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Measuring Product Type With Dynamics of Online Product Review Variance

Implications for Research and Practice, Proceedings of the 33rd International Conference on Information Systems (ICIS), Orlando, FL, 2012

18 Pages Posted: 21 Aug 2012 Last revised: 17 Mar 2013

Yili Hong

Arizona State University (ASU) - W.P. Carey School of Business

Pei-Yu Chen

Arizona State University (ASU) - Department of Information Systems

Lorin M. Hitt

University of Pennsylvania - Operations & Information Management Department

Date Written: August 20, 2012

Abstract

The concept of “product type” (experience versus search product) is increasingly important in business research and practice. However, it is not defined or measured precisely in the Internet age due to significantly lower search cost and changes in consumer information search behavior resulting from reliance on information and communications technology.. However, correctly understanding product type has important strategic implications for marketing strategy and digital market design. We take advantage of the greatly available micro level online word-of-mouth data and infer product type (experience versus search product) based on statistical properties of online word of mouth (specifically, online product reviews). We draw on the law of large numbers (L.L.N), and the literature on informational content and online product reviews to analytically propose a mechanism to classify products. Our theoretical analyses indicate that, for a pure search product, when number of reviews (i.e. review sample size) increases as more consumers rate the product, variance of the mean rating will decrease. And for a product with more experience attributes, when number of reviews increases, the variance of the mean rating will not decrease and may instead increase depending on how dominant these experience attributes are. We collect archival data from three different websites (Amazon, Yelp and Ctrip) that collect and publish consumer product reviews to categorize the products and services. Implications of this analytical tool and empirical findings for research, theory and managerial practice are discussed.

Keywords: Online Product Reviews, Product Type, Law of Large Numbers, Information Content, Product Quality, Business Analytics

Suggested Citation

Hong, Yili and Chen, Pei-Yu and Hitt, Lorin M., Measuring Product Type With Dynamics of Online Product Review Variance (August 20, 2012). Implications for Research and Practice, Proceedings of the 33rd International Conference on Information Systems (ICIS), Orlando, FL, 2012. Available at SSRN: https://ssrn.com/abstract=2132864

Yili Hong (Contact Author)

Arizona State University (ASU) - W.P. Carey School of Business ( email )

Tempe, AZ 85287-3706
United States

HOME PAGE: http://my.wpcarey.asu.edu/directory/people/profile.cfm?person=2255148

Pei-Yu Chen

Arizona State University (ASU) - Department of Information Systems ( email )

Tempe, AZ
United States

Lorin Hitt

University of Pennsylvania - Operations & Information Management Department ( email )

571 Jon M. Huntsman Hall
Philadelphia, PA 19104
United States
215-898-7730 (Phone)
215-898-3664 (Fax)

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