Do customers share the same perspective? A study on online OTAs ratings versus user ratings of Hong Kong hotels

Abstract

Hotel service levels and pricing range are often denoted by the “star” rating system predominant in that country. This rating system depends considerably on travel agencies to disseminate information to consumers to assist them in their hotel selection. Given the popularity of online travel agencies (OTAs) and review websites, consumers can now compare published star and user ratings of hotels online to obtain a complete idea of the hotel service standards from the perspective of other users. This study attempted to analyze the difference among the star and user ratings published in eight popular OTAs. Findings showed that Priceline and Ctrip had the lowest website star ratings, whereas Bookings.com and Agoda had the highest for both local chain and independent hotels. A comparison of the star and user ratings indicated that Priceline, TripAdvisor, and Hotels.com had no statistical difference, but the other five OTAs exhibited statistical differences. The findings also indicated that Ctrip had higher user rating scores among the OTAs, possibly indicating that Chinese users rate hotels higher than other nationalities do.

Online travel review rating scales and effects on hotel scoring and competitiveness

“…The singular scales of Booking.com (2.5-10) and Agoda (2-10) provide apparently better scores for the hotels when compared with the other OTAs analysed. This is also confirmed by Leung et al (2018) with their study comparing several OTAs, but the study did not take into account that the Booking.com scale starts on 2.5 instead of 1.…”

“…With the present study, we cannot point to a single reason such as the measurement scale, method of collecting reviews, textual description of positive and negative aspects, old review policies of OTAs or even that different platforms might have different users that might come with some sample self-selection issues on platforms. There are more factors to be considered such as the user's nationality that can be more or less demanding of hotel services (Au et al, 2014;Leung et al, 2018), cultural differences in responses to a Likert scale (Lee et al, 2002). Another factor could be the percentage of reviews collected on each OTA through either mobile phones or personal computers, since the analysis carried out by Mariani et al (2019) determined that the ratings on Booking.com were higher for responses collected with smartphones.…”

“…Since then, most authors have taken this scale into account when they have used data from Booking.com (24 citations in 3 years). However, many authors are repeating the mistake of considering that all OTAs use a scale of 0-10 or 1-10 ( Abrate and Viglia, 2016;Castro and Ferreira, 2018;Ert et al, 2016;Kim and Park, 2017;Leung et al, 2018;Pokryshevskaya and Antipov, 2017, among others). Even a UNWTO report indicated that Booking.com uses a 1-10 scale (Blomberg-Nygard and Anderson, 2016).…”

“…Often, this error does not affect the results of the studies carried out, because they are qualitative studies based on the content of the reviews. The most important inaccuracies occur when using scores from different websites and trying to homogenize scales as stated by Leung et al (2018):…”

A Client-Centric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP

“…Indeed, tourism and hotel services are among the most expensive services, which imply a considerable level of risk and uncertainty vis-à-vis travel-related decision-making [26], which has the effect of customers reviewing the reviews and opinions available online in order to minimize the perceived risk to their purchasing decision [25], which makes the level of reliance on online reviews of the very high consumer share, which has a profound impact on the sale of rooms between different hotel segments [27]. Consequently, a number of companies have set up average scores which summarize the information concerning the evaluations of tourist establishments in order to make them more easily accessible to customers, in this case, the star rating system [28] whose robustness is criticized because of the large differences in assigned scores and descriptive words across different systems [10] or because of the distribution of review scores in the same system [29]. Psychic and geographic distance even happens to impact rating scores.…”