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Explaining reviewing effort: Existing reviews as potential driver

Author

Listed:
  • Christoph Rohde

    (University of Innsbruck)

  • Alexander Kupfer

    (University of Innsbruck)

  • Steffen Zimmermann

    (Ulm University)

Abstract

Online review systems try to motivate reviewers to invest effort in writing reviews, as their success crucially depends on the helpfulness of such reviews. Underlying cognitive mechanisms, however, might influence future reviewing effort. Accordingly, in this study, we analyze whether existing reviews matter for future textual reviews. From analyzing a dataset from Google Maps covering 40 sights across Europe with over 37,000 reviews, we find that textual reviewing effort, as measured by the propensity to write an optional textual review and (textual) review length, is negatively related to the number of existing reviews. However, and against our expectations, reviewers do not increase textual reviewing effort if there is a large discrepancy between the existing rating valence and their own rating. We validate our findings using additional review data from Yelp. This work provides important implications for online platforms with review systems, as the presentation of review metrics matters for future textual reviewing effort.

Suggested Citation

  • Christoph Rohde & Alexander Kupfer & Steffen Zimmermann, 2022. "Explaining reviewing effort: Existing reviews as potential driver," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1169-1185, September.
    • Handle: RePEc:spr:elmark:v:32:y:2022:i:3:d:10.1007_s12525-022-00595-3
    DOI: 10.1007/s12525-022-00595-3
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    References listed on IDEAS

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    1. Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107611252, September.
    2. Yili (Kevin) Hong & Paul A. Pavlou, 2014. "Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents," Information Systems Research, INFORMS, vol. 25(2), pages 328-344, June.
    3. Alton Y.K. Chua & Snehasish Banerjee, 2015. "Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 354-362, February.
    4. Nachiketa Sahoo & Chrysanthos Dellarocas & Shuba Srinivasan, 2018. "The Impact of Online Product Reviews on Product Returns," Information Systems Research, INFORMS, vol. 29(3), pages 723-738, September.
    5. Gordon Burtch & Yili Hong & Ravi Bapna & Vladas Griskevicius, 2018. "Stimulating Online Reviews by Combining Financial Incentives and Social Norms," Management Science, INFORMS, vol. 64(5), pages 2065-2082, May.
    6. Yi-Chun (Chad) Ho & Junjie Wu & Yong Tan, 2017. "Disconfirmation Effect on Online Rating Behavior: A Structural Model," Information Systems Research, INFORMS, vol. 28(3), pages 626-642, September.
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    Cited by:

    1. Rainer Alt, 2022. "Electronic Markets on platform culture," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1019-1031, September.

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    More about this item

    Keywords

    Online reviews; Reviewing effort; Online review platform; Existing reviews;
    All these keywords.

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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