BEHAVIORAL IMPLICATIONS OF AI-SUPPORTED MANAGEMENT ACCOUNTING INFORMATION ON MANAGERIAL JUDGEMENT AND PERFORMANCE

Authors

  • Christina Sososutiksno Universitas Pattimura, Indonesia Author
  • Irga Anugrah Safira Harahap Universitas Pattimura, Indonesia Author
  • Nurul Amalia Ali Slamat Universitas Pattimura, Indonesia Author

Keywords:

Artificial Intelligence, Management Accounting Information, Managerial Judgment, Behavioral Implications, Managerial Performance

Abstract

The development of artificial intelligence has brought significant changes to management accounting information systems, particularly in providing faster, more accurate, and predictive information. The application of AI-supported management accounting information not only impacts the technical aspects of decision-making but also raises behavioral implications that influence managerial judgment and organizational performance. This study aims to systematically examine the behavioral implications of the use of AI-based management accounting information on managerial judgment processes and managerial performance. The method used is a literature review by examining relevant scientific articles from reputable international journals in the fields of management accounting, information systems, and organizational behavior. The results of the study indicate that AI support in management accounting can improve judgment quality by providing comprehensive data analysis, reducing certain cognitive biases, and increasing the speed and consistency of decisions. However, excessive reliance on AI systems also has the potential to create behavioral risks, such as decreased professional skepticism, overreliance on algorithmic recommendations, and a reduced role of intuition and managerial experience. These behavioral implications directly and indirectly affect managerial performance, both in terms of decision-making effectiveness, accountability, and the achievement of the organization's strategic goals. This research contributes to the development of management accounting literature by emphasizing the importance of a socio-technical approach to AI implementation, where technology integration needs to be balanced with an understanding of human behavioral factors to optimize performance benefits sustainably.

Downloads

Download data is not yet available.

References

Acciarini, C., Brunetta, F., & Boccardelli, P. (2020). Cognitive biases and decision-making strategies in times of change: A systematic literature review. Management Decision, 59(3), 638–652. https://doi.org/10.1108/MD-07-2019-1006

Advancing Interdisciplinary Studies on Social Sciences – Social Sciences Bibliography Indexes and Archives Data. (n.d.). Retrieved January 13, 2026, from https://sobiad.org/

Aliah, N., & Faridani, M. R. (2025). Transforming Accounting Through Artificial Intelligence: A Systematic Literature Review of Opportunities, Challenges, and Ethical Imperatives. Proceedings of International Conference on Islamic Community Studies, 677–696.

Buschmeyer, K., Hatfield, S., & Zenner, J. (2023). Psychological assessment of AI-based decision support systems: Tool development and expected benefits. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1249322

Carter, L., & Liu, D. (2025). How was my performance? Exploring the role of anchoring bias in AI-assisted decision making. International Journal of Information Management, 82, 102875. https://doi.org/10.1016/j.ijinfomgt.2025.102875

Çeri, Ş. Ö., & Erhan, T. (2025). A Conceptual Study on the Effects of Artificial Intelligence in Managerial Decision-Making. European Journal of Digital Economy Research, 6(1), 5–21. https://doi.org/10.5281/zenodo.15660090

Chen, Y., Kirshner, S. N., Ovchinnikov, A., Andiappan, M., & Jenkin, T. (2025). A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do? Manufacturing & Service Operations Management, 27(2), 354–368. https://doi.org/10.1287/msom.2023.0279

Dietzmann, C., & Duan, Y. (2022). Artificial Intelligence for Managerial Information Processing and Decision-Making in the Era of Information Overload. Hawaii International Conference on System Sciences 2022 (HICSS-55). https://aisel.aisnet.org/hicss-55/os/ai_and_organizing/3

Eng’airo, P. (2024a). The Impact of AI-Driven Performance Evaluation on Organizational Outcomes in Kenya: A Systematic Literature Review. Journal of Information, Technology and Data Science, 8(2), 1–15. https://doi.org/10.53819/81018102t7040

