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Journals / Journal of Innovative Healthcare Practices / 2026 / Cilt: 7 Sayı: 2

The Mediating Role of Workload in the Effect of Digital Fatigue on Employee Performance

Dijital Yorgunluğun Çalışan Performansı Üzerindeki Etkisinde İş Yükünün Aracı Rolü

Hasan Saltuk Durak(DÜZCE ÜNİVERSİTESİ, SOSYAL BİLİMLER MESLEK YÜKSEKOKULU)0000-0003-1994-0011
Pages
90–101
Publication Date
2026-08-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Furthermore, it was found that digital fatigue increased workload (β = 0.37; p < 0.001), and workload decreased performance (β = −0.37; p < 0.001).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to examine the effect of digital fatigue on employee performance in healthcare and to reveal the mediating role of workload in this relationship.
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Key Findings & Data:The rapid spread of digitalization in the healthcare sector creates new cognitive and psychological demands on employees; this has significant consequences in terms of performance outcomes.
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Conclusion & Impact:Furthermore, it was found that digital fatigue increased workload (β = 0.37; p < 0.001), and workload decreased performance (β = −0.37; p < 0.001).
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Dijital yorgunluk · iş yükü · çalışan performansı

Abstract

The aim of this study is to examine the effect of digital fatigue on employee performance in healthcare and to reveal the mediating role of workload in this relationship. The rapid spread of digitalization in the healthcare sector creates new cognitive and psychological demands on employees; this has significant consequences in terms of performance outcomes. Therefore, this study aims to contribute to the literature by addressing the relationships between digital fatigue, workload, and performance within a holistic framework. A quantitative method was adopted in the research, and data were collected through surveys from 630 healthcare workers working in the Western Black Sea Region. The validity and reliability of the measurement model were tested with PLS-SEM analysis; hypotheses were evaluated through structural modeling. The findings show that digital fatigue has a negative and significant effect on employee performance (β = −0.41; p < 0.001). It was determined that all sub-dimensions of digital fatigue negatively affect performance (β values ​​ranged from −0.25 to −0.34). Furthermore, it was found that digital fatigue increased workload (β = 0.37; p < 0.001), and workload decreased performance (β = −0.37; p < 0.001). Mediation analysis revealed that workload played a partial mediating role in the relationship between digital fatigue and performance (indirect effect β = −0.14; p < 0.001). This study contributes to the literature by presenting a holistic model explaining healthcare worker performance by considering digital fatigue and workload together. In addition, the findings offer important practical implications for healthcare managers regarding improving employee performance through managing digital demands and balancing workload.

Özet

Bu çalışmanın amacı, sağlık hizmetlerinde dijital yorgunluğun çalışan performansı üzerindeki etkisini incelemek ve bu ilişkide iş yükünün aracı rolünü ortaya koymaktır. Dijitalleşmenin sağlık sektöründe hızla yaygınlaşması, çalışanlar üzerinde yeni bilişsel ve psikolojik talepler oluşturmakta; bu durum performans çıktıları açısından önemli sonuçlar doğurmaktadır. Bu nedenle çalışma, dijital yorgunluk, iş yükü ve performans arasındaki ilişkileri bütüncül bir çerçevede ele alarak literatüre katkı sağlamayı hedeflemektedir. Araştırmada nicel yöntem benimsenmiş ve veriler Batı Karadeniz Bölgesi’nde görev yapan 630 sağlık çalışanından anket yoluyla toplanmıştır. Ölçüm modelinin geçerlilik ve güvenirliği PLS-SEM analizi ile test edilmiş; yapısal model aracılığıyla hipotezler değerlendirilmiştir. Elde edilen bulgular, dijital yorgunluğun çalışan performansı üzerinde negatif ve anlamlı bir etkiye sahip olduğunu göstermektedir (β = −0,41; p < 0,001). Dijital yorgunluğun tüm alt boyutlarının performansı olumsuz etkilediği belirlenmiştir (β değerleri −0,25 ile −0,34 arasında). Ayrıca dijital yorgunluğun iş yükünü artırdığı (β = 0,37; p < 0,001), iş yükünün ise performansı düşürdüğü (β = −0,37; p < 0,001) tespit edilmiştir. Aracılık analizi, iş yükünün dijital yorgunluk ile performans arasındaki ilişkide kısmi aracı rol oynadığını ortaya koymaktadır (dolaylı etki β = −0,14; p < 0,001). Bu çalışma, dijital yorgunluk ve iş yükünü birlikte ele alarak sağlık çalışan performansını açıklayan bütüncül bir model sunması bakımından literatüre katkı sağlamaktadır. Ayrıca bulgular, sağlık yöneticilerine dijital taleplerin yönetimi ve iş yükünün dengelenmesi yoluyla çalışan performansının artırılmasına yönelik önemli uygulama çıkarımları sunmaktadır.

