Modeling Consumer Learning from Online Product Reviews

Forthcoming, Marketing Science

46 Pages Posted: 9 May 2011 Last revised: 10 Sep 2014

Yi Zhao

Georgia State University - Department of Marketing

Sha Yang

University of Southern California - Marshall School of Business

Vishal Narayan

NUS Business School

Ying Zhao

Hong Kong University of Science & Technology (HKUST)

Multiple version iconThere are 2 versions of this paper

Date Written: September 20, 2012

Abstract

We propose a structural model to study the effect of online product reviews on consumer purchases of experiential products. Such purchases are characterized by limited repeat purchase behavior of the same product item (such as a book title), but significant past usage experience with other products of the same type (such as books of the same genre). To cope with the uncertainty in quality of the product item, we posit that consumers may learn from their experience with the same type of product, and others’ experiences with the product item. We model the credibility of product reviews and how it evolves over time. We apply the model to a panel dataset of 1,919 book purchases by 243 consumers. We find that consumers learn more from online reviews of book titles than from their own experience with other books of the same genre. We estimate the profit impact of product reviews and how it varies with the number of reviews. We find evidence of diminishing returns to increasing number of reviews. Under certain conditions, additional reviews might even lead to lower profits. We estimate the optimum number of reviews for a representative product.

Keywords: Learning Models, Choice Models, Product Reviews

JEL Classification: D12, D83, M31

Suggested Citation

Zhao, Yi and Yang, Sha and Narayan, Vishal and Zhao, Ying, Modeling Consumer Learning from Online Product Reviews (September 20, 2012). Forthcoming, Marketing Science. Available at SSRN: https://ssrn.com/abstract=1832763 or http://dx.doi.org/10.2139/ssrn.1832763

Yi Zhao (Contact Author)

Georgia State University - Department of Marketing ( email )

United States

Sha Yang

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA 90089
United States

Vishal Narayan

NUS Business School

Singapore

Ying Zhao

Hong Kong University of Science & Technology (HKUST) ( email )

Kowloon

Paper statistics

Downloads
953
Rank
18,235
Abstract Views
4,200