Eng’airo, P. (2024b). The Impact of AI-Driven Performance Evaluation on Organizational Outcomes in Kenya: A Systematic Literature Review. Journal of Information, Technology and Data Science, 8(2), 1–15. https://doi.org/10.53819/81018102t7040

Hristov, I., Camilli, R., & Mechelli, A. (2022). Cognitive biases in implementing a performance management system: Behavioral strategy for supporting managers’ decision-making processes. Management Research Review, 45(9), 1110–1136. https://doi.org/10.1108/MRR-11-2021-0777

Huong, N. T. Q., & Thanh, N. T. M. (2025). MANAGEMENT ACCOUNTING IN THE ERA OF DIGITAL TRANSFORMATION – TRENDS AND CHALLENGES. 6(11).

Ikwuo, A., Nworie, G., & Moedu, V. (2024). Implementation of AI-driven automation: A game-changer in accounting research. International Journal of Financial, Accounting, and Management, 6. https://doi.org/10.35912/ijfam.v6i3.2522

Limba, F. B., Sapulette, S. G., & Sitanala, T. F. (2025). AUDITOR PERCEPTION OF CLOUD TECHNOLOGY-BASED AUDITS. INTERNATIONAL JOURNAL OF ECONOMIC LITERATURE, 3(5), 291–306.

Massaro, M., Secinaro, S., Bagnoli, C., & Calandra, D. (2025a). Guest editorial: Artificial Intelligence (AI) for management decision-making processes. From measurement to strategy. Management Decision, 63(10), 3213–3225. https://doi.org/10.1108/MD-10-2025-377

Massaro, M., Secinaro, S., Bagnoli, C., & Calandra, D. (2025b). Guest editorial: Artificial Intelligence (AI) for management decision-making processes. From measurement to strategy. Management Decision, 63(10), 3213–3225. https://doi.org/10.1108/MD-10-2025-377

Menguy, B., & El Khoury, K. (2025). Reframing Financial Decision-Making in the Age of AI: Rethinking Oversight, Accountability, and Control in the Algorithmic Era. https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-214602

Proenca, J. C. (2024). Business Innovation Self-Assessment With Artificial Intelligence Support For Small And Medium-Sized Enterprises. Бизнес Управление, 4, 5–17.

Rahman, M. A., Shaon, S. H., & Islam, S. (2025a). Trust over ease? Examining the mediated effects of profitability on AI adoption in accounting. Technological Sustainability, 4(4), 402–423. https://doi.org/10.1108/TECHS-05-2025-0098

Rahman, M. A., Shaon, S. H., & Islam, S. (2025b). Trust over ease? Examining the mediated effects of profitability on AI adoption in accounting. Technological Sustainability, 4(4), 402–423. https://doi.org/10.1108/TECHS-05-2025-0098

Rastogi, C., Zhang, Y., Wei, D., Varshney, K. R., Dhurandhar, A., & Tomsett, R. (2022). Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. Proc. ACM Hum.-Comput. Interact., 6(CSCW1), 83:1-83:22. https://doi.org/10.1145/3512930

The Influence of Artificial Intelligence on Accounting Industry Practices: A Qualitative Exploratory Case Study - ProQuest. (n.d.-a). Retrieved January 13, 2026, from https://www.proquest.com/openview/35e902b3a519a245c69e3acf2d3289da/1?pq-origsite=gscholar&cbl=18750&diss=y

The Influence of Artificial Intelligence on Accounting Industry Practices: A Qualitative Exploratory Case Study - ProQuest. (n.d.-b). Retrieved January 13, 2026, from https://www.proquest.com/openview/35e902b3a519a245c69e3acf2d3289da/1?pq-origsite=gscholar&cbl=18750&diss=y

Zamil, M. H. (2025). AI-DRIVEN BUSINESS ANALYTICS FOR FINANCIAL FORECASTING: A SYSTEMATIC REVIEW OF DECISION SUPPORT MODELS IN SMES. Review of Applied Science and Technology, 4(02), 86–117. https://doi.org/10.63125/gjrpv442

Downloads

Published

2026-01-16