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Cite This Article

Durak, H. S. (2026). The Mediating Role of Workload in the Effect of Digital Fatigue on Employee Performance. Journal of Innovative Healthcare Practices, Cilt: 7 Sayı: 2, 90–101. https://doi.org/10.58770/joinihp.1941947

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References (35)

  1. Achat, H. M., Mittal, R., Stubbs, J. M., Gilroy, N., Schindeler, S. K., Shaban, R. Z., & Solano, T. (2022). Managing COVID-19 in an Australian designated isolation facility: Implications for current and future healthcare crises based on in-depth interviews. PloS one, 17(12), e0278479. https://doi.org/10.1371/journal.pone.0278479
  2. Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2010). Research commentary—The digital transformation of healthcare: Current status and the road ahead. Information Systems Research, 21(4), 796–809. https://doi.org/10.1287/isre.1100.0327
  3. Alobayli, F., O'Connor, S., Holloway, A., & Cresswell, K. (2023). Electronic health record stress and burnout among clinicians in hospital settings: A systematic review. Digital Health, 9, 20552076231220241. https://doi.org/10.1177/20552076231220241
  4. Aycan, Z., & Eskin, M. (2005). Childcare, Spousal, and Organizational Support in Predicting WorkFamily Conflict for Females and Males in Dual-Earner Families with Preschool Children. Sex Roles, 53(7), 453-471.
  5. Bakker, A. B., & Demerouti, E. (2017). Job demands–resources theory. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056
  6. Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123–1131. https://doi.org/10.1377/hlthaff.2014.0041
  7. Beasley, J. W., Wetterneck, T. B., Temte, J., Lapin, J. A., Smith, P., Rivera-Rodriguez, A. J., & Karsh, B. T. (2011). Information chaos in primary care: Implications for physician performance and patient safety. Journal of the American Board of Family Medicine, 24(6), 745–751. https://doi.org/10.3122/jabfm.2011.06.100255
  8. Bullock, A., Colvin, A. D., & Jackson, M. S. (2022). Zoom fatigue in the age of COVID- 19. Journal of Social Work in the Global Community, 7(1), 1- 15. https://doi.org/10.5590/jswgc.2022.07.1.01
  9. Campanella, P., Lovato, E., Marone, C., vd. (2016). The impact of electronic health records on healthcare quality: A systematic review and meta-analysis. European Journal of Public Health, 26(1), 60–64. https://doi.org/10.1093/eurpub/ckv122
  10. Demir, Y., Dağ, E., Nal, B., Nal, M., & Bektaş, G. (2025). Determinants of nurses’ job performance: the interaction of perceived occupational stress, psychological safety, and digital fatigue. BMC nursing, 24(1), 1443.
  11. Dey, P., & Bhattacharjee, S. (2025). İş Stresinin Çalışan Sağlığı ve Performansı Üzerindeki Etkisi: Assam'ın Hailakandi Bölgesindeki Özel Bankalardan Elde Edilen Kanıtlar. FinTech ve Sürdürülebilir İnovasyon, 1-6. https://doi.org/10.47852/bonviewFSI52026057
  12. Duxbury, L., Higgins, C.A., & Ivey, R. (2003). Work-life Conflict in Canada in the New Millennium - a Status Report. https://api.semanticscholar.org/CorpusID:155062257
  13. Dyrbye, L. N., Shanafelt, T. D., Sinsky, C. A., vd. (2020). Burnout among health care professionals: A call to explore and address this underrecognized threat to safe, high-quality care. NAM Perspectives. https://doi.org/10.31478/202007b
  14. Fernemark, H., Skagerström, J., Seing, I., vd. (2022). Working conditions in primary health care during the COVID-19 pandemic: A qualitative interview study with physicians in Sweden. BMJ Open, 12, e055035. https://doi.org/10.1136/bmjopen-2021-055035
  15. 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 Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.
  16. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  17. Hill, N. S., Axtell, C., Raghuram, S., & Nurmi, N. (2022). Unpacking virtual work's dual effects on employee well-being: An integrative review and future research agenda. Journal of Management, 48(8), 2279-2324. https://doi.org/10.1177/01492063221131535
  18. Hilty, D. M., Armstrong, C. M., Smout, S. A., Crawford, A., Maheu, M. M., Drude, K. P., Chan, S., Yellowlees, P., & Krupinski, E. (2022). Provider technology, fatigue and well-being: A scoping review. Journal of Medical Internet Research, 24, e34451. https://doi.org/10.2196/34451
