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Peer-reviewedCC BY 4.0Frontiers in Public Health

Artificial intelligence (AI) anxiety and nurses’ turnover intention during healthcare digital transformation: the mediating role of occupational fatigue

  • Lidan Zhu1
  • Feng Zang2
  • Guangxu Mao3
  • Xiang Li4
  • Xiaofang Shen5
5 affiliations
  1. 1Party Committee Office, Xinghua People's Hospital Affiliated to Yangzhou University
  2. 2Department of Infection Control, The First Affiliated Hospital with Nanjing Medical University
  3. 3Department of Epidemiology and Infection Control, Xinghua People's Hospital Affiliated to Yangzhou University
  4. 4Science and Education Department, Xinghua People's Hospital Affiliated to Yangzhou University
  5. 5Human Resources Department, Xinghua People's Hospital Affiliated to Yangzhou University
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Abstract

Background: The rapid integration of artificial intelligence (AI) into healthcare is associated with new psychological challenges. However, the extent of its impact on nursing workforce stability is unclear.

Aim: To explore the relationship between AI anxiety and turnover intention of clinical nurses, and the mediating effect of occupational fatigue and the moderating effect of emotional intelligence (EI).

Methods: A cross-sectional survey was conducted from March to June 2026 among 404 clinical nurses from four hospitals in Jiangsu Province, China. Data were collected using the AI Anxiety Scale (AIAS), Health Workers’ Occupational Fatigue Scale (HWOFS), Turnover Intention Scale (TIS) and Wong and Law Emotional Intelligence Scale (WLEIS). Mediation was tested using structural equation modelling with bootstrap resampling, while moderation was tested using moderated regression analysis.

Results: Differences in baseline turnover intentions across age, department, and professional title level were statistically significant (all p < 0.001). Path analysis showed that AI anxiety was positively associated with occupational fatigue (β = 0.611, p < 0.001) and turnover intention (β = 0.530, p < 0.001). Occupational fatigue partially mediated the association between AI anxiety and turnover intention (indirect effect = 0.299, 95% CI: 0.129, 0.379). EI had a significant negative main effect on occupational fatigue (b = −0.257, p < 0.001), but it did not significantly moderate the association between AI anxiety and occupational fatigue.

Conclusion: AI anxiety is significantly correlated with the turnover intention of clinical nurses, and occupational fatigue may represent a potential pathway linking AI anxiety and turnover intention. Although EI is associated with lower levels of occupational fatigue, it fails to cushion the impact of AI anxiety, a specific stressor. Hospital administrators should focus on targeted AI literacy improvement plans and specific fatigue relief strategies to retain clinical nursing staff.

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Cite

BibTeXRIS
BibTeX
@article{zhu2026artificial,
  title = {{Artificial intelligence (AI) anxiety and nurses’ turnover intention during healthcare digital transformation: the mediating role of occupational fatigue}},
  author = {Lidan Zhu and Feng Zang and Guangxu Mao and Xiang Li and Xiaofang Shen},
  journal = {Frontiers in Public Health},
  year = {2026},
  month = oct,
  doi = {10.3389/fpubh.2026.1982743},
  url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1982743/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Artificial intelligence (AI) anxiety and nurses’ turnover intention during healthcare digital transformation: the mediating role of occupational fatigue
AU  - Lidan Zhu
AU  - Feng Zang
AU  - Guangxu Mao
AU  - Xiang Li
AU  - Xiaofang Shen
PY  - 2026
DA  - 2026/10/22
JO  - Frontiers in Public Health
DO  - 10.3389/fpubh.2026.1982743
UR  - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1982743/full
AB  - Background: The rapid integration of artificial intelligence (AI) into healthcare is associated with new psychological challenges. However, the extent of its impact on nursing workforce stability is unclear.  Aim: To explore the relationship between AI anxiety and turnover intention of clinical nurses, and the mediating effect of occupational fatigue and the moderating effect of emotional intelligence (EI).  Methods: A cross-sectional survey was conducted from March to June 2026 among 404 clinical nurses from four hospitals in Jiangsu Province, China. Data were collected using the AI Anxiety Scale (AIAS), Health Workers’ Occupational Fatigue Scale (HWOFS), Turnover Intention Scale (TIS) and Wong and Law Emotional Intelligence Scale (WLEIS). Mediation was tested using structural equation modelling with bootstrap resampling, while moderation was tested using moderated regression analysis.  Results: Differences in baseline turnover intentions across age, department, and professional title level were statistically significant (all p < 0.001). Path analysis showed that AI anxiety was positively associated with occupational fatigue (β = 0.611, p < 0.001) and turnover intention (β = 0.530, p < 0.001). Occupational fatigue partially mediated the association between AI anxiety and turnover intention (indirect effect = 0.299, 95% CI: 0.129, 0.379). EI had a significant negative main effect on occupational fatigue (b = −0.257, p < 0.001), but it did not significantly moderate the association between AI anxiety and occupational fatigue.  Conclusion: AI anxiety is significantly correlated with the turnover intention of clinical nurses, and occupational fatigue may represent a potential pathway linking AI anxiety and turnover intention. Although EI is associated with lower levels of occupational fatigue, it fails to cushion the impact of AI anxiety, a specific stressor. Hospital administrators should focus on targeted AI literacy improvement plans and specific fatigue relief strategies to retain clinical nursing staff.
SN  - 2296-2565
ER  -