ARTIFICIAL INTELLIGENCE ANXIETY, JOB STRESS, AND TURNOVER INTENTION: AN ANALYSIS ON HEALTHCARE WORKERS

Abstract

Artificial intelligence (AI) anxiety refers to the discomfort or fear individuals may experience due to the expected negative outcomes and risks associated with the deployment of artificial intelligence in various social fields. In this context, the aim of the study is to analyze the impact of healthcare workers' AI anxiety perceptions on their job stress and turnover intentions. The sample of the research consists of 275 healthcare workers employed in a private hospital. The impact of AI anxiety on job stress and turnover intention was assessed using structural equation modeling. According to the findings, healthcare workers report very high levels of job stress, high levels of turnover intention, and above-average levels of AI anxiety. The analysis results indicate that AI anxiety has a statistically significant and positive effect on both job stress and turnover intention. In this context, health institutions and administrators have important duties to eliminate anxiety caused by artificial intelligence. In conclusion, in this era of digital and intelligent transformation, researching how healthcare workers perceive AI and how they respond to it is of vital importance.

Keywords

• Artificial intelligence anxiety
• Job stress
• Turnover intention
• Healthcare workers
• Healthcare institutions

YAPAY ZEKÂ KAYGISI, İŞ STRESİ VE İŞTEN AYRILMA NİYETİ: SAĞLIK ÇALIŞANLARI ÜZERİNDE BİR ANALİZ

Abstract

Yapay zekâ kaygısı, bireylerin çeşitli toplumsal alanlarda yapay zekânın konuşlandırılmasıyla ilişkili beklenen olumsuz sonuçlar ve riskler nedeniyle yaşayabilecekleri huzursuzluk veya korkuyu ifade etmektedir. Bu kapsamda çalışmanın amacı sağlık çalışanlarının yapay zekâ kaygısı algılarının, iş stresi algıları ve işten ayrılma niyetleri üzerindeki etkisini analiz etmektedir. Araştırmanın örneklemini bir özel hastanede görev yapan 275 sağlık çalışanı oluşturmaktadır. Yapay zekâ kaygısının iş stresi ve işten ayrılma niyeti üzerindeki etkisi yapısal eşitlik modellemesi kullanılarak değerlendirilmiştir. Araştırmadan elde edilen bulgulara göre sağlık çalışanlarının iş stresi seviyelerinin çok yüksek olduğu, işten ayrılma niyetlerinin yüksek olduğu ve yapay zekâ kaygı seviyelerinin ortalamanın üzerinde olduğu tespit edilmiştir. Yapılan analiz sonuçlarına göre yapay zekâ kaygısının iş stresi ve işten ayrılma niyeti üzerinde istatistiksel olarak anlamlı ve pozitif bir etkisi bulunmuştur. Bu kapsamda yapay zekâ kaynaklı kaygının ortadan kaldırılması için sağlık kurumları ve yöneticilere önemli görevler düşmektedir. Sonuç olarak dijital ve akıllı dönüşümün bu çağında, sağlık çalışanlarının yapay zekayı nasıl algıladıklarını ve buna nasıl tepki verdiklerini araştırmak hayati önem taşımaktadır.

Keywords

• Yapay zekâ kaygısı
• İş stresi
• İşten ayrılma niyeti
• Sağlık çalışanları
• Sağlık kurumları

