Essays on online reviews: Reviewers' strategic behaviors and contributions over time
Abstract
Online reviews play an important role in consumers’ purchasing decisions. Researchers are increasingly interested in studying the dynamic impact of online reviews on product sales. However, the antecedent of online reviews, online reviewers’ behaviors, has not been fully explored. Understanding how online reviewers make review decisions can assist companies to better predict the impact of online reviews on product sales. In addition, it can help researchers to better understand how online users contribute to various online communities. This dissertation consists of two studies investigating how online reviewers make review decisions. The first study tries to understand how online reviewers compete for the scarce resource of attention when writing online reviews. It theorizes the strategies for online reviewers to choose the right product and the right rating strategy when posting reviews so as to compete for attention. The results suggest that online reviewers strategically choose certain products to review and post certain ratings under different conditions. It is the first study to understand how reviewers’ review decisions can be driven by social incentives such as gaining attention. The second study examines the impact of social dynamics on reviewers’ contribution decisions over time. It develops a dynamic structural model of learning to address how online reviewers learn about the value or the attractiveness level of their reviews through the signals from social interactions. Different from the existing studies which applies structural models to study consumers’ learning about brand quality, this study tries to understand how reviewers learn about the value of the reviews provided by themselves. The results show that reviewers’ decisions are affected by the belief of the value or the attractiveness of their reviews. In addition, reviewers are risk averse towards the uncertainty of their belief. Social feedback can serve as a signal to reduce the uncertainty and thus help reviewers to learn about the attractiveness of their reviews over time.
Degree
Ph.D.
Advisors
Hu, Purdue University.
Subject Area
Marketing|Information Technology
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