A Deep Learning Framework to Model the Moderating Effect of Ethical Leadership on Emerging Technology’s Impact on Auditors’ Professional Judgment

Document Type : Original Article

Authors

Amir Avazpor ¹

Hashem Valipour ²

Hamid Salehi ³

¹ Department of Accounting, Yas. C., Islamic Azad University, Yasuj, Iran,

² Department of Accounting, Fir.C., Islamic Azad University, Firuzabad, Iran.

³ Department of Accounting, Fir.C., Islamic Azad University, Firuzabad, Iran

10.22067/ijaaf.2026.47435.1581

Abstract

This study introduced a Deep Moderated Neural Network (DMNN) to model the non-linear interaction between emerging technology adoption and ethical leadership in shaping auditors’ professional judgment. Drawing on survey data from 151 auditors, were normalized five technology usage items and five ethical leadership items to [0,1] and constructed the target judgment score as the average of five professional-judgment items. We benchmarked the DMNN against four alternative methods ordinary least squares regression (OLS), OLS with explicit pairwise interactions (OLS+Int), support vector regression (SVR), and random forest (RF) using RMSE and R² on a held-out test set. The DMNN obtained an RMSE of 0.1014 and an R² of 0.7562 better than those by OLS (RMSE = 0.1035, R² = 0.703), OLS+Int (RMSE = 0.1333, R² = 0.508), SVR (RMSE = 0.1818, R² = 0.084) and RF (RMSE = 0.1006, R² =0.719). These results showed that the DMNN learns complex moderation effects much better and also achieved higher predictive accuracy and explained variance than linear- and traditional machine learning approaches. The DMNN demonstrated enhanced performance as well as theoretical interpretability to shed light on auditors’ judgment processes and represents a promising new analytical tool for auditing research moving forward.

Keywords

Ethical Leadership

Emerging Technologies

Auditors’ Professional Judgment

Deep Moderated Neural Network (DMNN)

Machine Learning

Subjects

• Auditing

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References

1. Abbaskhani, H. Pakmaram, A. Rezaei, N. and Bahri Sales, J. (2024). Enhancing going concern prediction models: integrating text mining with data mining approaches. Iranian Journal of Accounting, Auditing and Finance, 8(3), pp 27-42 (In Persian). https://doi.org/10.22067/ijaaf.2024.43123.1217
2. Baron, R. M. and Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), pp. 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173
3. Brown‑Liburd, H. L., Issa, H. and Lombardi, D. (2020). Behavioral implications of big data’s impact on audit judgment and decision making and future research directions. Accounting Horizons, 34(4), pp. 451–468. https://doi.org/10.2308/horizons-19-116
4. Dutta, I., Bouri, A. and Adhikari, B. (2024). Reading between the lines: Improving financial fraud detection using a dual-channel text-numeric deep learning model. International Journal of Accounting Information Systems, 54, pp. 100674. https://doi.org/10.1016/j.accinf.2024.100674
5. Faraj Gumar, H. Piri, P. and Heydari, M. (2025). Modeling the relationship between financial stability and banking risks: artificial intelligence approach. Iranian Journal of Accounting, Auditing and Finance, 9(2), pp 113-133 (In Persian). https://doi.org/10.22067/ijaaf.2025.45952.1498
6. Filsaraei, M. and Sadeghi, M. S. (2024). The impact of professional ethics, social structure, and religious attitude on auditors’ judgments: a comparison of the environments in India and Iran. Iranian Journal of Accounting, Auditing and Finance, 8(4), pp 51-69 (In Persian). https://doi.org/10.22067/ijaaf.2024.45130.1425
7. Haghighi, R., Bagherpour, M. A., Ghanaei Chamanabad, A. and Abbaszadeh, M. R. (2024). Are auditors really independent in making professional judgment?. Iranian Journal of Accounting, Auditing and Finance, 8(3), pp 111-130 (In Persian). https://doi.org/10.22067/ijaaf.2024.44946.1432
8. Hussain, K., Ahmed, I. and Aamir, M. (2022). Ethical leadership and auditor’s underreporting of audit time: mediating role of work engagement. Review of Applied Management and Social Sciences, 5(2), pp. 231-242. https://doi.org/10.47067/ramss.v5i2.231
9. Nasirpour, M. Jabbarzadeh Kangarloie, S. Bahri Sales, J. and Badavar Nahandi, Y. (2023). The effect of ethical leadership and spirituality at work on the auditor’s dysfunctional behavior with the mediating role of moral atmosphere and moderating role of professional skepticism. Journal of Management Accounting and Auditing Knowledge, 12(46), pp. 267-287. https://doi.org/10.30473/JMAAK.2022.62883.2505
10. Ogiriki, T and Odoni, J. A. (2025). Auditor behaviour, judgment, and decision-making: Exploring the interplay. International Journal of Innovative Finance and Economics Research, 13(1), pp. 372–385. https://doi.org/10.5281/zenodo.15748941
11. Rahimi, K. Mehrazin, A. Noori Toupkanlu, Z. and Moradi, M. (2025). Judgment and decision making in accounting and auditing: Person, task and environment perspectives. Iranian Journal of Accounting, Auditing and Finance, 10(1), pp 117-149. https://doi.org/10.22067/ijaaf.2025.90979.1509
12. Rawashdeh, A. (2024). A deep learning-based SEM-ANN analysis of the impact of AI-based audit services on client trust. Journal of Applied Accounting Research, 25(3), pp. 594-622. https://doi.org/10.1108/JAAR-10-2022-0273
13. Samiolo, R. Spence, C. and Toh, D. (2024). Auditor judgment in the fourth industrial revolution. Contemporary Accounting Research, 41(1), pp. 498-528. https://doi.org/10.1111/1911-3846.12901
14. Sun, T. and Vasarhelyi, M. A. (2017). Deep learning and the future of auditing: how an evolving intelligent technology could transform analysis and improve judgment. Journal of Emerging Technologies in Accounting, 14(1), pp. 77–84. https://doi.org/10.2308/jeta-51785
15. Zimmerman, C. and Yen, D. (2023). Moral disengagement and auditor judgment under ethical climate variations. Journal of Accounting, Organizations and Society, 105, A. 101355. https://doi.org/10.1016/j.aos.2023.101355