AI Adoption in Management Accounting: Its Impact on Decision-Making Efficiency and Managerial Performance in the Digital Transformation Era

Siti Kholifah; Vivi Kumalasari Subroto; Eni Endaryati

doi:10.61132/maeswara.v3i6.2462

Authors

Siti Kholifah Universitas Sains dan Teknologi Komputer
Vivi Kumalasari Subroto Universitas Sains dan Teknologi Komputer
Eni Endaryati Universitas Sains dan Teknologi Komputer

DOI:

https://doi.org/10.61132/maeswara.v3i6.2462

Keywords:

Artificial Intelligence (AI), Technology Adoption

Abstract

In the current era of digital transformation, organizations are increasingly turning to Artificial Intelligence (AI) to enhance the precision and agility of managerial decisions. This study investigates how AI adoption within management accounting systems influences decision-making efficiency and managerial performance. Using survey data collected from managers and accountants in medium-to-large firms across Central Java, the research applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the hypothesized relationships. The results reveal that AI adoption significantly improves decision-making efficiency, which in turn enhances managerial performance. Furthermore, leadership support strengthens the relationship between AI adoption and decision-making efficiency, indicating that human and organizational factors remain critical for realizing technological benefits. The findings contribute to management accounting literature by elucidating the mechanism through which AI technologies create managerial value specifically, by accelerating analytical processes and improving the quality of managerial judgments. Practically, the study suggests that firms should focus on leadership involvement, data governance, and employee training to ensure that AI systems complement, rather than replace, human judgment. Overall, this study highlights that successful AI adoption in management accounting is not merely a technical transition but a strategic and cultural evolution toward evidence-based decision-making.

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References

Andani, G., Lindrianasari, L., Oktavia, R., & Septiyanti, R. (2022a). Indonesian Accounting Students' Self-Confidence To Adopt Artificial Intelligence (AI). Jurnal Akuntansi Dan Keuangan Indonesia, 19(1), 2. https://doi.org/10.21002/jaki.2022.02

Bag, S., Dhamija, P., Gupta, S., & Sivarajah, U. (2021). Examining the role of procurement 4.0 towards remanufacturing operations and circular economy. Production Planning and Control, 32(16), 1368-1383. https://doi.org/10.1080/09537287.2020.1817602

Bashkirova, A., & Krpan, D. (2024). Confirmation bias in AI-assisted decision-making: AI triage recommendations congruent with expert judgments increase psychologist trust and recommendation acceptance. Computers in Human Behavior: Artificial Humans, 2(1), 100066. https://doi.org/10.1016/J.CHBAH.2024.100066

Budiherwanto, I. (2025). The Influence of Artificial Intelligence and the Technology Acceptance Model on Sustainable Financial Governance. Jurnal Ilmiah Manajemen Kesatuan, 13(3), 1791-1802. https://doi.org/10.37641/JIMKES.V13I3.3256

Choukir, J. (2025). Leadership and Job Demands on Managerial Decision-Making Effectiveness through Emotional Exhaustion in the Aviation Industry. Qubahan Academic Journal, 5(4), 227-243. https://doi.org/10.48161/QAJ.V5N4A2163

Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services - the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320. https://doi.org/10.1108/JOSM-10-2020-0378

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of the Structural Model. In: Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Classroom Companion: Business. Springer, Cham. In Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, Issue 1). https://doi.org/10.1007/978-3-030-80519-7_1

Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, 102497. https://doi.org/10.1016/J.IJINFOMGT.2022.102497

Kovari, A. (2024). AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors. Information 2024, Vol. 15, Page 725, 15(11), 725. https://doi.org/10.3390/INFO15110725

Manel, H. A., Sania, W., Fadhillah, N., & Mahmud, A. (2023). Implementasi Artificial Intelligence dalam Sistem Informasi Akuntansi dan Manajemen. Jurnal Akuntansi Bisnis Dan Ekonomi, 9(2), 3460-3467. https://doi.org/10.33197/JABE.VOL9.ISS2.2023.1181

Maria, S., Alka'awneh, N., Abdul-Halim, H., Hasliza, N., & Saad, M. (2025). A Review of Diffusion of Innovations Theory (DOI) and Technology, Organization, and Environment Framework (TOE) in the Adoption of Artificial Intelligence. International Journal of Academic Research in Business and Social Sciences, 15(3), 2118-2129. https://doi.org/10.6007/IJARBSS/V15-I3/24804

Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/J.IM.2019.05.004

Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/J.JSIS.2024.101885

Pratama, B. B., Eltivia, N., & Ekasari, K. (2021). REVOLUSI AKUNTAN 4.0. Jurnal Akuntansi Multiparadigma, 12(3), 547-564. https://doi.org/10.21776/UB.JAMAL.2021.12.3.31

Priyanto, P., & Suhandi, N. P. M. (2022). Robotic Process Automation In Accounting Curriculum And Profession. Jurnal Akuntansi Multiparadigma, 13(3), 576-591. https://doi.org/10.21776/UB.JAMAL.2022.13.3.42

Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation-Augmentation Paradox. Https://Doi.Org/10.5465/Amr.2018.0072, 46(1), 192-210. https://doi.org/10.5465/AMR.2018.0072

Ranta, M., Ylinen, M., & Järvenpää, M. (2023). Machine Learning in Management Accounting Research: Literature Review and Pathways for the Future. European Accounting Review, 32(3), 607-636. https://doi.org/10.1080/09638180.2022.2137221

Supriadi, I. (2024). The audit revolution: Integrating artificial intelligence in detecting accounting fraud. Akuntansi Dan Teknologi Informasi, 17(1), 48-61. https://doi.org/10.24123/JATI.V17I1.6279

Sutjahyani, D. (2025). The Role of Performance Measurement Systems in Enhancing Managerial Decision Making: A Management Accounting Perspective. The Journal of Academic Science, 2(5), 1373-1382. https://doi.org/10.59613/3Y8YX124

Tempomona, I. A. O., Yusuf, R. M., & Paranoan, S. (2023). Urgensi Artificial Intelligence Dalam Transformasi Akuntan(si). Jurnal Akuntansi Multiparadigma, 14(3), 536-551. https://doi.org/10.21776/UB.JAMAL.2023.14.3.37

Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management, 68, 102588. https://doi.org/10.1016/J.IJINFOMGT.2022.102588

Wadipalapa, R. P., Katharina, R., Nainggolan, P. P., Aminah, S., Apriani, T., Ma'rifah, D., & Anisah, A. L. (2024). An Ambitious Artificial Intelligence Policy in a Decentralised Governance System: Evidence From Indonesia. Journal of Current Southeast Asian Affairs, 43(1), 65-93. https://doi.org/10.1177/18681034231226393

Zárate-Torres, R., Rey-Sarmiento, C. F., Acosta-Prado, J. C., Gómez-Cruz, N. A., Rodríguez Castro, D. Y., & Camargo, J. (2025). Influence of Leadership on Human-Artificial Intelligence Collaboration. Behavioral Sciences 2025, Vol. 15, Page 873, 15(7), 873. https://doi.org/10.3390/BS15070873