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Understanding online product ratings: A customer satisfaction model

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  • Engler, Tobias H.
  • Winter, Patrick
  • Schulz, Michael

Abstract

Online product ratings have become a major information source for customers, retailers, and manufacturers. Both practitioners and researchers predominantly interpret them as a reflection of product quality. We argue that they in fact represent the customer's satisfaction with the product. Accordingly, we present a customer satisfaction model of online product ratings which incorporates the customer's pre-purchase expectations and actual product performance as determinants of ratings. We validate our model by applying it to two datasets collected at the German website of Amazon.com. The results indicate that both factors have a significant influence on online product ratings, supporting the proposed interpretation of ratings.

Suggested Citation

  • Engler, Tobias H. & Winter, Patrick & Schulz, Michael, 2015. "Understanding online product ratings: A customer satisfaction model," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 113-120.
    • Handle: RePEc:eee:joreco:v:27:y:2015:i:c:p:113-120
    DOI: 10.1016/j.jretconser.2015.07.010
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    References listed on IDEAS

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    1. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    2. Jarvis, Cheryl Burke & MacKenzie, Scott B & Podsakoff, Philip M, 2003. "A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(2), pages 199-218, September.
    3. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    4. Yoon, Eunsang & Guffey, Hugh J. & Kijewski, Valerie, 1993. "The effects of information and company reputation on intentions to buy a business service," Journal of Business Research, Elsevier, vol. 27(3), pages 215-228, July.
    5. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-754, July/Aug..
    6. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    7. Ruth N. Bolton, 1998. "A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction," Marketing Science, INFORMS, vol. 17(1), pages 45-65.
    8. 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.
    9. Justin Malbon, 2013. "Taking Fake Online Consumer Reviews Seriously," Journal of Consumer Policy, Springer, vol. 36(2), pages 139-157, June.
    10. Tibor Scitovszky, 1944. "Some Consequences of the Habit of Judging Quality by Price," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 12(2), pages 100-105.
    Full references (including those not matched with items on IDEAS)

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