Online reviews, a form of electronic word-of-mouth (eWOM), often provide vital information to consumers. In the last few years, the number of online review platforms available to consumers has expanded dramatically.
As a result, modern consumers have instant access to thousands of peer-to-peer reviews on platforms dedicated to a wide variety of products and services, including air travel (Kayak), tourism (TripAdvisor), home remodeling (Angie’s List), restaurants (Yelp!), electronics (Shopping.com), and music (Amazon).
Given the abundance of online reviews, and given their important role in purchase decisions, marketing academics have spent considerable effort studying the transmission and impact of online reviews.
Until recently, however, marketers knew relatively little about the underlying processes that shape consumers’ receptions of online reviews.
That knowledge gap started to shrink around 2010, after several studies investigating review and reviewer characteristics began to appear in major marketing and management information systems journals.
For instance, a 2012 study published by the author and another colleague examined the factors that contributed to consumers’ perceptions of the usefulness (or helpfulness) of online reviews.
Using a random sample of 3,000 Yelp reviews of various service providers throughout the United States, the researchers discovered that a reviewer’s reputation was positively correlated with perceived usefulness.
Surprisingly, a reviewer’s expertise was negatively correlated with perceived usefulness. Similarly, a review’s rating (i.e., one-star, two-star, three-star, or four-star) exhibited a U-shaped relationship with usefulness in the sense that extreme reviews (with a one-star or four-star rating) were typically perceived as more useful than two-star or three-star ratings.
However, the length of the review did not show any significant correlation with perceived usefulness. Thus, longer reviews were not necessarily more useful, which was a counterintuitive result.
Since the time of that study, the landscape of online reviews has changed in several ways. First, an entire cottage industry devoted to generating fake reviews has emerged.
Now a business can inflate its brand image by contracting with an agent to flood online review platforms with fake, overly enthusiastic reviews.
Another interesting development spurred on by academic and professional interest in big data is the application of text mining software to recognize problems and opportunities conveyed by online reviews.
Finally, recent advances in image recognition software have led to research about how consumers increasingly use photos and other images to convey information in their reviews.
This new line of research has significant potential because researchers (and marketing analysts) are currently limited to coding images by hand.
Wesley Friske is an assistant professor of marketing at Missouri State University. His areas of specialization include marketing research and marketing management.
This article appeared in the June 18, 2017 edition of the News-Leader and can be accessed online here