IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v20y2020i4d10.1007_s10660-018-09326-3.html
   My bibliography  Save this article

How can social commerce be boosted? The impact of consumer behaviors on the information dissemination mechanism in a social commerce network

Author

Listed:
  • Narisa Zhao

    (Dalian University of Technology)

  • Hui Li

    (Dalian University of Technology)

Abstract

This paper investigates how various consumer behaviors influence sales volume in social commerce through information dissemination. We use a modified SIR dynamic model to study these effects and how they vary under different maximum message lifetimes and as the number of network nodes varies. We conduct parameter sensitivity analysis to measure the influences of consumer behaviors on the sales volume. The simulation results indicate that consumer behaviors during information dissemination can significantly influence the outbreak size of the information and the sales volume. Specifically, a higher information-spread probability will increase the outbreak size of the message. A higher first-purchase probability could accelerate the increase in sales volume, especially when the maximum message lifetime is long and the number of nodes is large. Besides, the regular-purchase behavior has a positive impact on the acceleration, while the discard-information behavior after the first purchase has an opposite effect. We verify our model using data collected from two websites: Sina Weibo and Taobao. This study helps social commerce marketers understand how consumer behaviors can influence sales volume through information spread and aids them in modeling this mechanism.

Suggested Citation

  • Narisa Zhao & Hui Li, 2020. "How can social commerce be boosted? The impact of consumer behaviors on the information dissemination mechanism in a social commerce network," Electronic Commerce Research, Springer, vol. 20(4), pages 833-856, December.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:4:d:10.1007_s10660-018-09326-3
    DOI: 10.1007/s10660-018-09326-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-018-09326-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-018-09326-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Zhiguo, 2013. "Discovering the influential users oriented to viral marketing based on online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3459-3469.
    2. Christian Hildebrand & Gerald Häubl & Andreas Herrmann & Jan R. Landwehr, 2013. "When Social Media Can Be Bad for You: Community Feedback Stifles Consumer Creativity and Reduces Satisfaction with Self-Designed Products," Information Systems Research, INFORMS, vol. 24(1), pages 14-29, March.
    3. Xuemei Tian & Libo Liu, 2017. "Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research," Electronic Commerce Research, Springer, vol. 17(1), pages 169-183, March.
    4. Arthur Campbell, 2013. "Word-of-Mouth Communication and Percolation in Social Networks," American Economic Review, American Economic Association, vol. 103(6), pages 2466-2498, October.
    5. Yang, Jianmei & Zhuang, Dong & Xie, Weicong & Chen, Guangrong, 2013. "A study of design approach of spreading schemes for viral marketing based on human dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6494-6505.
    6. Carolina Herrando & Julio Jiménez-Martínez & María José Martín-De Hoyos, 2017. "Passion at first sight: how to engage users in social commerce contexts," Electronic Commerce Research, Springer, vol. 17(4), pages 701-720, December.
    7. Ralf van der Lans & Gerrit van Bruggen & Jehoshua Eliashberg & Berend Wierenga, 2010. "A Viral Branching Model for Predicting the Spread of Electronic Word of Mouth," Marketing Science, INFORMS, vol. 29(2), pages 348-365, 03-04.
    8. Satsuma, J & Willox, R & Ramani, A & Grammaticos, B & Carstea, A.S, 2004. "Extending the SIR epidemic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 369-375.
    9. Li, Mingjie & Orgun, Mehmet A. & Xiao, Jinghua & Zhong, Weicai & Xue, Liyin, 2012. "The impact of human activity patterns on asymptomatic infectious processes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(14), pages 3718-3728.
    10. Zhao, Narisa & Cui, Xuelian, 2017. "Impact of individual interest shift on information dissemination in modular networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 232-242.
    11. Mauro Bampo & Michael T. Ewing & Dineli R. Mather & David Stewart & Mark Wallace, 2008. "The Effects of the Social Structure of Digital Networks on Viral Marketing Performance," Information Systems Research, INFORMS, vol. 19(3), pages 273-290, September.
