Factors Affecting the Adoption of Artificial Intelligence (AI) among Accountants in Malaysia

Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial Intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100218. https://doi.org/10.1016/j.joitmc.2024.100218
Ahmed, I. (2020). Technology organization environment framework in cloud computing. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(2), 716-725. http://doi.org/10.12928/telkomnika.v18i2.13871
Akinadewo, I. S. (2021). Artificial intelligence and accountants' approach to accounting functions. Covenant University Journal of Politics & International Affairs (Special Edition). https://journals.covenantuniversity.edu.ng/index.php/cujpiase/article/view/2756
Akter, M., Kummer, T.-F., & Yigitbasioglu, O. (2024). Looking beyond the hype: The challenges of blockchain adoption in accounting. International Journal of Accounting Information Systems, 53, 100681. https://doi.org/10.1016/j.accinf.2024.100681
Alamin, A. A., Wilkin, C. L., Yeoh, W., & Warren, M. (2020). The impact of self-efficacy on accountants’ behavioral intention to adopt and use Accounting Information Systems. Journal of Information Systems, 34(3), 31–46. https://doi.org/10.2308/isys-52617
Alamin, A., Yeoh, W., Warren, M., & Salzman, S. (2015). An empirical study of factors influencing accounting information systems adoption. https://doi.org/10.18151/7217259
Alaskar, T. H., Mezghani, K., & Alsadi, A. K. (2020). Examining the adoption of big data analytics in supply chain management under competitive pressure: Evidence from Saudi Arabia. Journal of Decision Systems, 30(2–3), 300–320. https://doi.org/10.1080/12460125.2020.1859714
Al-Azizi, L., Al-Badi, A. H., & Al-Zrafi, T. (2018). Exploring the factors influencing employees’ willingness to use mobile applications in Oman: Using UTAUT model. Journal of E-Government Studies and Best Practices, 2018, 1–27. https://doi.org/10.5171/2018.553293
Al Hadwer, A., Tavana, M., Gillis, D., & Rezania, D. (2021). A systematic review of organizational factors impacting cloud-based technology adoption using technology-organization-environment framework. Internet of Things, 15, 100407.
https://doi.org/10.1016/j.iot.2021.100407
Alsheibani, S., Cheung, Y., & Messom, C. (2018). Artificial Intelligence Adoption: AI-readiness at firm-level. PACIS 2018 Proceedings. 37. https://aisel.aisnet.org/pacis2018/37/
Attuquayefio, S., & Addo, H. (2014). Using the UTAUT model to analyze students’ ICT adoption. International Journal of Education and Development using ICT, 10(3). https://www.learntechlib.org/p/148478/
Awa, H. O., Ojiabo, O. U., & Orokor, L. E. (2017). Integrated technology-organization-environment (T-O-E) taxonomies for technology adoption. Journal of Enterprise Information Management, 30(6), 893–921. https://doi.org/10.1108/jeim-03-2016-0079
Ayaz, A., & Yanarta?, M. (2020). An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS). Computers in Human Behavior Reports, 2, 100032. https://doi.org/10.1016/j.chbr.2020.100032
Baiod, W., & Hussain, M. M. (2024). The impact and adoption of emerging technologies on accounting: Perceptions of Canadian companies. International Journal of Accounting & Information Management, 32(4), 557–592. https://doi.org/10.1108/ijaim-05-2023-0123
Ban?a, V.-C., Rînda?u, S.-M., T?nasie, A., & Cojocaru, D. (2022). Artificial Intelligence in the accounting of international businesses: A perception-based approach. Sustainability, 14(11), 6632. https://doi.org/10.3390/su14116632
Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial Intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4). https://doi.org/10.2139/ssrn.3741983
Bernama. (2019). MDEC to complete National AI Framework by year-end. New Straits Times. https://www.nst.com.my/news/nation/2019/04/475361/mdec-complete-national-ai-framework-year-end
