Consumers frequently read online consumer reviews before purchasing products both online and offline (at stores). Yet, reviews are known to have certain biases. This paper surveys 17 types of biases that previous studies identified. The effects of these biases are intertwined and hard to isolate from one another. It is then difficult to assess the impact of each bias on how consumers rate the helpfulness of reviews. Although extant studies use different terminologies, review biases can be summarized into three basic categories: selection biases, system biases, and attribution biases. Focusing on major categories of goods, the paper then considers the overestimation of review helpfulness due to system and non-system (selection and attribution) biases. Using Amazon.com reviews on six bestselling products and the data from a survey questionnaire to 294 consumers, the paper shows the following: (1) the overestimation of review helpfulness due to non-system biases is smaller in the order of search, experience and credence goods and (2) the overestimation of review helpfulness due to system biases is more pronounced with hedonic goods than non-hedonic goods.
Nakayama, Makoto, "Biases in Consumer Reviews: Implications for Different Categories of Goods" (2013). CONF-IRM 2013 Proceedings. 62.