The Impact of Technology on Business in the United Arab Emirates: A Technology Acceptance Model Perspective

Saira Saira 1, Sheeraz Ali 2, Callistus D. Odeh 1

1 Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russia
2 Dadabhoy Institute of Higher Education, Karachi, Pakistan

Abstract

The rapid embracement of technology in business operations within the United Arab Emirates (UAE) highlights the growing need to understand the determinants of technology adoption. As companies continue to mechanize their processes, it is more critical to explore how employees perceive and utilize these technological systems. The current study examined the impact of technology on UAE companies according to the Technology Acceptance Model (TAM), a widely applied conceptual model for user acceptance. The main objectives were to quantify the influence of perceived usefulness (PU), perceived ease of use (PEU), and attitude towards the system (ATT) on actual system use (ASU). The study postulated that PU and PEU positively influence ASU, whereas ATT's influence can be contingent upon the situation. A quantitative survey was conducted among 100 UAE employees representing various industries through a standardized questionnaire. Data analysis was carried out using SPSS and Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings indicated PEU to be the strongest predictor of ASU (β = 0.781, p < 0.001), validating that usability is a strong driver for technology use. PU was also found to significantly influence ASU (β = 0.001, p < 0.001), which means perceived utility as a critical factor. Theoretically, this research builds upon TAM theory by putting more emphasis on usability in emerging markets. Practically, it encourages UAE firms to invest in simple-to-use systems and training initiatives to make digital transformation less agonizing.

Keywords

perceived usefulness (PU); perceived ease of use (PEU); attitude towards the system (ATT); Technology Acceptance Model (TAM); digital transformation; UAE businesses

JEL classification

References

1. Jo, H., Bang, Y. (2023). Understanding continuance intention of enterprise resource planning (ERP): TOE, TAM, and IS success model. Heliyon, Vol. 9, Issue 10, e21019. doi.org/10.1016/j.heliyon.2023.e21019

         2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, Vol. 50, Issue 2, 179–211. doi.org/10.1016/0749-5978(91)90020-T

         3. Alsyouf, A., Lutfi, A., Alsubahi, N., Alhazmi, F.N., Al-Mugheed, K., Anshasi, R.J., Albugami, M. (2023). The use of a technology acceptance model (TAM) to predict patients’ usage of a personal health record system: the role of security, privacy, and usability. International Journal of Environmental Research and Public Health, Vol. 20, Issue 2, 1347. doi.org/10.3390/ijerph20021347

         4. Rukhiran, M., Wong-In, S., Netinant, P. (2023). User acceptance factors related to biometric recognition technologies of examination attendance in higher education: TAM model. Sustainability, Vol. 15, Issue 4, 3092. doi.org/10.3390/su15043092

         5. Ki, H.T.T., Tapanainen, T., Zaza, S., Huvila, I. (2024). Antecedents of Perceived Usefulness (PU) and Perceived Ease-of-Use (PEOU) in the Heuristic-Systematic Model: The Context of Online Diabetes Risk Test. Journal of Information Technology Applications and Management, Vol. 31, Issue 5, 17–39. doi.org/10.21219/jitam.2024.31.5.017

         6. Kim, J., Park, E. (2022). Understanding social resistance to determine the future of Internet of Things (IoT) services. Behaviour & Information Technology, Vol. 41, Issue 3, 547–557. doi.org/10.1080/0144929X.2020.1827033

         7. Saputra, D.E.E., Maulida Rahma, V., Eliyana, A., Pratama, A.S., Anggraini, R.D., Kamil, N.L.M., Ismail, I. (2023). Do system quality and information quality affect job performance? The mediation role of users’ perceptions. PlosOne, Vol. 18, Issue 6, e0285293. doi.org/10.1371/journal.pone.0285293

         8. Turel, O., Qahri-Saremi, H. (2023). Responses to ambivalence toward social networking sites: A typological perspective. Information Systems Journal, Vol. 33, Issue 2, 385–416. doi.org/10.1111/isj.12407

