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Artificial Intelligence Dependence, Auditors’ Experience and Professional Judgment: Evidence from Selected Audit Firms in Oyo State, Nigeria

Gbadamosi, Azeez Babatunde, Adedipe Oluwaseyi Ayodele Ph.D

Abstract

Artificial intelligence has been incorporated into auditing practice, increasing coverage and reach, but poses the unsolved problem of the erosion of professional judgment due to the reliance on the output of algorithms. Evidence from developing countries on this issue is scarce, and Nigeria-focused studies have been mostly conceptual. This study investigated the dependence on artificial intelligence and its effect on auditors’ professional judgment, as well as the moderating effect of auditors’ experience, the complexity of audit engagements, and firm policies on the selected audit firms in Oyo State (Nigeria). The study employed a descriptive and explanatory survey research design. The researcher used purposive sampling to distribute 320 questionnaires to audit and assurance staff from junior associates to partners; 278 completed questionnaires were returned and 261 retained for analysis (an effective response rate of 81.6%). All constructs-maintained reliability and met the criteria for convergent validity (with Cronbach’s alpha between 0.854 and 0.891, composite reliability between 0.868 and 0.902, and average variance extracted between 0.58 and 0.65). The principal methods of statistical analysis were the Pearson correlation and hierarchical regression. The dependence on artificial intelligence significantly and negatively influences professional judgment (B = −0.433, β = −0.412, R2 = 0.170, p < 0.001). This relationship was meaningfully and positively influenced by auditors’ experience (B = 0.142, ΔR2 = 0.038, p < 0.001), with the negative slope roughly halved for auditors with low and high experience, respectively (−0.543 and −0.259) when taking engagement complexity (B = 0.098, ΔR2 = 0.020, p = 0.030) and firm policies (B = 0.156, ΔR2 = 0.037, p < 0.001) into consideration. The study defines AI dependence as a verifiable but contingent threat to judgment quality, and argues that this threat is largely within the control of the firm. It advises the implementation of active dependence protocols, independent assessments of significant AI outputs, differentiated oversight, and the provision of a regulatory framework concerning manageable technologies within the audit profession.

Keywords

Artificial IntelligenceDependenceProfessional JudgmentAuditorExperience.