References

1. Abuselidze, G., & Mamaladze, L. (2021). The impact of artificial intelligence on employment before and during pandemic: A comparative analysis. Journal of Physics: Conference Series, 1840(1), 012040. https://doi.org/10.1088/1742-6596/1840/1/012040
2. Akçakanat, Ö. (2024). Yapay zekâ kaygısının teknoloji kaynaklı işsizlik endişesi üzerine etkisi: Muhasebe meslek mensupları üzerine bir araştırma. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(Özel Sayı), 53–67. https://doi.org/10.33707/akuiibfd.1458358
3. Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zekâ kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125–1146. https://doi.org/10.29023/alanyaakademik.833668
4. Ali, T., Hussain, I., Hassan, S., & Anwer, S. (2024). Examine how the rise of AI and automation affects job security, stress levels, and mental health in the workplace. Bulletin of Business and Economics (BBE), 13(2), 1180–1186. https://doi.org/10.61506/01.00506
5. Aydoğdu, F. (2023). AVE ve CR hesaplama. Fuat Aydoğdu Resmi Web Sitesi. https://www.fuataydogdu.com/avecr
6. Babapour, A. R., Gahassab-Mozaffari, N., & Fathnezhad-Kazemi, A. (2022). Nurses’ job stress and its impact on quality of life and caring behaviors: A cross-sectional study. BMC Nursing, 21(1), 75. https://doi.org/10.1186/s12912-022-00852-y Bartram, T., Casimir, G., Djurkovic, N., Leggat, S. G., & Stanton, P. (2012). Do perceived high performance work systems influence the relationship between emotional labour, burnout and intention to leave? A study of Australian nurses. Journal of Advanced Nursing, 68(7), 1567–1578. https://doi.org/10.1111/j.1365-2648.2012.05968.x
7. Başer, A., Altuntaş, S. B., Kolcu, G., & Özceylan, G. (2021). Artificial intelligence anxiety of family physicians in Turkey. Proceedings of the National Academy of Sciences, 23(S2). https://doi.org/10.23751/pn.v23iS2.12003
8. Bonneau-Diesce, J., & Chan, A. (2022). Will artificial intelligence ever be a threat to humankind? Journal of Student Research, 11(2). https://doi.org/10.47611/jsrhs.v11i2.2511

9. Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257. https://doi.org/10.1017/jmo.2016.55
10. Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257. https://doi.org/10.1017/jmo.2016.55
11. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press.
12. Cooper, C. L., & Barling, J. (Eds.). (2008). The SAGE handbook of organizational behavior. SAGE Publications.
13. Cugurullo, F., & Acheampong, R. A. (2024). Fear of AI: An inquiry into the adoption of autonomous cars in spite of fear, and a theoretical framework for the study of artificial intelligence technology acceptance. AI & Society, 39(4), 1569–1584. https://doi.org/10.1007/s00146-022-01598-6
14. Çetiner, N., & Çetinkaya, F. Ö. (2024). Çalışanların yapay zekâ kaygısı ile motivasyon düzeyleri arasındaki ilişki: Turizm çalışanları üzerine bir araştırma. Alanya Akademik Bakış, 8(1), 159–173. https://doi.org/10.29023/alanyaakademik.1297394
15. Dawson, A. J., Stasa, H., Roche, M. A., Homer, C. S., & Duffield, C. (2014). Nursing churn and turnover in Australian hospitals: Nurses’ perceptions and suggestions for supportive strategies. BMC Nursing, 13, Article 11. https://doi.org/10.1186/1472-6955-13-11
16. De Bruin, G. P. (2006). The dimensionality of the general work stress scale: A hierarchical exploratory factor analysis. SA Journal of Industrial Psychology, 32(4), 68–75.
17. Dou, Y. (2023). Does the application of artificial intelligence technology affect employees' turnover intention? In Proceedings of the 2023 6th International Conference on Information Management and Management Science (pp. 283–287). https://doi.org/10.1145/3625469.3625488
18. Elliott, D., & Soifer, E. (2022). AI technologies, privacy, and security. Frontiers in Artificial Intelligence, 5, 826737. https://doi.org/10.3389/frai.2022.826737
19. Erkutlu, H., Ergün, E. E., Köseoğlu, İ., & Vurgun, T. (2023). Yapay zekâ ve örgütsel davranış. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, 13(3), 1403–1417. https://doi.org/10.30783/nevsosbilen.1246678
20. Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
21. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 Gebrekidan, A. Y., Enaro, E. Y., Azeze, G., Adella, G. A., Kassie, G. A., Haile, K. E., & Asgedom, Y. S. (2023). Turnover intention among healthcare workers in Ethiopia: A systematic review and meta-analysis. BMJ Open, 13(5), e067266. https://doi.org/10.1136/bmjopen-2022-067266
22. Gilbreath, B., & Karimi, L. (2012). Supervisor behavior and employee presenteeism. International Journal of Leadership Studies, 7(1), 114–131.
23. Güdük, Ö., Vural, A., & Dişiaçık, G. (2025). Investigating the effect of perceived empowerment on artificial intelligence anxiety levels in healthcare workers. Çalışma ve Toplum, 1(84), 285–310. https://doi.org/10.54752/ct.1624015
24. Hahs-Vaughn, D. L., & Lomax, R. (2020). An introduction to statistical concepts (4th ed.). Routledge. https://doi.org/10.4324/9781315624358
25. Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
26. Hayes, L. J., O’Brien-Pallas, L., Duffield, C., Shamian, J., Buchan, J., Hughes, F., ... & Stone, P. W. (2006). Nurse turnover: A literature review. International Journal of Nursing Studies, 43(2), 237–263. https://doi.org/10.1016/j.ijnurstu.2005.02.007
27. Hopcan, S., Türkmen, G., & Polat, E. (2024). Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Education and Information Technologies, 29(6), 7281–7301. https://doi.org/10.1007/s10639-023-12086-9
28. Ibrahim Hassan, A. H., Baquero, A., Salama, W. M., & Ahmed Khairy, H. (2024). Engaging hotel employees in the era of artificial intelligence: The interplay of artificial intelligence awareness, job insecurity, and technical self-efficacy. https://doi.org/10.33168/JLISS.2024.0530
29. Johnson, D. G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267–2270. https://doi.org/10.1002/asi.23867
30. Kang, J., Shin, H., & Kang, C. (2024). Hospitality labor leakage and dynamic turnover behaviors in the age of artificial intelligence and robotics. Journal of Hospitality and Tourism Technology. Advance online publication. https://doi.org/10.1108/JHTT-12-2023-0411
31. Karimian, G., Petelos, E., & Evers, S. M. (2022). The ethical issues of the application of artificial intelligence in healthcare: A systematic scoping review. AI and Ethics, 2(4), 539–551. https://doi.org/10.1007/s43681-021-00131-7
32. Kato, T., & Koizumi, M. (2023). Effects of artificial intelligence and robots on job satisfaction and turnover intention. In 2023 IEEE 6th International Conference on Knowledge Innovation and Invention (ICKII) (pp. 773–777). IEEE. https://doi.org/10.1109/ICKII58656.2023.10332717
33. Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2022). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2022.2151730
34. Kılıç, Y. E. (2023). Yapay zekâ farkındalığı ile işten ayrılma niyeti ve performans arasındaki ilişkide örgütsel destek ve rekabetçi psikolojik iklimin rolü (Master’s thesis, Aksaray Üniversitesi Sosyal Bilimler Enstitüsü).
35. Kim, B. J., & Lee, J. (2024). The mental health implications of artificial intelligence adoption: The crucial role of self-efficacy. Humanities and Social Sciences Communications, 11(1), 1–15. https://doi.org/10.1057/s41599-024-04018-w