         9. Shahin, M., Chen, F.F., Hosseinzadeh, A., Koodiani, H.K., Shahin, A., Nafi, O.A. (2023). A smartphone-based application for an early skin disease prognosis: Towards a lean healthcare system via computer-based vision. Advanced Engineering Informatics, Vol. 57, 102036. doi.org/10.1016/j.aei.2023.102036

         10. Daryanto, E. (2022). The Influence of Information System Quality, Information Quality and Perceived Usefulness on User Satisfaction of Personnel Information Systems (Study at The Indonesian Army Crypto and Cyber Center). Journal of Positive School Psychology, Vol. 6, No. 3, 9814–9830. Available at: www.journalppw.com/index.php/jpsp/article/view/5457/3644

         11. Rezvani, S., Heidari, S., Roustapisheh, N., Dokhanian, S. (2022). The effectiveness of system quality, habit, and effort expectation on library application use intention: the mediating role of perceived usefulness, perceived ease of use, and user satisfaction. International Journal of Business Information Systems, Vol. 1, Issue 1, 1–18. dx.doi.org/10.1504/IJBIS.2022.10049515

         12. Senna, P.P., Ferreira, L.M.D., Barros, A.C., Roca, J.B., Magalhães, V. (2022). Prioritizing barriers for the adoption of Industry 4.0 technologies. Computers & Industrial Engineering, Vol. 171, 108428. doi.org/10.1016/j.cie.2022.108428

         13. Iyanna, S., Kaur, P., Ractham, P., Talwar, S., Islam, A.N. (2022). Digital transformation of healthcare sector. What is impeding adoption and continued usage of technology-driven innovations by end-users? Journal of Business Research, Vol. 153, 150–161. doi.org/10.1016/j.jbusres.2022.08.007

         14. Qi, Y., Sajadi, S.M., Baghaei, S., Rezaei, R., Li, W. (2024). Digital technologies in sports: Opportunities, challenges, and strategies for safeguarding athlete wellbeing and competitive integrity in the digital era. Technology in Society, Vol. 77, 102496. doi.org/10.1016/j.techsoc.2024.102496

         15. Hussain, A., Zhiqiang, M., Li, M., Jameel, A., Kanwel, S., Ahmad, S., Ge, B. (2025). The mediating effects of perceived usefulness and perceived ease of use on nurses’ intentions to adopt advanced technology. BMC Nursing, Vol. 24, 33. dx.doi.org/10.1186/s12912-024-02648-8

         16. Wang, N., Zhang, J., Yu, X. (2024). Sustainability-driven technology road mapping: a strategic framework for sustainable development with moderation of ease of use. International Journal of Information Systems and Change Management, Vol. 14, No. 4, 406–422. doi.org/10.1504/IJISCM.2024.144422

         17. Malmous, M., Zaidoune, S. (2025). Student Adoption of AI Writing Tools: A PLS-SEM and TAM-Based Analysis in Moroccan Higher Education. In: Next-Generation AI Methodologies in Education. IGI Global Scientific Publishing, 209–254. doi.org/10.4018/979-8-3693-7220-3.ch009

         18. Ibrahim, A., Shiring, E. (2022). The Relationship between Educators' Attitudes, Perceived Usefulness, and Perceived Ease of Use of Instructional and Web-Based Technologies: Implications from Technology Acceptance Model (TAM). International Journal of Technology in Education, Vol. 5, No. 4, 535–551. doi.org/10.46328/ijte.285

         19. Hidayat, H., Mohd Isa, M.R., Tanucan, J.C.M., Harmanto, D., Anwar, M., Hanesman, H., Dewi, F.K. (2024). Analysis of Computational Thinking Skill Through Technology Acceptance Model Approach Using Augmented Reality in Electronics Engineering Education. TEM Journal, Vol. 13, Issue 2, 1423–1431. dx.doi.org/10.18421/TEM132-56

         20. Magboul, I., Jebreel, M., Shawtri, F., Qabajeh, M., Shanti, A., Alqudah, M., Abu Huson, Y. (2024). Factors influencing accounting information system usage by oil companies & performance outcomes. Cogent Business & Management, Vol. 11, Issue 1, 2369211. doi.org/10.1080/23311975.2024.2369211

