Governing AI in Management Accounting: Evidence from an Emerging Industrial Economy
DOI:
https://doi.org/10.54259/akua.v5i3.7415Keywords:
AI Governance, Artificial Intelligence, Management AccountingAbstract
The increasing use of artificial intelligence (AI) in management accounting offers significant potential for improving information quality while simultaneously raising concerns about accountability and managerial decision discipline, particularly in emerging industrial contexts. This study aims to explore in depth how AI governance is practiced within management accounting systems and how management control mechanisms frame AI use to support accountability and organizational value creation. The study draws on the literature on management accounting digitalization, AI governance, and management control theory. A qualitative case study approach was employed in the Batam industrial area, using semi-structured interviews, internal document analysis, and limited observation, with data analyzed through thematic analysis. The findings indicate that AI is predominantly positioned as decision support rather than a decision driver, enhancing managerial sensemaking while preserving human judgment through decision ownership, review routines, and documentation. These results suggest that the value of AI in management accounting depends less on technological sophistication and more on the maturity of governance and control mechanisms that ensure disciplined and accountable decision-making.
Downloads
References
K. Abbas, “Management accounting and artificial intelligence: A comprehensive literature review and recommendations for future research,” Br. Account. Rev., 2025, doi: 10.1016/j.bar.2025.101551.
P. Rikhardsson and O. M. Yigitbasioglu, “Business intelligence & analytics in management accounting research: Status and future focus,” Int. J. Account. Inf. Syst., vol. 29, pp. 37–58, 2018, doi: 10.1016/j.accinf.2018.03.001.
M. Cinelli, W. Quattrociocchi, and A. Galeazzi, “The echo chamber effect on social media,” Proc. Natl. Acad. Sci., vol. 118, no. 9, 2021.
D. Appelbaum, A. Kogan, M. A. Vasarhelyi, and Z. Yan, “Impact of business analytics and enterprise systems on managerial accounting,” Int. J. Account. Inf. Syst., vol. 25, pp. 29–44, 2017, doi: 10.1016/j.accinf.2017.03.003.
K. Möller, U. Schäffer, and F. H. M. Verbeeten, “Digitalization in management accounting and control: An editorial,” J. Manag. Control, vol. 31, no. 1–2, pp. 1–8, 2020, doi: 10.1007/s00187-020-00300-5.
U. N. C. on T. and Development, “Technology and Innovation Report 2025: Inclusive artificial intelligence for development,” 2025. [Online]. Available: https://unctad.org/publication/technology-and-innovation-report-2025
M. Schnegg and K. Möller, “Strategies for data analytics projects in business performance forecasting: A field study,” J. Manag. Control, vol. 33, no. 2, pp. 241–271, 2022, doi: 10.1007/s00187-022-00338-7.
A. Bhimani, “Digital data and management accounting: Why we need to rethink research methods,” J. Manag. Control, vol. 31, no. 1, pp. 9–23, 2020, doi: 10.1007/s00187-020-00295-z.
K. A. Merchant and W. A. Van der Stede, Management control systems, 5th ed. Pearson, 2023.
R. Simons, Levers of control: How managers use innovative control systems to drive strategic renewal. Harvard Business School Press, 1995.
T. H. Davenport and R. Ronanki, “Artificial intelligence for the real world,” Harv. Bus. Rev., 2018, [Online]. Available: https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
C. Zhang, W. Zhu, J. Dai, Y. Wu, and X. Chen, “Ethical impact of artificial intelligence in managerial accounting,” Int. J. Account. Inf. Syst., vol. 49, p. 100619, 2023, doi: 10.1016/j.accinf.2023.100619.
M. N. K. Saunders, P. Lewis, and A. Thornhill, Research methods for business students, 9th ed. Pearson, 2023.
M. B. Miles, A. M. Huberman, and J. Saldaña, Qualitative data analysis: A methods sourcebook, 4th ed. SAGE Publications, 2020.
V. Braun and V. Clarke, Thematic analysis: A practical guide. Sage, 2021.
J. Saldaña, The coding manual for qualitative researchers, 4th ed. SAGE Publications, 2021.
D. Appelbaum, A. Kogan, M. Vasarhelyi, and Z. Yan, “Impact of Big Data on Audit Evidence,” Manag. Audit. J., vol. 32, no. 3, pp. 206–231, 2017, doi: 10.1108/MAJ-06-2016-1394.
R. Sentinella, M. Schnegg, and K. Möller, “A management control oriented governance framework for artificial intelligence,” Die Unternehmung, vol. 77, no. 2, pp. 162–184, 2023, doi: 10.5771/0042-059X-2023-2-162.
D. Horneber and S. Laumer, “Algorithmic accountability,” Bus. Inf. Syst. Eng., vol. 65, no. 6, pp. 723–730, 2023.
B. Aysolmaz, R. Müller, and D. Meacham, “The public perceptions of algorithmic decision-making systems: Results from a large-scale survey,” Telemat. Informatics, vol. 79, p. 101954, 2023, doi: 10.1016/j.tele.2023.101954.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Martius Martius, M. Iqbal Yusuf Conoras, Desti Asfina
This work is licensed under a Creative Commons Attribution 4.0 International License.
Hak cipta pada setiap artikel adalah milik penulis.
Penulis mengakui bahwa AKUA (Jurnal Akuntansi dan Keuangan) sebagai publisher yang mempublikasikan pertama kali dengan lisensi Creative Commons Attribution 4.0 International License.
Penulis dapat memasukan tulisan secara terpisah, mengatur distribusi non-ekskulif dari naskah yang telah terbit di jurnal ini kedalam versi yang lain, seperti: dikirim ke respository institusi penulis, publikasi kedalam buku, dan lain-lain. Dengan mengakui bahwa naskah telah terbit pertama kali pada AKUA (Jurnal Akuntansi dan Keuangan).
