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

Year: 2026

Type: article

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.

Source: Acta Psychologica

DOI: 10.1016/j.actpsy.2026.108044

Authors: Yue Wang, Yuan Zhao, Walton Wider, Hao Wu

Institutions: INTI International University, Shandong University of Political Science and Law, Shinawatra University, Wekerle Business School, International Institute of Management and Business

Language: English

Topic: Sleep and related disorders

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,670