Skip to main content
SpringerLink
Log in

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

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Sources: Adapted from Engel et al. [55], Darley et al. [53], and Wen et al. [54]

Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Liang, T., & Turban, E. (2011). Introduction to the special issue social commerce: A research framework for social commerce. International Journal of Electronic Commerce, 16(2), 5–14.

    Google Scholar 

  2. Wang, R., Rho, S., Chen, B.-W., & Cai, W. (2017). Modeling of large-scale social network services based on mechanisms of information diffusion: Sina Weibo as a case study. Future Generation Computing Systems, 74, 291–301.

    Google Scholar 

  3. Mamonov, S., & Benbunan-Fich, R. (2017). Exploring factors affecting social e-commerce service adoption: The case of Facebook Gifts. International Journal of Information Management, 37(6), 590–600.

    Google Scholar 

  4. Liang, T.-P., Ho, Y.-T., Li, Y.-W., & Turban, E. (2011). What drives social commerce: The role of social support and relationship quality. International Journal of Electronic Commerce, 16(2), 69–90.

    Google Scholar 

  5. Song, T., Yi, C., & Huang, J. (2017). Whose recommendations do you follow? An investigation of tie strength, shopping stage, and deal scarcity. Information & Management, 54(8), 1072–1083.

    Google Scholar 

  6. Choon, J., Ng, Y., Meng, M., Huang, D., & Liu, Y. (2016). The ‘feminine’ entrepreneurial personality trait: The competitive advantage of female college-student entrepreneurs in Chinese wei-shang businesses? Asian Business & Management, 15(5), 343–369.

    Google Scholar 

  7. Lien, C. H., & Cao, Y. (2014). Examining WeChat users’ motivations, trust, attitudes, and positive word-of-mouth: Evidence from China. Computers in Human Behavior, 41, 104–111.

    Google Scholar 

  8. Lee, S. Y. T., & Phang, C. W. (2015). Leveraging social media for electronic commerce in Asia: Research areas and opportunities. Electronic Commerce Research and Applications, 14(3), 145–149.

    Google Scholar 

  9. China E-Commerce Research Center. (2018). China social commerce industry development report in 2017. http://b2b.toocle.com/detail--6405503.html. Accessed 15 Dec 2018.

  10. IResearch. (2018). China E-commerce industry report. http://news.iresearch.cn/content/2018/02/273033.shtml. Accessed 15 Dec 2018.

  11. Kim, D. (2013). Under what conditions will social commerce business models survive? Electronic Commerce Research and Applications, 12(2), 69–77.

    Google Scholar 

  12. Culnan, M. J., Mchugh, P. J., & Zubillaga, J. I. (2010). How large U.S. companies can use Twitter and other social media to gain business value. MIS Quarterly Executive, 9(4), 243–259.

    Google Scholar 

  13. Zhao, N., & Cui, X. (2017). Impact of individual interest shift on information dissemination in modular networks. Physica A: Statistical Mechanics and its Applications, 466, 232–242.

    Google Scholar 

  14. Xiong, F., Liu, Y., & Zhang, H.-F. (2015). Multi-source information diffusion in online social networks. Journal of Statistical Mechanics: Theory and Experiment, 2015(7), P07008.

    Google Scholar 

  15. Daley, J., & Gani, D. J. (2001). Epidemic modelling: An introduction. Cambridge: Cambridge University Press.

    Google Scholar 

  16. Cheng, X., Fu, S., & de Vreede, G.-J. (2017). Understanding trust influencing factors in social media communication: A qualitative study. International Journal of Information Management, 37(2), 25–35.

    Google Scholar 

  17. Zhou, L., Zhang, P., & Zimmermann, H.-D. (2013). Social commerce research: An integrated view. Electronic Commerce Research and Applications, 12(2), 61–68.

    Google Scholar 

  18. Zhou, G., Xu, K., & Liao, S. S. Y. (2013). Do starting and ending effects in fixed-price group-buying differ? Electronic Commerce Research and Applications, 12(2), 78–89.

    Google Scholar 

  19. Herrando, C., Jiménez-Martínez, J., & Martín-De Hoyos, M. J. (2017). Passion at first sight: How to engage users in social commerce contexts. Electronic Commerce Research, 17(4), 701–720.

    Google Scholar 

  20. Hildebrand, C., Häubl, G., Herrmann, A., & Landwehr, J. R. (2013). When social media can be bad for you: Community feedback stifles consumer creativity and reduces satisfaction with self-designed products. Information Systems Research, 24(1), 14–29.

