The The impact of using intelligent accounting algorithms on the quality of accounting information

An exploratory study of the opinions of accounting faculty members at universities and higher institutes.

Authors

  • Abdalbaset A.Mohamed Omar جامعة السيد محمد بن علي السنوسي الإسلامية Author
  • Ali Omar Ali Al-Kaseh Sayyid Muhammad bin Ali al-Sanusi Islamic University Author

Keywords:

Intelligent accounting algorithms, quality of accounting information

Abstract

The study focused on understanding the impact of intelligent accounting algorithm dimensions on the quality of accounting information in the Libyan environment by exploring the opinions of accounting faculty members at universities and higher institutes in eastern Libya.

The study sample consisted of 94 participants, and the study employed descriptive tests and simple and multiple regression tests to test the hypotheses.

The study concluded that the four dimensions of the algorithms (technical dimension, functional dimension, analytical dimension, and control and governance dimension) positively impact the quality of accounting information.

The study recommended focusing on further research to understand intelligent accounting algorithms and their dimensions in detail, thereby enhancing the Libyan business environment's understanding of them and its ability to effectively utilize them in the future.

References

Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on management accounting. International Journal of Accounting Information Systems, 25, 29-44. https://www.sciencedirect.com/science/article/abs/pii/S1467089517300490

Atrous, R. (2025). Artificial intelligence and the evolution of accounting and audit practices: A systematic review. International Journal of Economics, Business and Management Research, 9(1), 125-143. https://doi.org/10.51505/IJEBMR.2025.9110

Bodnar, G. H., & Hopwood, W. S. (2019). Accounting information systems (15th ed.). Pearson.

Carollo, M., Lauri, L., Bardozzo, F. et al. (2026) Exploring the efficiency of GPT models in automating trial balance mapping: applications and insights. Soft Comput 30, 1693–1709. https://doi.org/10.1007/s00500-025-10983-4

COSO. (2017). Enterprise risk management: Integrating with strategy and performance. COSO, https://www.coso.org/Documents/2017-COSO-ERM-Integrating-with-Strategy-and-Performance-Executive-Summary.pdf

Deloitte. (2025). The role of algorithms in financial reporting and decision making. https://www.deloitte.com/reports/example.

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.

George, D., & Mallery, P. (2010). SPSS for Windows Step by Step: A Simple Guide and Reference, 17.0 Update (10th ed.). Allyn & Bacon.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.

Hnylytska, L., & Sattarov, A. (2025). Conceptual foundations of integrating intelligent agents into the enterprise accounting ecosystem. Digital Economy and Economic Security, (6-21), 21-29. https://doi.org/10.32782/dees.21-4

Ionescu, L. (2022). Big data algorithms and artificial intelligence technologies in cloud-based accounting information systems. Analysis and Metaphysics, (21), 42-57. https://www.ceeol.com/search/article-detail?id=1088894

Kieso, D. E., Weygandt, J. J., & Warfield, T. D. (2019). Intermediate accounting (17th ed.). Wiley.

Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308

Laudon, K. C., & Laudon, J. P. (2012). Management information systems: Managing the digital firm (12th ed.). Prentice Hall.

Li, H., Gao, H., Wu, C., & Vasarhelyi, M. A. (2025). Extracting financial data from unstructured sources: Leveraging large language models. Journal of Information Systems, 39(1), 135-156. https://doi.org/10.2308/ISYS-2023-047

Menard, S. (2002). Applied Logistic Regression Analysis. SAGE Publications.

Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10.

https://doi.org/10.2308/jeta-51935

Nobes, C., & Parker, R. (2020). Comparative international accounting (14th ed.). Pearson.

Nuritdinovich, A. S., Karimov, B., & Soliev, I. (2025). Integration of artificial intelligence and blockchain in modern accounting systems: Enhancing transparency and efficiency. Journal of Advanced Research in Economics and Administrative Sciences, 6(1), 45-62. https://doi.org/10.47631/jareas.v6i1.1042

Pallant, J. (2020). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS (7th ed.). Routledge.

Reinking, J., et al. (2020). Artificial intelligence in accounting: The importance of understanding AI. Journal of Information Systems, 34(1), 35–60.

https://doi.org/10.2308/isys-52609

Romney, M. B., Steinbart, P. J., Summers, S. L., & Wood, D. A. (2021). Accounting information systems (15th ed.). Pearson.

Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Sampaio, J., & Silva, R. (2025). Digital transformation in accounting: An assessment of automation and AI integration. International Journal of Financial Studies, 13(1), 15-32. https://doi.org/10.3390/ijfs13010015.

Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach (7th ed.). John Wiley & Sons.

Sekinobe, M., Mukasa, K., Nayebale, F. I., & Kato, J. (2025). An interdisciplinary framework for the development of intelligent accounting automation systems integrating predictive risk analytics and dynamic internal control mechanisms to enhance regulatory compliance and fraud mitigation in high-risk economic sectors. International Journal of Innovative Science and Research Technology, 10(12), 2520-2533. https://ideas.repec.org/a/cvr/ijisrt/202512ijisrt25dec1138.html

Sutton, S. G., Holt, M., & Arnold, V. (2016). The reports of my death are greatly exaggerated—Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 22, 60–73.https://doi.org/10.1016/j.accinf.2016.07.005

Tiron-Tudor, A., & Deliu, D. (2021). The future of accounting profession in the digital era: The impact of technology. Journal of Risk and Financial Management, 14(8), 382. https://doi.org/10.3390/jrfm14080382

Warren, J. D., Moffitt, K. C., & Byrnes, P. (2015). How Big Data will Control the History of Accounting and Outcomes. Accounting Horizons, 29(2), 397-407. https://www.scirp.org/reference/referencespapers?referenceid=2371058

Zhang, C., Sun, T., & Vasarhelyi, M. A. (2020). Framework for managing the algorithmic risk of artificial intelligence in auditing. Journal of Emerging Technologies in Accounting, 17(2), 1-16. https://doi.org/10.2308/JETA-2020-025

Downloads

Published

2026-06-13

Issue

Section

المقالات

How to Cite

The The impact of using intelligent accounting algorithms on the quality of accounting information: An exploratory study of the opinions of accounting faculty members at universities and higher institutes. (2026). Al-Ru’ya Journal of Economic and Political Sciences, 11(1), 175-207. https://journals.ius.edu.ly/index.php/rouya_Journal/article/view/34

Similar Articles

You may also start an advanced similarity search for this article.