Intangible Service” with “Empathy”, “Physical Environment” with “Tangibles”, “Location” with “Access”.
First of all, the group representing the highest beta coefficient was “Empathy” in the linear regression analysis, and the related words were ‘staff’, ‘service’, ‘care’, and ‘friend’ through the factor analysis. Especially, ‘staff’ and ‘service’ were most frequent words in the online hotel review, and through the CONCOR analysis, “Intangible Service” group was the biggest group compared with “Physical Environment”, “Purpose”, and “Location”. According to Lee [
49] the service from hotel staff is the most important attribute to making customers satisfied rather than the other luxurious or new facilities. In addition, Han and Chung [
50] examined customers satisfied with excellent service by hotel staff rather than physical environment, such as room cleanness, comfort, and room condition. The results of this study show the same results as many prior studies show that intangible service has the greatest impact on customer experience and satisfaction. Therefore, Service by staff is an essential key attribute to create a good reputation in the service industry and can still be seen as a part of the hotel that must be managed at all times to keep up the image of the hotel. Therefore, it is important to improve the attitude of employees through systematic service training. In addition, providing an appropriate working environment to enhance employee satisfaction to produce better service to customers can be another way.
The second highest beta value was “F&B” in the linear regression analysis, and the related words were ‘food’, ‘breakfast’, ‘drink’, and ‘dining’ through the factor analysis and those keywords are recorded a very high position in the frequency analysis. In hotels with a high satisfaction index, F&B facilities should also be well equipped and restaurants providing high-quality food to customers are significant. Especially, as keywords for breakfast are derived, it will be helpful for hotel business to focus on breakfast rather than on other meals.
This study shows the academic implication that the study has extended its application area of the semantic network analysis. While given the significance of the hotel segment in the tourism industry, this study empirically explores hotel experience and satisfaction by big data analytics. Along the way, the hotel industry has the opportunity to gain an understanding of attributes on the online review, so as to infiltrate into this market and investigate corresponding marketing strategies for their significant advantages. Understanding online reviews as a manifestation of customers’ experiences can help the hotel industry to identify the main attributes required to achieve positive post-purchase behaviors and to minimize negative intentions. Thus, the online reviews not only provide an efficient way for the hotel industry to collect feedback from hotel customers, but also provide an opportunity to discover how to generate positive intent after the experience. To create a high satisfaction score and a positive eWOM, the hotel industry should consider “F&B”, “Purpose”, and “Empathy”. Among them, “Empathy” was the most influential attribute in the regression analysis. These key factors may be used to examine the customer satisfaction or to test theoretical models to have a better understanding of a hotel customer’s behavior.
In practice, the analysis of online reviews can be used as a marketing tool by managers since customer review is an important source for a hotel to improve service and to create some promotion regarding profit. The analysis also provides the level of importance of these service attributes so the hotel industry can allocate their resources accordingly. The online review analyses can provide reliable satisfaction assessment. The hotel industry can also use this method to analyze their competitors’ customer reviews so that they can benchmark themselves against competitors in terms of customer satisfaction. These reviews can be used for sustainable strategic marketing decisions against competitors.
However, this study shows limitations in data collection. Firstly, the data collected in this study is limited, because this study focuses on only the top 25 hotels in the world for the sample. That is why future research should collect online review data from more hotel industries to generalize the findings. Secondly, the collected text was analyzing based on the frequency of individual words, therefore, it is difficult to understand the additional meaning of words. In future studies, further analysis of positives and negatives and sentimental analysis is expected to be carried out to better understand the customer’s experience and satisfaction. Therefore, it can provide stronger strategies to the hotel industry.