Skip to main content

Advertisement

SpringerLink
Log in

User behaviors in consumer-generated media under monetary reward schemes

  • Research Article
  • Published:
Journal of Computational Social Science Aims and scope Submit manuscript

Abstract

We investigate both the influence of monetary reward schemes on user behaviors and the quality of articles posted by users in consumer-generated media (CGM), such as social networking services (SNSs). Recently, CGM platforms have implemented monetary rewards to incentivize users to post articles and comments. However, the effect of monetary rewards on user behavior merits further investigation. Given that quality articles require more time and effort for preparation, we analyze user-dominant behaviors, including posting and commenting activities, and the quality of posted articles, using different monetary reward schemes. Therefore, we propose a monetary reward SNS-norms game by extending a conventional SNS-norms game, a social networking services model based on evolutionary game theory, and then introduce three monetary reward schemes with different monetary reward timings. We further incorporate efforts to improve the quality and preferences for monetary rewards, psychological rewards, and article quality in the agents, that is, our model of users. We have found that the timing of providing monetary rewards strongly influences the number and/or quality of articles posted using a game with monetary reward schemes on several types of user network structures, including a stochastic block model and an instance of the Facebook network. These results indicate that monetary rewards must be carefully designed in terms of timing and amount, depending on their purpose in the CGM.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Availability of data and materials

The dataset used in this study is available from the http://snap.stanford.edu/data. This code is available online at https://github.com/usui324/SocialMediaSimulation.

References

  1. Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), 531–558.

    Article  Google Scholar 

  2. Nadkarni, A., & Hofmann, S. G. (2012). Why do people use facebook? Personality and Individual Differences, 52(3), 243–249. https://doi.org/10.1016/j.paid.2011.11.007.

    Article  Google Scholar 

  3. Zhao, D., & Rosson, M. B. (2009). How and why people twitter: The role that micro-blogging plays in informal communication at work. ACM International Conference on Supporting Group Work. https://doi.org/10.1145/1531674.1531710.

    Article  Google Scholar 

  4. Razmerita, L., Kirchner, K., & Nielsen, P. (2016). What factors influence knowledge sharing in organizations? A social dilemma perspective of social media communication. Journal of Knowledge Management. https://doi.org/10.1108/JKM-03-2016-0112.

    Article  Google Scholar 

  5. Berry, N., Lobban, F., Belousov, M., Emsley, R., Nenadic, G., & Bucci, S. (2017). Understanding why people use twitter to discuss mental health problems. Journal of Medical Internet Research, 19(4), 107. https://doi.org/10.2196/jmir.6173.

    Article  Google Scholar 

  6. Toriumi, F., Yamamoto, H., & Okada, I. (2012). Why do people use social media? agent-based simulation and population dynamics analysis of the evolution of cooperation in social media.y. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2, 43–50 (IEEE).

    Google Scholar 

  7. Axelrod, R. (1986). An evolutionary approach to norms. The American Political Science Review, 80, 1095–1111.

    Article  Google Scholar 

  8. Hirahara, Y., Toriumi, F., & Sugawara, T. (2014). Evolution of cooperation in SNS-norms game on complex networks and real social networks. International conference on social informatics (pp. 112–120). Springer.

    Chapter  Google Scholar 

  9. Usui, Y., Toriumi, F., & Sugawara, T. (2022). Impact of monetary rewards on user’s behavior in social media. In R. M. Benito, C. Cherifi, H. Cherifi, E. Moro, L. M. Rocha, & M. Sales-Pardo (Eds.), Complex networks and their applications X (pp. 632–643). Springer.

    Chapter  Google Scholar 

  10. Alalwan, A. A. (2018). Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, 42, 65–77. https://doi.org/10.1016/j.ijinfomgt.2018.06.001.

    Article  Google Scholar 

  11. Kumar, A., Bezawada, R., Rishika, R., Janakiraman, R., & Kannan, P. (2016). From social to sale: The effects of firm-generated content in social media on customer behavior. Journal of Marketing, 80(1), 7–25.

    Article  Google Scholar 

  12. Toker-Yildiz, K., Trivedi, M., Choi, J., & Chang, S. R. (2017). Social interactions and monetary incentives in driving consumer repeat behavior. Journal of Marketing Research, 54(3), 364–380. https://doi.org/10.1509/jmr.13.0482.

    Article  Google Scholar 

  13. Arora, A., Bansal, S., Kandpal, C., Aswani, R., & Dwivedi, Y. (2019). Measuring social media influencer index- insights from facebook, twitter and instagram. Journal of Retailing and Consumer Services, 49, 86–101. https://doi.org/10.1016/j.jretconser.2019.03.012.

    Article  Google Scholar 

  14. Lovejoy, K., & Saxton, G. D. (2012). Information, community, and action: How nonprofit organizations use social media. Journal of Computer-Mediated Communication, 17(3), 337–353.

    Article  Google Scholar 

  15. Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of facebook friends: Social capital and college students use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168.

    Article  Google Scholar 

  16. Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169–2188.

    Article  Google Scholar 

  17. Shahbaznezhad, H., Dolan, R., & Rashidirad, M. (2021). The role of social media content format and platform in users’ engagement behavior. Journal of Interactive Marketing, 53, 47–65.

    Article  Google Scholar 

  18. Ostic, D., Qalati, S. A., Barbosa, B., Shah, S. M. M., Galvan Vela, E., Herzallah, A. M., & Liu, F. (2021). Effects of social media use on psychological well-being: A mediated model. Frontiers in Psychology, 12, 2381.

    Article  Google Scholar 

  19. Yoo, K.-H., & Gretzel, U. (2011). Influence of personality on travel-related consumer-generated media creation. Computers in Human Behavior, 27(2), 609–621. https://doi.org/10.1016/j.chb.2010.05.002 (Web 2.0 in Travel and Tourism: Empowering and Changing the Role of Travelers).

    Article  Google Scholar 

  20. Tang, Q., Gu, B., & Whinston, A. B. (2012). Content contribution for revenue sharing and reputation in social media: A dynamic structural model. Journal of Management Information Systems, 29(2), 41–76. https://doi.org/10.2753/MIS0742-1222290203.

    Article  Google Scholar 

  21. Toubia, O., & Stephen, A. T. (2013). Intrinsic vs. image-related utility in social media: Why do people contribute content to twitter? Marketing Science, 32(3), 368–392.

    Article  Google Scholar 

  22. Miura, Y., Toriumi, F., & Sugawara, T. (2021). Modeling and analyzing users’ behavioral strategies with co-evolutionary process. Computational Social Networks, 8(1), 11. https://doi.org/10.1186/s40649-021-00092-1.

    Article  Google Scholar 

  23. Yan, Y., Toriumi, F., & Sugawara, T. (2021). Understanding how retweets influence the behaviors of social networking service users via agent-based simulation. Computational Social Networks, 8(1), 18. https://doi.org/10.1186/s40649-021-00099-8.

    Article  Google Scholar 

  24. Gneezy, U., & Rustichini, A. (2000). Pay enough or don’t pay at all. The Quarterly Journal of Economics, 115(3), 791–810. https://doi.org/10.1162/003355300554917.

    Article  Google Scholar 

  25. Lu, Y., Ou, C., & Angelopoulos, S. (2018). Exploring the effect of monetary incentives on user behavior in online sharing platforms. Proceedings of Hawaii International Conference on System Sciences, 5, 5. https://doi.org/10.24251/HICSS.2018.436.

    Article  Google Scholar 

  26. Mustafa, G., & Ali, N. (2019). Rewards, autonomous motivation and turnover intention: Results from a non-western cultural context. Cogent Business and Management, 6(1), 1676090. https://doi.org/10.1080/23311975.2019.1676090.

    Article  Google Scholar 

  27. Chen, H., Hu, Y. J., & Huang, S. (2019). Monetary incentive and stock opinions on social media. Journal of Management Information Systems, 36(2), 391–417. https://doi.org/10.1080/07421222.2019.1598686.

    Article  Google Scholar 

  28. López, M., Sicilia, M., & Verlegh, P. W. J. (2021). How to motivate opinion leaders to spread e-wom on social media: Monetary vs non-monetary incentives. Journal of Research in Interactive Marketing. https://doi.org/10.1108/JRIM-03-2020-0059.

    Article  Google Scholar 

  29. Jing, D., Jin, Y., & Liu, J. (2019). The impact of monetary incentives on physician prosocial behavior in online medical consulting platforms: Evidence from China. Journal of Medical Internet Research, 21(7), 14685. https://doi.org/10.2196/14685.

    Article  Google Scholar 

  30. Okada, I., Yamamoto, H., Toriumi, F., & Sasaki, T. (2015). The effect of incentives and meta-incentives on the evolution of cooperation. PLoS Computational Biology, 11(5), 1004232.

    Article  Google Scholar 

  31. Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983). Stochastic blockmodels: First steps. Social Networks, 5(2), 109–137. https://doi.org/10.1016/0378-8733(83)90021-7.

    Article  Google Scholar 

  32. Leskovec, J., & Sosič, R. (2016). Snap: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology, 5, 5. https://doi.org/10.1145/2898361.

    Article  Google Scholar 

  33. Lee, C., & Wilkinson, D. J. (2019). A review of stochastic block models and extensions for graph clustering. Applied Network Science. https://doi.org/10.1007/s41109-019-0232-2.

    Article  Google Scholar 

Download references

Funding

This work was supported in part by JSPS KAKENHI (grant numbers 18H03498, 19H02376, and 20H04245).

Author information

Authors and Affiliations

  1. Department of Computer Science and Communications Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo, 1698555, Japan

    Yutaro Usui & Toshiharu Sugawara

  2. Department of Systems Innovation, The University of Tokyo, Tokyo, 1138654, Japan

    Fujio Toriumi

Authors
  1. Yutaro Usui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fujio Toriumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Toshiharu Sugawara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yutaro Usui.

Ethics declarations

Conflict of interest

All the authors state 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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Usui, Y., Toriumi, F. & Sugawara, T. User behaviors in consumer-generated media under monetary reward schemes. J Comput Soc Sc 6, 389–409 (2023). https://doi.org/10.1007/s42001-022-00187-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42001-022-00187-3

Keywords

Search

Navigation