Araştırma Makalesi
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The effects of healthcare workers’ technostress and change fatigue levels on their turnover intentions

Yıl 2025, Cilt: 6 Sayı: 3, 143 - 153
https://doi.org/10.51753/flsrt.1672644

Öz

This study aims to examine the effects of technostress and change fatigue levels on turnover intention among healthcare workers. The widespread use of digital technologies in healthcare has increased stress factors such as information overload, uncertainty, and constant connectivity. In this context, the predictive effects of the subdimensions of technostress (techno-overload, techno-invasion, and techno-uncertainty) and change fatigue on turnover intention were analysed. The research was conducted using a cross-sectional survey design with 162 healthcare workers employed at Akyazi State Hospital. Data were collected through a questionnaire, and statistical analyses, including correlation, multiple regression, and simple regression, were performed. According to the findings, techno-overload has a positive effect, while techno-invasion and techno-uncertainty have negative and statistically significant effects on turnover intention. The explanatory power of the model was 18.7%. Furthermore, a positive and significant relationship was found between change fatigue and turnover intention, with an explanatory power of 13.4%. In conclusion, technological stressors and ongoing organizational changes influence the turnover intentions of healthcare workers. Therefore, it is recommended that training and support programs be implemented to facilitate employees’ adaptation to digital systems and to manage change processes more effectively.

Etik Beyan

Ethical approval for this study was obtained from the Ethics Committee of Sakarya University of Applied Sciences (No. E-26428519-050.99-145523; 11 October 2024). Informed consent was obtained from all participants.