References

Abdelsamee, A. (2024). The impact of artificial intelligence on audit quality: A study of the mediating role of professional judgment. Journal of Accounting and Auditing Research, 12(3), 45–67. Abdullah, Z., Zahra, F., & Hadi, A. (2025). The function of artificial intelligence in audit judgment: A systematic literature review. International Journal of Accounting Information Systems, 48, 100–125. Akinadewo, J. O. (2021). Artificial intelligence and accountants’ approach to accounting functions in Nigeria. Nigerian Journal of Accounting Research, 17(2), 78–102. Akinadewo, J. O., Olakojo, Y. A., Abe, O. O., & Ogundele, O. J. (2025). Artificial intelligence and professional ethics in Nigerian audit procedures: A survey of registered accounting firms in Lagos. Journal of Accounting and Taxation, 17(1), 15– 32. Baharom, A. (2025). Artificial intelligence applications in fraud detection: Capabilities, limitations, and behavioral implications for auditors. Journal of Forensic Accounting Research, 10(1), 34–56. Bell, E., Bryman, A., & Harley, B. (2022). Business research methods (6th ed.). Oxford University Press. Cummings, M. L. (2021). Rethinking human-automation interaction in decision support systems. Human Factors, 63(4), 567–582. De Luna, M. R., Santos, J. P., Cruz, A. M., & Reyes, T. D. (2025). Artificial intelligence in financial auditing and fraud detection: Evidence from medium-sized accounting firms in Metro Manila. Proceedings of the International Conference on E-Society, 234–249. Deliu, D. (2024). Artificial intelligence vs. human intelligence in auditing: Implications for professional judgment and professional skepticism. Audit Financiar, 22(1), 45–68. Eze, E., & Balogun, T. (2024). Artificial intelligence, professional skepticism, and audit judgment: Evidence from audit firms in Nigeria. Nigerian Journal of Management Studies, 18(2), 89–112. Financial Times. (2025, March 15). Audit quality under scrutiny amid AI adoption. Financial Times, p. 12. Gasperz, S., Titaley, S., & Latuconsina, Z. (2026). The skepticism continuum: Professional inquiry in AI-integrated auditing environments. Journal of Emerging Technologies in Accounting, 23(1), 78–95. Goddard, K., Roudsari, A., & Wyatt, J. C. (2022). Automation bias: A systematic review of frequency, effect mediators, and mitigative interventions. Journal of the American Medical Informatics Association, 29(3), 512–523. Gopalan, S., Sreedharan, V., & Sreejith, S. (2021). Auditors’ perceptions of artificial intelligence in Oman: Experience-based variations in professional skepticism. Oman Journal of Accounting and Finance, 5(2), 45–67. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications. Handoko, B. L., Rosita, N., & Hazim, H. (2025). Remote audit, cloud computing, artificial intelligence, and auditor work experience: An integrated analysis of audit procedure implementation. Proceedings of the Asia Conference on Machine Learning and Computing, 156–172. Hilario, M., Santos, R., Oliveira, P., & Costa, T. (2024). The impact of artificial intelligence on systems audit processes: Engagement characteristics as moderating factors. Journal of Information Systems, 38(2), 89–112. Hurtt, R. K. (2020). Development of a scale to measure professional skepticism. Auditing: A Journal of Practice & Theory, 29(1), 149–171. Ibrahim, M. A., & Danjuma, I. (2023). Artificial intelligence adoption and audit quality in Nigerian listed firms: A panel data analysis. Journal of Accounting and Finance, 11(2), 134–156. Kamareldawla, M. (2025). External auditors’ perceptions of artificial intelligence acceptance in the Egyptian audit profession. Corporate Ownership & Control, 22(1), 78–95. Köhler, A., & Hirt, R. (2023). Automation bias in professional decision-making: A meta- analysis. Journal of Behavioral Decision Making, 36(2), 201–223. Leocádio, D., Malheiro, L., & Reis, P. (2024). Artificial intelligence and its impact on auditors’ professional skepticism and judgment: A behavioral analysis. RSM Global Publication Series on AI and Auditing, 2024-02, 1–34. Musa, H., & Adebayo, K. (2024). Artificial intelligence adoption and audit precision in Nigerian audit firms: The role of firm-level policies. Nigerian Journal of Accounting and Finance, 9(1), 45–68. Nainggolan, Y., Tan, S., & Lee, J. (2025). Audit technology and skills gaps: A mixed- methods investigation of experience-based variations. Asian Journal of Accounting Research, 10(2), 112–134. Okafor, C., & Eze, P. (2023). Automation bias and professional skepticism: Risks of AI integration in Nigerian audit practice. Journal of Accounting and Financial Management, 9(3), 67–89. Okoye, E. I., & Egbunike, F. C. (2022). Artificial intelligence maturity model and audit processes in Nigeria: A comparative analysis of large and medium-sized firms. Nigerian Journal of Auditing, 8(1), 23–48. Olatunji, O. R., & Onuoha, L. N. (2024). Artificial intelligence maturity and audit effectiveness in Nigeria: Empirical evidence from audit firms. West African Journal of Industrial and Academic Research, 30(1), 56–78. Parasuraman, R., & Manzey, D. H. (2020). Complacency and bias in human use of automation: An attentional integration. Human Factors, 62(3), 381–410. Permata Suyono, N., Wijaya, H., Sari, R., & Prasetyo, B. (2025). Predictive modeling, machine learning, and natural language processing in modern auditing: Capabilities and applications. Journal of Emerging Technologies in Accounting, 22(1), 45–67. Peters, G. (2024). The black box problem in AI-based auditing: Neural systems opacity and challenges for auditor accountability. International Journal of Digital Accounting Research, 24, 89–112. Salawu, R. O., & Elegbede, T. (2025). Artificial intelligence and audit performance metrics: Empirical evidence from audit firms in Nigeria. Journal of Finance and Accounting, 13(1), 34–56. Saunders, M., Lewis, P., & Thornhill, A. (2023). Research methods for business students (9th ed.). Pearson Education. Sutton, S. G., Arnold, V., & Holt, M. (2018). The theory of technological dominance: A comprehensive framework for understanding expert decision support systems. International Journal of Accounting Information Systems, 30, 1–16. Usman, A. B., & Adeyemi, S. B. (2023). Artificial intelligence and auditor professional skepticism in Nigeria: Preliminary evidence. Journal of Accounting and Management, 13(2), 78–95. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2023). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2024). Artificial intelligence adoption in professional services: Cognitive and organizational implications. Journal of Business Research, 158, 113–134.

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