Bervell, B., & Arkorful, V. (2020). LMS-enabled blended learning utilization in distance tertiary education: Establishing the relationships among facilitating conditions, voluntariness of use and use behaviour. International Journal of Educational Technology in Higher Education, 17(1), 6. https://doi.org/10.1186/s41239-020-0183-9
Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: A cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 49(5), 831–846. https://doi.org/10.1080/03075079.2024.2333937
Cambridge. (1999). “Work experience” Cambridge Dictionary, Cambridge University Press. https://dictionary.cambridge.org/dictionary/english/work-experience
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
Cimperman, M., Bren?i?, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—applying an extended UTAUT model. International Journal of Medical Informatics, 90, 22–31. https://doi.org/10.1016/j.ijmedinf.2016.03.002
Coghlan, D., & Brydon-Miller, M. (2014). The Sage encyclopedia of action research. Sage https://doi.org/10.4135/9781446294406
Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019). Understanding CRM adoption stages: empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13. https://doi.org/10.1016/j.compind.2019.03.007
Dartnall, J. (1998). A most delicate monster: The one-professional special library. Elsevier. https://doi.org/10.1016/B978-0-949060-40-2.50031-5
Dissanayake, C. A. K., Jayathilake, W., Wickramasuriya, H. V. A., Dissanayake, U., & Wasala, W. M. C. B. (2022). A review on factors affecting technology adoption in agricultural sector. Journal of Agricultural Sciences–Sri Lanka, 17(2), 280-296 https://doi.org/10.4038/jas.v17i2.9743
Dong, X. (2019). Performance expectancy, effort expectancy, social influence, facilitating conditions, and relative advantage affecting Chinese customers’ decision to use mobile payment in Bangkok. DSpace at Bangkok University: Home. http://dspace.bu.ac.th/jspui/handle/123456789/3779
Ferguson, S. M., & Olfert, M. R. (2015). Competitive pressure and technology adoption: Evidence from a policy reform in Western Canada. American Journal of Agricultural Economics, 98(2), 422–446. https://doi.org/10.1093/ajae/aav018
Gangwar, H., & Date, H. (2016). Understanding cloud computing adoption: A model comparison approach. Human Systems Management, 35(2), 93–114. https://doi.org/10.3233/hsm-150857
Glass, R., & Li, S. (2010). Social influence and instant messaging adoption. Journal of Computer Information Systems, 51(2), 24–30. https://doi.org/10.1080/08874417.2010.11645465
Gnaneswaran, D. (2019). Artificial Intelligence to nearly double the rate of innovation in Malaysia by 2021: Microsoft Study. Microsoft Malaysia News. https://news.microsoft.com/en-my/2019/04/02/artificial-intelligence-to-nearly-double-the-rate-of-innovation-in-malaysia-by-2021-microsoft-study/
Gupta, V. (2024). An empirical evaluation of a generative artificial intelligence technology adoption model from entrepreneurs’ perspectives. Systems, 12(3), 103.
https://doi.org/10.3390/systems12030103
Haleem, A. (2020). Owner manager’s acceptance of cloud accounting: an evaluation based on UTAUT model. Journal of Information Systems & Information Technology, 5(1), 75-88. http://ir.lib.seu.ac.lk/handle/123456789/5436
Her, Q. L., & Wong, J. (2019). Significant correlation versus strength of correlation. American Journal of Health-System Pharmacy, 77(2), 73–75. https://doi.org/10.1093/ajhp/zxz280
Jena, R. (2024). Factors affecting AI adoption in accounting: Insights from multi-method analysis using SEM, FSQCA, and NCA. https://doi.org/10.2139/ssrn.4948235
Kholilah, K., Kawulur, H. R., & Subekti, I. (2022). Perceived usefulness, perceived ease of use, facilitating condition, social influence, and personal innovativeness of accounting students cloud computing adoption. Organum: Jurnal Saintifik Manajemen Dan Akuntansi, 5(2), 141–151.
