Analysis of Artificial Intelligence (AI) Technology Acceptance Among Accounting Employees: A Model Based on UTAUT-3

Authors

  • Novia Permatasari Program Studi S2 Akuntansi, STIESIA Surabaya, Indonesia
  • Mia Ika Rahmawati Program Studi S2 Akuntansi, STIESIA Surabaya, Indonesia

Keywords:

ESG Risk Rating, Capital Structure, Profitability, Firm Value

Abstract

This study aims to analyze the factors that influence the acceptance and use of artificial intelligence (AI) technology by accounting employees using the UTAUT-3 model. Using a quantitative approach, data were collected from 162 accounting employees of an Internet Service Provider (ISP) company in East Java via a questionnaire and analyzed using PLS-SEM via SmartPLS 4. The results indicate that performance expectations, effort expectations, and social impact positively influence behavioral intention, while facility conditions, hedonic motivation, habit, and personal innovation do not have a significant effect. Habit influences actual usage behavior, but behavioral intention does not have a significant effect. These findings indicate the dominance of functional factors over pleasure or infrastructure in driving AI adoption. This study enriches the behavioral accounting literature and provides managerial implications for organizations in strategically adopting AI in financial reporting.

References

Abdillah, W., & Jogiyanto. (2015). Partial Least Square (PLS) Alternatif Structural Equational Modeling (SEM) dalam Penelitian Bisnis (1st ed.). Penerbit Andi.

Ajzen, I. (1991). Theory of Planned Behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.47985/dcidj.475

Alalwan, A. A., Dwivedi, Y. K., & Williams, M. D. (2016). Customers’ Intention and Adoption of Telebanking in Jordan. Information Systems Management, 33(2), 154–178. https://doi.org/10.1080/10580530.2016.1155950

Ali, M. B., Tuhin, R., Alim, M. A., Rokonuzzaman, M., Rahman, S. M., & Nuruzzaman, M. (2024). Acceptance and use of ICT in tourism: the modified UTAUT model. Journal of Tourism Futures, 10(2), 334–349. https://doi.org/10.1108/JTF-06-2021-0137

Alkhwaldi, A. F., Alidarous, M. M., & Alharasis, E. E. (2024). Antecedents and outcomes of innovative blockchain usage in accounting and auditing profession: an extended UTAUT model. Journal of Organizational Change Management, 37(5), 1102–1132. https://doi.org/10.1108/JOCM-03-2023-0070

Almaiah, M. A., Al-Rahmi, A. M., Alturise, F., Alrawad, M., Alkhalaf, S., Lutfi, A., Al-Rahmi, W. M., & Awad, A. B. (2022). Factors influencing the adoption of internet banking: An integration of ISSM and UTAUT with price value and perceived risk. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.919198

Almaqtari, F. A. (2024). The Role of IT Governance in the Integration of AI in Accounting and Auditing Operations. Economies, 12(8). https://doi.org/10.3390/economies12080199

Bakri, M. H., Almansoori, K. K. S. M., & Azlan, N. S. M. (2023). Determinants intention usage of Islamic E-Wallet Among Millennials. Global Business and Finance Review, 28(1), 11–32. https://doi.org/10.17549/gbfr.2023.28.1.11

Butt, S., Mahmood, A., & Saleem, S. (2022). The role of institutional factors and cognitive absorption on students’ satisfaction and performance in online learning during COVID 19. In PLoS ONE (Vol. 17, Issue 6 June). https://doi.org/10.1371/journal.pone.0269609

Butt, S., Mahmood, A., Saleem, S., Murtaza, S. A., Hassan, S., & Molnár, E. (2023). The Contribution of Learner Characteristics and Perceived Learning to Students’ Satisfaction and Academic Performance during COVID-19. Sustainability (Switzerland), 15(2). https://doi.org/10.3390/su15021348

Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201(January), 123247. https://doi.org/10.1016/j.techfore.2024.123247

Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(JULY), 1–14. https://doi.org/10.3389/fpsyg.2019.01652

Davis, F. D. (1989). Scientific Commons: A technology acceptance model for empirically testing new end-user information systems : theory and results (1985), 1985 [Davis, Fred D]. MIS Quarterly: Management Information Systems, 13(3), 319. http://en.scientificcommons.org/7894517

Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2. Interactive Technology and Smart Education, 14(4), 329–348. https://doi.org/10.1108/ITSE-06-2016-0015

Fu, J., Mouakket, S., & Sun, Y. (2024). Factors Affecting Customer Readiness to Trust Chatbots in an Online Shopping Context. Journal of Global Information Management, 32(1), 1–23. https://doi.org/10.4018/JGIM.347503

Gama, M. A. (2021). Pengaruh Task-Technology Fit Terhadap Prestasi Belajar Mahasiswa Akuntansi Dimediasi Oleh Pemanfaatan Smartphone Suwardi Bambang Fidiana Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA) Surabaya. Jurnal Ilmu Dan Riset Akuntansi, 8(10).

