SavedAdd your journal

Journals / Ege Üniversitesi Hemşirelik Fakültesi Dergisi / 2026 / Cilt: 40 Sayı: 1

Nurses' Robot Use Self-Efficacy: Mediation Effect in The Relationship Between Robot Anxiety and Preference of Automation Levels

Hemşirelerin Robot Kullanımına Dair Öz Yeterliği: Robot Kaygısı ve Otomasyon Seviyesi Tercihleri İlişkisinde Aracılık Etkisi

Serkan Erebak(TÜRKİYE SANAYİ SEVK ve İDARE ENSTİTÜSÜ)0000-0002-3777-7249
Necla Kasımoğlu(ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ)0000-0001-9957-0959
Pages
47–56
Publication Date
2024-04-26
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

Conclusion: In order to achieve effective nurse-robot cooperation, identifying robot use self-efficacy during employee selection and training of current employees may ease the adoption process of this technology.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objective: Healthcare organizations may develop their employees’ competencies to adapt to robot technologies by identifying potential challenges beforehand.
📊
Key Findings & Data:The psychological characteristics of the employees are among these challenges.
💡
Conclusion & Impact:Conclusion: In order to achieve effective nurse-robot cooperation, identifying robot use self-efficacy during employee selection and training of current employees may ease the adoption process of this technology.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Otomasyon Seviyesi · Sağlık Hizmeti · Hemşire

Abstract

Objective: Healthcare organizations may develop their employees’ competencies to adapt to robot technologies by identifying potential challenges beforehand. The psychological characteristics of the employees are among these challenges. In the current study, we focused on the influence of nurses' robot anxiety on their preferred level of automation and also the role of robot use self-efficacy between these two variables. Methods: Nurses working in a hospital answered paper-based surveys. Results: The analysis showed that robot use self-efficacy had a full mediation effect between their self-efficacy and preference of automation levels. Conclusion: In order to achieve effective nurse-robot cooperation, identifying robot use self-efficacy during employee selection and training of current employees may ease the adoption process of this technology.

Özet

Amaç: Sağlık kuruluşlarında her geçen yıl robotik teknolojilerin kullanımı gittikçe artmaktadır. Bu süreçte kuruluşlar çeşitli problemlerle karşılaşabilirler. Sağlık kuruluşları, çalışanlarının robot teknolojilerine uyum sağlama yetkinliklerini geliştirerek bu konuda karşılaşabilecekleri olası problemlere dair önlem alabilirler. Bu çalışmada, hemşirelerin robot kaygısının tercih ettikleri otomasyon seviyesi üzerindeki etkisine ve ayrıca robot kullanımı öz yeterliğinin bu iki değişken arasındaki rolüne odaklanılmıştır. Yöntem: Bir hastanede çalışan hemşireler kendilerine verilen anketleri yanıtlamıştır. Bulgular: Analiz sonuçları, robot kullanımı öz-yeterliğinin, öz-yeterlilik ile otomasyon seviyelerinin tercihi arasında tam aracılık etkisine sahip olduğunu göstermiştir. Sonuç: Etkili hemşire-robot işbirliğinin sağlanabilmesi için çalışan seçimi ve mevcut çalışanların eğitimleri sırasında robot kullanım öz yeterliklerinin belirlenmesi bu teknolojinin benimsenme sürecini kolaylaştırabilir.

🔖

Cite This Article

Erebak, S., Kasımoğlu, N. (2024). Nurses' Robot Use Self-Efficacy: Mediation Effect in The Relationship Between Robot Anxiety and Preference of Automation Levels. Ege Üniversitesi Hemşirelik Fakültesi Dergisi, Cilt: 40 Sayı: 1, 47–56. https://doi.org/10.53490/egehemsire.1084354

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (52)

