Are Online Reviews Helpful for Consumers?: Big Data Evidence From Services Industry

Are Online Reviews Helpful for Consumers?: Big Data Evidence From Services Industry

David D'Acunto (University of Pisa, Italy), Annamaria Tuan (University of Bologna, Italy) and Daniele Dalli (University of Pisa, Italy)
DOI: 10.4018/978-1-5225-8575-6.ch012
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This chapter explores the elements influencing online reviews' usefulness by focusing on the language that consumers use when writing online reviews and on reviewers' attributes. By using text mining tools, the authors investigate how reviews' language affects their usefulness perception (i.e., the number of times readers have marked them as useful). The dataset consists of more than 54,000 online reviews from the most frequently used e-WOM source currently available and covers the period 2005-2017. The results suggest that word count and some of reviews' linguistic features (e.g., the subjectivity score, authenticity score) influence their usefulness perception. Reviewers' attributes (i.e., their number of reviews, age, class, and gender) also affect their reviews' perceived usefulness. The chapter concludes by describing the study results' implications for theory development, for empirical research, and for managerial practice.
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The advent of online communication channels, such as web-based opinion platforms, has led to marketers increasingly focusing their attention on electronic word of mouth (eWOM) (e.g. Liu et al. 2018, Roy et al. 2018, Chevalier & Mayzlin, 2006; Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004). eWOM has been defined as “any positive or negative statement made by potential, actual or former customers about a product or company […] made available to a multitude of people and institutions via the internet” (Hennig-Thurau et al., 2004, p. 39). Filieri and McLaey (2014) suggested that, given that reviews consist of “comments published by travelers on the tourism products, services and brands they experience” (p. 1), online reviews are an example of eWOM.

It is difficult to evaluate the products and services within the tourism and hospitality domain, because they are mainly intangible goods. Consequently, consumers’ online reviews play a critical role (Del Chiappa et al., 2018, Schuckert et al., 2015; Liu and Park, 2015) and have important consequences from a managerial viewpoint. When communicating the company’s products and services, customers become “objective voices” (Vermeulen and Seegers, 2009), with more than 75% of consumers taking peer reviews into account when planning a holiday (Xie et al., 2014). Peers’ feedbacks are, in fact, perceived as more trustworthy and credible than company-generated information (Filieri et al., 2015; Park, Lee and Han, 2007), which is one of the major reasons why potential customers read online reviews to inform their decision-making processes (Zhu & Zhang, 2010).

Reviews’ usefulness can facilitate consumers’ decision-making and is therefore important (Lee, 2013). This metric, which is available on the majority of online platforms such as Tripadvisor, suggests how many times others have marked a review as useful. Previous research has investigated the determinants of usefulness by focusing, for instance, on the score rating, the reviewer’s background, and the service providers’ response (Filieri et al., 2018).

Nevertheless, which of reviews’ linguistic attributes stimulate consumers to mark them as useful? And, is there a connection between the language used in the reviews and their usefulness perception?

We analyze a sample of Tripadvisor reviews by means of automated text analysis and linguistic analysis to answer these questions. Identifying the factors affecting perceived usefulness − measured as the number of helpful votes that a review received − is indeed crucial in the tourism and hospitality domain, as well as in marketing. These votes allow online platforms to provide travelers and consumers with greater value and to assist their decision-making processes (Sussman & Siegal, 2003).

Key Terms in this Chapter

Online Reviews Platforms: Website which collects online reviews. The most famous in the hospitality industry is Tripadvisor.

Online Reviews: A review of a product or service made by a consumer who has experienced a service or purchased a product.

eWOM: Electronic version of traditional word of mouth.

Sentiment Analysis: Process of categorizing opinions expressed in text in order to understand consumers’ attitudes toward a topic.

Linguistic-Cognitive Analysis: Analysis of grammar choices in texts which can reveal something about the author than the content itself.

Usefulness: Number of helpful votes of a review.

Automated Text Analysis: Research method that analyses texts by means of software (e.g., LIWC).

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