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- Title
Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application.
- Authors
Chen, Peiyu; Hitt, Lorin M.; Hong, Yili; Wu, Shinyi
- Abstract
Search and experience goods, as well as vertical and horizontal differentiation, are fundamental concepts of great importance to business operations and strategy. In our paper, we propose a set of theory-grounded data-driven measures that allow us to measure not only product type (search vs. experience and horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. We used product rating data from Amazon.com to illustrate 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, whereas 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. Industry practitioners could utilize our approaches to quantitatively measure product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products. 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.
- Subjects
AMAZON.COM Inc.; PRODUCT differentiation; PRODUCT positioning; CONSUMERS' reviews; CONSUMER education; PRODUCT reviews; RATINGS of hospitals
- Publication
Information Systems Research, 2021, Vol 32, Issue 4, p1470
- ISSN
1047-7047
- Publication type
Article
- DOI
10.1287/isre.2021.1041