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Research on online shopping user behavior based on information cascade

Published: 22 November 2021 Publication History

Abstract

Information cascades are common in real social life, because of information cascades can lead to blind follow-up by users, it is easy to have a bad impact on society. This paper proposes a new method to research on the online shopping user behavior based on information cascades. By collecting date from jd between October 1, 2019 and September 30, 2020, this paper constructs unbalanced panel data, empirically tests the information cascade effect in online shopping users’ Behavior, and study the mixed impact of information cascade and online word-of-mouth on users’ shopping behavior. The results show that the relative popularity of goods has a significant impact on its sales, showing an obvious information cascade effect. The number of comments has no impact on the sales volume of high ranked goods, but has a positive and significant impact on the sales volume of low ranked goods, and the user score has no impact on the sales volume of goods. The law of demand is still valid for high ranked commodity sales. For the lower ranked products, the price represents the quality information of the products and has a positive impact on the sales volume.

References

[1]
P. Zhu, Z. Wang, X. Li, Y. H. Liu, and X. Zhu, “Understanding promotion framing effect on purchase intention of elderly mobile app consumers,” Electron. Commer. Res. Appl., vol. 44, p. 101010, Nov. 2020.
[2]
P. Zhu, J. Hu, Y. Zhang, and X. Li, “Enhancing Traceability of Infectious Diseases: A Blockchain-Based Approach,” Inf. Process. Manag., vol. 58, no. 4, p. 102570, Jul. 2021.
[3]
P. Zhu, J. Hu, Y. Zhang, and X. Li, “A Blockchain Based Solution for Medication Anti-Counterfeiting and Traceability,” IEEE Access, vol. 8, pp. 184256–184272, 2020.
[4]
H. Sun, “A longitudinal study of herd behavior in the adoption and continued use of technology,” MIS Q. Manag. Inf. Syst., vol. 37, no. 4, pp. 1013–1041, 2013.
[5]
P. Zhu, J. Hu, X. Li, and Q. Zhu, “Using Blockchain Technology to Enhance the Traceability of Original Achievements,” IEEE Trans. Eng. Manag., pp. 1–15, 2021.
[6]
X. Li, “Informational cascades in IT adoption,” Commun. ACM, vol. 47, no. 4, pp. 93–97, 2004.
[7]
J. P. Onnela and F. Reed-Tsochas, “Spontaneous emergence of social influence in online systems,” Proc. Natl. Acad. Sci. U. S. A., vol. 107, no. 43, pp. 18375–18380, 2010.
[8]
E. a Walden, “Journal of the Association for Information Systems Sequential Adoption Theory: A Theory for Understanding Herding Behavior in Early Adoption of Novel Technologies * Sequential Adoption Theory: A Theory for Understanding Herding Behavior in Early Adoptio,” Jais, vol. 10, no. 1, pp. 31–62.
[9]
S. Bikhchandani, D. Hirshleifer, and I. Welch, “A theory of fads, fashion, custom, and cultural change as informational cascades,” J. Polit. Econ., vol. 100, no. 5, pp. 992–1026, 1992.
[10]
P. Konana and S. Balasubramanian, “The Social-Economic-Psychological model of technology adoption and usage: An application to online investing,” Decis. Support Syst., vol. 39, no. 3, pp. 505–524, May 2005.
[11]
P. Zhu, F. Jia, and J. Zhang, “A copyright protection watermarking algorithm for remote sensing image based on binary image watermark,” Opt. - Int. J. Light Electron Opt., vol. 124, pp. 4177–4181, 2013.
[12]
W. Duan, B. Gu, and A. B. Whinston, “Informational cascades and software adoption on the Internet: An empirical investigation,” MIS Q. Manag. Inf. Syst., vol. 33, no. 1, pp. 23–48, 2009.
[13]
M. Cha, F. Benevenuto, Y. Y. Ahn, and K. P. Gummadi, “Delayed information cascades in Flickr: Measurement, analysis, and modeling,” Comput. Networks, vol. 56, no. 3, pp. 1066–1076, 2012.
[14]
C. Hui, Y. Tyshchuk, W. A. Wallace, M. Magdon-Ismail, and M. Goldberg, “Information cascades in social media in response to a crisis: A preliminary model and a case study,” WWW’12 - Proc. 21st Annu. Conf. World Wide Web Companion, pp. 653–656, 2012.
[15]
P. Zhu, F. Jia, and P. Wu, “A novel information retrieval algorithm based on association diagram extension of key words,” Information, vol. 15, no. 10, pp. 4065–4079, 2012.
[16]
U. Simonsohn and D. Ariely, “Ebay's Happy Hour: Non-rational Herding in Online Auctions,” 2005.
[17]
Y. F. Chen, “Herd behavior in purchasing books online,” Comput. Human Behav., vol. 24, no. 5, pp. 1977–1992, 2008.
[18]
E. Lee, B. Lee, and M. Chae, “Association for Information Systems AIS Electronic Library (AISeL) Herding Behavior In Online P2P Lending: An Empirical Investigation,” 2011.

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        cover image ACM Other conferences
        ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided Education
        September 2021
        2972 pages
        ISBN:9781450390255
        DOI:10.1145/3482632
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 22 November 2021

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