Skip to main content
SpringerLink
Log in

An interaction model among enterprise and government actions and public opinion dissemination in negative events

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

The development of Internet information technology has accelerated the spread of online public opinion in negative enterprise events. Moreover, enterprises release information through online social networks and cooperate with the government to handle the status of events, which affects the spread of public opinion and the progress of negative events. Based on the multi-molecular reaction model, we explore the interaction among four factors: enterprise online opinion dissemination, the development of negative events, government regulation, and enterprise response strategies. And then, the stability of the model is analyzed using Routh stability theory. Furthermore, we discuss the Baidu Wei Zexi incident that occurred in China as a case study, and offer courses of action for policy makers to navigate through crises of public opinion. The research results show that the linkage approach with government regulation out in front and the enterprise response following is the best approach to mitigating the spread of public opinion.

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

Similar content being viewed by others

References

  1. Qi, Y., Li, X. H., & Cao, X. (2020). A research on the evolution of net-mediated public sentiment in enterprise social responsibility negative events and government-enterprise cooperation. Systems Engineering-Theory Practice, 40(07), 1792–1805. (in Chinese).

    Google Scholar 

  2. An, L., Zhou, W., Ou, M. H., Li, G., Yu, C. M., & Wang, X. F. (2021). Measuring and profiling the topical influence and sentiment contagion of public event stakeholders. International Journal of Information Management, 58, 102327.

    Article  Google Scholar 

  3. Yin, J. L., Feng, J. Y., & Wang, Y. Y. (2015). Social media and multinational corporations’ enterprise social responsibility in China: The case of ConocoPhillips oil spill incident. IEEE Transactions on Professional Communication, 58(2), 135–153.

    Article  Google Scholar 

  4. Deephouse, D. L. (2000). Media reputation as a strategic resource: An integration of mass communication and resource-based theories. Journal of Management, 26(6), 1091–1112.

    Article  Google Scholar 

  5. Zhang, Z. L., & Zhang, Z. Q. (2009). An interplay model for rumour spreading and emergency development. Physica A: Statistical Mechanics and its Applications, 388(19), 4159–4166.

    Article  Google Scholar 

  6. Huo, L. A., Huang, P. Q., & Fang, X. (2011). An interplay model for authorities’ actions and rumor spreading in emergency event. Physica A: Statistical Mechanics and its Applications, 390(20), 3267–3274.

    Article  Google Scholar 

  7. Zhao, L. J., Wang, Q., Cheng, J. J., Zhang, D., Ma, T., Chen, Y. C., & Wang, J. J. (2012). The impact of authorities’ media and rumor dissemination on the evolution of emergency. Physica A: Statistical Mechanics and its Applications, 391(15), 3978–3987.

    Article  Google Scholar 

  8. Spence, P. R., Lachlan, K. A., Lin, X. L., & Greco, M. L. (2015). Variability in Twitter content across the stages of a natural disaster: Implications for crisis communication. Communication Quarterly, 63(2), 171–186.

    Article  Google Scholar 

  9. Meng, L. P., Kang, Q., Han, C. F., & Zhang, B. K. (2016). A multi-agent model for simulation of public crisis information dissemination. International Journal of Wireless and Mobile Computing, 11(1), 33–41.

    Article  Google Scholar 

  10. Wang, J., Wang, X., & Fu, L. (2020). Evolutionary game model of public opinion information dissemination in online social networks. IEEE Access, 8, 127732–127747.

    Article  Google Scholar 

  11. Yu, L., Li, L., Tang, L., Dai, W., & Hanachi, C. (2017). A multi-agent-based online opinion dissemination model for China’s crisis information release policy during hazardous chemical leakage emergencies into rivers. Online Information Review, 41(4), 537–557.

    Article  Google Scholar 

  12. Pfeffer, J., Zorbach, T., & Carley, K. M. (2014). Understanding online firestorms: Negative word-of-mouth dynamics in social media networks. Journal of Marketing Communications, 20(1–2), 117–128.

    Article  Google Scholar 

  13. Ming, H. L., Gang, L., Hua, H., et al. (2022). Modeling the influencing factors of electronic word-of-mouth about CSR on social networking sites. Environmental Science and Pollution Research, 2022, 1–18.

    Google Scholar 

  14. Yan, H. Y., Zhan, L. Y., Chen, M. M., & Qu, H. N. (2021). Research on the dissemination and response of online public opinion on enterprise crisis events based on system dynamics. Chinese Journal of Systems Science, 29(01), 92–97. (in Chinese).

    Google Scholar 

  15. Coombs, W. T. (2004). Impact of past crises on current crisis communication: Insights from situational crisis communication theory. The Journal of Business Communication, 41(3), 265–289.

    Article  Google Scholar 

  16. Qi, L. Y., Guo, Y. N., & Zhang, B. B. (2017). Research on information disclosure of enterprise social responsibility based on system dynamics—Take sudden events as examples. Systems Engineering-Theory Practice, 37(11), 2871–2881. (in Chinese).

    Google Scholar 

  17. Eppler, M. J., & Mengis, J. (2004). The concept of information overload-a review of literature from organization science, accounting, marketing, mis, and related disciplines. The Information Society, 20(5), 271–305.

    Article  Google Scholar 

  18. Lee, E. K., Maheshwary, S., Mason, J., & Glisson, W. (2006). Large-scale dispensing for emergency response to bioterrorism and infectious-disease outbreak. Interfaces, 36(6), 591–607.

    Article  Google Scholar 

  19. Lin, P., Xie, Y. H., & Wei, J. (2018). The medias’ influence on the hot degree of network public opinion. Journal of Modern Information, 38(05), 94–99. (in Chinese).

    Google Scholar 

  20. Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 786, 1360–1380.

    Article  Google Scholar 

  21. Kaiser, J., Keller, T. R., & Kleinen-von Königslöw, K. (2021). Incidental news exposure on Facebook as a social experience: The influence of recommender and media cues on news selection. Communication Research, 48(1), 77–99.

    Article  Google Scholar 

  22. Wu, J. Q., Tang, L., Li, L., & Yu, L. A. (2015). System-dynamics-based emergency strategy analysis for urban water supply crisis in water pollution accidents caused by dangerous chemicals-A case study of Harbin water crisis caused by explosion of Jilin Chemical Company. Systems Engineering-Theory Practice, 35(03), 677–686. (in Chinese).

    Google Scholar 

  23. Qi, L. Y., & Su, S. (2018). The evolution of CSR in PetroChina: A case study based on the perspective of organizational meaning construction and institutional integration. Journal of Management Case Studies, 11(06), 565–576. (in Chinese).

    Google Scholar 

  24. Angus-Leppan, T., Metcalf, L., & Benn, S. (2010). Leadership styles and CSR practice: An examination of sensemaking, institutional drivers and CSR leadership. Journal of Business Ethics, 93(2), 189–213.

    Article  Google Scholar 

  25. Mittal, R. C., & Jiwari, R. (2011). Numerical solution of two-dimensional reaction-diffusion Brusselator system. Applied Mathematics and Computation, 217, 5404–5415.

    Article  Google Scholar 

  26. Li, Z. X., & Chen, L. S. (2010). Dynamical behaviors of a trimolecular response model with impulsive input. Nonlinear Dynamics, 62, 167–176.

    Article  Google Scholar 

Download references

Acknowledgements

This work is funded by the national foundation for philosophy and Social Sciences (No. 19BG234), the youth project of the National Natural Science Foundation of China (No. 71503163) and the Shanghai planning foundation for philosophy and Social Sciences (No. 2020BGl005, No. 2021EGL004)

Author information

Authors and Affiliations

  1. School of Management, Shanghai University of Engineering Science, Shanghai, 201620, People’s Republic of China

    Xiaoli Wang, Shuqin Chen, Yanxi Xie & Jing Zhang

Authors
  1. Xiaoli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuqin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanxi Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuqin Chen.

Appendix

Appendix

See Table 6.

Table 6 Basic parameters of the model
Full size table

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

Wang, X., Chen, S., Xie, Y. et al. An interaction model among enterprise and government actions and public opinion dissemination in negative events. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09767-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10660-023-09767-5

Keywords

Search

Navigation