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The effects of prior reviews on perceived review helpfulness: A configuration perspective

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  • Zhu, Yongmin
  • Liu, Miaomiao
  • Zeng, Xiaohua
  • Huang, Pei

Abstract

Understanding what makes a review helpful is important for consumers and online review management. Previous studies have explored the effects of review attributes, with the underlying assumption being that consumers assess each review independently of prior reviews of the product. Drawing on configuration theory and the literature on prior knowledge influencing consumer information search behavior, we propose that there should be a fit between prior reviews and focal review attributes in determining perceived review helpfulness. Using data from Amazon, we empirically demonstrate their complex interdependency through fuzzy-set qualitative comparative analysis. The results show that descriptive reviews with more words and moderate ratings are perceived as more helpful when all prior reviews have been posted recently, while evaluative reviews with extreme ratings are more helpful when prior reviews exhibit greater disagreement. Our findings help reconcile some conflicting results in the previous literature and provide guidance on review management.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jbrese:v:110:y:2020:i:c:p:484-494
    DOI: 10.1016/j.jbusres.2020.01.027
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    as
    1. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    2. Herr, Paul M & Kardes, Frank R & Kim, John, 1991. "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 454-462, March.
    3. Dezhi Yin & Sabyasachi Mitra & Han Zhang, 2016. "Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth," Information Systems Research, INFORMS, vol. 27(1), pages 131-144, March.
    4. Roberto Garcia-Castro & Claude Francoeur, 2016. "When more is not better: Complementarities, costs and contingencies in stakeholder management," Strategic Management Journal, Wiley Blackwell, vol. 37(2), pages 406-424, February.
    5. Ragin, Charles C., 2000. "Fuzzy-Set Social Science," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226702773, September.
    6. Rao, Akshay R & Sieben, Wanda A, 1992. "The Effect of Prior Knowledge on Price Acceptability and the Type of Information Examined," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(2), pages 256-270, September.
    7. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    8. Kostyra, Daniel S. & Reiner, Jochen & Natter, Martin & Klapper, Daniel, 2016. "Decomposing the effects of online customer reviews on brand, price, and product attributes," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 11-26.
    9. Fang, Bin & Ye, Qiang & Kucukusta, Deniz & Law, Rob, 2016. "Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics," Tourism Management, Elsevier, vol. 52(C), pages 498-506.
    10. Cheng, Yi-Hsiu & Ho, Hui-Yi, 2015. "Social influence's impact on reader perceptions of online reviews," Journal of Business Research, Elsevier, vol. 68(4), pages 883-887.
    11. Brucks, Merrie, 1985. "The Effects of Product Class Knowledge on Information Search Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(1), pages 1-16, June.
    12. Singh, Jyoti Prakash & Irani, Seda & Rana, Nripendra P. & Dwivedi, Yogesh K. & Saumya, Sunil & Kumar Roy, Pradeep, 2017. "Predicting the “helpfulness” of online consumer reviews," Journal of Business Research, Elsevier, vol. 70(C), pages 346-355.
    13. 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.
    14. 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.
    15. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    16. 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.
    17. Filieri, Raffaele, 2016. "What makes an online consumer review trustworthy?," Annals of Tourism Research, Elsevier, vol. 58(C), pages 46-64.
    18. 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.
    19. repec:ucp:bkecon:9780226702766 is not listed on IDEAS
    20. Omar A. El Sawy & Arvind Malhotra & YoungKi Park & Paul A. Pavlou, 2010. "Research Commentary ---Seeking the Configurations of Digital Ecodynamics: It Takes Three to Tango," Information Systems Research, INFORMS, vol. 21(4), pages 835-848, December.
    21. Park, Sangwon & Nicolau, Juan L., 2015. "Asymmetric effects of online consumer reviews," Annals of Tourism Research, Elsevier, vol. 50(C), pages 67-83.
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