https://doi.org/10.35138/organum.v5i2.257
Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416. https://doi.org/10.1016/j.ijmedinf.2008.12.005
Komathi, W., & Sim, C. H. (2024). Shaping a digital future: Examining Technology, Organization and Wnvironment (TOE) framework. Journal of Technology Management and Business, 11(1), 80-97. https://doi.org/10.30880/jtmb.2024.11.01.005
Koul, S., & Eydgahi, A. (2019). The impact of social influence, technophobia, and perceived safety on autonomous vehicle technology adoption. Periodica Polytechnica Transportation Engineering, 48(2), 133–142. https://doi.org/10.3311/pptr.11332
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610. https://doi.org/10.1177/001316447003000308
Kulviwat, S., Bruner II, G. C., & Al-Shuridah, O. (2009). The role of social influence on adoption of high tech innovations: The moderating effect of public/private consumption. Journal of Business Research, 62(7), 706–712. https://doi.org/10.1016/j.jbusres.2007.04.014
Kumar, A., & Shankar, A. (2024). Building a sustainable future with enterprise metaverse in a data-driven era: A technology-organization-environment (TOE) perspective. Journal of Retailing and Consumer Services, 81, 103986.
https://doi.org/10.1016/j.jretconser.2024.103986
Lacity, M. C., & Willcocks, L. P. (2021). Becoming strategic with intelligent automation. MIS Quarterly. Executive, 20(2), 7. https://aisel.aisnet.org/misqe/vol20/iss2/7
Lada, S., Chekima, B., Karim, Mohd. R. A., Fabeil, N. F., Ayub, M. S., Amirul, S. M., Ansar, R., Bouteraa, M., Fook, L. M., & Zaki, H. O. (2023). Determining factors related to artificial intelligence (AI) adoption among Malaysia’s small and medium-sized businesses. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100144. https://doi.org/10.1016/j.joitmc.2023.100144
Lee, C. S., & Tajudeen, F. P. (2020). Usage and Impact of Artificial Intelligence on Accounting: Evidence from Malaysian Organisations. Asian Journal of Business and Accounting, 13(1), 213–240. https://doi.org/10.22452/ajba.vol13no1.8
Li, Z., & Zheng, L. (2018). The impact of artificial Intelligence on accounting. In 2018 4th International Conference on Social Science and Higher Education (ICSSHE 2018). Atlantis Press, 181, 813-816. 10.2991/icsshe-18.2018.203
Liu, L., Cruz, A. M., Rincon, A. R., Buttar, V., Ranson, Q., & Goertzen, D. (2014). What factors determine therapists’ acceptance of new technologies for rehabilitation – A study using the unified theory of acceptance and use of technology (UTAUT). Disability and Rehabilitation, 37(5), 447–455. https://doi.org/10.3109/09638288.2014.923529
Luo, J., Meng, Q., & Cai, Y. (2018). Analysis of the impact of artificial intelligence application on the development of accounting industry. Open Journal of Business and Management, 6(4), 850-856. 10.4236/ojbm.2018.64063
Lutfi, A. (2022). Factors influencing the continuance intention to use accounting information system in Jordanian smes from the perspectives of UTAUT: Top management support and self-efficacy as predictor factors. Economies, 10(4), 75.
https://doi.org/10.3390/economies10040075
Malik, S., Chadhar, M., Vatanasakdakul, S., & Chetty, M. (2021). Factors affecting the organizational adoption of blockchain technology: Extending the technology–organization–environment (TOE) framework in the Australian context. Sustainability, 13(16), 9404. https://doi.org/10.3390/su13169404
Marei, A., Mustafa, J. A., Othman, M., Daoud, L., Lutfi, A., & Al-Amarneh, A. A. (2023). The moderation of organizational readiness on the relationship between toe factors and fintech adoption and financial performance. Journal of Law and Sustainable Development, 11(3), 1-36. https://doi.org/10.55908/sdgs.v11i3.730
Marikyan, D., & Papagiannidis, S. (2023). Unified theory of acceptance and use of technology: A review. TheoryHub Book. https://open.ncl.ac.uk ISBN: 9781739604400
McKinnie, M. (2016). Cloud computing: Toe adoption factors by service model in manufacturing. ScholarWorks @ Georgia State University.
