State anxiety moderates the association between motivations and excessive smartphone use
Excessive smartphone use has recently attracted researchers’ attention. Previous studies have suggested that state anxiety and motivations are important predictors of excessive smartphone use. However, few studies have investigated how motivations and state anxiety interact with each other, and the subsequent impact on excessive smartphone use. In the current study, based on the Compensatory Internet Use theory, we analyzed the moderating role of state anxiety on the relationship between two types of motivations (i.e. entertainment and social interaction) and excessive smartphone use. Using the Smartphone Addiction Scale for College Students (SAS-C), Smartphone Usage Motivation Scale and State Anxiety Scale (S-Anxiety), we investigated 600 Chinese college students who identified themselves as smartphone users. Results indicated that: (1) for the high smartphone-use group, state anxiety moderates the relationship between entertainment and social interaction motivations and excessive smartphone use; (2) for the low smartphone-use group, state anxiety does not moderate the relationship between entertainment and social interaction motivations and excessive smartphone use. Our study emphasized the importance of psychological well-being variables (i.e. anxiety in this study) in facilitating excessive smartphone use, and may provide guidance for the design of interventions targeted at people suffering from excessive smartphone use.
KeywordsState anxiety Entertainment motivation Social interaction motivation Excessive smartphone use
This work was supported by the Key Cultivating Project in Southwest University [grant number is SWU1809006]. Grant title is The Psychological Mechanism and Prevention of Mobile Gaming Addiction among Young Individuals.
Compliance with Ethical Standards
Conflict of Interest
All authors declare that they have no conflict of interest.
- Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions: Sage.Google Scholar
- Blumler, J. G., Katz, E., & Blumler, J. G. (1974). The uses of mass communications: Current perspectives on gratifications research (Vol. 3): Sage publications Beverly Hills, CA.Google Scholar
- Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013). Applied multiple regression/correlation analysis for the behavioral sciences: Routledge.Google Scholar
- Muthén, L. K., & Muthén, B. O. (2010). Mplus User's Guide: Statistical Analysis with Latent Variables: User'ss Guide: Muthén & Muthén.Google Scholar
- Spielberger, C. D. (1985). Assessment of trait and state anxiety: Conceptual and methodological issues. The Southern Psychologist, 2(4), 6-16.Google Scholar
- Su, S., Pan, T. T., Liu, X. Q., Chen, X. W., Wang, Y. J., & Li, M. Y. (2014). Development of the smartphone addiction scale for college students. Chinese Mental Health Journal, 28(5), 392–397.Google Scholar
- Thomée, S., Eklöf, M., Gustafsson, E., Nilsson, R., & Hagberg, M. (2007). Prevalence of perceived stress, symptoms of depression and sleep disturbances in relation to information and communication technology (ICT) use among young adults–an explorative prospective study. Computers in Human Behavior, 23(3), 1300–1321.CrossRefGoogle Scholar
- Wang, L. J. (2008). Investigating into State anxiety among primary and middle school teachers in China. (Doctoral dissertation), Southwest University.Google Scholar
- Young, K. S. (2004). Internet addiction: A new clinical phenomenon and its consequences. American Behavioral Scientist, 48(4), 402-15.Google Scholar