  19. Jeilani, A., & Hussein, A. (2025). Impact of digital health technologies adoption on healthcare workers’ performance and workload: Perspective with DOI and TOE models. BMC Health Services Research, 25, Article 271. https://doi.org/10.1186/s12913-025-12414-4
  20. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
  21. Kruse, C. S., Krowski, N., Rodriguez, B., vd. (2018). Telehealth and patient satisfaction: A systematic review and narrative analysis. BMJ Open, 7(8), e016242. https://doi.org/10.1136/bmjopen-2017-016242
  22. Küçükaltan, B., Kazançoğlu, İ., Önbıçak, A. E., & Ercan, A. (2019). İş stresi, iş tatmini, örgütsel kıvanç ve işten ayrılma niyeti etkileşiminin iş performansıyla ilintisi. Yaşar Üniversitesi E-Dergisi, 14(55), 296-308. https://doi.org/10.19168/jyasar.631342
  23. Ning, L., Jia, H., Gao, S., Liu, M., Xu, J., Ge, S., Li, M., & Yu, X. (2023). The mediating role of job satisfaction and presenteeism on the relationship between job stress and turnover intention among primary health care workers. International Journal for Equity in Health, 22(1), 128. https://doi.org/10.1186/s12939-023-01971-x
  24. Rotenstein, L. S., Hendrix, N., Phillips, R. L., & Adler-Milstein, J. (2024). Team and electronic health record features and burnout among family physicians. JAMA Network Open, 7(11), e2442687. https://doi.org/10.1001/jamanetworkopen.2024.42687
  25. Sarraf, B., & Ghasempour, A. (2025). Impact of artificial intelligence on electronic health record-related burnouts among healthcare professionals: A systematic review. Frontiers in Public Health, 13, 1628831. https://doi.org/10.3389/fpubh.2025.1628831
  26. Sehitoglu, Y., & Zehir, C., (2010). The Analysis of Employee Performance in the Context of Employee Silence and Organizational Citizenship Behavior in Turkish Public Institutions. Amme Idaresi Dergisi, 43(2), 87-110.
  27. Shanafelt, T. D., Dyrbye, L. N., Sinsky, C., Hasan, O., Satele, D., Sloan, J., & West, C. P. (2016). Relationship between clerical burden and burnout among physicians. Mayo Clinic Proceedings, 91(7), 836–848. https://doi.org/10.1016/j.mayocp.2016.05.007
  28. Sohn, N., Rallis, G., Lad, N., & Gokcen, E. (2022). Patient safety issues from information overload in electronic medical records. Journal of Patient Safety, 18(6), e999–e1003. https://doi.org/10.1097/PTS.0000000000001002 Topol, E. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300- 7
  29. Tüfekçi Yaman, Ü., Yıldız Bağdoğan, S. ve Keser, A. (2023). Covid-19 sürecinde uzaktan çalışmada iş stresi, iş tatmini ve işten ayrılma niyeti. Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 14 (1), 163-184. https://doi.org/10.54558/jiss.1241552
  30. Tutar, H., & Mutlu, H. T. (2024). Dijital Yorgunluk Ölçeği (DİYÖ): Geçerlilik ve Güvenirlik Çalışması. İletişim Kuram ve Araştırma Dergisi, 67, 56-74. https://doi.org/10.47998/ikad.1426571
  31. Wu, Y., Wu, M., Wang, C., Lin, J., Liu, J., & Liu, S. (2024). Evaluating the prevalence of burnout among health care professionals related to electronic health record use: A systematic review and meta-analysis. JMIR Medical Informatics, 12, e54811. https://doi.org/10.2196/54811
  32. Yiğit, İ., & Özkan, F. (2023). Anxiety and work overload perception levels of primary healthcare professionals during the COVID-19 pandemic. Work, 76(3), 921–930. https://doi.org/10.3233/WOR-220495
  33. Young, G., Zavelina, L., & Hooper, V. (2008). Assessment of workload using NASA Task Load Index in perianesthesia nursing. Journal of perianesthesia nursing: official journal of the American Society of PeriAnesthesia Nurses, 23(2), 102–110. https://doi.org/10.1016/j.jopan.2008.01.008
  34. Dey, P., & Bhattacharjee, S. (2025). İş Stresinin Çalışan Sağlığı ve Performansı Üzerindeki Etkisi: Assam'ın Hailakandi Bölgesindeki Özel Bankalardan Elde Edilen Kanıtlar. FinTech ve Sürdürülebilir İnovasyon, 1-6. https://doi.org/10.47852/bonviewFSI52026057
  35. Sehitoglu, Y., & Zehir, C., (2010). The Analysis of Employee Performance in the Context of Employee Silence and Organizational Citizenship Behavior in Turkish Public Institutions. Amme Idaresi Dergisi, 43(2), 87-110.