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Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews

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  • Wu, Xiaoyue
  • Jin, Liyin
  • Xu, Qian

Abstract

Online reviews have become an important source of information for consumers’ purchase decisions. Drawing upon the consumer expertise and persuasion literature, this study proposes that consumers are more willing to accept a reviewer's recommendation when his/her historical ratings in a certain product domain display greater variance. Five experiments provide consistent support for this hypothesis and the underlying process. Study 1 tests the proposed effect of a reviewer's rating variance on consumers’ willingness to accept the reviewer's recommendation. Studies 2 and 3 show that this effect can be attributed to perceptions regarding the reviewer's expertise. Moreover, this “variance-expert inference” effect is attenuated when the consumption experience of the reviewer is limited (Study 4) and when the consumers are familiar with the products (Study 5). The theoretical implications for the online review and persuasion literature and practical implications for online retailers are discussed.

Suggested Citation

  • Wu, Xiaoyue & Jin, Liyin & Xu, Qian, 2021. "Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews," Journal of Retailing, Elsevier, vol. 97(2), pages 238-250.
    • Handle: RePEc:eee:jouret:v:97:y:2021:i:2:p:238-250
    DOI: 10.1016/j.jretai.2020.05.006
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    References listed on IDEAS

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    Cited by:

    1. Moon, Sangkil & Kim, Seung-Wook & Iacobucci, Dawn, 2024. "Dynamic relationship changes between reviewers and consumers in online product reviews," Journal of Retailing, Elsevier, vol. 100(1), pages 70-84.
    2. Yang, Wenjuan & Zhang, Jiantong & Yan, Hong, 2022. "Promotions of online reviews from a channel perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    3. Marder, Ben & Angell, Rob & Boyd, Eric, 2023. "How and why (imagined) online reviews impact frontline retail encounters," Journal of Retailing, Elsevier, vol. 99(2), pages 265-279.
    4. Bigne, Enrique & Ruiz, Carla & Curras-Perez, Rafael, 2024. "How consumers process online review types in familiar versus unfamiliar destinations. A self-reported and neuroscientific study," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    5. Ravula, Prashanth & Bhatnagar, Amit & Gauri, Dinesh K, 2023. "Role of gender in the creation and persuasiveness of online reviews," Journal of Business Research, Elsevier, vol. 154(C).
    6. Ravula, Prashanth & Jha, Subhash & Biswas, Abhijit, 2022. "Relative persuasiveness of repurchase intentions versus recommendations in online reviews," Journal of Retailing, Elsevier, vol. 98(4), pages 724-740.
    7. Zhao Du & Fang Wang & Shan Wang, 2021. "Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    8. Nima Jalali & Sangkil Moon & Moon-Yong Kim, 2023. "Profiling diverse reviewer segments using online reviews of service industries," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 130-148, June.

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    More about this item

    Keywords

    Rating variance; Perceived expertise; Online review; Product recommendation; Persuasion;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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