         21. Wai, L.C., Isa, A.M.M., Bhandari, P., Senathirajah, A.R.B.S., Haque, R., Devasia, S.N., Al-Hunaiyyan, A. (2024). Factors influencing job satisfaction to ensure sustainable growth amongst family-owned organizations in Malaysia. Kurdish Studies, Vol. 12, Issue 1, 3144–3173. dx.doi.org/10.58262/ks.v12i1.227

         22. Lavuri, R., Jindal, A., Akram, U. (2022). How perceived utilitarian and hedonic value influence online impulse shopping in India? Moderating role of perceived trust and perceived risk. International Journal of Quality and Service Sciences, Vol. 14, No. 4, 615–634. doi.org/10.1108/IJQSS-11-2021-0169

         23. Al-Mamary, Y.H.S. (2022). Why do students adopt and use learning management systems? Insights from Saudi Arabia. International Journal of Information Management Data Insights, Vol. 2, Issue 2, 100088. doi.org/10.1016/j.jjimei.2022.100088

         24. Martínez-Peláez, R., Ochoa-Brust, A., Rivera, S., Félix, V.G., Ostos, R., Brito, H., Mena, L.J. (2023). Role of digital transformation for achieving sustainability: mediated role of stakeholders, key capabilities, and technology. Sustainability, Vol. 15, Issue 14, 11221. doi.org/10.3390/su151411221

         25. Alalwan, A.A., Baabdullah, A.M., Fetais, A.H.M., Algharabat, R.S., Raman, R., Dwivedi, Y.K. (2024). SMEs entrepreneurial finance-based digital transformation: towards innovative entrepreneurial finance and entrepreneurial performance. Venture Capital, Vol. 26, Issue 4, 401–429. doi.org/10.1080/13691066.2023.2195127

         26. Prastiawan, D.I., Aisjah, S., Rofiaty, R. (2021). The Effect of Perceived Usefulness, Perceived Ease of Use, and Social Influence on the Use of Mobile Banking through the Mediation of Attitude Toward Use. Asia Pacific Management and Business Application, Vol. 9, No. 3, 243–260. dx.doi.org/10.21776/ub.apmba.2021.009.03.4

         27. Ajibade, S., Zaidi, A.T. (2023). Technological Acceptance Model for Social Media Networking in e-Learning in Higher Educational Institutes. International Journal of Information and Education Technology, Vol. 13, No. 2, 239–246. dx.doi.org/10.18178/ijiet.2023.13.2.1801

         28. Abdul-Halim, N.A., Vafaei-Zadeh, A., Hanifah, H., Teoh, A.P., Nawaser, K. (2022). Understanding the determinants of e-wallet continuance usage intention in Malaysia. Quality & Quantity, Vol. 56, 3413–3439. doi.org/10.1007/s11135-021-01276-7

         29. Almaiah, M.A., Al-Rahmi, A., Alturise, F., Hassan, L., Lutfi, A., Alrawad, M., Aldhyani, T.H. (2022). Investigating the effect of perceived security, perceived trust, and information quality on mobile payment usage through near-field communication (NFC) in Saudi Arabia. Electronics, Vol. 11, Issue 23, 3926. doi.org/10.3390/electronics11233926

         30. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, Vol. 13, No. 3, 319–340. doi.org/10.2307/249008

         31. Leong, Y.R., Tajudeen, F.P., Chung, Y.W. (2024). Usage and impact of the internet-of-things-based smart home technology: a quality-of-life perspective. Universal Access in the Information Society, Vol. 23, 345–364. doi.org/10.1007/s10209-022-00937-0

         32. Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human-Computer Interaction, Vol. 40, Issue 2, 497–514. doi.org/10.1080/10447318.2022.2151730

         33. Al-Rahmi, A.M., Al-Rahmi, W.M., Alturki, U., Aldraiweesh, A., Almutairy, S., Al-Adwan, A.S. (2022). Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Education and Information Technologies, Vol. 27, 7805–7826. doi.org/10.1007/s10639-022-10934-8

         34. Derecho, K.C., Cafino, R., Aquino-Cafino, S.L., Isla Jr, A., Esencia, J.A., Lactuan, N.J., Velasco, L.C.P. (2024). Technology adoption of electronic medical records in developing economies: A systematic review on physicians’ perspective. Digital Health, Vol. 10, 20552076231224605. doi.org/10.1177/20552076231224605

