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Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand

Author

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  • Steffen Zimmermann

    (School of Management, University of Innsbruck, 6020 Innsbruck, Austria)

  • Philipp Herrmann

    (Faculty of Business and Economics, Paderborn University, 33098 Paderborn, Germany)

  • Dennis Kundisch

    (Faculty of Business and Economics, Paderborn University, 33098 Paderborn, Germany)

  • Barrie R. Nault

    (Haskayne School of Business, University of Calgary, Calgary, Alberta T2N 1N4, Canada)

Abstract

Consumer ratings play a decisive role in purchases by online shoppers. Although the effects of the average and the number of consumer ratings on future product pricing and demand have been studied with some conclusive results, the effects of the variance of these ratings are less well understood. We develop a model where we decompose the variance of consumer ratings into two sources: taste differences about search and experience attributes of a durable good, and quality differences among instances of this good in the form of product failure. We find that (i) optimal price increases and demand decreases in variance caused by taste differences, (ii) optimal price and demand decrease in variance caused by quality differences, and (iii) when holding the average rating as well as the total variance constant, for products with low total variance, both price and demand increase in the relative share of variance caused by taste differences. Counter to intuition, we demonstrate that risk-averse consumers may prefer a higher-priced product with a higher variance in ratings when deciding between two similar products with the same average rating.

Suggested Citation

  • Steffen Zimmermann & Philipp Herrmann & Dennis Kundisch & Barrie R. Nault, 2018. "Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand," Information Systems Research, INFORMS, vol. 29(4), pages 984-1002, December.
  • Handle: RePEc:inm:orisre:v:29:y:2018:i:4:p:984-1002
    DOI: 10.1287/isre.2017.0764
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    References listed on IDEAS

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    3. Yinan Yu & Liangfei Qiu & Hailiang Chen & Benjamin Yen, 2023. "Movie fit uncertainty and interplay between traditional advertising and social media marketing," Marketing Letters, Springer, vol. 34(3), pages 429-448, September.
    4. Jürgen Neumann, 2021. "When Biased Ratings Benefit the Consumer - An Economic Analysis of Online Ratings in Markets with Variety-Seeking Consumers," Working Papers Dissertations 77, Paderborn University, Faculty of Business Administration and Economics.
    5. Salvatore Carta & Andrea Medda & Alessio Pili & Diego Reforgiato Recupero & Roberto Saia, 2018. "Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data," Future Internet, MDPI, vol. 11(1), pages 1-19, December.
    6. Holger Karl & Dennis Kundisch & Friedhelm Meyer auf der Heide & Heike Wehrheim, 2020. "A Case for a New IT Ecosystem: On-The-Fly Computing," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(6), pages 467-481, December.
    7. Chinonso E. Etumnu & Kenneth Foster & Nicole O. Widmar & Jayson L. Lusk & David L. Ortega, 2020. "Does the distribution of ratings affect online grocery sales? Evidence from Amazon," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 501-521, October.
    8. Duan, Yongrui & Liu, Tonghui & Mao, Zhixin, 2022. "How online reviews and coupons affect sales and pricing: An empirical study based on e-commerce platform," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    9. Bikram P. Ghosh & Michael R. Galbreth, 2023. "The weight of the crowd, social information credibility, and firm strategy," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1079-1095, April.
    10. Hoyer, B. & van Straaten, D., 2022. "Anonymity and self-expression in online rating systems—An experimental analysis," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).
    11. Jianqing Chen & Zhiling Guo & Jian Huang, 2022. "An Economic Analysis of Rebates Conditional on Positive Reviews," Information Systems Research, INFORMS, vol. 33(1), pages 224-243, March.
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    13. Qian Li & Yuanyuan Tang & Wei Xu & Mingming Wang, 2023. "Variance does matter in affecting the box office: a multi-aspect investigation," Electronic Commerce Research, Springer, vol. 23(2), pages 659-679, June.

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