Psychometric properties of the Arabic version of the Unified Theory of Acceptance and Use of Technology (UTAUT-2012) Among Nursing Students
DOI:
https://doi.org/10.19173/irrodl.v26i4.8827Keywords:
psychometric properties, UTAUT 2012 model, artificial intelligence, structural equation modeling, nursing education, nursing studentsAbstract
The integration of artificial intelligence (AI) into nursing education is essential for equipping future nurses with the skills required to navigate an increasingly technology-driven healthcare environment. This study aimed to validate the Arabic version of the Unified Theory of Acceptance and Use of Technology (UTAUT-2012) model in assessing factors influencing nursing students’ acceptance and use of AI in healthcare education. A cross-sectional pilot study was conducted with 200 nursing students to evaluate the psychometric properties of the Arabic-translated UTAUT (2012) instrument. Confirmatory factor analysis was performed using covariance-based structural equation modeling (CB-SEM) in SmartPLS (Version 4.1.0). Confirmatory factor analysis supported the construct validity of the nine UTAUT 2012 constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, behavioral intention, and use behavior. All items showed acceptable factor loadings (> .5), composite and construct reliability (> 0.7), and average variance extracted (> 0.5). Discriminant validity was confirmed using the Fornell-Larcker criterion and the heterotrait-monotrait ratio. The findings offer valuable insights into the factors influencing Arabic-speaking nursing students’ acceptance and use of AI in healthcare education, supporting the model’s validity in this cultural context.
References
Ab Hamid, M. R., Sami, W., & Mohmad Sidek, M. H. (2017). Discriminant validity assessment: Use of Fornell and Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series, 890, Article 012163. https://doi.org/10.1088/1742-6596/890/1/012163
Abry, S., Mehrabian, F., Omidi, S., Karimy, M., Kasmaei, P., & Haryalchi, K. (2022). Investigation of factors related to the behavior of reporting clinical errors in nurses working in educational and medical centers in Rasht city, Iran. BMC Nursing, 21, Article 348. https://doi.org/10.1186/s12912-022-01134-3
Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: An investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5). https://doi.org/10.19173/irrodl.v14i5.1631
Ahmad, F. B., Al-Nawaiseh, S. J., & Al-Nawaiseh, A. J. (2023). Receptivity level of faculty members in universities using digital learning tools: A UTAUT perspective. International Journal of Emerging Technologies in Learning, 18(13), 209–219. https://doi.org/10.3991/ijet.v18i13.39763
Akbar, G. G., Muchtar, Wahid, A. A., Kania, I., Putri, A. D., & Alamanda, D. T. (2023). Applying UTAUT model to understand use of behavior health applications user in Indonesia during the COVID-19. Jurnal Ilmu Administrasi: Media Pengembangan Ilmu dan Praktek Administrasi, 20(1), 29–45. https://jia.stialanbandung.ac.id/index.php/jia/article/view/900
Alenezi, F. Y. (2023). Artificial intelligence versus Arab universities: An enquiry into the Saudi context. The Scientific Journal of King Faisal University: Humanities and Management Sciences, 24(1), 82–88. https://doi.org/10.37575/h/edu/220038
Alhamazani, F. H. (2020). Exploratory factor analysis toward adoption of cloud computing by faculty members in Saudi Arabian universities for teaching and learning. International Journal of Computer Science and Network Security, 20(2), 8–15. https://doi.org/10.22937/IJCSNS.2020.20.2.2
Al-Qeisi, K., Dennis, C., Hegazy, A., & Abbad, M. (2015). How viable is the UTAUT model in a non-Western context? International Business Research, 8(2), 204–219. https://doi.org/10.5539/ibr.v8n2p204
Alshammari, S. H. (2021). Determining the factors that affect the use of virtual classrooms: A modification of the UTAUT model. Journal of Information Technology Education: Research, 20, 117–135. https://doi.org/10.28945/4709
Bandyopadhyay, K., & Fraccastoro, K. A. (2007). The effect of culture on user acceptance of information technology. Communications of the Association for Information Systems, 19, Article 23. https://doi.org/10.17705/1cais.01923
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, Article 639. https://doi.org/10.1186/s12913-021-06628-5
Chang, T.-F., Chao, C.-M., & Cheng, B.-W. (2015). Framework and verification of a blended e-learning system behavioral intention model among clinical nurses. Journal of Baltic Science Education, 14(6), 733–743. https://files.eric.ed.gov/fulltext/EJ1427489.pdf
Consiglio, C., Borgogni, L., Di Tecco, C., & Schaufeli, W. B. (2016). What makes employees engaged with their work? The role of self-efficacy and employee’s perceptions of social context over time. The Career Development International, 21(2), 125–143. https://psycnet.apa.org/record/2016-19688-002
