Autonomy satisfaction and bedtime procrastination among generation Z university students: The serial mediating roles of general self-efficacy and AI dependence

Wang Y 1 , Zhao Y 2 , Wider W 3 , Wu H 4

Affiliations 

  • 1 Faculty of Education and Liberal Arts, INTI International University, Nilai, 71800, Malaysia; Police Officer Academy, Shandong University of Political Science and Law, Jinan, 250014, China. Electronic address: sdjnwangyue@163.com
  • 2 Police Officer Academy, Shandong University of Political Science and Law, Jinan, 250014, China
  • 3 Faculty of Business and Communications, INTI International University, Nilai, 71800, Malaysia; Department of Applied Economic Sciences, Wekerle Business School, Budapest, 1083, Hungary; Faculty of Management, Shinawatra University, Pathum Thani, 12160, Thailand; Department of Economics, Management, and Law, International Institute of Management and Business, Minsk, 220007, Belarus
  • 4 Faculty of Business and Communications, INTI International University, Nilai, 71800, Malaysia
Acta Psychol (Amst), 2026 Oct 30;271:108044.
PMID: 42962395 DOI: 10.1016/j.actpsy.2026.108044

Abstract

BACKGROUND: With the rapid integration of artificial intelligence (AI) into higher education, university students' daily behaviors are increasingly shaped by technology use. Bedtime procrastination has emerged as a common self-regulation problem among Generation Z students. However, the psychological mechanisms linking environmental resources and technology-related behaviors to sleep outcomes remain insufficiently understood. This study examined the associations between autonomy satisfaction and bedtime procrastination and the mediating roles of general self-efficacy and AI dependence.

METHODS: A cross-sectional survey was conducted among 717 Chinese university students using cluster sampling. Standardized self-report measures assessed autonomy satisfaction, general self-efficacy, AI dependence, and bedtime procrastination. Data were analyzed using SPSS 26.0 and Mplus 8.0. Structural equation modeling and bias-corrected bootstrap (5000 resamples) were employed to test direct and indirect effects.

RESULTS: Autonomy satisfaction was positively associated with general self-efficacy and negatively associated with AI dependence and bedtime procrastination. General self-efficacy was negatively associated with both AI dependence and bedtime procrastination, while AI dependence was positively associated with bedtime procrastination. Mediation analysis revealed that both general self-efficacy and AI dependence served as significant mediators, with AI dependence accounting for a larger proportion of the indirect effect. A significant serial mediation pathway was also observed.

CONCLUSIONS: The findings suggest that autonomy satisfaction was associated with bedtime procrastination through both cognitive and behavioral mechanisms. AI dependence plays a particularly important role as a proximal behavioral pathway. These results highlight the need to promote balanced AI use and strengthen students' self-regulation in technology-rich learning environments.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

Similar publications