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The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation

Author

Listed:
  • Qihua Liu

    (Jiangxi University of Finance and Economics)

  • Xiaoyu Zhang

    (Jiangxi University of Finance and Economics)

  • Liyi Zhang

    (Wuhan University)

  • Yang Zhao

    (Wuhan University)

Abstract

While ranking systems, electronic word of mouth (eWOM) channels and recommendation systems might appear as three separate tools that influence consumer choice, consumers at online reading platforms are often exposed to all three simultaneously during a searching session of e-books. This study conducts an empirical analysis to examine the interaction effects of these three decision-supporting tools on online reading behavior. To do so, we collect a 25-week panel data set on Yuedu.163.com, which is one of the earliest online reading platforms in China. Our results indicate that informational cascades are particularly prominent on the online reading market. Under the influence of informational cascades, eWOM volume and valence have no impact on the clicks of e-books with high rankings, but have positive impact on the clicks of e-books with low rankings. Recommendation strength has a positive impact on popular e-books clicks, but has no impact on the clicks of less popular e-books. Moreover, we find that eWOM valence and recommendation strength have a substitute relationship in affecting the clicks of e-books with high rankings. However, eWOM and recommendation system have a complementary relationship in affecting the clicks of less popular e-books. To our best knowledge, this paper is the first to investigate the interaction effects of information cascades, eWOM and recommendation systems on online user behavior. Our findings provide important theoretical contributions and managerial implications.

Suggested Citation

  • Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.
  • Handle: RePEc:spr:elcore:v:19:y:2019:i:3:d:10.1007_s10660-018-9312-0
    DOI: 10.1007/s10660-018-9312-0
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    as
    1. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 57946, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    3. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 59346, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    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. 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.
    6. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    7. Yang Liu & Juan Feng & Xiuwu Liao, 2017. "When Online Reviews Meet Sales Volume Information: Is More or Accurate Information Always Better?," Information Systems Research, INFORMS, vol. 28(4), pages 723-743, December.
    8. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    9. Herzenstein, Michal & Dholakia, Utpal M. & Andrews, Rick L., 2011. "Strategic Herding Behavior in Peer-to-Peer Loan Auctions," Journal of Interactive Marketing, Elsevier, vol. 25(1), pages 27-36.
    10. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    11. Nanda Kumar & Izak Benbasat, 2006. "Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites," Information Systems Research, INFORMS, vol. 17(4), pages 425-439, December.
    12. Qihua Liu & Shan Huang & Liyi Zhang, 2016. "The influence of information cascades on online purchase behaviors of search and experience products," Electronic Commerce Research, Springer, vol. 16(4), pages 553-580, December.
    13. Rex E. Pereira, 2001. "Influence of Query-Based Decision Aids on Consumer Decision Making in Electronic Commerce," Information Resources Management Journal (IRMJ), IGI Global, vol. 14(1), pages 31-48, January.
    14. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65605, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    15. Bin Gu & Jaehong Park & Prabhudev Konana, 2012. "Research Note ---The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products," Information Systems Research, INFORMS, vol. 23(1), pages 182-196, March.
    16. Gediminas Adomavicius & Jesse C. Bockstedt & Shawn P. Curley & Jingjing Zhang, 2013. "Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects," Information Systems Research, INFORMS, vol. 24(4), pages 956-975, December.
    17. Shyam Gopinath & Jacquelyn S. Thomas & Lakshman Krishnamurthi, 2014. "Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance," Marketing Science, INFORMS, vol. 33(2), pages 241-258, March.
    18. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    19. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
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