ENHANCING DIGITAL TRANSFORMATION IN THE UNITED ARAB EMIRATES: EXAMINING THE TECHNOLOGICAL, ORGANIZATIONAL, AND ENVIRONMENTAL DRIVERS SHAPING AI ADOPTION INTENTIONS AMONG GOVERNMENT EMPLOYEES
DOI:
https://doi.org/10.35631/IJLGC.1144015Keywords:
Artificial Intelligence (AI) Adoption, Government Employee, UAE, Consumers, Technology Acceptance Model (TAM), Technology-Organization-Environment (TOE)Abstract
Artificial Intelligence (AI) has become a key driver of digital transformation in the United Arab Emirates (UAE), yet its adoption within government institutions remains uneven despite substantial national investments and policy support. This study investigates the technological, organizational, and environmental factors influencing government employees’ intentions to adopt AI. Drawing upon the Technology Acceptance Model (TAM) and the Technology–Organization–Environment (TOE) framework, the study proposes an integrated model that examines the effects of System Quality, Service Quality, Information Quality, Technical Support, AI Awareness, Top Management Support, Government Policy, Industry Pressure, and Data Privacy Concerns on Perceived Usefulness, Perceived Ease of Use, Trust, and Intention to Adopt AI. User Readiness was also examined as a moderating variable. A quantitative research design was employed, and data were collected from 425 government employees across the seven emirates of the UAE. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that technological quality factors, organizational support, and environmental conditions significantly influence Perceived Usefulness and Perceived Ease of Use. Consistent with TAM, Perceived Ease of Use positively affects Perceived Usefulness, while both constructs significantly enhance Trust. Trust emerged as the strongest predictor of Intention to Adopt AI, highlighting its critical role in fostering AI acceptance among public-sector employees. In addition, User Readiness significantly strengthened the relationship between technology perceptions and adoption intention. The model demonstrated substantial explanatory power, accounting for 78.1% of the variance in Intention to Adopt AI. The study contributes to the AI adoption literature by integrating TAM and TOE within a public-sector context and highlighting the importance of trust and user readiness in technology acceptance. The findings provide practical insights for policymakers and government leaders seeking to accelerate AI-driven digital transformation through improved technological infrastructure, organizational preparedness, employee awareness, and trust-building initiatives.
Downloads
References
Abd Aziz, A., Nor, R. N. H., Jusoh, Y. Y., Rahman, W. N. W. A., & Ali, N. M. (2024). Factors influencing information quality of information systems: A systematic literature review. JOIV: International Journal on Informatics Visualization, 8(3-2), 1923-1931. https://doi.org/10.62527/joiv.8.3-2.3483
Aboelazm, K. S. (2025). A new era of public procurement: critical issues of procuring artificial intelligence systems to produce public services. International Journal of Law and Management. https://doi.org/10.1108/ijlma-06-2024-0208
Al Ali, F. A., & Khalil, E. (2025). The use of artificial intelligence in government communication in the UAE. The Egyptian Journal of Media Research, 90, 95-129.
Alshehhi, K., Cheaitou, A., & Rashid, H. (2024). Procurement of artificial intelligence systems in UAE public sectors: an interpretive structural modeling of critical success factors. Sustainability, 16(17), 7724. https://doi.org/10.3390/su16177724
Al Tawhidi, F., & Bourini, I. (2024). Leveraging Market Agility Through AI-Enabled Capabilities in the United Arab Emirates (UAE). In Achieving Sustainable Business Through AI, Technology Education and Computer Science: Volume 3: Business Sustainability and Artificial Intelligence Applications (pp. 41-52). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73632-2_4
Archana, T. (2025). Artificial intelligence (AI) and digital competencies in the public sector. In Digital competency development for public officials: Adapting new technologies in public services (pp. 95-120). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6547-2.ch005
Arpaci, I., Cetin Yardimci, Y., Ozkan, S., & Turetken, O. (2012). Organizational adoption of information technologies: A literature review. International Journal of eBusiness and eGovernment Studies, 4(2), 37-50.
