Measuring the Value of Different Types of Product Information in the Electronic Retail of Books

The value of different kinds of book information in electronic retail is measured using best–worst scaling, an approach making the respondent consider several information items simultaneously and choose the most and least preferred among them. Our respondents are both customers using municipal libraries and students. Theory on digital nativity, Hierarchical Bayes estimation and Latent Class analysis are used to study the heterogeneity of the preferences. Customer reviews give the most valuable information on an average. Especially digital immigrants rely more on samples, expert reviews, author presentations, blogs, and friends’ ratings. Preference clusters were identified where the extremes groups were those who highly valued peer information but not expert information and those who valued them vice versa. Customer reviews are appreciated by almost everyone, regardless of their state of digital nativity. The electronic retailer should make all efforts to provide the personalized information when the customer is browsing books online and offer a rich set of customer reviews.