         35. Vhatkar, M.S., Raut, R.D., Gokhale, R., Kumar, M., Akarte, M., Ghoshal, S. (2024). Leveraging digital technology in retailing business: Unboxing synergy between omnichannel retail adoption and sustainable retail performance. Journal of Retailing and Consumer Services, Vol. 81, 104047. doi.org/10.1016/j.jretconser.2024.104047

         36. Nicolescu, L., Tudorache, M. T. (2022). Human-computer interaction in customer service: the experience with AI chatbots – A systematic literature review. Electronics, Vol. 11, Issue 10, 1579. doi.org/10.3390/electronics11101579

         37. Kaplan, A.D., Kessler, T.T., Brill, J.C., Hancock, P.A. (2023). Trust in artificial intelligence: Meta-analytic findings. Human Factors, Vol. 65, Issue 2, 337–359. doi.org/10.1177/00187208211013988

         38. Stahl, B.C., Schroeder, D., Rodrigues, R. (2023). Ethics of Artificial Intelligence: Case Studies and Options for Addressing Ethical Challenges. Springer Cham, 116 p. dx.doi.org/10.1007/978-3-031-17040-9

         39. Song, Y., Gui, L., Wang, H., Yang, Y. (2023). Determinants of continuous usage intention in community group buying platform in China: Based on the information system success model and the expanded technology acceptance model. Behavioral Sciences, Vol. 13, Issue 11, 941. doi.org/10.3390/bs13110941

         40. Lutfi, A., Alsyouf, A., Almaiah, M.A., Alrawad, M., Abdo, A.A.K., Al-Khasawneh, A.L., Saad, M. (2022). Factors influencing the adoption of big data analytics in the digital transformation era: Case study of Jordanian SMEs. Sustainability, Vol. 14, Issue 3, 1802. https://doi.org/10.3390/su14031802

         41. Park, I., Kim, D., Moon, J., Kim, S., Kang, Y., Bae, S. (2022). Searching for new technology acceptance model under social context: Analyzing the determinants of acceptance of intelligent information technology in digital transformation and implications for the requisites of digital sustainability. Sustainability, Vol. 14, Issue 1, 579. doi.org/10.3390/su14010579

         42. Zimba, O., Gasparyan, A.Y. (2023). Designing, conducting, and reporting survey studies: a primer for researchers. Journal of Korean Medical Science, Vol. 38, Issue 48, e403. doi.org/10.3346/jkms.2023.38.e403

         43. Hair, J.F., Risher, J.J., Sarstedt, M., Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, Vol. 31, No. 1, 2–24. doi.org/10.1108/EBR-11-2018-0203

         44. Yusoff, A.S.M., Peng, F.S., Abd Razak, F.Z., Mustafa, W.A. (2020). Discriminant validity assessment of religious teacher acceptance: The use of HTMT criterion. Journal of Physics: Conference Series, Vol. 1529, 042045. doi.org/10.1088/1742-6596/1529/4/042045

         45. Chin, W.W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, Vol. 22, No. 3, 313–334. doi.org/10.2307/249668

         46. Dwivedi, Y.K., Rana, N.P., Jeyaraj, A., Clement, M., Williams, M.D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, Vol. 21, 719–734. doi.org/10.1007/s10796-017-9774-y

         47. Venkatesh, V., Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, Vol. 46, No. 2, 186–204. doi.org/10.1287/mnsc.46.2.186.11926

         48. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, Vol. 27, No. 3, 425–478. doi.org/10.2307/30036540

         49. Schepman, A., Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human-Computer Interaction, Vol. 39, Issue 14, 2724–2741. doi.org/10.1080/10447318.2022.2085409

         50. Islam, A., Naseem, A. (2025). Role of Industry 4.0 tools in organizational performance of the IT sector. Kybernetes, Vol. 54, No. 3, 1752–1779. dx.doi.org/10.1108/K-09-2023-1697

         51. Al-Adwan, A.S., Li, N., Al-Adwan, A., Abbasi, G.A., Albelbisi, N.A., Habibi, A. (2023). Extending the technology acceptance model (TAM) to Predict University Students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, Vol. 28, 15381–15413. doi.org/10.1007/s10639-023-11816-3

