Abstract
The rapid advancements in artificial intelligence (AI) technologies have significantly impacted various industries, including the accounting profession. This paper examines the adoption of AI in the accounting profession using the Technology Acceptance Model (TAM) as a framework. The TAM provides a theoretical foundation to understand the factors influencing the acceptance and adoption of AI in accounting, including perceived usefulness, perceived ease of use, attitudes towards AI, and external factors. The paper also discusses the implications of AI adoption for accountants and the challenges associated with integrating AI into accounting practices. Finally, recommendations are provided to facilitate successful AI adoption in the accounting profession.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abousaber, I., & Abdalla, H. F. (2023). Review of using technologies of artificial intelligence in companies. International Journal of Communication Networks and Information Security (IJCNIS), 15(11), 101–108.
Agarwal, P., & Gaur, F. (2020). A historical perspective of artificial intelligence in accounting: Evolution, current developments, and future opportunities. Journal of Accounting and Organizational Change, 16(1), 1–12. https://doi.org/10.1108/AAAJ-09-2020-4934
Alshurafat, H. (2023). The usefulness and challenges of chatbots for accounting professionals: Application on ChatGPT. Available at SSRN: https://doi.org/10.2139/ssrn.4345921.
Askary, S., Abu-Ghazaleh, N., & Tahat, Y. A. (2018). Artificial intelligence and reliability of accounting information. In Challenges and opportunities in the digital era (p. 11195). Springer. https://doi.org/10.1007/978-3-030-02131-3_28
Baldwin, A. A., Brown, C. E., & Trinkle, B. S. (2006). Opportunities for artificial intelligence development in the accounting domain: The case for auditing. International Journal of Intelligent Systems in Accounting, Finance and Management, 14(3), 77–86.
Berdiyeva, O., Islam, M. U., & Saeedi, M. (2021). Artificial intelligence in accounting and finance: meta-analysis. NUST Business Review, 3(1), 57–79. Available at SSRN: https://ssrn.com/Abstract=3897848
Bertino, E., Kantarcioglu, M., Akcora, C. G., Samtani, S., Mittal, S., & Gupta, M. (2021). AI for security and security for AI. Association for Computing Machinery Journal, 11, 333–334. https://doi.org/10.1145/3422337.3450357
Bozkurt, A., et al. (2023). Speculative futures on ChatGPT and Generative Artificial Intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53–130. https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/709
Chu, M., & Yong, K. (2021). Big data analytics for business intelligence in accounting and audit. Open Journal of Social Sciences, 9, 42–52. https://doi.org/10.4236/jss.2021.99004
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
Goh, Clarence, et al. (2019). Charting the future of accountancy with AI. Retrieved from https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2833&context=soa_research
Hasan, A. R. (2022). Artificial Intelligence (AI) in accounting & auditing: A literature review. Open Journal of Business and Management, 10(1), 440–465. https://doi.org/10.4236/ojbm.2022.101026
Jammalamadaka, K. R., & Itapu, S. (2022). Responsible AI in automated credit scoring systems. AI Ethics Publication, 1–11. https://doi.org/10.1007/s43681-022-00175-3.
Lehner, O. M., Ittonen, K., Silvola, H., & Ström, E. (2022). Artificial intelligence-based decision-making in accounting and auditing: Ethical challenges and normative thinking. Accounting, Auditing & Accountability Journal, 35(9), 109–135. https://doi.org/10.1108/AAAJ-09-2020-4934
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, 850–856. https://doi.org/10.4236/ojbm.2018.64063
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualisation, measurement calibration, and empirical study on its impact on organisational creativity and firm performance. Journal of Information & Management, 58(3), 1–20. https://doi.org/10.1016/j.im.2021.103434
Sarker, I. H. (2022). AI-based modelling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3, 158. https://doi.org/10.1007/s42979-022-01043-x
Shaffer, K. J., Gaumer, C. J., & Bradley, K. P. (2020). Artificial intelligence products reshape accounting: Time to re-train. Development and Learning in Organisations, 34(6), 41–43. https://doi.org/10.1108/DLO-10-2019-0242
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 1–10. https://doi.org/10.1016/j.ijhcs.2020.102551
Smith, L. L. (2023). Decreading the digital skills gap in South Africa through income sharing agreements. Available from https://www.umuzi.org/articles/isa-product-offering# (Assessed 20 May 2023).
Sutton, R. S. (2020). John McCarthy’s definition of intelligence. Journal of Artificial General Intelligence, 11(2), 66–67. https://doi.org/10.2478/jagi-2020-0003
Tater, T., et al. (2022). AI driven accounts payable transformation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11). https://doi.org/10.1609/aaai.v36i11.21506
Vărzaru, A. A. (2022). Assessing artificial intelligence technology acceptance in managerial accounting. Electronics, 11, 1–13. https://doi.org/10.3390/electronics11142256
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. IPR2014-00023, No. 1048 Exhibit - Venkatest and Bala 2008 (P.T.A.B. Sep. 25, 2014) (docketalarm.com)
Zhang, Y., **ong, F., **e, Y., Fen, X., & Gu, H. (2019). The impact of artificial intelligence and blockchain on the accounting profession. IEEE Open Access Journal, 8, 110461–110477. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9110603
Zhang, C., Zhu, W., Dai, J., Wu, Y., & Chen, X. (2023). Ethical impact of artificial intelligence in managerial accounting. International Journal of Accounting Information Systems, 49, 1–19. https://doi.org/10.1016/j.accinf.2023.100619
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kayser, K., Telukdarie, A. (2024). Literature Review: Artificial Intelligence Adoption Within the Accounting Profession Applying the Technology Acceptance Model (3). In: Moloi, T., George, B. (eds) Towards Digitally Transforming Accounting and Business Processes. ICAB 2023. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-46177-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-46177-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46176-7
Online ISBN: 978-3-031-46177-4
eBook Packages: Business and ManagementBusiness and Management (R0)