Treffer: Generative artificial intelligence acceptance, anxiety, and behavioral intention in the middle east: a TAM-based structural equation modelling approach.
Title:
Generative artificial intelligence acceptance, anxiety, and behavioral intention in the middle east: a TAM-based structural equation modelling approach.
Authors:
Mohamed MG; Adult Health Nursing. RAK College of Nursing, RAK Medical and Health Sciences University, Al Qusaidat, Near RAK Hospital, PO Box: 11172, Ras Al-Khaimah, UAE. mona@rakmhsu.ac.ae., Goktas P; UCD School of Computer Science, University College Dublin, Belfield, Dublin, Ireland., Khalaf SA; College of Applied Medical Sciences in Yanbu Governorate, Taibah University, Yanbu, KSA, Saudi Arabia.; Department of Community Health Nursing, Faculty of Nursing, Assiut University, Assiut, Egypt., Kucukkaya A; Institute of Graduate Studies, Istanbul University-Cerrahpasa, Istanbul, Turkey., Al-Faouri I; Dean of RAK College of Nursing, RAK Medical and Health Sciences University, Ras Al-Khaimah, UAE.; Jordan University of Science and Technology, Ar-Ramtha, Jordan., Seleem EAES; Faculty of Nursing, Zarqa University, Zarqa, Jordan.; Nursing Administration Department, Assiut University, Assiut, Egypt., Ibraheem A; College of Applied Medical Sciences in Yanbu Governorate, Taibah University, Yanbu, KSA, Saudi Arabia., Abdelhafez AM; Gynecological and Obstetric Nursing, Faculty of Nursing, Assiut University, Assiut, Egypt., Abdullah SO; Department of Psychiatric and Mental Health Nursing, Faculty of Medicine & Health Science, Hodeidah University, Al Hudaydah, Yemen., Zaki HN; College of Applied Medical Sciences in Yanbu Governorate, Taibah University, Yanbu, KSA, Saudi Arabia.; Psychiatric and Mental Health Nursing, Faculty of Nursing, Benha University, Benha, Egypt., Nashwan AJ; Nursing & Midwifery Research Department (NMRD), Hamad Medical Corporation, Doha, Qatar. anashwan@hamad.qa.; Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar. anashwan@hamad.qa.
Source:
BMC nursing [BMC Nurs] 2025 Jul 01; Vol. 24 (1), pp. 703. Date of Electronic Publication: 2025 Jul 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 101088683 Publication Model: Electronic Cited Medium: Print ISSN: 1472-6955 (Print) Linking ISSN: 14726955 NLM ISO Abbreviation: BMC Nurs Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: London : BioMed Central, [2002]-
Comments:
Erratum in: BMC Nurs. 2025 Aug 1;24(1):1003. doi: 10.1186/s12912-025-03640-6.. (PMID: 40751148)
References:
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Mohamed MG, Islam MR, Ahmed SK, Khalaf SA, Abdelall HA, Mahmood KA, et al. Assessment of knowledge, attitude, anxiety level and perceived mental healthcare needs toward Mpox infection among nursing students: A multi-center cross-sectional study. Glob Transit. 2024;6:203–11. https://doi.org/10.1016/j.glt.2024.10.001 . (PMID: 10.1016/j.glt.2024.10.001)
Dai HM, Teo T, Rappa NA, Huang F. Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Comput Educ. 2020;150:103850. https://doi.org/10.1016/j.compedu.2020.103850 . (PMID: 10.1016/j.compedu.2020.103850)
Granić A. Educational technology adoption: A systematic review. Educ Inf Technol (Dordr). 2022;27:9725–44. https://doi.org/10.1007/s10639-022-10951-7 . (PMID: 10.1007/s10639-022-10951-7)
Ahmed SK, Dabou EA, Abdelsamad S, Mohamed MG, Chandran D, Chakraborty S, et al. Monkeypox virus infection and myocarditis: A review of current evidence and possible pathogenesis. Narra J. 2023;3:e104. https://doi.org/10.52225/narra.v3i1.104 . (PMID: 10.52225/narra.v3i1.104)
Alzaabi D. The impact of technology and digital transformation on innovation in the UAE. Int J Technol Syst. 2023;8:82–99. https://doi.org/10.47604/ijts.2170 . (PMID: 10.47604/ijts.2170)
