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

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.

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Publication Details

Journal
Acta Psychologica
Published
2026-10-30
DOI
https://doi.org/10.1016/j.actpsy.2026.108044
Primary Topic
Sleep and related disorders
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Yuan Zhao, Walton Wider, Yue Wang, Hao Wu
Acta Psychologica
Sleep and related disorders
article

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

Yuan Zhao, Walton Wider, Yue Wang, Hao Wu
article en

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.

Acta PsychologicaVol. 271
INTI International University (MY), Shinawatra University (TH), Wekerle Business School (HU), Shandong University of Political Science and Law (CN), International Institute of Management and Business (BY)
Openalex Percentile: Top 8%
Sleep and related disorders
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