https://doi.org/10.57709/8571911
Meyer, J. (2011). Workforce age and technology adoption in small and medium-sized service firms. Small Business Economics, 37, 305–324. https://doi.org/10.1007/s11187-009-9246-y
Milliou, C., & Petrakis, E. (2011). Timing of technology adoption and product market competition. International Journal of Industrial Organization, 29(5), 513–523. https://doi.org/10.1016/j.ijindorg.2010.10.003
Misraini, G. D., & Muda, I. (2025). The influence of performance expectancy, effort expectancy, social influence, and facilitating conditions on behavioral intention to use accounting information system in MSMEs Banda Aceh city. Computers In Education Journal, 9–17. https://coed-journal.org/wp-content/uploads/2025-01-02.pdf
Mohd Faizal, S., Jaffar, N., & Mohd nor, A. S. (2022). Integrate the adoption and readiness of digital technologies amongst accounting professionals towards the fourth industrial revolution. Cogent Business & Management, 9(1). https://doi.org/10.1080/23311975.2022.2122160
Nascimento, A. M., & Meirelles, F. de S. (2022). Factors Influencing the Adoption Intention of Artificial Intelligence in Small Businesses. ISLA 2022 Proceedings. 20. https://aisel.aisnet.org/isla2022/20
Nnaji, C., Gambatese, J., Karakhan, A., & Eseonu, C. (2019). Influential Safety Technology adoption predictors in construction. Engineering, Construction and Architectural Management, 26(11), 2655–2681. https://doi.org/10.1108/ecam-09-2018-0381
Norzelan, N. A., Mohamed, I. S., & Mohamad, M. (2024). Technology acceptance of artificial Intelligence (AI) among heads of finance and accounting units in the shared service industry. Technological Forecasting and Social Change, 198, 123022. https://doi.org/10.1016/j.techfore.2023.123022
Nurfatimah, S. N., Rahmawati, T., Wiharno, H., Yusuf, F., & Darmawan, E. (2024). Determining factors in adoption of accounting application. Indonesian Journal Of Business And Economics, 7(2), 1027–1036. https://doi.org/10.25134/ijbe.v7i2.11186
Peng, G., & Mu, J. (2011). Technology adoption in online social networks. Journal of Product Innovation Management, 28(s1), 133–145. https://doi.org/10.1111/j.1540-5885.2011.00866.x
Pentina, I., Koh, A. C., & Le, T. T. (2012). Adoption of social networks marketing by SMEs: Exploring the role of social influences and experience in technology acceptance. International Journal of Internet Marketing and Advertising, 7(1), 65–82. https://doi.org/10.1504/ijima.2012.044959
Rawashdeh, A., Bakhit, M., & Abaalkhail, L. (2023). Determinants of artificial intelligence adoption in SMEs: The mediating role of accounting automation. International Journal of Data and Network Science, 7(1), 25–34. https://doi.org/http://dx.doi.org/10.5267/j.ijdns.2022.12.010
Rizkalla, N., Tannady, H., & Bernando, R. (2024). Analysis of the influence of performance expectancy, effort expectancy, social influence, and attitude toward behavior on intention to adopt live.on. Multidisciplinary Reviews, 6.
