Volume 7, Issue 2 (June 2025)
IEEPJ 2025, 7(2): 1-15 |
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Farhadian A, Azizimohammadi S, Haddadi K, Moradi R. (2025). Structural Model of Imposter Syndrome and Techno-Stress on Faculty Job Burnout: Mediating Role of Digital Work Addiction and the Moderating Role of AI Literacy. IEEPJ. 7(2), 1-15.
URL: http://ieepj.hormozgan.ac.ir/article-1-1142-en.html
URL: http://ieepj.hormozgan.ac.ir/article-1-1142-en.html
1- Assistant Professor of Psychology, Department of Counseling and Guidance, Farhangian University, Tehran, Iran
2- Assistant Professor of Psychology, Department of Counseling and Guidance, Farhangian University, Tehran, Iran ,S_mm173@yahoo.com
3- Assistant Professor of Psychology, Central Tehran Branch, Islamic Azad University, Tehran, Iran
4- M.A. in Educational Psychology, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Assistant Professor of Psychology, Department of Counseling and Guidance, Farhangian University, Tehran, Iran ,
3- Assistant Professor of Psychology, Central Tehran Branch, Islamic Azad University, Tehran, Iran
4- M.A. in Educational Psychology, Science and Research Branch, Islamic Azad University, Tehran, Iran
Abstract: (707 Views)
Objective: Higher education in the AI era faces a paradigmatic rupture, exposing faculty to novel psychological challenges. This study aimed to develop and test a structural model examining how imposter syndrome and techno-stress affect faculty job burnout, with digital work addiction as a mediator and AI literacy as a moderator.
Methods: This applied, correlational study employed structural equation modeling (SEM). The statistical population comprised all full-time faculty at Tehran’s public universities (~7,500 individuals) in the 2024–2025 academic year; 400 were selected via stratified random sampling. Instruments included: Maslach’s Burnout Inventory (1996), Clance’s Imposter Phenomenon Scale (1985), Tarafdar’s Techno-Stress Inventory (2007), Digital Work Addiction Scale (2017), and AI Literacy Scale (2024). Data analysis was conducted using SPSS-27 and AMOS-26.
Results: Results indicated significant direct effects of techno-stress and imposter syndrome on job burnout. Digital work addiction fully mediated the pathways from technological stress and internal doubts to burnout. Multi-group analysis in AMOS confirmed AI literacy’s significant moderating role: among faculty with high AI literacy, the harmful impacts of techno-stress and imposter syndrome on digital work addiction and subsequent burnout were significantly reduced.
Conclusions: AI literacy transcends technical proficiency and functions as a psychological protective buffer. Universities are advised to prioritize capacity-building strategies grounded in critical AI literacy and sustainable digital well-being protocols to prevent burnout and safeguard faculty mental health.
Methods: This applied, correlational study employed structural equation modeling (SEM). The statistical population comprised all full-time faculty at Tehran’s public universities (~7,500 individuals) in the 2024–2025 academic year; 400 were selected via stratified random sampling. Instruments included: Maslach’s Burnout Inventory (1996), Clance’s Imposter Phenomenon Scale (1985), Tarafdar’s Techno-Stress Inventory (2007), Digital Work Addiction Scale (2017), and AI Literacy Scale (2024). Data analysis was conducted using SPSS-27 and AMOS-26.
Results: Results indicated significant direct effects of techno-stress and imposter syndrome on job burnout. Digital work addiction fully mediated the pathways from technological stress and internal doubts to burnout. Multi-group analysis in AMOS confirmed AI literacy’s significant moderating role: among faculty with high AI literacy, the harmful impacts of techno-stress and imposter syndrome on digital work addiction and subsequent burnout were significantly reduced.
Conclusions: AI literacy transcends technical proficiency and functions as a psychological protective buffer. Universities are advised to prioritize capacity-building strategies grounded in critical AI literacy and sustainable digital well-being protocols to prevent burnout and safeguard faculty mental health.
Keywords: Job Burnout, Techno-Stress, Imposter Syndrome, Digital Work Addiction, AI Literacy, Faculty Members
Type of Study: Original |
Subject:
Evolutionary Psychology
Received: 2024/11/25 | Accepted: 2025/02/12 | Published: 2025/06/1
Received: 2024/11/25 | Accepted: 2025/02/12 | Published: 2025/06/1
References
1. Aghaee, A., Rafiei, E., & Saadatmand, Z. (2023). Investigating the mediating role of occupational passion in the relationship between techno-stress and job performance among faculty members in smart learning environments. Journal of Educational Technology, 17(3), 541-556. [DOI:10.22059/jet.2023.367891.1234]
2. Baqeri, S., et al. (2024). AI literacy and teaching self-efficacy in the digital era: Developing a structural model for faculty at medical universities. Journal of Advances in Medical Education, 21(1), 45-58.
