Article Sidebar

Submitted
August 7, 2025
Accepted
November 17, 2025
Published
November 25, 2025
Download
PDF
Statistic
Read Counter : 65 Download : 50

Downloads

Download data is not yet available.

Main Article Content

Abstract

The aim of this study is to analyze the influence of readiness and knowledge on the adoption of Artificial Intelligence (AI), as well as the mediating role of user perceptions on this influence. The Technology Acceptance Model (TAM) theory, which is a popular model for predicting people's attitudes when they decide whether or not to embrace a technology system, is used in this study. The sampling strategy employs a random sampling technique, in which the sample is selected at random to create an unrepresentative skew of the entire population. There were 439 responders in all, and the sample consisted of D3, D4, and S1 accounting students from the city of Mataram. A questionnaire is used to collect data. The clever PLS 3.0 application was utilized to apply the Partial Least Square (PLS) technique in order to evaluate the hypothesis. His research's findings demonstrate that knowledge and readiness have a favorable impact on AI adoption, that knowledge and readiness have a positive impact on AI user perceptions, and that user views can mediate the relationship between knowledge and readiness and AI adoption. This study aids in recognizing patterns in how AI is perceived in accounting settings and helps comprehend and predict how these technological advancements will affect students' capacity to adapt.

Article Details

License

Copyright (c) 2025 Nurabiah Nurabiah, Herlina Pusparini, Bambang, Nur Fitriyah

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Hak Cipta (Copyright)

Penulis yang mempublikasikan naskahnya pada Jurnal ini menyetujui ketentuan berikut:

Hak cipta pada setiap artikel adalah milik penulis.