36. Konnopka, A., & König, H. (2020). Economic burden of anxiety disorders: A systematic review and meta-analysis. Pharmacoeconomics, 38, 25–37. https://doi.org/10.1007/s40273-019-00849-7
37. Lemay, D. J., Basnet, R. B., & Doleck, T. (2020). Fearing the robot apocalypse: Correlates of AI anxiety.
38. Lestari, N. S., Rosman, D., & Millenia, E. (2023). The association between smart technology, artificial intelligence, robotics, and algorithms (STARA) awareness, job stress, job insecurity, and job satisfaction among hotel employees during COVID-19 pandemic. In E3S Web of Conferences (Vol. 388, p. 03021). EDP Sciences. https://doi.org/10.1051/e3sconf/202338803021
39. Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, 101410. https://doi.org/10.1016/j.techsoc.2020.101410
40. Lin, C. P., Tsai, Y. H., & Mahatma, F. (2017). Understanding turnover intention in cross-country business management. Personnel Review, 46(8), 1717–1737. https://doi.org/10.1108/PR-07-2016-0176
41. Manyika, J. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute, 150.
42. Mathieu, C., & Gilbreath, B. (2023). Measuring presenteeism from work stress: The job stress-related Presenteeism Scale. Journal of Occupational and Environmental Medicine, 65(3), 210–216. https://doi.org/10.1097/JOM.0000000000002753
43. Nam, T. (2019). Technology usage, expected job sustainability, and perceived job insecurity. Technological Forecasting and Social Change, 138, 155–165. https://doi.org/10.1016/j.techfore.2018.08.017
44. Russon, A. E., Josefowitz, N., & Edmonds, C. V. (1994). Making computer instruction accessible: Familiar analogies for female novices. Computers in Human Behavior, 10(2), 175–187. https://doi.org/10.1016/0747-5632(94)90001-9
45. Saha, D., Sinha, R., & Bhavsar, K. (2011). Understanding job stress among healthcare staff. Online Journal of Health and Allied Sciences, 10(1).
46. Scanlan, J. N., & Still, M. (2019). Relationships between burnout, turnover intention, job satisfaction, job demands and job resources for mental health personnel in an Australian mental health service. BMC Health Services Research, 19, Article 62. https://doi.org/10.1186/s12913-018-3841-z
47. Scherer, M. U. (2015). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harvard Journal of Law & Technology, 29, 353–400.
48. Selvi, S. (2017). İş yaşam kalitesinin işten ayrılma niyeti üzerine etkisi: Sağlık sektöründe bir uygulama (Master’s thesis, Avrasya Üniversitesi, Sağlık Bilimleri Enstitüsü).
49. Seoni, S., Jahmunah, V., Salvi, M., Barua, P. D., Molinari, F., & Acharya, U. R. (2023). Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023). Computers in Biology and Medicine, 107441. https://doi.org/10.1016/j.compbiomed.2023.107441
50. Sharma, K., Davidson, B. G. J., George, J. P., & Muttungal, P. V. (2024). Breeding distrust during artificial intelligence (AI) era: How technological advancements, job insecurity and job stress fuel organizational cynicism? Evidence-based HRM: A Global Forum for Empirical Scholarship. Advance online publication. https://doi.org/10.1108/EBHRM-05-2024-0159
51. Stănescu, D. F., & Romașcanu, M. C. (2024). The influence of AI anxiety and neuroticism in attitudes toward artificial intelligence. European Journal of Sustainable Development, 13(4), 191. https://doi.org/10.14207/ejsd.2024.v13n4p191
52. Teleş, M. (2021). Validity and reliability of the Turkish version of the General Work Stress Scale. Journal of Nursing Management, 29(4), 710–720. https://doi.org/10.1111/jonm.13211
53. Türkmen, S., Atay, L., & Türkmen, E. (2018). Destinasyon kişiliği, memnuniyet ve davranışsal niyetler arasındaki ilişkilerin incelenmesi: Çanakkale örneği. Yaşar Üniversitesi E-Dergisi, 13(49), 22–32. https://doi.org/10.19168/jyasar.330474
54. Uygungil-Erdogan, S., Şahin, Y., Sökmen-Alaca, A. İ., Oktaysoy, O., Altıntaş, M., & Topçuoğlu, V. (2025). Assessing the effect of artificial intelligence anxiety on turnover intention: The mediating role of quiet quitting in Turkish small and medium enterprises. Behavioral Sciences, 15(3), 249. https://doi.org/10.3390/bs15030249
55. Wang, Y. S. (2007). Development and validation of a mobile computer anxiety scale. British Journal of Educational Technology, 38(6), 990–1009. https://doi.org/10.1111/j.1467-8535.2006.00687.x
56. Wang, Y. Y., & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887
57. Wellcome Trust. (2020). The role of science in mental health: Insights from the Wellcome Global Monitor. Wellcome. https://cms.wellcome.org/sites/default/files/2021-10/wellcome-global-monitor-mental-health.pdf
58. Xu, G., Xue, M., & Zhao, J. (2023). The association between artificial intelligence awareness and employee depression: The mediating role of emotional exhaustion and the moderating role of perceived organizational support. International Journal of Environmental Research and Public Health, 20(6), 5147. https://doi.org/10.3390/ijerph20065147
59. Yanan, Li. (2023). Relationship between perceived threat of artificial intelligence and turnover intention in luxury hotels. Heliyon, 9(8), e18520. https://doi.org/10.1016/j.heliyon.2023.e18520
60. Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3), e12962. https://doi.org/10.1111/soc4.12962