Arunachalam, H. (2025). Organizational Readiness and Change Management in the Age of AI-Driven Business Analytics. AI in Business Analytics and Decision-Making, 15. https://doi.org/10.47715/978-93-86388-87-2/ch2
Benson, T. (2019). Digital innovation evaluation: user perceptions of innovation readiness, digital confidence, innovation adoption, user experience and behaviour change. BMJ health & care informatics, 26(1), e000018. https://doi.org/10.1136/bmjhci-2019-000018
Bin-Nashwan, S. A., & Li, J. Z. (2025). What shapes AI adoption in financial service-oriented contexts? The game-changing role of innovation climate. Information Discovery and Delivery. https://doi.org/10.1108/idd-12-2024-0199
Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human-Computer Interaction, 39(9), 1727-1739. https://doi.org/10.1080/10447318.2022.2050543
Custers, B., Sears, A. M., Dechesne, F., Georgieva, I., Tani, T., & Van der Hof, S. (2019). EU personal data protection in policy and practice (Vol. 29, pp. 1-249). The Hague, The Netherlands: TMC Asser Press. https://doi.org/10.1007/978-94-6265-282-8_2
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Drazin, R. (1991). The processes of technological innovation: David A. Tansik book review editor Louis G. Tornatzky and Mitchell Fleischer. Lexington, MA: DC Heath & Company, 1990. 298 pages. https://doi.org/10.1007/bf02371446
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Elliott, A. (2019). The culture of AI: Everyday life and the digital revolution. Routledge. https://doi.org/10.4324/9781315387185
Feldstein, S. (2021). The rise of digital repression: How technology is reshaping power, politics, and resistance. Oxford University Press. https://doi.org/10.1093/oso/9780190057497.001.0001
Felemban, H., Sohail, M., & Ruikar, K. (2024). Exploring the readiness of organisations to adopt artificial intelligence. Buildings, 14(8), 2460. https://doi.org/10.3390/buildings14082460
Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services-the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320. https://doi.org/10.1108/josm-10-2020-0378
Fu, C. J., Silalahi, A. D. K., Shih, I. T., Phuong, D. T. T., Eunike, I. J., & Jargalsaikhan, S. (2024). Assessing ChatGPT’s information quality through the lens of user information satisfaction and information quality theory in higher education: A theoretical framework. Human Behavior and Emerging Technologies, 2024(1), 8114315. https://doi.org/10.1155/2024/8114315
Gkikas, D. C., & Gkikas, M. C. (2026). AI in Citizen-Centric Smart Cities: Exploring Data Privacy, Algorithmic Transparency and Trustworthiness Through Regulation. In Human-Smart City Interactions and User-Citizen Experiences (pp. 75-99). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-06364-9_5
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications. https://doi.org/10.54055/ejtr.v6i2.134
Hamzah, Z. (2025). AI innovation, intellectual property commercialization, and the rise of the intellectual capital economy: challenges and prospects for GCC economies. Intellectual Property and Innovation: Contemporary Developments in the GCC Member States, 167-188. https://doi.org/10.1007/978-981-96-4020-1_8
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
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/imds-09-2015-0382
Hong, S. J., & Cho, H. (2023). The role of uncertainty and affect in decision-making on the adoption of AI-based contact-tracing technology during the COVID-19 pandemic. Digital Health, 9, 20552076231169836. https://doi.org/10.1177/20552076231169836
Horani, O. M., Al-Adwan, A. S., Yaseen, H., Hmoud, H., Al-Rahmi, W. M., & Alkhalifah, A. (2025). The critical determinants impacting artificial intelligence adoption at the organizational level. Information Development, 41(3), 1055-1079. https://doi.org/10.1177/02666669231166889
Khalil, E., & Al-Ali, F. A. (2026). AI adoption in government public relations: Technology acceptance, social influence, and organizational dynamics in the UAE. Public Relations Inquiry, 15(1), 31-51. https://doi.org/10.1177/2046147x251383106