  1. Penulis mengakui bahwa Jurnal  Current : Jurnal Kajian Akuntansi dan Bisnis Terkini berhak sebagai yang mempublikasikan pertama kali dengan lisensi Creative Commons Attribution 4.0 International License.
  2. Penulis dapat memasukan tulisan secara terpisah, mengatur distribusi non-ekskulif  dari naskah yang telah terbit di jurnal ini kedalam versi yang lain (misal: dikirim ke respository institusi penulis, publikasi kedalam buku, dll), dengan mengakui bahwa naskah telah terbit pertama kali pada Jurnal  Current :Jurnal Kajian Akuntansi dan Bisnis Terkini.
How to Cite
Nurabiah, N., Pusparini, H., Bambang, & Fitriyah, N. (2025). PERAN PERSEPSI PENGGUNA DALAM MEMEDIASI ADOPSI ARTIFICIAL INTELLIGENCE (AI) DI KALANGAN MAHASISWA AKUNTANSI. CURRENT: Jurnal Kajian Akuntansi Dan Bisnis Terkini, 6(3), 716–730. https://doi.org/10.31258/current.6.3.716-730
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Abdillah, W., & Jogiyanto. (2015). Partial Least Square (PLS), Alternative Structural Equation Model (SEM) Dalam Penelitian Bisnis. Yogyakarta : Andi.
  2. Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888–918. https://doi.org/10.1037/0033-2909.84.5.888
  3. Al Frijat, Y. S., & Al-Hajaia, M. E. (2021). The role of practical experience requirement in improving the accountant work performance in the business sector. Journal of Governance and Regulation, 10(2), 63–73. https://doi.org/10.22495/jgrv10i2art6
  4. Albrecht, W. S., & Sack, R. J. (2001). The Perilous Future of Accounting Education. The CPA Journal, 3(71), 18–23. http://dx.doi.org/10.1016/j.jaci.2012.05.050
  5. Amdanata, D. D., Burhan, B., Seswandi, A., & Annisava, A. R. (2023). Siapkah Mahasiswa Akuntansi Menghadapi Artificial Intelligence Dalam Akuntansi? Jurnal Akuntansi Kompetif, 6(1), 163–174. https://doi.org/10.35446/akuntansikompetif.v6i1.1282
  6. Amoroso, D. L., & Hunsinger, S. (2009). Measuring the Acceptance of Internet Technology by Consumers. International Journal of E-Adoption, 1(3), 48–81. https://doi.org/10.4018/jea.2009092903
  7. Arif, W. (2006). Kajian tentang perilaku pengguna sistem informasi dengan pendekatan Technology Acceptance Model(TAM). Academia, 4, 1–8.
  8. Athania Purba, K., & Dewayanto, T. (2023). Penerapan Artificial Intelligence, Machine Learning Dan Deep Learning Pada Kurikulum Akuntansi-a Systematic Literature Review. Diponegoro Journal of Accounting, 12, 1–15. http://ejournal-s1.undip.ac.id/index.php/accounting
  9. Awayiga, J. Y., Onumah, J. M., & Tsamenyi, M. (2010). Knowledge and skills development of accounting graduates: The perceptions of graduates and employers in Ghana. Accounting Education, 19(1–2), 139–158. https://doi.org/10.1080/09639280902903523
  10. Bueno, S., & Salmeron, J. L. (2008). TAM-based success modeling in ERP. Interacting with Computers, 20(6), 515–523. https://doi.org/10.1016/j.intcom.2008.08.003
  11. Burnett, S. (2003). The Future of Accounting Education: A Regional Perspective. Journal of Education for Business, 78(3), 129–134. https://doi.org/10.1080/08832320309599709
  12. Byrne, M., & Flood, B. (2003). Assessing the teaching quality of accounting programmes: An evaluation of the course experience questionnaire. Assessment and Evaluation in Higher Education, 28(2), 135–145. https://doi.org/10.1080/02602930301668
  13. Chukwuani, V. N., & Egiyi, M. A. (2020). Automation of Accounting Processes: Impact of Artificial Intelligence. International Journal of Research and Innovation in Social Science (IJRISS), 4(8), 444–449. www.rsisinternational.org
  14. Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107–130. https://doi.org/10.1080/09639284.2021.1872035
  15. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
  16. Emetaram, E., & Uchime, H. N. (2021). Impact of Artificial Intelligence (AI) on Accountancy Profession. Journal of Accounting and Financial Management , 7(2), 2695–2211.
  17. Ghozali, I. (2011). Structural Equation Modeling Metode Alternatif dengan Partial Least Square (PLS) (3rd ed.). Semarang : Undip.
  18. Hasan, A. R. (2022). Artificial Intelligence (AI) in Accounting & Auditing: A Literature Review. Open Journal of Business and Management, 10(01), 440–465. https://doi.org/10.4236/ojbm.2022.101026
  19. Heiat, A., Brown, D., & Johnson, D. M. (2007). An Empirical Analysis Of Underlying Factors Affecting The Choice Of Accounting Major. Journal of College Teaching & Learning (TLC), 4(8). https://doi.org/10.19030/tlc.v4i8.1558
  20. Hidayanti, W., & Azmiyanti, R. (2023). Dampak Penggunaan Chat GPT pada Kompetensi Mahasiswa Akuntansi: Literature Review. Seminar Nasional Akuntansi Dan Call for Paper, 3(1), 83–91. https://senapan.upnjatim.ac.id/index.php/senapan/article/view/288
  21. Howieson, B. (2003). Accounting practice in the new millennium: Is accounting education ready to meet the challenge? In British Accounting Review (Vol. 35, Issue 2). https://doi.org/10.1016/S0890-8389(03)00004-0
  22. Huang, F., No, W. G., Vasarhelyi, M. A., & Yan, Z. (2022). Audit data analytics, machine learning, and full population testing. Journal of Finance and Data Science, 8, 138–144. https://doi.org/10.1016/j.jfds.2022.05.002