  1. Achim, N., & Kassim, A. A. (2015). Computer Usage: The Impact of Computer Anxiety and Computer Self-efficacy. Procedia - Social and Behavioral Sciences, 172, 701–708. https://doi.org/10.1016/j.sbspro.2015.01.422
  2. Alaiad, A., & Zhou, L. (2014). The determinants of home healthcare robots adoption: An empirical investigation. International Journal of Medical Informatics, 83(11), 825-840. https://doi.org/10.1016/j.ijmedinf.2014.07.003
  3. Bandura, A. (2006). Guide for constructing self-efficacy scales. Self-Efficacy Beliefs of Adolescents, 5(1), 307-337.
  4. Bandura, A. (2010). Self‐efficacy. In The Corsini Encyclopedia of Psychology (eds I. B. Weiner and W. E. Craighead). doi:10.1002/9780470479216.corpsy0836
  5. Bartneck, C., Suzuki, T., Kanda, T., & Nomura, T. (2007). The influence of people’s culture and prior experiences with Aibo on their attitude towards robots. Ai & Society, 21(1-2), 217-230. https://doi.org/10.1007/s00146-006-0052-7
  6. Beedholm, K., Frederiksen, K., Frederiksen, A. M. S., & Lomborg, K. (2015). Attitudes to a robot bathtub in Danish elder care: A hermeneutic interview study. Nursing & Health Sciences, 17(3), 280-286. https://doi.org/10.1111/nhs.12184
  7. Broadbent, E., Tamagawa, R., Patience, A., Knock, B., Kerse, N., Day, K., & MacDonald, B. A. (2012). Attitudes towards health‐care robots in a retirement village. Australasian Journal on Ageing, 31(2), 115-120. https://doi.org/10.1111/j.1741-6612.2011.00551.x
  8. Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021). Predicted influences of artificial intelligence on nursing education: Scoping review. JMIR Nursing, 4(1), e23933.
  9. Chen, A. I., Balter, M. L., Maguire, T. J., & Yarmush, M. L. (2015). Real-time needle steering in response to rolling vein deformation by a 9-DOF image-guided autonomous venipuncture robot. Paper presented at the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2015.7353736
  10. Coeckelbergh, M. (2011). Human development or human enhancement? A methodological reflection on capabilities and the evaluation of information technologies. Ethics and Information Technology, 13(2), 81-92. https://doi.org/10.1007/s10676-010-9231-9
  11. de Graaf, M. M., & Allouch, S. B. (2013). The relation between people's attitude and anxiety towards robots in human-robot interaction. Paper presented at the 2013 IEEE RO-MAN. https://doi.org/10.1109/roman.2013.6628419
  12. Erebak, S., & Turgut, T. (2019). Caregivers’ attitudes toward potential robot coworkers in elder care. Cognition, Technology & Work, 21(2), 327–336. https://doi.org/10.1007/s10111-018-0512-0
  13. Fang, B., Mei, G., Yuan, X., Wang, L., Wang, Z., & Wang, J. (2021). Visual SLAM for robot navigation in healthcare facility. Pattern Recognition, 107822.
  14. Frazier, R. M., Carter-Templeton, H., Wyatt, T. H., & Wu, L. (2019). Current Trends in Robotics in Nursing Patents—A Glimpse Into Emerging Innovations. CIN: Computers, Informatics, Nursing, 37(6), 290-297.
  15. Hasan, B. (2006). Delineating the effects of general and system-specific computer self-efficacy beliefs on IS acceptance. Information & Management, 43(5), 565-571. https://doi.org/10.1016/j.im.2005.11.005
  16. Hayes, A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling [White paper]. Retrieved from http://www.afhayes.com/public/process2012.pdf
  17. Hilburn, B. (2017). Dynamic decision aiding: the impact of adaptive automation on mental workload. Engineering Psychology Cognitive Ergonomics: Volume 1: Transportation Systems. https://doi.org/10.4324/9781315094496-19
  18. Hsu, M.-H., & Chiu, C.-M. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems, 38(3), 369-381. https://doi.org/10.1016/j.dss.2003.08.001
  19. Hu, J., Edsinger, A., Lim, Y.-J., Donaldson, N., Solano, M., Solochek, A., & Marchessault, R. (2011). An advanced medical robotic system augmenting healthcare capabilities-robotic nursing assistant. Paper presented at the 2011 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2011.5980213
  20. Izard, C. E. (2013). Human emotions. Springer Science & Business Media.
  21. Jenkins, S., & Draper, H. (2015). Care, monitoring, and companionship: Views on care robots from older people and their carers. International Journal of Social Robotics, 7(5), 673-683. https://doi.org/10.1007/s12369-015-0322-y
  22. Kanade, P., Akhtar, M., & David, F. (2021). Remote Monitoring Technology for COVID-19 Patients. European Journal of Electrical Engineering and Computer Science, 5(1), 44-47.
  23. Koceski, S., & Koceska, N. (2016). Evaluation of an assistive telepresence robot for elderly healthcare. Journal of Medical Systems, 40(5), 121. https://doi.org/10.1007/s10916-016-0481-x
  24. Lee, H., Piao, M., Lee, J., Byun, A., & Kim, J. (2020). The purpose of bedside robots: Exploring the needs of inpatients and healthcare professionals. CIN: Computers, Informatics, Nursing, 38(1), 8-17.
  25. Ma, Q., & Liu, L. (2005). The role of Internet self-efficacy in the acceptance of web-based electronic medical records. Journal of Organizational and End User Computing, 17(1), 38-57. https://doi.org/10.4018/9781605660509.ch115
  26. Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60.
  27. Marakas, G. M., Yi, M. Y., & Johnson, R. D. (1998). The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information Systems Research, 9(2), 126-163. https://doi.org/10.1287/isre.9.2.126
