TY - JOUR
T1 - Measuring Product Type and Purchase Uncertainty with Online Product Ratings
T2 - A Theoretical Model and Empirical Application
AU - Chen, Peiyu
AU - Hitt, Lorin M.
AU - Hong, Yili
AU - Wu, Shinyi
N1 - Funding Information:
The authors thank the Senior Editor Alok Gupta, the Associate Editor Michael Zhang, and the anonymous reviewers for the helpful feedback and suggestions. The authors also thank Gordon Burtch, Ni Huang, Xinxin Li, and Paul Pavlou for early discussions, as well as participants in the Workshop on Information Systems and Economics and International Conferences on Information Systems. The authors contributed equally to the work, and the names are listed alphabetically.
Publisher Copyright:
© 2021 INFORMS
PY - 2021/12
Y1 - 2021/12
N2 - 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 versus experience and horizontal versus 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.
AB - 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 versus experience and horizontal versus 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.
KW - consumer reviews
KW - data-driven approach
KW - experience goods
KW - online product ratings
KW - product differentiation
KW - product type
KW - search goods
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U2 - 10.1287/ISRE.2021.1041
DO - 10.1287/ISRE.2021.1041
M3 - Article
AN - SCOPUS:85128547602
SN - 1047-7047
VL - 32
SP - 1470
EP - 1489
JO - Information Systems Research
JF - Information Systems Research
IS - 4
ER -