Dikkema, Y., Nieuwenhuis, M. K., van der Schans, C. P., & Mouton, L. J. (2021). Questionnaires to assess facilitators and barriers of early mobilization in critically ill patients: Which one to choose? A systematic review. Clinical Nursing Research, 30(4), 442–454. https://doi.org/10.1177/1054773820948268
Drissi, N., Ouhbi, S., Amiri, L., Al Mugaddam, F., Jan, R. K., & Isomursu, M. (2022). A conceptual framework to design connected mental health solutions in the United Arab Emirates: Questionnaire study. JMIR Formative Research, 6(2), Article e27675. https://doi.org/10.2196/27675
Gabriel, R. (2023). Correlations between learning style preferences and Arab-speaking Gulf region first-year college students’ EFL performance: A literature review. Journal of Language Teaching and Research, 14(3), 709–714. https://doi.org/10.17507/jltr.1403.18
Gonzalez, I. P., Jr., & dos Santos, E. M. (2017). A proposal for UTAUT model extension in the virtual learning environments use as presential learning support context. International Journal of Technology and Human Interaction, 13(3), 33–46. https://doi.org/10.4018/IJTHI.2017070103
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433. https://doi.org/10.1007/s11747-011-0261-6
Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Kwak, Y., Seo, Y. H., & Ahn, J. W. (2022). Nursing students’ intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Education Today, 119, Article 105541. https://doi.org/10.1016/j.nedt.2022.105541
Lee, Y., Lim, W., & Eng, H. S. (2024). A systematic review of UTAUT2 constructs’ analysis among MSMEs in non-OECD countries. Journal of Science and Technology Policy Management, 15(4), 765–793. https://doi.org/10.1108/JSTPM-08-2022-0140
McCoy, S., Galletta, D. F., & King, W. R. (2007). Applying TAM across cultures: The need for caution. European Journal of Information Systems, 16(1), 81–90. https://doi.org/10.1057/palgrave.ejis.3000659
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430–445. https://doi.org/10.1037/0033-2909.105.3.430
O’Connor, S., Peltonen, L.-M., Ronquillo, C., Chu, C. H., Topaz, M., Chen, L.-Y. A., & Lee, J. J. (2023, February 20). A framework for embedding artificial intelligence in nursing: Education, innovation, collaboration, and implementation—The ALIGN Model. OSF Preprints. https://doi.org/10.31219/osf.io/3b8pc
Or, C. (2023). Revisiting Unified Theory of Technology and Use of Technology using meta-analytic structural equation modelling. International Journal of Technology in Education and Science, 7(1), 83–103. https://doi.org/10.46328/ijtes.420
Polit, D. F., & Beck, C. T. (2016). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Lippincott Williams & Wilkins.
Rouibah, K. (2008). Social usage of instant messaging by individuals outside the workplace in Kuwait: A structural equation model. Information Technology & People, 21(1), 34–68. https://doi.org/10.1108/09593840810860324
Sidani, S., Guruge, S., Miranda, J., Ford-Gilboe, M., & Varcoe, C. (2010). Cultural adaptation and translation of measures: An integrated method. Research in Nursing & Health, 33(2), 133–143. https://doi.org/10.1002/nur.20364
Speyer, R., Cordier, R., Kertscher, B., & Heijnen, B. J. (2014). Psychometric properties of questionnaires on functional health status in oropharyngeal dysphagia: A systematic literature review. BioMed Research International, 2014(1), Article 458678. https://doi.org/10.1155/2014/458678
Tanaka, J. S., & Huba, G. J. (1985). A fit index for covariance structure models under arbitrary GLS estimation. British Journal of Mathematical and Statistical Psychology, 38(2), 197–201. https://psycnet.apa.org/record/1986-10882-001
Taskiran, N. (2023). Effect of an artificial intelligence course in nursing on students’ medical artificial intelligence readiness: A comparative quasi-experimental study. Nurse Educator, 48(5), e147–e152. https://doi.org/10.1097/NNE.0000000000001446
Terwee, C. B., Bot, S. D. M., de Boer, M. R., van der Windt, D. A. W. M., Knol, D. L., Dekker, J., Bouter, L. M., & de Vet, H. C. W. (2007). Quality criteria were proposed for the measurement properties of health status questionnaires. Journal of Clinical Epidemiology, 60(1), 34–42. https://doi.org/10.1016/j.jclinepi.2006.03.012
Thabet, Z., Albashtawi, S., Ansari, H., Al-Emran, M., Al-Sharafi, M. A., & AlQudah, A. A. (2023). Exploring the factors affecting telemedicine adoption by integrating UTAUT2 and IS success model: A hybrid SEM–ANN approach. IEEE Transactions on Engineering Management, 71, 8938–8950. https://ieeexplore.ieee.org/abstract/document/10195252
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The Unified Theory of Acceptance and Use of Technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. https://doi.org/10.1108/JEIM-09-2014-0088
Zhou, L. L., Owusu-Marfo, J., Asante Antwi, H., Antwi, M. O., Kachie, A. D. T., & Ampon-Wireko, S. (2019). Assessment of the social influence and facilitating conditions that support nurses’ adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model. BMC Medical Informatics and Decision Making, 19, Article 230. https://doi.org/10.1186/s12911-019-0956-z
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