    Google Scholar 

  21. Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246–259.

    Google Scholar 

  22. Huang, Z., & Benyoucef, M. (2017). The effects of social commerce design on consumer purchase decision-making: An empirical study. Electronic Commerce Research and Applications, 25, 40–58.

    Google Scholar 

  23. Gorner, J., Zhang, J., & Cohen, R. (2013). Improving trust modeling through the limit of advisor network size and use of referrals. Electronic Commerce Research and Applications, 12(2), 112–123.

    Google Scholar 

  24. Tian, X., & Liu, L. (2017). Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research. Electronic Commerce Research, 17(1), 169–183.

    Google Scholar 

  25. Pastor-Satorras, R., & Vespignani, A. (2001). Epidemic spreading in scale-free networks. Physical Review Letters, 86(14), 3200–3203.

    Google Scholar 

  26. Parshani, R., Carmi, S., & Havlin, S. (2010). Epidemic threshold for the susceptible-infectious-susceptible model on random networks. Physical Review Letters, 104(25), 258701.

    Google Scholar 

  27. Zanette, D. H. (2002). Dynamics of rumor propagation on small-world networks. Physical Review E, 65(4), 041908.

    Google Scholar 

  28. Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81(2), 591–646.

    Google Scholar 

  29. Zhou, T., Liu, J.-G., Bai, W.-J., Chen, G., & Wang, B.-H. (2006). Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity. Physical Review E, 74(5), 056109.

    Google Scholar 

  30. Satsuma, J., Willox, R., Ramani, A., Grammaticos, B., & Carstea, A. (2004). Extending the SIR epidemic model. Physica A: Statistical Mechanics and its Applications, 336(3–4), 369–375.

    Google Scholar 

  31. Rizzo, A., Frasca, M., & Porfiri, M. (2014). Effect of individual behavior on epidemic spreading in activity-driven networks. Physical Review E, 90(4), 042801.

    Google Scholar 

  32. Li, M., Orgun, M. A., Xiao, J., Zhong, W., & Xue, L. (2012). The impact of human activity patterns on asymptomatic infectious processes in complex networks. Physica A: Statistical Mechanics and its Applications, 391(14), 3718–3728.

    Google Scholar 

  33. Funk, S., Salathé, M., & Jansen, V. A. A. (2010). Modelling the influence of human behaviour on the spread of infectious diseases: A review. Journal of the Royal Society, Interface, 7(50), 1247–1256.

    Google Scholar 

  34. Sotiriadis, M. D., & van 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.

    Google Scholar 

  35. Daugherty, T., & Hoffman, E. (2014). eWOM and the importance of capturing consumer attention within social media. Journal of Marketing Communications, 20(1–2), 82–102.

    Google Scholar 

  36. Yang, F. X. (2017). Effects of restaurant satisfaction and knowledge sharing motivation on eWOM intentions. Journal of Hospitality & Tourism Research, 41(1), 93–127.

    Google Scholar 

  37. Wang, Q., Xu, Z., Cui, X., Wang, L., & Ouyang, C. (2017). Does a big Duchenne smile really matter on e-commerce websites? An eye-tracking study in China. Electronic Commerce Research, 17(4), 609–626.

    Google Scholar 

  38. Zhang, K. Z. K., Benyoucef, M., & Zhao, S. J. (2016). Building brand loyalty in social commerce: The case of brand microblogs. Electronic Commerce Research and Applications, 15, 14–25.

    Google Scholar 

  39. Libai, B., et al. (2010). Customer-to-customer interactions: Broadening the scope of word of mouth research. Journal of Service Research, 13(3), 267–282.

    Google Scholar 

  40. Bruce, N. I., Foutz, N. Z., & Kolsarici, C. (2012). Dynamic effectiveness of advertising and word of mouth in sequential distribution of new products. Journal of Marketing Research, 49(4), 469–486.

    Google Scholar 

  41. Jin, S.-A. A., & Phua, J. (2014). Following celebrities’ Tweets about brands: The impact of Twitter-based electronic word-of-mouth on consumers’ source credibility perception, buying intention, and social identification with celebrities. Journal of Advertising, 43(2), 181–195.

    Google Scholar 

  42. Li, F., & Du, T. C. (2011). Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs. Decision Support Systems, 51(1), 190–197.

