Average scores integration in official star rating scheme

Abstract

Purpose Evidence suggests that electronic word-of-mouth (eWOM) plays a highly influential role in decision-making when booking hotel rooms. The number of online sources where consumers can obtain information on hotel ratings provided has grown exponentially. Hence, a number of companies have developed average scores to summarize this information and to make it more easily available to consumers. Furthermore, official star rating schemes are starting to provide these commercially developed average scores to complement the information their schemes offer. The purpose of this paper is to examine the robustness of these systems. Design/methodology/approach Average scores from different systems, and the scores provided by one rating site were collected for 200 hotels and compared. Findings Findings suggested important differences in the ratings and assigned descriptive word across websites. Research limitations/implications The results imply that the application of average scores by official organizations is not legitimate and identifies a research gap in the area of consumer and star rating standardization. Originality/value The paper is of value to the industry and academia related to the examination of rating scales adopted by major online review tourism providers. Evidence of malpractice has been identified and the adoption of this type of scales by official star rating schemes is questioned.

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

“…Therefore, these OTAs use a scale that is not the usual one and leads users to believe that a hotel with a score of 5 or 6 on average in the assessment on Agoda and on Booking.com is a hotel with a "pass mark" when it is actually a failure. This perception is reinforced when words such as "passable", "pleasant", "acceptable" or "above average" are included next to these scores (Mellinas and Reino, 2019).…”

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.…”

“…However, since the customer experience is a determining factor that can have an impact on the recommendations of the latter and taking into account that products related to hosting cannot be tried before purchase [7], consumer interest in listings displayed on P2P platforms (of which Airbnb is one) is influenced by online user reviews [8]. It even happens that the latter have an impact on the purchasing decision since they constitute a major source of information [9] and thus contribute to electronic word of mouth (eWOM) which has radically restructured the relationship between the business and the consumer [10].…”

Quality Audit Indicators for Inbound Tourism: A Qualitative Study on Malaysian Travel Agencies

“…In the case of Star Ratings Australia, the indicators are determined by more than 200 indicators which have been ranked by travelers on quality accommodation which reflects cleanliness, quality, and condition of facilities (https://www.starratings.com.au/). In the United Kingdom, Mellina and Reino (2019) found that online review rating or electronic word-of-mouth (eWOM) plays a significant role in the hotel's classifications.…”