Kaynakça

  • Andrulli, R., & Gerards, R. (2023). How new ways of working during COVID-19 affect employee well-being via technostress, need for recovery, and work engagement. Computers in Human Behavior, 139, 107560.
  • Asad, M. M., Erum, D., Churi, P., & Moreno Guerrero, A. J. (2023). Effect of technostress on psychological well-being of post-graduate students: A perspective and correlational study of higher education management. International Journal of Information Management Data Insights, 3(1), 100149.
  • Baek, G., Lee, Y. J., & Lee, E. (2025). The impact of technostress, nursing informatics competency and knowledge-sharing behaviour on nursing work performance among tertiary hospital nurses. Journal of Advanced Nursing, 81(8), 4734-4745.
  • Bao, Y., Zhang, X., & Hua, M. (2024). Relating technostress and turnover intention: A three-wave study. Journal of Computer Information Systems. Advance online publication.
  • Bernerth, J. B., Walker, H. J., & Harris, S. G. (2011). Change fatigue: Development and initial validation of a new measure. Work & Stress, 25(4), 321-337.
  • Bicer, I., & Sarigul, S. S. (2025). Sağlık kurumlarında ekosentrik liderlik: Türkçe geçerlik ve güvenirlik çalışması. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(1), 72-88. Brooks, S., & Califf, C. (2017). Social media-induced technostress: Its impact on the job performance of IT professionals and the moderating role of job characteristics. Computer Networks, 114, 143-153.
  • Cao, S., Lin, J., Liang, Y., & Qin, Y. (2024). A concept analysis of change fatigue among nurses based on Walker and Avant’s method. Journal of Nursing Management. Advance online publication.
  • Demirci, B., & Seçilmiş, C. (2020). Turnover intention scale: Turkish adaptation, validity and reliability. Business & Management Studies: An International Journal, 8(1), 435-455.
  • Dhaouadi, A., & Chouikha, M. (2024). The adverse consequences of technostress on strain and turnover intentions: The mediating role of burnout. Annals of Management and Organization, 23(3), 551-570.
  • Duan, H., He, D., Zeng, Y., Ma, X., Li, Q., & Zhou, X. (2025). Organizational change fatigue among nurses and its impact on work engagement: A qualitative study. Applied Nursing Research, 152018.
  • Dyrbye, L. N., Shanafelt, T. D., Johnson, P. O., Johnson, L. A., Satele, D., & West, C. P. (2019). A cross-sectional study exploring the relationship between burnout, absenteeism, and job performance among American nurses. BMC Nursing, 18, 57.
  • Ekingen, S., & Yıldız, B. (2021). Değişim yorgunluğu ölçeğinin Türkçe uyarlaması: Geçerlik ve güvenirlik çalışması. Sağlık ve Hemşirelik Yönetimi Dergisi, 8(1), 33-42.
  • Fernemark, H., Karlsson, N., Skagerström, J., Seing, I., Karlsson, E., Brulin, E., & Nilsen, P. (2024). Psychosocial work environment in Swedish primary healthcare: Job satisfaction, turnover intention, social support, leadership climate and change fatigue. Human Resources for Health, 22, 70.
  • Galvin, J., Richards, G., & Smith, A. (2022). Digital communication overload, techno-invasion and mental health: Evidence from healthcare workers. Journal of Advanced Nursing, 78(10), 3271-3284.
  • Genc, E. (2020). COVID-19 and the rise of technostress in healthcare. Journal of Healthcare Management, 65(5), 356-362.
  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
  • Ilgaz, H., Çetin, B., & Sünbül, A. M. (2016). Teknoloji stres ölçeğinin Türkçeye uyarlanması: Geçerlik ve güvenirlik çalışması. Eğitim Teknolojisi Kuram ve Uygulama, 6(1), 17-33.
  • Kaltenegger, H. C., Marques, M. D., Becker, L., Rohleder, N., Nowak, D., Wright, B. J., & Weigl, M. (2024). Prospective associations of technostress at work, burnout symptoms, hair cortisol, and chronic low-grade inflammation. Brain, Behavior, and Immunit, 117, 320-329.
  • Keshavarz, H., Saeidnia, H. R., & Wang, T. (2025). Navigating technostress: a deep dive into health practitioners' technological challenges in hospital settings. BMC Health Services Res, 25(1), 18.
  • Ki, J., & Choi-Kwon, S. (2022). Health problems, turnover intention, and actual turnover among shift work female nurses: Analyzing data from a prospective longitudinal study. PLOS ONE, 17(7), e0270958.
  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press. Kopuz, K., Turgut, M., & Aydın, G. (2025). Technostress among nurses: boon or bane? The moderated mediation model. BMC nursing, 24(1), 1092.
  • Kumar, P. S. (2024). Technostress: A comprehensive literature review on antecedents and consequences. Technology in Society, 78, 102356.
  • La Torre, G., Esposito, A., Sciarra, I., & Chiappetta, M. (2019). Definition, symptoms and risk of technostress: A systematic review. International Journal of Environmental Research and Public Health, 16(21), 4021.
  • Lay, K. S. M., & Masingboon, K. (2025). Turnover prevalence and the relationship between transition shock and turnover intention among new nurses: A meta-analysis. International Journal of Nursing Studies Advances, 100390.
  • Li, L., & Wang, X. (2021). Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education. Cognition, Technology & Work, 23(2), 315-330.
  • Lopes, A., Martins, M., & Ferreira, P. (2024). Digital intensification, technostress and mental health among hospital staff during COVID-19. Journal of Health Psychology, 29(6), 1215-1229.
  • Lv, M., Zhai, J., Zhang, L., Wang, H., Li, B. H., Zhang, T., & Moreira, P. (2025). Change Fatigue Among Clinical Nurses and Related Factors: A Cross-sectional Study in Public Hospitals. Health Services Insights, 18, 11786329251318586.
  • Mafula, M., Mothiba, T., & Malema, R. (2025). Nurse turnover intention, cost and patient outcomes: A systematic review. International Journal of Nursing Studies Advances, 7, 100146.
  • Muir, K. J., Wanchek, T. N., Lobo, J. M., Keim-Malpass, J., & Malpass, J. (2022). Evaluating the costs of nurse burnout-attributed turnover: A Markov modelling approach. Journal of Patient Safety, 18(4), 351- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
  • Poku, C. A., Bayuo, J., Agyare, V. A., Sarkodie, N. K., & Bam, V. (2025). Work engagement, resilience and turnover intentions among nurses: A mediation analysis. BMC Health Services Research, 25, 71.
  • Poku, C. A., Donkor, E., & Naab, F. (2022). Impacts of nursing work environment on turnover intentions: The mediating role of burnout in Ghana. Nursing Research and Practice, 2022, 1-10.
  • Polat, Ş., & Yeşil, A. (2025). Relationships between nurses' personal and professional characteristics and career decision regret, occupational stress and turnover intention: A descriptive cross-sectional study. International Journal of Nursing Practice, 31(4), e70040.
  • Sarigul, S. S., & Ugurluoglu, O. (2023). Change fatigue and perceived organizational culture, burnout, turnover intention, and commitment in nurses. Research and Theory for Nursing Practice, 37(3), 311-332.
  • Siddiqi, K. O., & Rahman, M. H. (2025). Effect of perceived supervisor support, perceived co-worker support, and technostress on turnover intention among nurses in Bangladesh. Journal of the Knowledge Economy. Advance online publication.
  • Shin, J., & Shin, H. (2024). Effects of technostress on psychological contract violation and organizational change resistance. Behavioral Sciences, 14(9), 768.
  • Stemmer, R., Bassi, E., Ezra, S., Harvey, C., Jojo, N., & Meyer, G. (2022). Unfinished nursing care and the impact on nurse outcomes: A systematic review. Journal of Advanced Nursing, 78(8), 2290-2303.
  • Suh, A., & Lee, J. (2017). Understanding teleworkers’ technostress and its influence on job satisfaction. Internet Research, 27(1), 140-159.
  • Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301-328.
  • Wang, Q., & Yao, N. (2025). Understanding the impact of technology usage at work on academics’ psychological well-being: A perspective of technostress. BMC Psychology, 13, 130.
  • Wayne, S. J., Shore, L. M., & Liden, R. C. (1997). Perceived organizational support and leader–member exchange: A social exchange perspective. Academy of Management Journal, 40(1), 82-111.
  • West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56-75). Sage.
  • Yu, M., Ji, J., Kan, D., Gu, D., Yin, X., Cai, S., … Jiang, L. (2025). Analysis of the current situation and influencing factors of change fatigue among clinical nurses. Frontiers in Public Health, 13, 1566534.
  • Zheng, J., Feng, S., Feng, Y., Wang, L., Gao, R., & Xue, B. (2024). Relationship between burnout and turnover intention among nurses: A network analysis. BMC Nursing, 23, 2624.