  23. Kavanagh, M. H., & Drennan, L. (2010). What skills and attributes does an accounting graduate need. CPA Journal, 80(7), 63–65.
  24. Lai, M. L. (2008). Technology Readiness, Internet Self-Efficacy and Computing Experience of Professional Accounting Students. Campus-Wide Information Systems, 25(1), 18–29., 25(1), 18–29. https://doi.org/https://doi.org/10.1108/1065074081 0849061
  25. Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141. https://doi.org/10.1016/j.elerap.2008.11.006
  26. Leitner-Hanetseder, S., Lehner, O. M., Eisl, C., & Forstenlechner, C. (2021). A profession in transition: actors, tasks and roles in AI-based accounting. Journal of Applied Accounting Research, 22(3), 539–556. https://doi.org/10.1108/JAAR-10-2020-0201
  27. Li, C.-F. (2013). The Revised Technology Acceptance Model and the Impact of Individual Differences in Assessing Internet Banking Use in Taiwan. International Journal of Business and Information, 8(1), 96–119.
  28. Lin, J. S. C., & Chang, H. C. (2011). The role of technology readiness in self-service technology acceptance. Managing Service Quality, 21(4), 424–444. https://doi.org/10.1108/09604521111146289
  29. Lin, J. S. C., & Hsieh, P. L. (2007). The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies. Computers in Human Behavior, 23(3), 1597–1615. https://doi.org/10.1016/j.chb.2005.07.006
  30. Mansor, N. A., Hamid, Y., Anwar, I. S. K., Mohd Isa, N. S., & Abdullah, M. Q. (2022). The Awareness and Knowledge on Artificial Intelligence among Accountancy Students. International Journal of Academic Research in Business and Social Sciences, 12(11), 1629–1640. https://doi.org/10.6007/ijarbss/v12-i11/15307
  31. Marioara, I., Valentin, B., Delia, D., & Amalia, N. Ş. (2022). Perception of Students and Master Students from the Western Part of Romania Over the Digitalization Process in the Accounting Education. Studies in Business and Economics, 17(1), 52–72. https://doi.org/10.2478/sbe-2022-0004
  32. Mohammad, S. J., Hamad, A. K., Borgi, H., Thu, P. A., Sial, M. S., & Alhadidi, A. A. (2020). How artificial intelligence changes the future of accounting industry. International Journal of Economics and Business Administration, 8(3), 478–488. https://doi.org/10.35808/ijeba/538
  33. Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374. https://doi.org/10.1016/j.chb.2014.07.044
  34. Nugroho, M. A. (2022). Hubungan Kesiapan Teknologi dengan Persepsi Kebermanfaatan Teknologi pada UMKM. Nominal Barometer Riset Akuntansi Dan Manajemen, 11(2), 297–306. https://doi.org/10.21831/nominal.v11i2.52425
  35. Parasuraman, A. (2000). Technology Readiness Index (TRI): A Multipleitem Scale To Measure Readiness To Embrace New Technologies. Journal Of Service Research, 2:307(May).
  36. Parasuraman, A., & Colby, C. L. (2015). An Updated and Streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
  37. Pratiwi, M. T., Indriani, F., & Sugiarto, J. (2018). Analisis Pengaruh Technology Readiness Terhadap Minat Menggunakan Tcash Di Kota Semarang. Jurnal Bisnis Strategi, 26(1), 76. https://doi.org/10.14710/jbs.26.1.76-88
  38. Putri Dwima Ernis, & Padli Pirdaus. (2022). Dampak Teknologi Artificial Intelligence Pada Profesi Akuntansi. EKOMA : Jurnal Ekonomi, Manajemen, Akuntansi, 2(1), 131–137. https://doi.org/10.56799/ekoma.v2i1.1154
  39. Qasim, A., & Kharbat, F. F. (2020). Blockchain technology, business data analytics, and artificial intelligence: Use in the accounting profession and ideas for inclusion into the accounting curriculum. Journal of Emerging Technologies in Accounting, 17(1), 107–117. https://doi.org/10.2308/jeta-52649
  40. Qin, X., Shi, Y., Lyu, K., & Mo, Y. (2020). Using a tam-toe model to explore factors of building information modelling (Bim) adoption in the construction industry. Journal of Civil Engineering and Management, 26(3), 259–277. https://doi.org/10.3846/jcem.2020.12176
  41. Rahayu, F. S., Budiyanto, D., & Palyama, D. (2017). Analisis Penerimaan E-Learning Menggunakan Technology Acceptance Model (Tam) (Studi Kasus: Universitas Atma Jaya Yogyakarta). Jurnal Terapan Teknologi Informasi, 1(2), 87–98. https://doi.org/10.21460/jutei.2017.12.20
  42. Sani, A., & Wiliani, N. (2019). Faktor Kesiapan Dan Adopsi Teknologi Informasi Dalam Konteks Teknologi Serta Lingkungan Pada Umkm Di Jakarta. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 5(1), 49–56. https://doi.org/10.33480/jitk.v5i1.616
  43. Sekaran, U., & Bougie, R. (2011). Research Methods for Business: A Skill-Building Approach, 6th Edition (6th ed.). Wiley.
  44. Spraakman, G., O’Grady, W., Askarany, D., & Akroyd, C. (2015). Employers’ Perceptions of Information Technology Competency Requirements for Management Accounting Graduates. Accounting Education, 24(5), 403–422. https://doi.org/10.1080/09639284.2015.1089177
  45. Sternad, S., & Bobek, S. (2013). Impacts of TAM-based External Factors on ERP Acceptance. Procedia Technology, 9, 33–42. https://doi.org/10.1016/j.protcy.2013.12.004
  46. Sugiyono. (2011). Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung : Alfabeta.
  47. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
  48. Venkatesh, V., & Davis, F. D. (2000). 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