39 Pages Posted: 9 Oct 2014
Date Written: September 30, 2014
A significant body of literature in information systems, marketing, and economics has shown the important implication of the distinction between experience products and search products ("product type") on consumer information search, marketplace design, and firm strategy. However, how to empirically measure product types remains a challenge, and this challenge is further complicated by the growth of online commerce and the increasing availability of online reviews that have transformed the nature of many products and altered the traditional perception of these products.
The objective of this research is to propose an online product review-based measure that could accurately reflect consumers' perception of a product, as search or experience dominated product. Based on the definitions of search and experience products - whether information can be easily transferred or not - we propose a data-driven method that can be used to infer product type from statistical analyses of online product reviews. Our theoretical analyses indicate that the variance of the ratings should decrease as more consumers rate a pure search product; for experience products however, the variance of the ratings may remain constant or increase depending on the importance of the experience attributes in determining consumer utility. We demonstrate the empirical applications of this approach at the category, product, and attribute levels using product reviews data from Amazon, Yelp, and Ctrip, respectively. In addition, a user study conducted on Amazon Mechanical Turk shows our review-based measure to outperform Nelson's (1970) product classification, which historically has been the standard in determining product type. Overall, this new measure provides an easy to implement, less subjective and more accurate measure of product type. Therefore, researchers and practitioners can use this measure to better understand how consumers perceive products and to design strategies accordingly.
Keywords: product type, online product reviews, user-generated content, data-driven approach
Suggested Citation: Suggested Citation
Hong, Yili and Chen, Pei-Yu and Hitt, Lorin M., Measuring Product Type with Dynamics of Online Product Review Variances: A Theoretical Model and the Empirical Applications (September 30, 2014). NET Institute Working Paper No. 14-03. Available at SSRN: https://ssrn.com/abstract=2506328 or http://dx.doi.org/10.2139/ssrn.2506328