    Google Scholar 

  43. Bampo, M., Ewing, M. T., Mather, D. R., Stewart, D., & Wallace, M. (2008). The effects of the social structure of digital networks on viral marketing performance. Information Systems Research, 19(3), 273–290.

    Google Scholar 

  44. Yang, J., Zhuang, D., Xie, W., & Chen, G. (2013). A study of design approach of spreading schemes for viral marketing based on human dynamics. Physica A: Statistical Mechanics and its Applications, 392(24), 6494–6505.

    Google Scholar 

  45. Zhu, Z. (2013). Discovering the influential users oriented to viral marketing based on online social networks. Physica A: Statistical Mechanics and its Applications, 392(16), 3459–3469.

    Google Scholar 

  46. Wang, Z. S., Zhao, H., Lai, Z. W., & Qin, X. Z. (2016). Improved SIR epiDEM model of social network marketing effectiveness and experimental simulation. Systems Engineering-Theory & Practice, 36(8), 2024–2034.

    Google Scholar 

  47. Marathe, M., & Vullikanti, A. K. S. (2013). Computational epidemiology. Communications of the ACM, 56(7), 88.

    Google Scholar 

  48. van der Lans, R., van Bruggen, G., Eliashberg, J., & Wierenga, B. (2010). A viral branching model for predicting the spread of electronic word of mouth. Marketing Science, 29(2), 348–365.

    Google Scholar 

  49. Campbell, A. (2013). Word-of-mouth communication and percolation in social networks. American Economic Review, 103(6), 2466–2498.

    Google Scholar 

  50. Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(5), 90–102.

    Google Scholar 

  51. Marsden, P. (2009). Top social commerce survey findings. https://Socialcommercetoday.com Online. Available: https://digitalwellbeing.org/top-social-commerce-survey-findings-ripple6/. Accessed 15 Dec 2018.

  52. Liang, T., & Lai, H. (2002). Effect of store design on consumer purchases: An empirical study of on-line bookstores. Information & Management, 39(6), 431–444.

    Google Scholar 

  53. Darley, W. K., Blankson, C., & Luethge, D. J. (2010). Toward an integrated framework for online consumer behavior and decision making process: A review. Psychology & Marketing, 27(2), 94–116.

    Google Scholar 

  54. Wen, C., Prybutok, V. R., Blankson, C., & Fang, J. (2014). The role of E-quality within the consumer decision making process. International Journal of Operations & Production Management, 34(12), 1506–1536.

    Google Scholar 

  55. Engel, J. F., Blackwell, R. D., & Miniard, P. W. (1995). Consumer behavior (8th ed.). Fort Worth, TX: Harcourt Education.

    Google Scholar 

  56. Wu, Y., Deng, S., & Huang, H. (2012). Information propagation through opportunistic communication in mobile social networks. Mobile Networks and Applications, 17(6), 773–781.

    Google Scholar 

  57. Xin, W., Member, Z., Li, S., He, Y., & Chang, E. Y. (2015). Connecting social media to e-commerce: Cold-start product recommendation using microblogging information. IEEE Transactions on Knowledge and Data Engineering, X(X), 1–14.

    Google Scholar 

  58. Liang, K., Jiang, C., & Lin, Z. (2016). The nature of sellers’ cyber credit in C2C e-commerce: The perspective of social capital. Electronic Commerce Research, 17(1), 133–147.

    Google Scholar 

  59. Zhou, L., Zhang, D., Yang, C., & Wang, Y. (2017). Harnessing social media for health information management. Electronic Commerce Research and Applications, 27, 139–151.

    Google Scholar 

Download references

Acknowledgments

The authors thank reviewers and editors for their valuable and constructive comments. This research is supported by the General Program of Natural Science Foundation of China (Grant No. 61471083), Program of the Ministry of Education of China (Grant No. 14YJA630044).

Author information

Authors and Affiliations

  1. Institute of Systems Engineering, Dalian University of Technology, No. 2, Linggong Road, Ganjingzi District, Dalian City, 116023, Liaoning Province, People’s Republic of China

    Narisa Zhao & Hui Li

Authors
  1. Narisa Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Li.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, N., Li, H. How can social commerce be boosted? The impact of consumer behaviors on the information dissemination mechanism in a social commerce network. Electron Commer Res 20, 833–856 (2020). https://doi.org/10.1007/s10660-018-09326-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-018-09326-3

Keywords

Search

Navigation