    12. Jhony Choon Yeong Ng & Metis Meng Die Huang & Yiping Liu, 2016. "The ‘feminine’ entrepreneurial personality trait: The competitive advantage of female college-student entrepreneurs in Chinese wei-shang businesses?," Asian Business & Management, Palgrave Macmillan, vol. 15(5), pages 343-369, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu Chen & Yingliang Wu & Rujie Zhong, 2021. "Research on Competitiveness of China’s Social Commerce Enterprises Based on Macro- and Micro-Niche," Sustainability, MDPI, vol. 13(1), pages 1-20, January.
    2. Wenyi Wang & Qiang Guo, 2023. "Subscription strategy choices of network video platforms in the presence of social influence," Electronic Commerce Research, Springer, vol. 23(1), pages 577-604, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tavasoli, Ali & Shakeri, Heman & Ardjmand, Ehsan & Young, William A., 2021. "Incentive rate determination in viral marketing," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1169-1187.
    2. Sarah Gelper & Ralf van der Lans & Gerrit van Bruggen, 2021. "Competition for Attention in Online Social Networks: Implications for Seeding Strategies," Management Science, INFORMS, vol. 67(2), pages 1026-1047, February.
    3. Pescher, Christian & Reichhart, Philipp & Spann, Martin, 2014. "Consumer Decision-making Processes in Mobile Viral Marketing Campaigns," Journal of Interactive Marketing, Elsevier, vol. 28(1), pages 43-54.
    4. Shareef, Mahmud Akhter & Mukerji, Bhasker & Alryalat, Mohammad Abdallah Ali & Wright, Angela & Dwivedi, Yogesh K., 2018. "Advertisements on Facebook: Identifying the persuasive elements in the development of positive attitudes in consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 258-268.
    5. Nelson-Field, Karen & Riebe, Erica & Newstead, Kellie, 2013. "The emotions that drive viral video," Australasian marketing journal, Elsevier, vol. 21(4), pages 205-211.
    6. Shaheer, Noman Ahmed & Li, Sali, 2020. "The CAGE around cyberspace? How digital innovations internationalize in a virtual world," Journal of Business Venturing, Elsevier, vol. 35(1).
    7. Jennifer K D’Angelo & Kristin Diehl & Lisa A Cavanaugh, 2019. "Lead by Example? Custom-Made Examples Created by Close Others Lead Consumers to Make Dissimilar Choices," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 750-773.
    8. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    9. Irina Heimbach & Oliver Hinz, 2018. "The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks," Information Systems Research, INFORMS, vol. 29(3), pages 592-611, September.
    10. Baxendale, Shane & Macdonald, Emma K. & Wilson, Hugh N., 2015. "The Impact of Different Touchpoints on Brand Consideration," Journal of Retailing, Elsevier, vol. 91(2), pages 235-253.
    11. Das, Kallol & Patel, Jayesh D. & Sharma, Anuj & Shukla, Yupal, 2023. "Creativity in marketing: Examining the intellectual structure using scientometric analysis and topic modeling," Journal of Business Research, Elsevier, vol. 154(C).
    12. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    13. ILes, Richard, 2017. "Government Doctor Absenteeism And Its Effects On Consumer Demand In Rural North India," Working Papers 2018-9, School of Economic Sciences, Washington State University, revised 12 2018.
    14. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2020. "Voluntary and Mandatory Social Distancing: Evidence on COVID-19 Exposure Rates from Chinese Provinces and Selected Countries," Globalization Institute Working Papers 382, Federal Reserve Bank of Dallas.
    15. Foster Provost & David Martens & Alan Murray, 2015. "Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design," Information Systems Research, INFORMS, vol. 26(2), pages 243-265, June.
    16. Elias Carroni & Paolo Pin & Simone Righi, 2020. "Bring a Friend! Privately or Publicly?," Management Science, INFORMS, vol. 66(5), pages 2269-2290, May.
    17. Fatao Wang & Lihui Ding & Hongxin Yu & Yuanjun Zhao, 0. "Big data analytics on enterprise credit risk evaluation of e-Business platform," Information Systems and e-Business Management, Springer, vol. 0, pages 1-40.
    18. Sha Yang & Yi Zhao & Ravi Dhar, 2010. "Modeling the Underreporting Bias in Panel Survey Data," Marketing Science, INFORMS, vol. 29(3), pages 525-539, 05-06.
    19. Li, Jian & Zhou, Junjie & Chen, Ying-Ju, 2021. "The Limit of Targeting in Networks," ISU General Staff Papers 202112081957590000, Iowa State University, Department of Economics.
    20. Gong, Qingbin & Diao, Xundi, 2023. "The impacts of investor network and herd behavior on market stability: Social learning, network structure, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1388-1398.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:20:y:2020:i:4:d:10.1007_s10660-018-09326-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.