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Measuring Product Type with Dynamics of Online Product Review Variances: A Theoretical Model and the Empirical Applications

Author

Listed:
  • Yili Hong

    (Department of Information Systems, Arizona State University)

  • Pei-yu Chen

    (Department of Information Systems, Arizona State University)

  • Lorin Hitt

    (Department of Operations and Information Management, University of Pennsylvania)

Abstract

A significant body of literature in information systems, marketing, and economics has shown the important implication of the distinction between experience products and search products (“product type†) on consumer information search, marketplace design, and firm strategy. However, how to empirically measure product types remains a challenge, and this challenge is further complicated by the growth of online commerce and the increasing availability of online reviews that have transformed the nature of many products and altered the traditional perception of these products. The objective of this research is to propose an online product review-based measure that could accurately reflect consumers’ perception of a product, as search or experience dominated product. Based on the definitions of search and experience products — whether information can be easily transferred or not — we propose a data-driven method that can be used to infer product type from statistical analyses of online product reviews. Our theoretical analyses indicate that the variance of the ratings should decrease as more consumers rate a pure search product; for experience products however, the variance of the ratings may remain constant or increase depending on the importance of the experience attributes in determining consumer utility. We demonstrate the empirical applications of this approach at the category, product, and attribute levels using product reviews data from Amazon.com, Yelp.com, and Ctrip.com, respectively. In addition, a user study conducted on Amazon Mechanical Turk shows our review-based measure to outperform Nelson’s (1970) product classification, which historically has been the standard in determining product type. Overall, this new measure provides an easy to implement, less subjective and more accurate measure of product type. Therefore, researchers and practitioners can use this measure to better understand how consumers perceive products and to design strategies accordingly.

Suggested Citation

  • Yili Hong & Pei-yu Chen & Lorin Hitt, 2014. "Measuring Product Type with Dynamics of Online Product Review Variances: A Theoretical Model and the Empirical Applications," Working Papers 14-03, NET Institute.
  • Handle: RePEc:net:wpaper:1403
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    References listed on IDEAS

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    1. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    2. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    3. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    4. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    5. Caves, Richard E & Williamson, Peter J, 1985. "What Is Product Differentiation, Really?," Journal of Industrial Economics, Wiley Blackwell, vol. 34(2), pages 113-132, December.
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    Cited by:

    1. Sharapudinov, S. & Zezerova, V. & Storchevoy, M., 2017. "Determinants of Online Word-of-Mouth: Evidence from Durable Goods Market," Working Papers 8721, Graduate School of Management, St. Petersburg State University.
    2. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.

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    More about this item

    Keywords

    product type; online product reviews; user-generated content; data-driven approach;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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