Gardner, B., Lally, P., & Rebar, A. L. (2020). Does habit weaken the relationship between intention and behaviour ? Revisiting the habit- intention interaction hypothesis. Social and Personality Psychology Compass, 14(8), 1–24. https://doi.org/10.1111/spc3.12553

Ghozali, I., & Kusumadewi, K. A. (2023). Partial Least Squares Konsep Teknik dan Aplikasi Menggunakan Program SmartPLS 4.0 untuk Penelitian Empiris (Edisi 1). Penerbit Andi Yogyakarta.

Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49(February), 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008

Guste, R. R. A., & Ong, A. K. S. (2024). Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology. Computers, 13(6). https://doi.org/10.3390/computers13060128

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

Hair, J., Sarstedt, M., & Ringle, C. M. (2017). Partial Least Squares Structural Equation Modeling (Issue September). https://doi.org/10.1007/978-3-319-05542-8

Hossain, M. A., Hasan, M. I., Chan, C., & Ahmed, J. U. (2017). Predicting user acceptance and continuance behaviour towards location-based services: The moderating effect of facilitating conditions on behavioural intention and actual use. Australasian Journal of Information Systems, 21, 1–22. https://doi.org/10.3127/ajis.v21i0.1454

Im, S., Bayus, B. L., & Mason, C. H. (2003). An Empirical Study of Innate Consumer Innovativeness , Personal Characteristics , and New-Product Adoption Behavior. Journal of the Academy of Marketing Science, 1(31), 61–73. https://doi.org/10.1177/0092070302238602

Isaac, O., Abdullah, Z., Aldholay, A. H., & Abdulbaqi Ameen, A. (2019). Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Asia Pacific Management Review, 24(4), 335–354. https://doi.org/10.1016/j.apmrv.2018.12.003

Kašparová, P. (2023). Intention to use business intelligence tools in decision making processes: applying a UTAUT 2 model. Central European Journal of Operations Research, 31(3), 991–1008. https://doi.org/10.1007/s10100-022-00827-z

Kumar, J., Rani, M., Rani, G., & Rani, V. (2024). Human-machine dialogues unveiled: an in-depth exploration of individual attitudes and adoption patterns toward AI-powered ChatGPT systems. Digital Policy, Regulation and Governance , 26(4), 435–449. https://doi.org/10.1108/DPRG-11-2023-0167

Lin, D., Fu, B., Xie, K., Zheng, W., Chang, L., & Lin, J. (2023). Research on the Improvement of Digital Literacy for Moderately Scaled Tea Farmers under the Background of Digital Intelligence Empowerment. Agriculture (Switzerland), 13(10). https://doi.org/10.3390/agriculture13101859

Liu, J. Y. W., Sorwar, G., Rahman, M. S., & Hoque, M. R. (2023). The role of trust and habit in the adoption of mHealth by older adults in Hong Kong: a healthcare technology service acceptance (HTSA) model. BMC Geriatrics, 23(1), 1–18. https://doi.org/10.1186/s12877-023-03779-4

Lu, L., Zhao, J., & Chen, H. (2024). Investigating OTA employees’ double-edged perceptions of ChatGPT: The moderating role of organizational support. International Journal of Hospitality Management, 120(February), 103753. https://doi.org/10.1016/j.ijhm.2024.103753

Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75(28), 102362. https://doi.org/10.1016/j.techsoc.2023.102362

Maisha, K., & Shetu, S. N. (2023). Influencing factors of e-learning adoption amongst students in a developing country: the post-pandemic scenario in Bangladesh. In Future Business Journal (Vol. 9, Issue 1). https://doi.org/10.1186/s43093-023-00214-3

Mangadi, T., & Petersen, F. (2024). Factors influencing the acceptance and use of a South African online bank. South African Journal of Information Management, 26(1), 1–12. https://doi.org/10.4102/sajim.v26i1.1759

Marikyan, D., Papagiannidis, S., & Alamanos, E. (2023). Cognitive Dissonance in Technology Adoption : A Study of Smart Home Users. 1, 1101–1123.

Meraghni, O., Bekkouche, L., & Demdoum, Z. (2021). IMPACT OF DIGITAL TRANSFORMATION ON ACCOUNTING INFORMATION SYSTEMS – EVIDENCE. 0394, 249–264.

Muchran, M., Khairudin, N. S., Arizah, A., Rayyani, W. O., Soraya, Z., Irwan, A., & Muchran. (2024). Integration of the UTAUT 2 Model and Awareness of Cybercrime as the Moderating Variable of Cashless Adoption in Indonesia. Review of Integrative Business and Economics Research, 13(3), 304–321.

