A structural equation modeling based on the conservation of resources theory: Analyzing the relations between artificial intelligence attitude, employability, and career stress in nursing students
Küçük Resim Yok
Tarih
2026
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Churchill Livingstone
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Background: The rapid rise of artificial intelligence (AI) in healthcare is reshaping nursing education and practice. There is no research examining the structural relationships between attitudes toward AI, perceived future employability, and career stress among nursing students, representatives of the healthcare sector. Aim: To examine the associations among AI attitude, perceived future employability, and career stress in nursing students, based on the Conservation of Resources (COR) theory. Design: A descriptive-correlational study. Settings: The study was conducted at the nursing faculties of two universities in T & uuml;rkiye. Participants: The study involved 521 third-and fourth-year undergraduate nursing students. Methods: Data were collected through face-to-face interviews using the Student Information Form, the General Attitudes Towards Artificial Intelligence Scale (GAAIS), the Perceived Future Employability Scale (PFES), and the Career Stress Scale (CSS). Structural equation modeling with bootstrapped indirect effects was used to test the hypothesized associations. Composite scores of validated scales were used, and the analysis was performed as structural model with observed variables. Results: Mean scores indicated high positive AI attitudes (X = 3.63, SD = 0.61) and low negative AI attitudes (X = 2.90, SD = 0.70). Perceived future employability was moderate (X = 109.86, SD = 17.99), and career stress was also moderate (X = 50.23, SD = 17.57). Correlation analysis showed positive associations between positive attitudes toward AI and perceived future employability (r = 0.332, p < .001), and negative associations between negative attitudes toward AI and perceived future employability (r =-0.188, p < .001). Perceived future employability was negatively associated with career stress (r =-0.299, p < .001), while negative attitudes toward AI were positively associated with career stress (r = 0.207, p < .001). No significant correlation was observed between positive attitudes toward AI and career stress (r = 0.055, p = .207). Regression analysis indicated that positive attitudes toward AI were associated with higher perceived future employability ((3 = 0.88, p < .001), whereas negative attitudes were associated with lower perceived future employability ((3 =-0.43, = .013). Positive attitudes did not show a significant association with career stress ((3 =-0.19, p = .469), while negative attitudes were positively related to career stress ((3 = 1.21, p = .002). In the mediating model, perceived future employability was associated with a positive indirect effect ((3 = 0.05, 95% CI = 0.02; 0.09) on the relationship between negative attitudes toward AI and career stress. Among PFES subdimensions, the indirect effect was significant through the Perceived Future Personal Traits subdimension ((3 = 0.04, 95% CI = 0.01; 0.07), while other subdimensions were not significant. Together, AI attitudes and perceived future employability accounted for 14% of the variance in career stress. Conclusions: Findings were consistent with a partial mediation pattern in which perceived future employability was associated with the relationship between AI attitudes and career stress. Nursing curricula may enhance technical competence and personal traits such as confidence, adaptability, self-efficacy, and resilience, encouraging students to view AI as a valuable professional development resource.
Açıklama
Anahtar Kelimeler
Artificial intelligence attitude, Career stress, COR theory, Nursing students, Perceived future employability, Structural equation modeling
Kaynak
Nurse Education Today
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
162