Pasumarti SSR, El Hajj C. AI for sustainable development: MENA strategies. AI in the middle East for growth and business. Cham: Springer Nature Switzerland; 2025. pp. 43–60. https://doi.org/10.1007/978-3-031-75589-7_4 . (PMID: 10.1007/978-3-031-75589-7_4)
Awogbemi O, Kallon DV, Von, Kumar KS. Contributions of artificial intelligence and digitization in achieving clean and affordable energy. Intell Syst Appl. 2024;22:200389. https://doi.org/10.1016/j.iswa.2024.200389 . (PMID: 10.1016/j.iswa.2024.200389)
Essa RA, Ahmed SK, Mohamed MG. Pulmonary hydatid cyst mimicking lung tumor in a heavy smoker patient— uniportal VATS management. Int J Surg Case Rep. 2022;95:107138. https://doi.org/10.1016/j.ijscr.2022.107138 . (PMID: 10.1016/j.ijscr.2022.107138)
Mannuru NR, Shahriar S, Teel ZA, Wang T, Lund BD, Tijani S, et al. Artificial intelligence in developing countries: the impact of generative artificial intelligence (AI) technologies for development. Inform Dev. 2023. https://doi.org/10.1177/02666669231200628 . (PMID: 10.1177/02666669231200628)
Harb Z, Arafat R. The adoption of artificial intelligence technologies in Arab newsrooms: potentials and challenges. Emerg Media. 2024;2:371–81. https://doi.org/10.1177/27523543241291068 . (PMID: 10.1177/27523543241291068)
Wanner J, Herm L-V, Heinrich K, Janiesch C. The effect of transparency and trust on intelligent system acceptance: evidence from a user-based study. Electron Markets. 2022;32:2079–102. https://doi.org/10.1007/s12525-022-00593-5 . (PMID: 10.1007/s12525-022-00593-5)
Chen C, Hu W, Wei X. From anxiety to action: exploring the impact of artificial intelligence anxiety and artificial intelligence self-efficacy on motivated learning of undergraduate students. Interact Learn Environ. 2024;1–16. https://doi.org/10.1080/10494820.2024.2440877 .
Sulaiman T, Mahomed A, Rahman A, Hassan M. Understanding antecedents of learning management system usage among university lecturers using an integrated TAM-TOE model. Sustainability. 2023;15:1885. https://doi.org/10.3390/su15031885 . (PMID: 10.3390/su15031885)
Gorian E, Osman ND, DIGITAL ETHICS OF ARTIFICIAL INTELLIGENCE (AI) IN SAUDI ARABIA AND UNITED ARAB EMIRATES. Malaysian J Syariah Law. 2024;12:583–97. https://doi.org/10.33102/mjsl.vol12no3.798 . (PMID: 10.33102/mjsl.vol12no3.798)
Al-Zahrani AM, Alasmari TM. Exploring the impact of artificial intelligence on higher education: the dynamics of ethical, social, and educational implications. Humanit Soc Sci Commun. 2024;11:912. https://doi.org/10.1057/s41599-024-03432-4 . (PMID: 10.1057/s41599-024-03432-4)
Alateeg S, Al-Ayed S. Exploring the role of artificial intelligence technology in empowering women-led startups. Knowl Perform Manage. 2024;8:28–38. https://doi.org/10.21511/kpm.08(2).2024.03 . (PMID: 10.21511/kpm.08(2).2024.03)
Sallam M, Salim NA, Barakat M, Al-Mahzoum K, Al-Tammemi AB, Malaeb D, et al. Assessing health students’ attitudes and usage of ChatGPT in jordan: validation study. JMIR Med Educ. 2023;9:e48254. https://doi.org/10.2196/48254 . (PMID: 10.2196/48254)
Ahmed SK, Abdulqadir SO, Omar RM, Hussein S, Qurbani K, Mohamed MG, et al. Knowledge, attitudes, and willingness of healthcare workers in iraq’s Kurdistan region to vaccinate against human monkeypox: A nationwide Cross-Sectional study. Vaccines (Basel). 2023;11:1734. https://doi.org/10.3390/vaccines11121734 . (PMID: 10.3390/vaccines11121734)
Tummalapenta SR, Pasupuleti RS, Chebolu RM, Banala TV, Thiyyagura D. Factors driving ChatGPT continuance intention among higher education students: integrating motivation, social dynamics, and technology adoption. J Computers Educ. 2024. https://doi.org/10.1007/s40692-024-00343-w . (PMID: 10.1007/s40692-024-00343-w)