The effects of healthcare workers’ technostress and change fatigue levels on their turnover intentions

Yıl 2025, Cilt: 6 Sayı: 3, 143 - 153
https://doi.org/10.51753/flsrt.1672644

Öz

This study aims to examine the effects of technostress and change fatigue levels on turnover intention among healthcare workers. The widespread use of digital technologies in healthcare has increased stress factors such as information overload, uncertainty, and constant connectivity. In this context, the predictive effects of the subdimensions of technostress (techno-overload, techno-invasion, and techno-uncertainty) and change fatigue on turnover intention were analysed. The research was conducted using a cross-sectional survey design with 162 healthcare workers employed at Akyazi State Hospital. Data were collected through a questionnaire, and statistical analyses, including correlation, multiple regression, and simple regression, were performed. According to the findings, techno-overload has a positive effect, while techno-invasion and techno-uncertainty have negative and statistically significant effects on turnover intention. The explanatory power of the model was 18.7%. Furthermore, a positive and significant relationship was found between change fatigue and turnover intention, with an explanatory power of 13.4%. In conclusion, technological stressors and ongoing organizational changes influence the turnover intentions of healthcare workers. Therefore, it is recommended that training and support programs be implemented to facilitate employees’ adaptation to digital systems and to manage change processes more effectively.

Etik Beyan

Ethical approval for this study was obtained from the Ethics Committee of Sakarya University of Applied Sciences (No. E-26428519-050.99-145523; 11 October 2024). Informed consent was obtained from all participants.

