Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application

Information Systems Research, Forthcoming

43 Pages Posted: 10 Apr 2014 Last revised: 19 May 2021

See all articles by Pei-Yu Chen

Pei-Yu Chen

Arizona State University (ASU) - Department of Information Systems

Lorin M. Hitt

University of Pennsylvania - Operations & Information Management Department

Yili Hong

University of Houston - C.T. Bauer College of Business

Shinyi Wu

Arizona State University (ASU)

Date Written: May 19, 2021

Abstract

Building on the distinction between search and experience goods as well as vertical and horizontal differentiation, we propose a set of theory-grounded, data-driven measures that allow us to measure not only product type (search vs. experience, horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. The proposed measures have two advantages over prior methods: 1) unlike prior categorization schemes that classified goods as either search or experience goods, our measure is continuous, allowing us to rank order the degree of search vs. experience and horizontal vs. vertical differentiation among products or categories; 2) Our approach is easier to implement than prior methods because it relies solely on consumer ratings information (as opposed to expert judgment) and can be employed at multiple levels (attributes, products or product categories).
We illustrate empirical applications of our proposed measures using product rating data from Amazon.com. Our data-driven measures reveal the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Our results also show that, while ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Our method and findings could facilitate further research on product review systems and enable quantitative measurement of product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.

Keywords: product type, search goods, experience goods, product differentiation, online product ratings, consumer reviews, data-driven approach

Suggested Citation

Chen, Pei-Yu and Hitt, Lorin M. and Hong, Yili and Wu, Shinyi, Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application (May 19, 2021). Information Systems Research, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2422686 or http://dx.doi.org/10.2139/ssrn.2422686

Pei-Yu Chen

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

Tempe, AZ
United States

Lorin M. 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)

Yili Hong (Contact Author)

University of Houston - C.T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

Shinyi Wu

Arizona State University (ASU) ( email )

Farmer Building 440G PO Box 872011
Tempe, AZ 85287
United States

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