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Context-Dependent Product Evaluations: An Empirical Analysis of Internet Book Reviews

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  • Hu, Ye
  • Li, Xinxin

Abstract

Using book review data on Amazon.com, the authors extend current research into online consumer reviews by empirically investigating the context dependence effect in the review writing process. They find that when product quality remains constant, later reviews tend to differ from previously posted ones, and the difference is moderated by the popularity of the product, the variance of previous reviews, whether later reviews explicitly refer to previous reviews, and the age of the product and the reviews. This phenomenon can be explained by both consumer expectation and self-selection effects in review writing. The implications of this research can help practitioners understand the reviewing process and provide some guidelines for improving the objectivity of online product reviews.

Suggested Citation

  • Hu, Ye & Li, Xinxin, 2011. "Context-Dependent Product Evaluations: An Empirical Analysis of Internet Book Reviews," Journal of Interactive Marketing, Elsevier, vol. 25(3), pages 123-133.
    • Handle: RePEc:eee:joinma:v:25:y:2011:i:3:p:123-133
    DOI: 10.1016/j.intmar.2010.10.001
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    2. Xingyu Chen & Xing Li & Dai Yao & Zhimin Zhou, 2019. "Seeking the support of the silent majority: are lurking users valuable to UGC platforms?," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 986-1004, November.
    3. Zhou, Shasha & Tu, Le, 2022. "The effect of social dynamics in online review voting behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    4. Richards, Timothy J. & Tiwari, Ashutosh, 2014. "Social Networks and Restaurant Choice," 2014 AAEA/EAAE/CAES Joint Symposium: Social Networks, Social Media and the Economics of Food, May 29-30, 2014, Montreal, Canada 166112, Agricultural and Applied Economics Association.
    5. Ziqiong Zhang & Zili Zhang & Rob Law, 2014. "Relative importance and combined effects of attributes on customer satisfaction," The Service Industries Journal, Taylor & Francis Journals, vol. 34(6), pages 550-566, April.
    6. Gao, Baojun & Li, Xiangge & Liu, Shan & Fang, Debin, 2018. "How power distance affects online hotel ratings: The positive moderating roles of hotel chain and reviewers’ travel experience," Tourism Management, Elsevier, vol. 65(C), pages 176-186.
    7. Young-Jin Lee & Kartik Hosanagar & Yong Tan, 2015. "Do I Follow My Friends or the Crowd? Information Cascades in Online Movie Ratings," Management Science, INFORMS, vol. 61(9), pages 2241-2258, September.
    8. Sarigul, Sercan & Rui, Huaxia, 2014. "Nowcasting Obesity in the U.S. Using Google Search Volume Data," 2014 AAEA/EAAE/CAES Joint Symposium: Social Networks, Social Media and the Economics of Food, May 29-30, 2014, Montreal, Canada 166113, Agricultural and Applied Economics Association.
    9. S. Cicognani & P. Figini & M. Magnani, 2016. "Social Influence Bias in Online Ratings: A Field Experiment," Working Papers wp1060, Dipartimento Scienze Economiche, Universita' di Bologna.
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