Kaynakça

  • Andrulli, R., & Gerards, R. (2023). How new ways of working during COVID-19 affect employee well-being via technostress, need for recovery, and work engagement. Computers in Human Behavior, 139, 107560.
  • Asad, M. M., Erum, D., Churi, P., & Moreno Guerrero, A. J. (2023). Effect of technostress on psychological well-being of post-graduate students: A perspective and correlational study of higher education management. International Journal of Information Management Data Insights, 3(1), 100149.
  • Baek, G., Lee, Y. J., & Lee, E. (2025). The impact of technostress, nursing informatics competency and knowledge-sharing behaviour on nursing work performance among tertiary hospital nurses. Journal of Advanced Nursing, 81(8), 4734-4745.
  • Bao, Y., Zhang, X., & Hua, M. (2024). Relating technostress and turnover intention: A three-wave study. Journal of Computer Information Systems. Advance online publication.
  • Bernerth, J. B., Walker, H. J., & Harris, S. G. (2011). Change fatigue: Development and initial validation of a new measure. Work & Stress, 25(4), 321-337.
  • Bicer, I., & Sarigul, S. S. (2025). Sağlık kurumlarında ekosentrik liderlik: Türkçe geçerlik ve güvenirlik çalışması. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(1), 72-88. Brooks, S., & Califf, C. (2017). Social media-induced technostress: Its impact on the job performance of IT professionals and the moderating role of job characteristics. Computer Networks, 114, 143-153.
  • Cao, S., Lin, J., Liang, Y., & Qin, Y. (2024). A concept analysis of change fatigue among nurses based on Walker and Avant’s method. Journal of Nursing Management. Advance online publication.
  • Demirci, B., & Seçilmiş, C. (2020). Turnover intention scale: Turkish adaptation, validity and reliability. Business & Management Studies: An International Journal, 8(1), 435-455.
  • Dhaouadi, A., & Chouikha, M. (2024). The adverse consequences of technostress on strain and turnover intentions: The mediating role of burnout. Annals of Management and Organization, 23(3), 551-570.
  • Duan, H., He, D., Zeng, Y., Ma, X., Li, Q., & Zhou, X. (2025). Organizational change fatigue among nurses and its impact on work engagement: A qualitative study. Applied Nursing Research, 152018.
  • Dyrbye, L. N., Shanafelt, T. D., Johnson, P. O., Johnson, L. A., Satele, D., & West, C. P. (2019). A cross-sectional study exploring the relationship between burnout, absenteeism, and job performance among American nurses. BMC Nursing, 18, 57.
  • Ekingen, S., & Yıldız, B. (2021). Değişim yorgunluğu ölçeğinin Türkçe uyarlaması: Geçerlik ve güvenirlik çalışması. Sağlık ve Hemşirelik Yönetimi Dergisi, 8(1), 33-42.
  • Fernemark, H., Karlsson, N., Skagerström, J., Seing, I., Karlsson, E., Brulin, E., & Nilsen, P. (2024). Psychosocial work environment in Swedish primary healthcare: Job satisfaction, turnover intention, social support, leadership climate and change fatigue. Human Resources for Health, 22, 70.
  • Galvin, J., Richards, G., & Smith, A. (2022). Digital communication overload, techno-invasion and mental health: Evidence from healthcare workers. Journal of Advanced Nursing, 78(10), 3271-3284.
  • Genc, E. (2020). COVID-19 and the rise of technostress in healthcare. Journal of Healthcare Management, 65(5), 356-362.
  • George, D., & Mallery, P. (2010). SPSS for Windows step by step: A simple guide and reference (10th ed.). Pearson.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
  • Ilgaz, H., Çetin, B., & Sünbül, A. M. (2016). Teknoloji stres ölçeğinin Türkçeye uyarlanması: Geçerlik ve güvenirlik çalışması. Eğitim Teknolojisi Kuram ve Uygulama, 6(1), 17-33.
  • Kaltenegger, H. C., Marques, M. D., Becker, L., Rohleder, N., Nowak, D., Wright, B. J., & Weigl, M. (2024). Prospective associations of technostress at work, burnout symptoms, hair cortisol, and chronic low-grade inflammation. Brain, Behavior, and Immunit, 117, 320-329.
  • Keshavarz, H., Saeidnia, H. R., & Wang, T. (2025). Navigating technostress: a deep dive into health practitioners' technological challenges in hospital settings. BMC Health Services Res, 25(1), 18.
  • Ki, J., & Choi-Kwon, S. (2022). Health problems, turnover intention, and actual turnover among shift work female nurses: Analyzing data from a prospective longitudinal study. PLOS ONE, 17(7), e0270958.
  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press. Kopuz, K., Turgut, M., & Aydın, G. (2025). Technostress among nurses: boon or bane? The moderated mediation model. BMC nursing, 24(1), 1092.
  • Kumar, P. S. (2024). Technostress: A comprehensive literature review on antecedents and consequences. Technology in Society, 78, 102356.
