How Does Perceived Overload in Mobile Social Media Influence Users’ Passive Usage Intentions? Considering the Mediating Roles of Privacy Concerns and Social Media Fatigue

ABSTRACT

Social media is becoming an important instrument for interpersonal communication. However, more and more users attempt to use social media passively and have negative emotional responses. The critical question arising is what causes people to use social media passively. Furthermore, the roles of social media fatigue and privacy concerns between the perceived overload and passive usage intentions have not yet been investigated in depth. The study aims to explore how perceived overload affects the passive usage intentions of social media users. The conceptual model incorporated “perceived overload,” “social media fatigue,” “privacy concerns,” and “passive usage intentions” into a cognition-affect-conation framework to reflect the influencing process. This study collected data from 335 users on mainstream social media platforms and analyzed it using the partial least square-structural equation model (PLS-SEM). The results show that perceived overload positively affects the passive usage intentions of mobile social media users. Particularly, it is found that privacy concerns and social media fatigue mediate the relationship between perceive overload and passive usage intentions. The mediating effect of social media fatigue is significantly stronger than privacy concerns. This study enriches the research of information system use. It also provides theoretical and practical implications for social media scholars and practitioners.

Acknowledgments

The authors thank all the questionnaire participants in this research. We also express our gratitude to the editors and reviewers for their helpful comments and suggestions.

Disclosure statement

There was no potential competing interest to report in this work. I would like to declare on behalf of my co-authors that the research described above has not been published before and has not been considered to be published anywhere.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under [Grant No.71771045] and [Grant No.72071035].

Notes on contributors

Jingyu Li

Jingyu Li is a master’s degree candidate of Management Science and Engineering at the School of Business Administration, Northeastern University, China. Her research interests include Human Factors, Social media Interaction, User Experience Design, and Human-computer Interaction.

Fu Guo

Fu Guo is a professor of Industrial Engineering at the School of Business Administration, Northeastern University, China. She is a peer reviewer for the National Natural Science Foundation of China. Her research interests include Human Factors, Kansei Engineering, User Experience Design, Human-Computer Interaction, Human-Robot Interaction, and Occupational Safety and Health.

Qing-Xing Qu

Qing-Xing Qu is a postdoctoral at the Department of Industrial Engineering, School of Business Administration, Northeastern University, China. He obtained his Ph.D. degree in Management Science and Engineering from Northeastern University in 2020. His research interests include Human Factors, Kansei Engineering, User Experience Design, and Human-Computer Interaction.

Deming Hao

Deming Hao is a master’s degree candidate at the Department of Industrial Engineering, School of Business Administration, Northeastern University, China. His research interests include Human Factors, User Experience Design, Human-Autonomous system Interaction, and Human-computer Interaction.