KUPCU C, BODUR G. Assessing nursing students’ e < math altimg=si0005.svg> <mo linebreak="badbreak”>–</mo> health literacy and foresights to global health challenges during covid < math altimg=si0006.svg> <mo linebreak="badbreak”>–</mo> 19 restrictions: A cross-sectional study with a machine learning approach. Nurse Educ Pract. 2024;81:104179. https://doi.org/10.1016/j.nepr.2024.104179 . (PMID: 10.1016/j.nepr.2024.104179)
Goktas P, Kucukkaya A, Karacay P. Leveraging the efficiency and transparency of artificial intelligence-driven visual chatbot through smart prompt learning concept. Skin Res Technol. 2023;29. https://doi.org/10.1111/srt.13417 .
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Contributed Indexing:
Keywords: Anxiety; Artificial intelligence; Behavioral intention; Nurse; Nursing education; Structural equation modeling; Technology acceptance model
Entry Date(s):
Date Created: 20250702 Latest Revision: 20250802
Update Code:
20250802
PubMed Central ID:
PMC12211728
DOI:
10.1186/s12912-025-03436-8
PMID:
40598322
Database:
MEDLINE
Weitere Informationen
Background: Adopting generative artificial intelligence (GenAI) in education rapidly transforms learning environments, yet nursing students' acceptance and anxiety toward these technologies remain underexplored in Middle Eastern contexts. This study extends the Technology Acceptance Model (TAM) by incorporating constructs such as Facilitating Conditions (FC) and Social Influence (SI). It investigates the moderating role of Anxiety on Behavioral Intention to Use (BIU) generative AI tools.
Methods: A cross-sectional study was conducted among 1,055 undergraduate nursing students across four Middle Eastern countries, including Egypt, Jordan, Saudi Arabia, and Yemen. Data were collected using a structured questionnaire comprising the Generative Artificial Intelligence Acceptance Scale and the Artificial Intelligence Anxiety Scale. Structural equation modeling was employed to evaluate relationships among Performance Expectancy (PE), Effort Expectancy (EE), FC, SI, and BIU, with Anxiety as a moderator. Descriptive statistics, confirmatory factor analysis, and path analysis were performed using SPSS and Python's semopy library.
Results: The model demonstrated strong explanatory power, with 75.09% of the variance in BIU explained by the TAM constructs and Anxiety. Path coefficients revealed significant positive relationships between PE (β = 0.477, p < 0.001), EE (β = 0.293, p < 0.001), FC (β = 0.189, p < 0.001), and SI (β = 0.308, p < 0.001) and BIU. Anxiety had the strongest moderating effect (β = 0.552, p < 0.001), indicating its critical role in shaping behavioral intentions. Gender, year of study, and access to technology emerged as significant demographic variables influencing acceptance and anxiety levels.
Conclusions: This study emphasizes the importance of reducing anxiety and enhancing support systems to foster GenAI acceptance among nursing students. The findings provide actionable insights for designing culturally tailored educational interventions to promote the effective integration of AI in nursing education.
Clinical Trial Number: Not applicable.
(© 2025. The Author(s).)
Declarations. Ethics approval and consent to participate: The research was conducted in accordance with the principles set out in the Declaration of Helsinki. Ethical approval and institutional permission were obtained from the Taibah University Ethics Committee (Approval No: AMSY/08/2024). Informed consent was also obtained from the participants to ensure the ethical suitability of the research. Consent for publication: Not applicable. Permission to reproduce material from other sources: Not applicable. Competing interests: The authors declare no competing interests.