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What makes population perception of review helpfulness: an information processing perspective

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  • Bin Guo

    (Zhejiang University)

  • Shasha Zhou

    (Zhejiang University of Finance and Economics)

Abstract

What makes online consumer reviews (OCRs) helpful to consumers has been an important issue to academics and practitioners. In this paper, we explicate the moderation role of the reviewer’s similarity to the vocal population on the relationship between review characteristics and population-perceived review helpfulness from an information processing perspective. Vocal population refers to those community members who regularly post and read OCRs, respond to other users’ posts, and evaluate other OCRs. We purposively focus on two types of similarity, i.e., linguistic style similarity and expertise similarity. The empirical results indicate that the two dimensions of similarity play different roles in shaping population perceptions of review helpfulness. Specifically, linguistic style similarity positively moderates the impact of review valence and review length on review helpfulness, while expertise similarity negatively moderates the effect of review valence and review length on review helpfulness. We also discuss the theoretical and managerial implications of our findings.

Suggested Citation

  • Bin Guo & Shasha Zhou, 2017. "What makes population perception of review helpfulness: an information processing perspective," Electronic Commerce Research, Springer, vol. 17(4), pages 585-608, December.
    • Handle: RePEc:spr:elcore:v:17:y:2017:i:4:d:10.1007_s10660-016-9234-7
    DOI: 10.1007/s10660-016-9234-7
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    3. Osterbrink Lars & Alpar Paul & Seher Alexander, 2020. "Influence of Images in Online Reviews for Search Goods on Helpfulness," Review of Marketing Science, De Gruyter, vol. 18(1), pages 43-73, September.
    4. Yang, Luming & Xu, Min & Xing, Lin, 2022. "Exploring the core factors of online purchase decisions by building an E-Commerce network evolution model," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    5. Jin, Wangyan & Chen, Yuangao & Yang, Shuiqing & Zhou, Shasha & Jiang, Hui & Wei, June, 2023. "Personalized managerial response and negative inconsistent review helpfulness: The mediating effect of perceived response helpfulness," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    6. Srikanth Parameswaran & Pubali Mukherjee & Rohit Valecha, 2023. "I Like My Anonymity: An Empirical Investigation of the Effect of Multidimensional Review Text and Role Anonymity on Helpfulness of Employer Reviews," Information Systems Frontiers, Springer, vol. 25(2), pages 853-870, April.
    7. Hu, Xin & He, Liuyi & Liu, Junjun, 2022. "Status reinforcing: Unintended rating bias on online shopping platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    8. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    9. Zhu, Yongmin & Liu, Miaomiao & Zeng, Xiaohua & Huang, Pei, 2020. "The effects of prior reviews on perceived review helpfulness: A configuration perspective," Journal of Business Research, Elsevier, vol. 110(C), pages 484-494.
    10. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    11. Baidyanath Biswas & Pooja Sengupta & Boudhayan Ganguly, 2022. "Your reviews or mine? Exploring the determinants of “perceived helpfulness” of online reviews: a cross-cultural study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1083-1102, September.

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