Analysis of the Perceived Value of Online Tourism Reviews: Influence of Readability and Reviewer Characteristics

By Bin Fang, Qiang Ye, D. Kucukusta, R. Law. 2016

600 citations recorded by the source corpus.

Online reviews provide additional product information to reduce uncertainty. Hence, consumers often rely on online reviews to form purchase decisions. However, an explosion of online reviews brings the problem of information overload to individuals. Identifying reviews containing valuable information from large numbers of reviews becomes increasingly important to both consumers and companies, especially for experience products, such as attractions. Several online review platforms provide a function for readers to rate a review as “helpful” when it contains valuable information. Different from consumers, companies want to detect potential valuable reviews before they are rated to avoid or promote their negative or positive influence, respectively. Using online attraction review data retrieved from TripAdvisor, we conduct a two-level empirical analysis to explore factors that affect the value of reviews. We introduc a negative binomial regression model at a review level to explore the effects of the actual reviews. Subsequently, we apply a Tobit regression model at the reviewer level to investigate the effects of reviewer characteristics inferred from properties of historical rating distribution. The empirical analysis results indicate that both text readability and reviewer characteristics affect the perceived value of reviews. These findings have direct implications for attraction managers in their improved identification of potential valuable reviews.

Subfields: Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c685373b1f2ff600c3fc7489d1bc520e59d3f674

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Citations in the corpus

The role of emotions and conflicting online reviews on consumers' purchase intentions (2018, background): “Previous research demonstrates the influence of reviews on product sales (Fang et al., 2016) and consumer decision making (Huang & Korfiatis, 2015); however, this impact differs depending on the review valence and sequence (Kim & Lee, 2015).”
The Impact of Perceived Usefulness of Online Reviews, Trust and Perceived Risk on Online Purchase Intention in Emerging Markets: A Mexican Perspective (2020, background): “Trust can be created through online reviews; according to Fang et al. (2016), consumers consider online reviews as a more credible as other traditional sources of information.”
Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews (2019, background): “Hotel or attraction ranking is relatively stable over a long period because of their relatively stable popularity (Fang et al., 2016).”
Sentiment Analysis in Tourism: Capitalizing on Big Data (2019, background): “In the literature, this form of implicit sentiment is referred to as objective polar utterance (Fang et al., 2016), evaluative fact (Gräbner et al., 2012), or it is denoted as a polar fact (Leung et al., 2013).”
Pictorial content, sequence of conflicting online reviews and consumer decision-making: The stimulus-organism-response model revisited (2020, background): “Previous research on eWOM in tourism has focused mainly on hotels, tourist destinations and/or attractions, paying little attention to restaurants (Fang et al., 2016; Ruiz et al, 2018).”
Effects of the Booking.com rating system: Bringing hotel class into the picture (2018, unknown): “They have collected and analysed online consumer reviews (OCRs) published on the most popular independent consumer review website TripAdvisor (Fang et al., 2016; Xie et al., 2014), Online Travel Agencies (OTAs) such as Expedia (e.g., Stringam & Gerdes, 2010) and Booking (e.g., Mellinas et al., 2016)”