  28. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. Cambridge, MA, US: The MIT Press
  29. Nomura, T., Kanda, T., Yamada, S., & Suzuki, T. (2011). Exploring influences of robot anxiety into HRI. Paper presented at the 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI). https://doi.org/10.1145/1957656.1957737
  30. Nomura, T., Suzuki, T., Kanda, T., & Kato, K. (2006). Measurement of negative attitudes toward robots. Interaction Studies, 7(3), 437-454.
  31. Olaronke, I., Oluwaseun, O., & Rhoda, I. (2017). State Of The Art: A Study of Human-Robot Interaction in Healthcare. International Journal of Information Engineering and Electronic Business, 9(3), 43-55. https://doi.org/10.5815/ijieeb.2017.03.06
  32. Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans, 30(3), 286-297.
  33. Pérez-Vidal, C., Carpintero, E., Garcia-Aracil, N., Sabater-Navarro, J., Azorín, J. M., Candela, A.,. Systems, A. (2012). Steps in the development of a robotic scrub nurse. Robotics and Autonomous Systems, 60(6), 901–911. https://doi.org/10.1016/j.robot.2012.01.005
  34. Pino, M., Boulay, M., Jouen, F., & Rigaud, A.-S. (2015). “Are we ready for robots that care for us?” Attitudes and opinions of older adults toward socially assistive robots. Frontiers in Aging Neuroscience, 7, 141. https://doi.org/10.3389/fnagi.2015.00141
  35. Rahman, M. S., Ko, M., Warren, J., & Carpenter, D. (2016). Healthcare Technology Self-Efficacy (HTSE) and its influence on individual attitude: An empirical study. Computers in Human Behavior, 58, 12-24. https://doi.org/10.1016/j.chb.2015.12.016
  36. Riley, V. (1989). A general model of mixed-initiative human-machine systems. Proceedings of the Human Factors Society Annual Meeting, 33(2), 124–128. https://doi.org/10.1177/154193128903300227
  37. Rosenthal-von der Pütten, A. M., Krämer, N. C., Hoffmann, L., Sobieraj, S., & Eimler, S. C. (2013). An experimental study on emotional reactions towards a robot. International Journal of Social Robotics, 5(1), 17-34. https://doi.org/10.1007/s12369-012-0173-8
  38. Saadé, R. G., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information Technology Education: Research, 8, 177-191. https://doi.org/10.28945/3386
  39. Sánchez, A., Poignet, P., Dombre, E., Menciassi, A., Dario, P. J. R., & Systems, A. (2014). A design framework for surgical robots: Example of the Araknes robot controller. Robotics and Autonomous Systems, 62(9), 1342-1352. https://doi.org/10.1016/j.robot.2014.03.020
  40. Savela, N., Turja, T., & Oksanen, A. (2018). Social acceptance of robots in different occupational fields: A systematic literature review. International Journal of Social Robotics, 10(4), 493-502. https://doi.org/10.1007/s12369-017-0452-5
  41. Sharkey, A., & Sharkey, N. (2012). Granny and the robots: ethical issues in robot care for the elderly. Ethics and Information Technology, 14(1), 27-40. https://doi.org/10.1007/s10676-010-9234-6
  42. Sheridan, T. B., & Verplank, W. L. (1978). Human and computer control of undersea teleoperators. Massachusetts Inst of Tech Cambridge Man-Machine Systems Lab. https://doi.org/10.21236/ada057655
  43. Stock, R., & Nguyen, M. A. (2019). Robotic Psychology. What Do We Know about Human-Robot Interaction and What Do We Still Need to Learn? Paper presented at the Proceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2019.234
  44. Strudwick, G. (2015). Predicting nurses' use of healthcare technology using the technology acceptance model: An integrative review. Comput Inform Nurs, 33(5), 189-198; quiz E181. https://doi.org/10.1097/01.ncn.0000465413.54230.e1
  45. Talebi, S., Zare, H., Sarmadi, M. R., & Saeedipour, B. (2012). Presenting the causal model of psychological variable (Computer experience, Subjective norm, Computer anxiety and Computer self efficacy) on actual use of information technology on the basis of Davis’s model. Life Science Journal, 9(4), 3263-3266.
  46. Turja, T., Rantanen, T., & Oksanen, A. (2017). Robot use self-efficacy in healthcare work (RUSH): development and validation of a new measure. AI & Society, 1-7. https://doi.org/10.1007/s00146-017-0751-2
  47. Turja, T., Van Aerschot, L., Särkikoski, T., & Oksanen, A. (2018). Finnish healthcare professionals' attitudes towards robots: Reflections on a population sample. Nursing Open, 5(3), 300-309. https://doi.org/10.1002/nop2.138
  48. Watson, D., Womack, J., & Papadakos, S. (2020). Rise of the robots: Is artificial intelligence a friend or foe to nursing practice?. Critical Care Nursing Quarterly, 43(3), 303-311.
  49. Worldometer (2021). COVID-19 coronavirus pandemic. https://www.worldometers.info/coronavirus/
  50. Weng, Y. H., Chen, C. H., & Sun, C. T. (2009). Toward the human–robot co-existence society: On safety intelligence for next generation robots. International Journal of Social Robotics, 1(4), 267.
  51. Zeng, Z., Chen, P. J., & Lew, A. A. (2020). From high-touch to high-tech: COVID-19 drives robotics adoption. Tourism Geographies, 22(3), 724-734.
  52. Pérez-Vidal, C., Carpintero, E., Garcia-Aracil, N., Sabater-Navarro, J., Azorín, J. M., Candela, A.,. Systems, A. (2012). Steps in the development of a robotic scrub nurse. Robotics and Autonomous Systems, 60(6), 901–911. https://doi.org/10.1016/j.robot.2012.01.005