  • La Torre, G., Esposito, A., Sciarra, I., & Chiappetta, M. (2019). Definition, symptoms and risk of technostress: A systematic review. International Journal of Environmental Research and Public Health, 16(21), 4021.
  • Lay, K. S. M., & Masingboon, K. (2025). Turnover prevalence and the relationship between transition shock and turnover intention among new nurses: A meta-analysis. International Journal of Nursing Studies Advances, 100390.
  • Li, L., & Wang, X. (2021). Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education. Cognition, Technology & Work, 23(2), 315-330.
  • Lopes, A., Martins, M., & Ferreira, P. (2024). Digital intensification, technostress and mental health among hospital staff during COVID-19. Journal of Health Psychology, 29(6), 1215-1229.
  • Lv, M., Zhai, J., Zhang, L., Wang, H., Li, B. H., Zhang, T., & Moreira, P. (2025). Change Fatigue Among Clinical Nurses and Related Factors: A Cross-sectional Study in Public Hospitals. Health Services Insights, 18, 11786329251318586.
  • Mafula, M., Mothiba, T., & Malema, R. (2025). Nurse turnover intention, cost and patient outcomes: A systematic review. International Journal of Nursing Studies Advances, 7, 100146.
  • Muir, K. J., Wanchek, T. N., Lobo, J. M., Keim-Malpass, J., & Malpass, J. (2022). Evaluating the costs of nurse burnout-attributed turnover: A Markov modelling approach. Journal of Patient Safety, 18(4), 351- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
  • Poku, C. A., Bayuo, J., Agyare, V. A., Sarkodie, N. K., & Bam, V. (2025). Work engagement, resilience and turnover intentions among nurses: A mediation analysis. BMC Health Services Research, 25, 71.
  • Poku, C. A., Donkor, E., & Naab, F. (2022). Impacts of nursing work environment on turnover intentions: The mediating role of burnout in Ghana. Nursing Research and Practice, 2022, 1-10.
  • Polat, Ş., & Yeşil, A. (2025). Relationships between nurses' personal and professional characteristics and career decision regret, occupational stress and turnover intention: A descriptive cross-sectional study. International Journal of Nursing Practice, 31(4), e70040.
  • Sarigul, S. S., & Ugurluoglu, O. (2023). Change fatigue and perceived organizational culture, burnout, turnover intention, and commitment in nurses. Research and Theory for Nursing Practice, 37(3), 311-332.
  • Siddiqi, K. O., & Rahman, M. H. (2025). Effect of perceived supervisor support, perceived co-worker support, and technostress on turnover intention among nurses in Bangladesh. Journal of the Knowledge Economy. Advance online publication.
  • Shin, J., & Shin, H. (2024). Effects of technostress on psychological contract violation and organizational change resistance. Behavioral Sciences, 14(9), 768.
  • Stemmer, R., Bassi, E., Ezra, S., Harvey, C., Jojo, N., & Meyer, G. (2022). Unfinished nursing care and the impact on nurse outcomes: A systematic review. Journal of Advanced Nursing, 78(8), 2290-2303.
  • Suh, A., & Lee, J. (2017). Understanding teleworkers’ technostress and its influence on job satisfaction. Internet Research, 27(1), 140-159.
  • Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301-328.
  • Wang, Q., & Yao, N. (2025). Understanding the impact of technology usage at work on academics’ psychological well-being: A perspective of technostress. BMC Psychology, 13, 130.
  • Wayne, S. J., Shore, L. M., & Liden, R. C. (1997). Perceived organizational support and leader–member exchange: A social exchange perspective. Academy of Management Journal, 40(1), 82-111.
  • West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56-75). Sage.
  • Yu, M., Ji, J., Kan, D., Gu, D., Yin, X., Cai, S., … Jiang, L. (2025). Analysis of the current situation and influencing factors of change fatigue among clinical nurses. Frontiers in Public Health, 13, 1566534.
  • Zheng, J., Feng, S., Feng, Y., Wang, L., Gao, R., & Xue, B. (2024). Relationship between burnout and turnover intention among nurses: A network analysis. BMC Nursing, 23, 2624.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistik (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Halil Demir 0000-0001-9374-9739

Gülsün Erigüç 0000-0001-5186-9345

Yayımlanma Tarihi 13 Kasım 2025
Gönderilme Tarihi 9 Nisan 2025
Kabul Tarihi 22 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 3

Kaynak Göster

APA Demir, H., & Erigüç, G. (t.y.). The effects of healthcare workers’ technostress and change fatigue levels on their turnover intentions. Frontiers in Life Sciences and Related Technologies, 6(3), 143-153. https://doi.org/10.51753/flsrt.1672644


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