Lee, J., Kim, H. J., & Ahn, M. J. (2011). The willingness of e-Government service adoption by business users: The role of offline service quality and trust in technology. Government information quarterly, 28(2), 222-230. https://doi.org/10.1016/j.giq.2010.07.007
Li, Y., & Xie, W. (2025). It makes me feel incompetent and anxious: an exploratory study of how techno-stressors affect older adult satisfaction toward e-government service. Journal of Technology in Human Services, 43(3), 207-233. https://doi.org/10.1080/15228835.2025.2511304
Lin, C. S., Kuo, Y. F., & Wang, T. Y. (2025). Trust and acceptance of AI caregiving robots: The role of ethics and self-efficacy. Computers in Human Behavior: Artificial Humans, 3, 100115. https://doi.org/10.1016/j.chbah.2024.100115
Mehrotra, S., Degachi, C., Vereschak, O., Jonker, C. M., & Tielman, M. L. (2024). A systematic review on fostering appropriate trust in Human-AI interaction: Trends, opportunities and challenges. ACM Journal on Responsible Computing, 1(4), 1-45. https://doi.org/10.1145/3696449
Mishra, A. K., Tyagi, A. K., Dananjayan, S., Rajavat, A., Rawat, H., & Rawat, A. (2024). Revolutionizing government operations: The impact of artificial intelligence in public administration. Conversational Artificial Intelligence, 607-634. https://doi.org/10.1002/9781394200801.ch34
Neumann, O., Guirguis, K., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114-141. https://doi.org/10.1080/14719037.2022.2048685
Oyetade, K., Harmse, A., & Zuva, T. (2024). Internal organizational factors influencing ICT adoption for sustainable growth. Discover Global Society, 2(1), 108. https://doi.org/10.1007/s44282-024-00136-7
Radwan, A. F., Mousa, S. A., Ayyad, K., & Abdulzaher, M. H. (2026). The integration of artificial intelligence in public relations practice in the UAE: Analyzing opportunities and challenges through the AMO theory framework. Public Relations Inquiry, 15(1), 53-73. https://doi.org/10.1177/2046147x251360062
Rane, N., Choudhary, S. P., & Rane, J. (2024). Acceptance of artificial intelligence: key factors, challenges, and implementation strategies. Journal of Applied Artificial Intelligence, 5(2), 50-70. https://doi.org/10.48185/jaai.v5i2.1017
Religia, Y., Ramawati, Y., Firdausi, A. S. M., & Nainggolan, D. S. (2025). Exploring digital leadership-TOE framework in CRM adoption by SMEs in developing countries. RAUSP Management Journal, 60(1), 52-68. https://doi.org/10.1108/rausp-09-2024-0187
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press. https://doi.org/10.1007/978-94-011-1771-5_2
Ryan, S. D., & Harrison, D. A. (2000). Considering social subsystem costs and benefits in information technology investment decisions: a view from the field on anticipated payoffs. Journal of Management Information Systems, 16(4), 11-40. https://doi.org/10.1080/07421222.2000.11518264
Saragih, H. H., Saifi, M., Nuzula, N. F., & Worokinasih, S. (2025). Exploring the nexus between corporate agility and sustainable strategy: the role of stakeholder engagement and external forces. Cogent Business & Management, 12(1), 2438864. https://doi.org/10.1080/23311975.2024.2438864
Satyro, W. C., Contador, J. C., Gomes, J. A., Monken, S. F. D. P., Barbosa, A. P., Bizarrias, F. S., ... & Prado, R. G. (2024). Technology-organization-external-sustainability (TOES) framework for technology adoption: critical analysis of models for industry 4.0 implementation projects. Sustainability, 16(24), 11064. https://doi.org/10.3390/su162411064
Spring, M., Faulconbridge, J., & Sarwar, A. (2022). How information technology automates and augments processes: Insights from Artificial‐Intelligence‐based systems in professional service operations. Journal of Operations Management, 68(6-7), 592-618. https://doi.org/10.1002/joom.1215
Tasnim, K., Abdullah, M. S., Karim, M. Z., & Hasan, R. (2025). AI-Driven Innovation, Privacy Issues, and Gaining Consumer Trust: The Future of Digital Marketing. Business and Social Sciences, 3(1), 1-7. https://doi.org/10.25163/business.3110212
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Zainuddin, M. T. (2016). Marketing: Conventional Approach and Complementary Views from Islamic Perspectives. USIM Press, Universiti Sains Islam Malaysia.