3. Bravata, D. M., Watts, S. A., Keefer, A. L., Madhusudhan, D. K., Taylor, K. T., Clark, D. M., Sanderson, B., & Hagg, H. K. (2020). Prevalence, predictors, and treatment of impostor syndrome: A systematic review. Journal of General Internal Medicine, 35(5), 1252-1275. [DOI:10.1007/s11606-019-05364-1]
4. Chen, L., & Li, R. (2025). AI literacy as a psychological buffer: Moderating the effects of technostress in digital environments. Technology in Society, 76, 102-118. [DOI:10.1016/j.techsoc.2024.102241]
5. Hair, J. F., Sarstedt, M., & Ringle, C. M. (2024). Advanced issues in partial least squares structural equation modeling. SAGE Publications. [DOI:10.15358/9783800671465]
6. Khaliqi, M., & Gholipour, M. (2024). Predicting faculty occupational burnout based on techno-intrusion and techno-overload resulting from university digitization mandates. Iranian Journal of Occupational Health, 21(1), 18-35.
7. King, L. (2025). The architecture of digital addiction: How AI-driven platforms reshape academic labor. Computers and Human Behavior, 142, 107-124. [DOI:10.1016/j.chb.2024.107612]
8. Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Press.
9. Li, X., Zhang, Y., & Wang, H. (2025). Generative AI and technostress: A longitudinal study among university professors. Information & Management, 62(2), 103-120. [DOI:10.1016/j.im.2024.103678]
10. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). Association for Computing Machinery. [DOI:10.1145/3313831.3376231]
11. Maslach, C., & Leiter, M. P. (2022). The burnout challenge: Managing people's relationships with their jobs. Harvard University Press. [DOI:10.4159/9780674287297]
12. Mohammadi, S., & Nouri, Z. (2024). AI literacy: Dimensions, components, and the necessity of integration into faculty professional development programs (A systematic review). Journal of Medical Education Strategies, 17(2), 89-104.
13. Nguyen, A., Ha, S., & Tan, G. (2024). Beyond technical skills: A multidimensional framework for AI literacy in higher education. Educational Technology Research and Development, 72(3), 889-912. [DOI:10.1007/s11423-024-10312-8]
14. Pakserestesh, S., Rahimi, M., & Soleimani, N. (2023). The mediating role of work addiction in the relationship between personality traits and occupational burnout among higher education staff. Journal of Public Management, 15(2), 210-234.
15. Patel, R. (2025). Digital transparency and the escalation of impostor syndrome in academia. Higher Education Quarterly, 79(1), 15-32. [DOI:10.1111/hequ.12456]
16. Quinones, C. (2017). The role of technology in work-related rumination and digital work addiction. Journal of Occupational Health Psychology, 22(4), 450-462. [DOI:10.1037/ocp0000069]
17. Razavi, F., & Asgari, M. (2023). Examining the relationship between imposter syndrome and emotional burnout and research performance among faculty members. Journal of Educational Psychology, 19(68), 127-145.
18. Sadeghi, A., Musavi, S. M., & Ahmadi, V. (2022). Analyzing the impact of techno-stress on work-life balance and occupational burnout among faculty during the transition to virtual education. Journal of Information Technology Management Studies, 11(42), 75-102.
19. Spagnoli, P., Molino, M., Molinaro, A. M., Giancaspro, M. L., Manuti, A., & Ghislieri, C. (2023). Workaholism and technostress during the COVID-19 pandemic: The moderating role of self-efficacy. International Journal of Environmental Research and Public Health, 20(3), 2451-2467. [DOI:10.3390/ijerph20032451]
20. Tarafdar, M., & Wang, J. (2024). Technostress 2.0: The impact of artificial intelligence on the professional well-being of knowledge workers. MIS Quarterly, 48(1), 211-235. [DOI:10.25300/MISQ/2023/18223]
21. Tarafdar, M., Maier, C., Laumer, S., & Weitzel, T. (2019). Explaining the link between technostress creators and counterproductive work behavior: A stressor-strain-outcome perspective. Journal of Information Technology, 34(1), 45-69. [DOI:10.1057/s41265-018-0064-2]
22. Wang, B., Kim, S., & Davis, G. (2024). The protective role of AI literacy in preventing burnout among digital-dependent professionals. Journal of Applied Psychology, 109(4), 567-584. [DOI:10.1037/apl0001182]
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