https://doi.org/10.31893/multirev.2023spe017
Rumangkit, S., Surjandy, & Billman, A. (2023). The effect of performance expectancy, facilitating condition, effort expectancy, and perceived easy to use on intention to using media support learning based on unified theory of acceptance and use of technology (utaut). E3S Web of Conferences, 426, 02004. https://doi.org/10.1051/e3sconf/202342602004
Sair, S. A., & Danish, R. Q. (2018). Effect of performance expectancy and effort expectancy on the mobile commerce adoption intention through personal innovativeness among Pakistani consumers. Pakistan Journal of Commerce and Social Sciences (PJCSS),12(2), 501-520. https://hdl.handle.net/10419/188355
Salimonu, R., Jimoh, G., Abdul, M., & Tomori, R. (2016). An empirical investigation of the moderating effects of work experience and position on the e-voting adoption. Annals Computer Science Series, 14(2), 84-96. https://anale-informatica.tibiscus.ro/download/lucrari/14-2-13-Jimoh.pdf
Sargent, K., Hyland, P., & Sawang, S. (2012). Factors influencing the adoption of information technology in a construction business. Australasian Journal of Construction Economics and Building, 12(2), 72-86. https://doi.org/10.5130/ajceb.v12i2.2448
Sayginer, C., & Ercan, T. (2020). Understanding determinants of cloud computing adoption using an integrated diffusion of innovation (doi)-technological, organizational and environmental (TOE) model. Humanities & Social Sciences Reviews, 8(1), 91–102. https://doi.org/10.18510/hssr.2020.8115
Shaikh, A. A., Glavee-Geo, R., & Karjaluoto, H. (2021). How relevant are risk perceptions, effort, and performance expectancy in mobile banking adoption? In Research Anthology on Securing Mobile Technologies and Applications, 692–716. IGI Global. https://doi.org/10.4018/978-1-7998-8545-0.ch038
Siegel, A. F., & Wagner, M. R. (2022). Multiple regression. In Practical Business Statistics, 371–431. Academic Press. https://doi.org/10.1016/b978-0-12-820025-4.00012-9
Sofyani, H., Murtin, A., Juanda, Utami, T. P., & Putra, A. Z. (2024). Employee intentions to adopt blockchain technology in Accounting Information Systems in local government: Testing the unified theory of acceptance and use of technology (UTAUT). In SHS Web of Conferences, 201, 03002. https://doi.org/10.1051/shsconf/202420103002
Strzelecki, A., & ElArabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology, 55(3), 1209–1230. https://doi.org/10.1111/bjet.13425
Sugandini, D., Margahana, H., & Rahatmawati, I. (2019). Managerial support, time constrain and user pressure on digital technology adoption. In Proceedings of the 2nd International Conference on Inclusive Business in the Changing World, 304–309. https://doi.org/10.5220/0008430603040309
Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre-and post-adopters. Computers in Human Behavior, 64, 602–620.
https://doi.org/10.1016/j.chb.2016.07.037
Sun, S., Lee, P. C., Law, R., & Hyun, S. S. (2020). An investigation of the moderating effects of current job position level and hotel work experience between technology readiness and technology acceptance. International Journal of Hospitality Management, 90, 102633. https://doi.org/10.1016/j.ijhm.2020.102633
Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751–752. https://doi.org/10.1126/science.aat5991
Travaglione, A., Scott-Ladd, B., Hancock, J., & Chang, J. (2017). Managerial support: Renewing the role of managers amidst declining union support for employees. Journal of General Management, 43(1), 24–32. https://doi.org/10.1177/0306307017723313
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
Xu, Z., Li, Y., & Hao, L. (2019). An empirical examination of UTAUT model and social network analysis. Library Hi Tech, 40(1), 18–32. https://doi.org/10.1108/lht-11-2018-0175
Zhai, H., Yang, X., Xue, J., Lavender, C., Ye, T., Li, J.-B., Xu, L., Lin, L., Cao, W., & Sun, Y. (2021). Radiation oncologists’ perceptions of adopting an artificial intelligence–assisted contouring technology: Model development and questionnaire study. Journal of Medical Internet Research, 23(9). https://doi.org/10.2196/27122
Zhao, Y., Hao, S., Chen, Z., Zhou, X., Zhang, L., & Guo, Z. (2023). Critical factors influencing the internet of things technology adoption behavior of construction companies: Evidence from China. Engineering, Construction and Architectural Management, 32(2), 760–784. https://doi.org/10.1108/ecam-01-2023-0045

Yiek, W. H., Heang, L. T., Ping, C. Y., & Mun, H. W. (2025). Factors Affecting the Adoption of Artificial Intelligence (AI) among Accountants in Malaysia. International Journal of Academic Research in Economics and Management Sciences, 14(4), 8-28.

Copyright: © 2025 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode