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.

Similar content being viewed by others

References
Adomavicius, G., Bockstedt, J., Curley, S., & Zhang, J. (2013). Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research, 24(4), 956–975.
Adomavicius, G., Bockstedt, J. C., Curley, S. P., & Zhang J. (2017). Effects of online recommendations on consumers’ willingness to pay. information systems research, published online in Articles in Advance 11 Dec 2017. https://doi.org/10.1287/isre.2017.0703.
Amazon. (2014). Feedback FAQ Retrieved. http://www.amazon. com/gp/help/customer/display.html? nodeId = 1161284. Accessed May 25, 2014.
Amblee, N., & Bui, T. (2011). Harnessing the influence of social proof in online shopping: the effect of electronic word of mouth on sales of digital microproducts. International Journal of Electronic Commerce, 16(2), 91–114.
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509.
Askalidis, G., & Malthouse, E. C. (2016). The value of online customer reviews. In Proceedings of the 10th ACM conf. on rec. sys., ACM, pp. 155–158.
Babić Rosario, A., Sotgiu, F., De Valck, K., & Bijmolt, T. H. (2016). The effect of electronic word of mouth on sales: a meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(3), 297–318.
Benlian, A., Titah, R., & Hess, T. (2012). Differential effects of provider recommendations and consumer reviews in E-commerce transactions: an experimental study. Journal of Management Information Systems, 29(1), 237–272.
Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026.
Bright Local. (2016). Local consumer review survey. https://www.brightlocal.com/learn/local-consumer-review-survey/.
Chen, H., Duan, W., & Zhou, W. (2017). The interplay between free sampling and word of mouth in the online software market. Decision Support Systems, 95, 82–90.
Chen, P. Y., Wu, S.Y., & Yoon, J. (2004). The impact of online recommendations and consumer feedback on sales. In Proceedings of the 25th international conference on information systems, pp. 711–724.
Cheng, J., Adamic, L. A., Dow, P. A., Kleinberg, J., & Leskovec J. (2014). Can cascades be predicted? In Proceedings of the 23rd international conference on World Wide Web.
Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.
Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.
Clemons, E. K., Gao, G. G., & Hitt, L. M. (2006). When online reviews meet hyper differentiation: A study of the craft beer industry. Journal of Management Information Systems., 23(2), 149–171.
CNBN. (2015). Alibaba’s anti-counterfeit on scalping of twenty-three merchants from Tmall.com, all have been repelled and ten more merchants’ logistics have been forced off. http://china.cnr.cn/xwwgf/20150507/t20150507_518489320.shtml.
Cosley, D., Lam, S., Albert, I., Konstan, J. A., & Riedl, J. (2003). Is seeing believing? How recommender interfaces affect users’ opinions. CHI 2003 Conf, ACM, New York, pp. 585–592.
Cresci, S., Pietro, R. D., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015). Fame for sale: Efficient detection of fake Twitter followers. Decision Support Systems, 80, 56–71.
Cui, G., Lui, H. K., & Guo, X. (2012). The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 17(1), 39–58.
Dellarocas, C., Zhang, X. Q., & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21(4), 23–45.
Diviani, N., & Meppelink, C. S. (2017). The impact of recommendations and warnings on the quality evaluation of health websites: An online experiment. Computers in Human Behavior, 71, 122–129.
Duan, W., & Zhang, J. (2014). The impact of referral channels in online customer journey. In Thirty fifth international conference on information systems, Auckland, pp. 1–15.
Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter? An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.
Duan, W., Gu, B., & Whinston, A. B. (2008). The dynamics of online word-of-mouth and product sales? An empirical investigation of the movie industry. Journal of Retailing, 84(2), 233–242.
Duan, W., Gu, B., & Whinston, A. B. (2009). Informational cascades and software adoption on the internet: An empirical investigation. MIS Quarterly, 33(1), 23–48.
Fleder, D., & Hosanagar, K. (2008). Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Science, 55(5), 697–712.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291–313.
Godes, D., & Mayzlin, D. (2004). Using online conversations to measure word-of-mouth communication. Marketing Science, 23(4), 545–560.
Gopinath, S., Thomas, J. S., & Krishnamurthi, L. (2014). Investigating the relationship between the content of online word of mouth, advertising, and brand performance. Marketing Science, 33(2), 241–258.
Gu, B., Park, J., & Konana, P. (2012). Research note-the impact of external word-of-mouth sources on retailer sales of high-involvement products. Information Systems Research, 23(1), 182–196.
Haubl, G., & Murray, K. B. (2006). Double agent: Assessing the role of electronic product-recommendation systems. Sloan Management Review, 47(3), 8–12.
Hervas-Drane, A. (2008). Word of mouth and recommender systems: A theory of the long tail. Working paper.
Herzenstein, M., Dholakia, U. M., & Andrews, R. L. (2011). Strategic herding behavior in peer-to-peer loan auctions. Journal of Interactive Marketing, 25(1), 27–36.
Hostler, R. E., Yoon, V. Y., & Guimaraes, T. (2005). Assessing the impact of internet agent on end users’ performance. Decision Support Systems, 41(1), 313–325.
Hsu, C. L., Yu, L. C., & Chang, K. C. (2017). Exploring the effects of online customer reviews, regulatory focus, and product type on purchase intention: Perceived justice as a moderator. Computers in Human Behavior, 69, 335–346.
iResearch. (2017). 2016 China digital reading industry annual report. http://www.iresearch.com.cn/report/2978.html.
Jabr, W., & Zheng, Z. (2014). Know yourself and know your enemy: An analysis of firm recommendations and consumer reviews in a competitive environment. MIS Quarterly, 38(3), 635–654.
Khan, M. R. (2017). Cascading behavior in yelp reviews. In Proceedings of ACM conference (Conference’17).
Kumar, N., & Benbasat, I. (2006). Influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research, 17(4), 425–439.
Lee, E., & Lee, B. (2012). Herding behavior in online P2P lending: An empirical investigation. Electronic Commerce Research and Applications, 11(5), 495–503.
Lee, E. J., & Shin, S. Y. (2014). When do consumers buy online product reviews? Effects of review quality, product type, and reviewer’s photo. Computers in Human Behavior, 31, 356–366.
Lee, M., & Youn, S. (2009). Electronic word of mouth (eWOM) How eWOM platforms influence consumer product judgement. International Journal of Advertising, 28(3), 473–499.
Lee, Y. J., Hosanagar, K., & Tan, Y. (2015). Do I follow my friends or the crowd? Information cascades in online movie ratings. Management Science, 61(9), 2241–2258.
Li, S. S., & Karahanna, E. (2015). Online recommendation systems in a B2C E-commerce context: A review and future directions. Journal of the Association for Information Systems, 16(2), 72–107.
Li, X., & Hitt, L. M. (2008). Self-selection and information role of online product reviews. Information Systems Research, 19(4), 456–474.
Lin, Z. (2014). An empirical investigation of user and system recommendations in e-commerce. Decision Support Systems, 68, 111–124.
Liu, Q., & Zhang, L. (2014). Information cascades in online reading: An empirical investigation of panel data. Library Hi Tech, 32(4), 687–705.
Liu, Q., Huang, S., & Zhang, L. (2016). The influence of information cascades on online purchase behaviors of search and experience products. Electronic Commerce Research, 16(4), 553–580.
Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.
Liu, Y., Feng, J., & Liao, X. (2017). When online reviews meet sales volume information: Is more or accurate information always better? Information Systems Research, 28(4), 723–743.
MarketWatch. (2013). Yelp Deems 20% of user reviews ‘suspicious’. Retrieved February 18, 2014.http://www.marketwatch.com/story/20-of-yelp-reviews-are-fake-2013-09-24.
Maslowska, E., Malthouse, E. C., & Bernritter, S. (2017). Too good to be true: The role of online reviews’ features in probability to buy. International Journal of Advertising, 36(1), 142–163.
Maslowska, E., Malthouse, E. C., & Viswanathan, V. (2017). Do customer reviews drive purchase decisions? The moderating roles of review exposure and price. Decision Support Systems, 98, 1–9.
Núñez-Valdez, E. R., Lovelle, J. M. C., Hernández, G. I., Fuente, A. J., & Labra-Gayo, J. E. (2015). Creating recommendations on electronic books: A collaborative learning implicit approach. Computers in Human Behavior, 51, 1320–1330.
Oestreicher-singer, G., & Sundararajan, A. (2012). Recommendation networks and the long tail of electronic commerce. MIS Quarterly, 36(1), 65–83.
Olson, E. L., & Widing, R. E. (2002). Are interactive decision aids better than passive decision aids? A comparison with implications for information providers on the internet. Journal of Interactive Marketing, 16(2), 22–33.
Onnela, J. P., & Reed-Tsochas, F. (2010). Spontaneous emergence of social influence in online systems. Proceedings of the National Academy of Sciences, 107(43), 18375–18380.
Panniello, U., Hill, S., & Gorgoglione, M. (2016). The impact of profit incentives on the relevance of online recommendations. Electronic Commerce Research and Applications, 2016(20), 87–104.
Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148.
Park, J., Gu, B., & Lee, H. (2012). The relationship between retailer-hosted and third-party hosted wom sources and their influence on retailer sales. Electronic Commerce Research and Applications, 11(3), 253–261.
Pathak, B., Garfinkel, R., Gopal, R. D., Venkatesan, R., & Yin, F. (2010). Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27(2), 159–188.
Pereira, R. E. (2001). Influence of query-based decision aids on consumer decision making in electronic commerce. Information Resources Management Journal, 14(1), 31–48.
Pramanik, S., Wang, Q., Danisch, M., Guillaume, J. L., & Mitra, B. (2017). Modeling cascade formation in Twitter amidst mentions and retweets. Social Network Analysis & Mining. https://doi.org/10.1007/s13278-017-0462-1.
Qiu, L., Pang, J., & Lim, K. H. (2012). Effects of conflicting aggregated rating on eWOM review credibility and diagnosticity: The moderating role of review valence. Decision Support Systems, 54(1), 631–643.
Smith, L., & Sorensen, P. N. (2000). Pathological outcomes of observational learning. Econometrica, 68(2), 371–398.
Sotiriadis, M. D., & Zyl, C. (2013). Electronic word-of-mouth and online reviews in tourism services: The use of twitter by tourists. Electronic Commerce Research, 13(1), 103–124.
Sun, H. (2013). A longitudinal study of herd behavior in the adoption and continued use of technology. MIS Quarterly, 37(4), 1013–1041.
Swaminathan, V. (2003). The impact of recommendation agents on consumer evaluation and choice: The moderating role of category risk, product complexity, and consumer knowledge. Journal of Consumer Psychology, 13(1–2), 93–101.
Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of Web personalization on user information processing and decision outcomes. MIS Quarterly, 30(4), 865–890.
TechNavio. Global E-book Market 2015–2019. https://www.giiresearch.com/report/infi310421-global-e-book-market.html.
Trenz, M., & Berger, B. (2013). Analyzing online customer reviews–an interdisciplinary literature review and research agenda. In Proceedings of the 21st European conference on information systems (ECIS), 2013, pp. 1–12.
Vijayasarathy, L. R., & Jones, J. M. (2001). Do internet shopping aids make a difference? An Empirical Investigation. Electronic Markets, 11(1), 75–83.
Wang, W., Qiu, L., Kim, D., & Benbasat, I. (2016). Effects of rational and social appeals of online recommendation agents on cognition- and affect-based trust. Decision Support Systems, 2016(86), 48–60.
Xiao, B., & Benbasat, I. (2007). E-Commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.
Zhang, J., & Liu, P. (2012). Rational herding in microloan markets. Management Science, 58(5), 892–912.
Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902–1911.
Acknowledgements
This paper is supported by the National Natural Science Foundation of China (No: 71764006, No: 71363022, No: 71373192, No: 71361012), Natural Science Foundation of Jiangxi, China (No: 20161BAB201029) and Foundation of Jiangxi Educational Committee (No: GJJ170335).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, Q., Zhang, X., Zhang, L. et al. The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation. Electron Commer Res 19, 521–547 (2019). https://doi.org/10.1007/s10660-018-9312-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10660-018-9312-0