Will I lose my job? A longitudinal cross-lagged panel model for technological unemployment anxiety, academic burnout, and quiet quitting
INTERNATIONAL JOURNAL FOR EDUCATIONAL AND VOCATIONAL GUIDANCE, cilt.27, ss.1-22, 2026 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 27
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s10775-026-09826-x
- Dergi Adı: INTERNATIONAL JOURNAL FOR EDUCATIONAL AND VOCATIONAL GUIDANCE
- Derginin Tarandığı İndeksler: Social Science Premium Collection (ProQuest), Education Collection (ProQuest), Education Source Ultimate (EBSCO), Health Research Premium Collection (ProQuest), Scopus, Agricultural & Environmental Science Database, IBZ Online, Educational research abstracts (ERA), ERIC (Education Resources Information Center), EBSCO Education Source, Psycinfo
- Sayfa Sayıları: ss.1-22
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Rapid advances in artificial intelligence and automation have heightened concerns about future employment, especially among university students. Technological unemployment anxiety has thus emerged as a salient stressor with implications for academic engagement and well-being. This study examined the directional relationships between technological unemployment anxiety, academic burnout, and quiet quitting. Using a two-wave half-longitudinal design, data were collected from 306 students (M = 21.76, standard deviation [SD] = 2.50) at 3-month intervals. Cross-lagged analyses showed that technological unemployment anxiety at T1 predicted burnout at T2, and burnout at T1 predicted quiet quitting at T2, supporting Job Demands–Resources (JD–R) and Conservation of Resources (COR) frameworks.
Rapid advances in artificial intelligence and automation have heightened concerns about future employment, especially among university students. Technological unemployment anxiety has thus emerged as a salient stressor with implications for academic engagement and well-being. This study examined the directional relationships between technological unemployment anxiety, academic burnout, and quiet quitting. Using a two-wave half-longitudinal design, data were collected from 306 students (M = 21.76, standard deviation [SD] = 2.50) at 3-month intervals. Cross-lagged analyses showed that technological unemployment anxiety at T1 predicted burnout at T2, and burnout at T1 predicted quiet quitting at T2, supporting Job Demands–Resources (JD–R) and Conservation of Resources (COR) frameworks.