A flourishing of the importance of customer reviews has been observed in this digital era. This is especially true in hotel sector, which allows guests to express their satisfaction towards the service in the form of open-structured online reviews and overall ratings over travel agency websites. Using reviews data of 2001 hotels from Tripadvisor.com, the chapter analyzes the overall hotel performances through linguistic features of e-WOM such as its length, readability, sentiment, and volume. The chapter develops a regression model for evaluating guest satisfaction by using overall ratings as its measure, validated through hotel review data. Data analysis result shows that review volume, sentiment index, and readability have significant positive affect over guest satisfaction whereas length shows the negative influence. This chapter discusses beneficial implications for researchers and practitioners working in this field.
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In the times of emerging technologies, digitalization has perforated every field. To survive in present marketing environment, practitioners need to have an edge over their competitors at all times. Businesses have the tendency to easily adapt to the environment like a malleable alloy, which is also a need of the hour. Majority of the customer base is turning tech-savvy, which in turn creates a necessity for the companies to keep up with their dynamic customer landscape. Market practitioners are now realigning their businesses to online domain in order to streamline their everyday operations. Automation of operations comes with its own set of benefits. Besides indulging digital customer at all touch points during customer experience lifecycle, it effectively leads to increased efficiency, reduced related cost, enables better analysis and downsizes humanly error.
Recreation and tourism is a major sector of consideration for a majority of economies around the world. It contributes in innumerable ways to social, economic and other intangible benefits to them (Wood, Guerry, Silver, & Lacayo, 2013). Information on tourist trends and behavior in this realm is collected using user generated content on social platforms. With the emergence of cybernated environment, service industry has ventured into online marketing in full bloom. In the Online Travel Agencies, provision of hotel booking and other services through online mode has given rise to an ever-increasing amount of online textual data in the form of feedbacks and reviews (Tandon, Sharma, & Aggarwal, 2019). With the technological advancements, Word of Mouth (WOM) has taken the electronic form (EWOM) with an additional benefit on businesses in the form of wide reach among audience beyond the geographical and time constraints (Yen & Tang, 2019). EWOM can be described as a form of casual information exchange with other consumers through online technological platforms talking about usage and characteristics of a product or service or their seller (De Pelsmacker, van Tilburg, & Holthof, 2018).
User Generated Content (UGC) on the websites provides travelers with non- commercial information and travel experiences shared by fellow travelers. These uncensored, straight from the reviewers, expressions of experience are considered to be more reliable and current (Gretzel & Yoo, 2008). Customers perceive the information provided by the members of travel fraternity to be more trustworthy and influential as compared to that provided by the service providers (Fotis, Buhalis, & Rossides, 2012). A recent survey report by Nielsen states that 63% of the travel services and product customers, did a prior online investigation before the purchase of product or service (McKenzie & Adams, 2018).These results bespeak of the emerging trend of traveler’s reliance on content available online as a part of decision making process. Today, these websites have become an important source of information for the travel destinations as well as the choice of activities to be conducted there (Xiang & Gretzel, 2010). A number of researchers have shown that EWOM axiomatically affect the product sales like books (Hong, Xu, Xu, Wang, & Fan, 2017), cars (Fan, Che, & Chen, 2017), movies (Duan, Gu, & Whinston, 2008), and cameras (Gu, Park, & Konana, 2012).