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

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.