Influence of Attitude toward Artificial Intelligence (AI) on Job Performance with AI in Nurses
DOI:
https://doi.org/10.56294/dm2025221Keywords:
Attached growth, Biological wastewater treatment, Biofilm, Rotating biological contactorsAbstract
AI has revolutionized the workplace, significantly impacting the nursing profession. Attitudes toward AI, defined as workers’ perceptions and beliefs about its utility and effectiveness, are critical for its adoption and efficient use in clinical settings. Factors such as age, marital status, and education level may influence this relationship, affecting job performance. This study examines the influence of attitude toward AI on job performance with AI among Peruvian nurses, while also assessing how sociodemographic characteristics moderate this relationship. A descriptive cross-sectional design was used with a sample of 249 Peruvian nurses aged 24 to 53 years (M = 35.58, SD = 8.3). Data were collected using two validated scales: the Brief Artificial Intelligence Job Performance Scale (BAIJPS) and the Attitude toward Artificial Intelligence Scale (AIAS-4). Descriptive statistics, Pearson correlations, and multiple linear regression were applied. A significant positive correlation was found between attitude toward AI and job performance with AI (r = 0.43, p < 0.01). Age (β = -0.177, p < 0.05), divorced marital status (β = -8.144, p < 0.01), and having a bachelor’s degree (β = -3.016, p < 0.05) were negatively associated with job performance, while being from the Selva region had a positive effect (β = 4.182, p < 0.05). A favorable attitude toward AI positively influences nurses’ job performance, highlighting the need for interventions that enhance AI perception. Age, marital status, and education moderate this relationship, suggesting AI adoption strategies should be tailored to different demographic groups.
References
Abuzaid, M. M., Elshami, W., & Fadden, S. M. (2022). Integration of artificial intelligence into nursing practice. Health and Technology, 12(6). https://doi.org/10.1007/s12553-022-00697-0
Alruwaili, M. M., Abuadas, F. H., Alsadi, M., Alruwaili, A. N., Elsayed Ramadan, O. M., Shaban, M., Al Thobaity, A., Alkahtani, S. M., & El Arab, R. A. (2024). Exploring nurses’ awareness and attitudes toward artificial intelligence: Implications for nursing practice. DIGITAL HEALTH, 10. https://doi.org/10.1177/20552076241271803
Altmiller, G., & Pepe, L. H. (2022). Influence of Technology in Supporting Quality and Safety in Nursing Education. In Nursing Clinics of North America (Vol. 57, Issue 4). https://doi.org/10.1016/j.cnur.2022.06.005
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice. In Journal of Organizational Behavior (Vol. 45, Issue 2). https://doi.org/10.1002/job.2735
Barchielli, C., Marullo, C., Bonciani, M., & Vainieri, M. (2021). Nurses and the acceptance of innovations in technology-intensive contexts: the need for tailored management strategies. BMC Health Services Research, 21(1). https://doi.org/10.1186/s12913-021-06628-5
Barnard, Y., Bradley, M. D., Hodgson, F., & Lloyd, A. D. (2013). Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Computers in Human Behavior, 29(4). https://doi.org/10.1016/j.chb.2013.02.006
Božić, V. (2024). Artifical Intelligence in Nurse Education. https://doi.org/10.1007/978-3-031-50300-9_9
Charness, N., & Boot, W. R. (2009). Aging and information technology use: Potential and barriers. Current Directions in Psychological Science, 18(5). https://doi.org/10.1111/j.1467-8721.2009.01647.x
Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and Aging, 21(2). https://doi.org/10.1037/0882-7974.21.2.333
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3). https://doi.org/10.2307/249008
Deranty, J. P., & Corbin, T. (2024). Artificial intelligence and work: a critical review of recent research from the social sciences. AI and Society, 39(2). https://doi.org/10.1007/s00146-022-01496-x
Dwivedi, A., & Kochhar, K. (2023). Employee’s Attitude Towards Artificial Intelligence in the Indian Banking Sector. International Journal of Professional Business Review, 8(11). https://doi.org/10.26668/businessreview/2023.v8i11.4099
Taşgit, Y., Baykal, Y., Can Aydin, U., Yakupoğlu, A., & Coşkuner, M. (2023). Do Employees’ Artificial Intelligence Attitudes Affect Individual Business Performance? Journal of Organisational Studies and Innovation, 10(2). https://doi.org/10.51659/josi.22.176
Erdfelder, E., FAul, F., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods 2009 41:4, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Glauberman, G., Ito-Fujita, A., Katz, S., & Callahan, J. (2023). Artificial Intelligence in Nursing Education: Opportunities and Challenges. In Hawaii Journal of Health and Social Welfare (Vol. 82, Issue 12).