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(September 2023), 123022. https://doi.org/10.1016/j.techfore.2023.123022

Ohtomo, S., & Kimura, R. (2022). The effect of habit on preventive behaviors : a two-wave longitudinal study to predict COVID-19 preventive behaviors. Health Psychology and Behavioral Medicine, 2850. https://doi.org/10.1080/21642850.2022.2075876

Orbell, S., & Sheetan, P. (1998). ‘ Inclined abstainers ’ : A problem for predicting health-related behaviour. Journal of Social Psychology, 37, 151–165.

Rahmawati, M. I., & Subardjo, A. (2022). A Bibliometric Analysis of Accounting in the Blockchain Era. Journal of Accounting and Investment, 23(1), 66–77. https://doi.org/10.18196/jai.v23i1.13302

Ren, Z., & Zhou, G. (2023). Analysis of Driving Factors in the Intention to Use the Virtual Nursing Home for the Elderly: A Modified UTAUT Model in the Chinese Context. Healthcare (Switzerland), 11(16). https://doi.org/10.3390/healthcare11162329

Sheeran, P. (2002). European Review of Social Psychology Intention — Behavior Relations : A Conceptual and Empirical Review. European Review of Social Psychology, 12(1), 37–41.

Sugianto, L. O., Hartono, A., Permatasari, N., & Ulfah, I. F. (2019). Integration of Information System Success Models to Explain End User Satisfaction of Debtor Information Systems. AFRE (Accounting and Financial Review), 2(1), 32–41. https://doi.org/10.26905/afr.v2i1.3260

Tanantong, T., & Wongras, P. (2024). A UTAUT-Based Framework for Analyzing Users’ Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand. Systems, 12(1). https://doi.org/10.3390/systems12010028

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540

Venkatesh, V., Tech, V., Weng, Q., Rai, A., & Maruping, L. M. (2023). Guidelines for the Development of Three-Level Models : Bridging Levels of Analysis and Integrating Contextual Influences in IS Guidelines for the Development of Three-Level Models : Bridging Levels of Analysis and Integrating Contextual Influences in IS R. Journal of the Association for Information Systems Volume, 24(1), 65–106. https://doi.org/10.17705/1jais.00778

Venkatesh, V., Thong, J. Y. ., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. Management Information Systems Research Center, 36(1), 157–178. https://doi.org/10.2307/41410412

Wang, L., & Li, W. (2024). The Impact of AI Usage on University Students’ Willingness for Autonomous Learning. Behavioral Sciences, 14(10). https://doi.org/10.3390/bs14100956

Wang, S., & Nah, K. (2024). Exploring Sustainable Learning Intentions of Employees Using Online Learning Modules of Office Apps Based on User Experience Factors: Using the Adapted UTAUT Model. Applied Sciences (Switzerland), 14(11). https://doi.org/10.3390/app14114746

Wu, C. G., & Ho, J. C. (2022). The influences of technological characteristics and user beliefs on customers’ perceptions of live chat usage in mobile banking. International Journal of Bank Marketing, 40(1), 68–86. https://doi.org/10.1108/IJBM-09-2020-0465

Wu, Y., Wu, X., Zheng, H., Han, F., & Huang, Y. (2025). Factors influencing behavioral intention to use e learning in higher education during the COVID 19 pandemic: A meta analytic review based on the UTAUT2 model. In Education and Information Technologies (Issue January). Springer US. https://doi.org/10.1007/s10639-024-13299-2

Yang, T., Lai, I. K. W., Fan, Z. Bin, & Mo, Q. M. (2020). Interactive service quality on the acceptance of self-service ordering systems for the restaurant industry. Journal of Hospitality and Tourism Technology, 12(2), 271–286. https://doi.org/10.1108/JHTT-02-2020-0041

Yasmine Fathy Abdel Moneim. (2024). The Impact of UTAUT, trust perspective and bank’s reputation on actual use of mobile banking with mediating role of behavioral intention: An empirical study on commercial banks in Egypt. Journal of Electrical Systems, 20(4s), 1553–1562. https://doi.org/10.52783/jes.2197

Zhan, Y., Sun, Y., & Xu, J. (2023). A Study on the Recycling Classification Behavior of Express Packaging Based on UTAUT under “Dual Carbon” Targets. Sustainability (Switzerland), 15(15), 1–23. https://doi.org/10.3390/su151511622

Downloads

Published

30-09-2025

How to Cite

Permatasari, N., & Rahmawati, M. I. (2025). Analysis of Artificial Intelligence (AI) Technology Acceptance Among Accounting Employees: A Model Based on UTAUT-3. E-Jurnal Akuntansi, 35(9). Retrieved from https://ejournal1.unud.ac.id/index.php/akuntansi/article/view/1983

Issue

Section

Articles