مقاله انگلیسی رایگان در مورد رضایت مشتری: شواهد کلان داده از بررسی آنلاین هتل – الزویر 2019

1 Introduction

In the e-tourism era, many customers book hotels online and post reviews after their stay. These online reviews, in the format of both textual reviews (comments) and ratings, generate an electronic-wordof-mouth (eWOM) effect, which influences future customer demand and hotels’ financial performance and thus have significant business value (Xie et al., 2014). Customers’ ratings indicate their satisfaction, whose antecedents and influence have been extensively studied in the literature (e.g., Banerjee and Chua, 2016; Schuckert et al., 2015). One of the biggest strengths of researching customer ratings is that ratings can show overall customer satisfaction in a direct way. Recently, many studies have focused on textual reviews (Xiang et al., 2015; Berezina et al., 2016). The strengths of researching customer textual reviews are that they can show customer consumption experiences, highlight the product and service attributes customers care about, and provide customers’ perceptions in a detailed way through the open-structure form. Researchers and hoteliers want to know both (a) the details about hotel guests’ experiences to improve the corresponding product and service attributes and (b) customers’ overall evaluation of the hotel stay experience to obtain a snapshot of the hotel’s operational performance and overall customer satisfaction or to develop marketing strategies to better promote the hotel (Cantallops and Salvi, 2014). However, two challenges exist when hoteliers try to understand both sides of the coin. The first challenge is the information overload of individual-level reviews or comments. Numerous comments in the open structure of online textual reviews or face-to-face conversations as feedback from hotel guests are available online and offline. The written comments often contain a substantial number of words and are time consuming to read one by one in detail. The second challenge is the lack of availability of a holistic satisfaction measure. In the face-to-face conversation environment, it is often hard to capture customers’ overall evaluation of their hotel experience directly. Customers may not reveal their true evaluation, especially when they have a negative perception, because of worries about breaking the customer–seller relationship or concerns about the hotel “losing face” (Au et al., 2010). In some cases, it may be infeasible to develop a specific scale by which customers can give a single rating to evaluate the whole product or service. In the comment card and online review environment, customer comments as verbal protocols in terms of customers’ online textual reviews, as opposed to direct measures, can avoid eliciting customers’ perceptions (Smith and Bolton, 2002). The direct measurement of customer ratings in terms of closed-ended survey questions can confound the data of customers’ true evaluation because of variations in survey design from different review platforms (Weber, 1985; Xiang et al., 2017). A technical approach to link the relationship between customers’ overall satisfaction and their textual comments is needed to address these major challenges. Technical attributes of textual reviews can explain significant variations in customer ratings, and technical attributes of online textual reviews can have a significant effect on customer ratings (Geetha et al., 2017). To link the two sides of the coin, this study uses customers’ online review behavior to predict their overall satisfaction with hotels. Many previous studies focus on the indications and contents of customer online reviews (e.g., Xiang et al., 2015; Xu and Li, 2016), but few studies discuss the linguistic style, namely the technical attributes of the online reviews themselves (e.g., Geetha et al., 2017). The main reasons lie in the fact that examining technical attributes of online textual reviews is an extremely costly task with unstable and difficult-to-interpret measurements (Chevalier and Mayzlin, 2006; Godes and Mayzlin, 2004).