Hasan, H. E., Jaber, D., Tabbah, S. Al, Lawand, N., Habib, H. A., & Farahat, N. M. (2024). Knowledge, attitude and practice among pharmacy students and faculty members towards artificial intelligence in pharmacy practice: A multinational cross-sectional study. PLoS ONE, 19(3 March). https://doi.org/10.1371/journal.pone.0296884
Hoerger, M., & Currell, C. (2011). Ethical issues in Internet research. In S. Knapp, M. Gottlieb, M. Handelsman, & L. VandeCreek (Eds.), APA handbook of ethics in psychology, Vol 2: Practice, teaching, and research. (Vol. 2, pp. 385–400). American Psychological Association. https://doi.org/10.1037/13272-018
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. In Stroke and Vascular Neurology (Vol. 2, Issue 4). https://doi.org/10.1136/svn-2017-000101
Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77. https://doi.org/10.1016/j.tele.2022.101925
Lähdepuro, A., Savolainen, K., Lahti-Pulkkinen, M., Eriksson, J. G., Lahti, J., Tuovinen, S., Kajantie, E., Pesonen, A. K., Heinonen, K., & Räikkönen, K. (2019). The Impact of Early Life Stress on Anxiety Symptoms in Late Adulthood. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-40698-0
Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C. P., & Stephan, A. (2023). An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. In npj Digital Medicine (Vol. 6, Issue 1). https://doi.org/10.1038/s41746-023-00852-5
Lomis, K., Jeffries, P., Palatta, A., Sage, M., Sheikh, J., Sheperis, C., & Whelan, A. (2021). Artificial Intelligence for Health Professions Educators. NAM Perspectives. https://doi.org/10.31478/202109a
Malamin, B. (2024). Attitudes of Graphic Designers and Copywriters in Bulgaria Towards Artificial Intelligence. Postmodernism Problems, 14(1), 55–73. https://doi.org/10.46324/PMP2401055
Mlambo, M., Silén, C., & McGrath, C. (2021). Lifelong learning and nurses’ continuing professional development, a metasynthesis of the literature. BMC Nursing, 20(1). https://doi.org/10.1186/s12912-021-00579-2
Morales-García, W. C., & Sairitupa-Sanchez, L. Z. (2024). Adaptation and Validation of a Brief Artificial Intelligence Job Performance Scale (BAIJPS) in Nurses. Interdisciplinary Advances in Health, 1.
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024). Adaptation and Psychometric Properties of an Attitude toward Artificial Intelligence Scale (AIAS-4) among Peruvian Nurses. Behavioral Sciences, 14(6), 437. https://doi.org/10.3390/bs14060437
Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). THE IMPACT OF ARTIFICIAL INTELLIGENCE ON WORKERS’ SKILLS: UPSKILLING AND RESKILLING IN ORGANISATIONS. Informing Science, 26. https://doi.org/10.28945/5078
Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2). https://doi.org/10.1111/j.1744-6570.2000.tb00206.x
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. In Journal of the Royal Society of Medicine (Vol. 112, Issue 1). https://doi.org/10.1177/0141076818815510
Rony, M. K. K., Kayesh, I., Bala, S. Das, Akter, F., & Parvin, M. R. (2024). Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study. Heliyon, 10(4). https://doi.org/10.1016/j.heliyon.2024.e25718
Sbarra, D. A. (2015). Divorce and health: Current trends and future directions. In Psychosomatic Medicine (Vol. 77, Issue 3). https://doi.org/10.1097/PSY.0000000000000168
Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, D., Biessmann, F., & Wolf-Ostermann, K. (2021). Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. In Journal of Medical Internet Research (Vol. 23, Issue 11). https://doi.org/10.2196/26522
Sharip, H., Che Zakaria, W. F. W., Leong, S. S., Ali Masoud, M., & Mohd Junaidi, M. Z. H. (2023). Radiographers’ Acceptance on the Integration of Artificial Intelligence into Medical Imaging Practice. Environment-Behaviour Proceedings Journal, 8(25). https://doi.org/10.21834/e-bpj.v8i25.4872
Hedge, J. W., Borman, W. C., & Lammlein, S. E. (2006). The aging workforce: realities, myths, and implications for organizations. Choice Reviews Online, 43(07). https://doi.org/10.5860/choice.43-4127
Topol, E. (2019). Deep Medicine - How Artificial Intelligence Can Make Healthcare Human Again. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9).
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2). https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2). https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3). https://doi.org/10.2307/30036540
Wang, X., Fei, F., Wei, J., Huang, M., Xiang, F., Tu, J., Wang, Y., & Gan, J. (2024). Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study. Frontiers in Public Health, 12. https://doi.org/10.3389/fpubh.2024.1433252
Wen, Z., & Huang, H. (2022). The potential for artificial intelligence in healthcare. Journal of Commercial Biotechnology, 27(4). https://doi.org/10.5912/jcb1327
Williams, R., Anderson, S., Cresswell, K., Kannelønning, M. S., Mozaffar, H., & Yang, X. (2024). Domesticating AI in medical diagnosis. Technology in Society, 76. https://doi.org/10.1016/j.techsoc.2024.102469
Xu, G., Xue, M., & Zhao, J. (2023). The Relationship of Artificial Intelligence Opportunity Perception and Employee Workplace Well-Being: A Moderated Mediation Model. International Journal of Environmental Research and Public Health, 20(3). https://doi.org/10.3390/ijerph20031974
Zhang, H. (2023). Artificial intelligence in healthcare: Opportunities and challenges. Theoretical and Natural Science, 21(1), 130–134. https://doi.org/10.54254/2753-8818/21/20230845
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Copyright (c) 2025 Wilter C. Morales-García, Liset Z. Sairitupa-Sanchez, Alcides Flores-Paredes , Mardel Morales-García, Fernando N. Gutierrez-Caballero (Author)
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