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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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    1. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    2. Zhiang (John) Lin & Mike W. Peng & Haibin Yang & Sunny Li Sun, 2009. "How do networks and learning drive M&As? An institutional comparison between China and the United States," Strategic Management Journal, Wiley Blackwell, vol. 30(10), pages 1113-1132, October.
    3. Friestad, Marian & Wright, Peter, 1994. "The Persuasion Knowledge Model: How People Cope with Persuasion Attempts," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 1-31, June.
    4. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    5. 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.
    6. Yang, Jun & Mai, Enping (Shirley), 2010. "Experiential goods with network externalities effects: An empirical study of online rating system," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1050-1057, September.
    7. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    8. Bagozzi, Richard P, 2000. "On the Concept of Intentional Social Action in Consumer Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(3), pages 388-396, December.
    9. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    10. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.
    11. Zhang, Jason Q. & Craciun, Georgiana & Shin, Dongwoo, 2010. "When does electronic word-of-mouth matter? A study of consumer product reviews," Journal of Business Research, Elsevier, vol. 63(12), pages 1336-1341, December.
    12. Ann E. Schlosser, 2005. "Posting versus Lurking: Communicating in a Multiple Audience Context," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 32(2), pages 260-265, September.
    13. Purnawirawan, Nathalia & De Pelsmacker, Patrick & Dens, Nathalie, 2012. "Balance and Sequence in Online Reviews: How Perceived Usefulness Affects Attitudes and Intentions," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 244-255.
    14. Sung Tae Kim & Choong Kwon Lee & Taewon Hwang, 2008. "Investigating the influence of employee blogging on IT workers' organisational citizenship behaviour," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 7(2), pages 178-189.
    15. Gupta, Pranjal & Harris, Judy, 2010. "How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1041-1049, September.
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    5. 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.
    6. 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).
    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.
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    10. 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.
    11. 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).

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