         52. Rožman, M., Oreški, D., Tominc, P. (2023). Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA Environment. Sustainability, Vol. 15, Issue 6, 5019. doi.org/10.3390/su15065019

         53. Emon, M.M.H., Khan, T., Rahman, M.A., Siam, S.A.J. (2024). Factors influencing the usage of artificial intelligence among Bangladeshi professionals: Mediating role of attitude towards the technology. IEEE International Conference on Computing, Applications and Systems (COMPAS). IEEE, 1–7. doi.org/10.1109/COMPAS60761.2024.10796110

         54. Arslan, A., Cooper, C., Khan, Z., Golgeci, I., Ali, I. (2022). Artificial intelligence and human workers interaction at team level: A conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, Vol. 43, No. 1, 75–88. doi.org/10.1108/IJM-01-2021-0052

         55. Hampel, N., Sassenberg, K., Scholl, A., Reichenbach, M. (2022). Introducing digital technologies in the factory: Determinants of blue-collar workers’ attitudes towards new robotic tools. Behaviour & Information Technology, Vol. 41, Issue 14, 2973–2987. doi.org/10.1080/0144929X.2021.1967448

         56. Péréa, C., Gérard, J., de Benedittis, J. (2023). Digital sobriety: From awareness of the negative impacts of IT usages to degrowth technology at work. Technological Forecasting and Social Change, Vol. 194, 122670. doi.org/10.1016/j.techfore.2023.122670

         57. Aruleba, K., Jere, N., Matarirano, O. (2022). An evaluation of technology adoption during remote teaching and learning at tertiary institution by gender. IEEE Transactions on Computational Social Systems, Vol. 10, Issue 3, 1335–1346. doi.org/10.1109/TCSS.2022.3163912

         58. Chanda, R.C., Vafaei-Zadeh, A., Hanifah, H, Ashrafi, D.M., Ahmed, T. (2024). Achieving a sustainable future by analyzing electric vehicle adoption in developing nations through an extended technology acceptance model. Sustainable Futures, Vol. 8, 100386. doi.org/10.1016/j.sftr.2024.100386

         59. Dadhich, M., Rathore, S., Gyamfi, B.A., Ajibade, S.S.M., Agozie, D.Q. (2023). Quantifying the Dynamic Factors Influencing New-Age Users’ Adoption of 5G Using TAM and UTAUT Models in Emerging Country: A Multistage PLS-SEM Approach. Education Research International, Vol. 2023, 5452563. doi.org/10.1155/2023/5452563

About Authors

Saira Saira

M.Sc, Post-Graduate Student, Institute of Economics and Management, Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russia (620002, Ekaterinburg, Mira street, 19); ORCID orcid.org/0009-0006-8983-6358 e-mail: sairamaitlo7381@gmail.com

Sheeraz Ali

LLB, Post-Graduate Student, Dadabhoy Institute of Higher Education, Karachi, Pakistan (SNPA, 17/B, Block-3 KCHSU off Shaheed-e-Millat Service Road, Karachi Memon Society P.E.C.H.S., Karachi, 75460, Pakistan); ORCID orcid.org/0009-0004-5128-0409 e-mail: sheerazmashooque@gmail.com

Callistus D. Odeh

M.Sc, Post-Graduate Student, Institute of Economics and Management, Ural Federal University named after the first President of Russia B.N. Yeltsin, Ekaterinburg, Russia (620002, Ekaterinburg, Mira street, 19); ORCID orcid.org/0009-0004-5441-9023 e-mail: callistusodeh@gmail.com

For citation

Saira, S., Ali, S., Odeh, C.D. (2025). The Impact of Technology on Business in the United Arab Emirates: A Technology Acceptance Model Perspective. Journal of Applied Economic Research, Vol. 24, No. 2, 462-490. doi.org/10.15826/vestnik.2025.24.2.016

Article info

Received March 11, 2025; Revised March 27, 2025; Accepted May 2, 2025.

DOI: https://doi.org/10.15826/vestnik.2025.24.2.016

Download full text article: