The Impact of Information Dissemination Mechanism in Interactive Apps on User Behavior
Abstract
With the development of social media tools and the widespread adoption of smart devices, social media has become a crucial information source for many individuals. However, the rapid dissemination of information can potentially influence users' behavioral intentions. This study explores the impact of the information dissemination mechanisms in interactive apps on users' behavioral intentions. Interactive apps exhibit instantaneous and diverse information characteristics, coupled with strong social attributes and the dissemination features of algorithmic recommendation mechanisms. These mechanisms exert a dual influence on users' behavioral intentions: high-quality information enhances users' positive behavioral intentions through the single-factor exposure effect, while false information misleads users into irrational behavioral intentions by evoking emotions, shaping subjective norms, and leading to cognitive solidification through information silos. Cross-platform dissemination further amplifies this negative impact. Based on this, the study proposes targeted countermeasures, including optimizing platform algorithms, improving user feedback channels, enhancing media literacy, and strengthening positive guidance, aimed at optimizing the information ecosystem of interactive apps.
References
- Wang, G., Wang, Y., Liu, K., & Li, J. (2019). Multidimensional Influencing Factors of Public Opinion Information Dissemination in Social Media: Evidence from Weibo Dataset. International Journal of Modern Physics B, 33(31).
- Zhou, Q., Li, B., Scheibenzuber, C., & Li, H. (2023). Fake News Land? Exploring the Impact of Social Media Affordances on User Behavioral Responses: A Mixed-Methods Research. Computers in Human Behavior, 148.
- Liu, X., He, D., Yang, L., & Liu, C. (2019). A Novel Negative Feedback Information Dissemination Model Based on Online Social Network. Physica A: Statistical Mechanics and Its Applications, 513, 371–389.
- Wang, L., Lei, C., Xu, Y., Yang, Y., Shan, S., & Xu, X. (2014). An Information Dissemination Model of Product Quality and Safety Based on Scale-Free Networks. Information Technology & Management, 15(3), 211–221.
- Trivedi, S. K., Patra, P., Srivastava, P. R., Kumar, A., & Ye, F. (2024). Exploring Factors Affecting Users’ Behavioral Intention to Adopt Digital Technologies: The Mediating Effect of Social Influence. IEEE Transactions on Engineering Management, 71, 13814–13826.
- Vaghefi, M. S., Beheshti, N., & Jain, H. (2024). Dissemination of Health Messages in Online Social Network: A Study of Healthcare Providers’ Content Generation and Dissemination on Twitter. Information & Management, 61(2), 103925.
- Huh, J., & Shin, W. (2014). Trust in Prescription Drug Brand Websites: Website Trust Cues, Attitude Toward the Website, and Behavioral Intentions. Journal of Health Communication, 19(2), 170–191.
- Zhu, H., Wu, H., Cao, J., Fu, G., & Li, H. (2018). Information Dissemination Model for Social Media with Constant Updates. Physica A: Statistical Mechanics and Its Applications, 502, 469–482.
- Engelhart, A., Thurston, I., Obiezu-Umeh, C., Nwaozuru, U., Pavlick, M., Mason, S., Anikamadu, O., Gbaja-Biamila, T., Howie, W., Herrera, C., Monks, S., Makanjuola, N. K., Lake, J., & Iwelunmor, J. (2023). Designing for Dissemination: Crowdsourcing Open Call to Identify Public Preferences for Health Information Dissemination. Implementation Science, 18(3).