Abstract
Online product ratings have become a major information source for customers, retailers, and manufacturers. Both practitioners and researchers predominantly interpret them as a reflection of product quality. We argue that they in fact represent the customer's satisfaction with the product. Accordingly, we present a customer satisfaction model of online product ratings which incorporates the customer's pre-purchase expectations and actual product performance as determinants of ratings. We validate our model by applying it to two datasets collected at the German website of Amazon.com. The results indicate that both factors have a significant influence on online product ratings, supporting the proposed interpretation of ratings.
1. Introduction
Along with the growing diffusion of e-commerce, online product reviews have become a major information source for customers, retailers, and manufacturers. On the one hand, reviews and ratings contributed by online shop customers provide product information for prospective consumers, thereby reducing their uncertainty about the product (Chen and Xie, 2008). Consistently, research has shown that they affect sales in various contexts (e.g., Chevalier and Mayzlin, 2006; Lin et al., 2011; Park et al., 2007). On the other hand, online retailers and manufacturers increasingly rely on customer feedback to enrich their marketing strategy (Chen and Xie, 2008; Cui et al. 2012), to adjust product listings (e.g. via relevance sorting), and to create additional revenue streams (Mudambi and Schuff, 2010). For these reasons, it is not surprising that nearly all major online retailers such as Amazon. com or Ebay.com have implemented product rating functionalities.
6. Implications, limitations, and conclusion
In this study, we have shown that the customer satisfaction model of online product ratings is better suited to explain the score of ratings than traditional quality-centered explanations. This means that customers' ratings of products depend on their expectation about these products and their performance. This finding has rich and concrete implications for both research and practice.
The development and empirical test of this model advances theoretical knowledge by introducing the customers' expectation as a determinant of online ratings. Thereby, we refine the current understanding of the baseline of online ratings. The empirical results suggest that the model provides a valuable tool to analyze online ratings and is a valid starting point to elaborate on biases more accurately.
