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, vol.27, pp.1-22, 2026 (Scopus)
- Publication Type: Article / Article
- Volume: 27
- Publication Date: 2026
- Doi Number: 10.1007/s10775-026-09826-x
- Journal Name: INTERNATIONAL JOURNAL FOR EDUCATIONAL AND VOCATIONAL GUIDANCE
- Journal Indexes: 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
- Page Numbers: pp.1-22
- Open Archive Collection: AVESIS Open Access Collection
- Yıldız Technical University Affiliated: Yes
Abstract
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.