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Impact of Data Analytics and Artificial Intelligence on Audit Quality of Listed Manufacturing Companies in Nigeria

Okolo Chibueze Friday Eno G. Ukpong PhD Uwem E. Uwah PhD

Abstract

This study examined the impact of data analytics and artificial intelligence (AI) on audit quality of listed manufacturing companies in Nigeria. Specifically, the study investigated the effects of audit data analytics adoption, artificial intelligence utilization, and auditor technological competence on audit quality. The study was anchored on the Technology Acceptance Model and Resource-Based View Theory. An ex-post facto research design was adopted, and data were obtained from annual reports and corporate disclosures of 25 listed manufacturing companies on the Nigerian Exchange Group covering the period 2015–2024. The study employed panel regression analysis. Audit quality was proxied by audit report quality, while data analytics adoption, AI utilization, and auditor technological competence served as explanatory variables. The findings revealed that audit data analytics adoption has a positive and significant effect on audit quality (β = 0.421, p = 0.000), artificial intelligence utilization exerts a positive and significant effect on audit quality (β = 0.387, p = 0.002), while auditor technological competence also has a positive and significant influence on audit quality (β = 0.294, p = 0.011). The model explained approximately 68.4% of the variation in audit quality (Adjusted R2 = 0.684). The study concluded that data analytics and artificial intelligence significantly enhance audit quality by improving audit efficiency, fraud detection capability, risk assessment accuracy, and financial reporting reliability. The study recommended increased investment in AI-enabled audit systems, continuous auditor training, and the establishment of regulatory guidelines for technology- assisted auditing.

Keywords

Artificial IntelligenceData AnalyticsAudit QualityManufacturing CompaniesNigeriaFinancial Reporting.

References

Akai, N. D., Ukpong, E. G., & Uwah, U. E. (2024). Integrated reporting and market value of listed industrial goods companies in Nigeria. IIARD International Journal of Economics and Business, 10(9), 174–186. Akpan, S. A., Ukpong, E. G., & Etim, U. (2024). Liquidity management and profitability of deposit money banks in Nigeria. International Journal of Banking and Finance, 10(8), 100–126. Akomolehin, A., Bello, T., & Yusuf, R. (2026). Artificial intelligence-enabled auditing and financial reporting quality among listed companies in Nigeria. Journal of Accounting and Digital Finance. Alles, M. (2024). Audit analytics and continuous auditing: Enhancing audit effectiveness through data-driven approaches. Accounting Horizons. Appelbaum, D., Kogan, A., & Vasarhelyi, M. (2023). Big data and analytics in auditing: Transforming audit practice. Journal of Emerging Technologies in Accounting. Bakarich, K. M., & O'Brien, P. E. (2023). The use of artificial intelligence in auditing: Implications for audit quality. International Journal of Accounting Information Systems. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. Bello, A. (2026). Artificial intelligence adoption and audit quality in Nigerian audit firms. Nigerian Journal of Accounting Research. Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. Davenport, T. H., & Harris, J. G. (2024). Competing on analytics: Updated perspectives on data-driven decision-making. Harvard Business Review Press. DeAngelo, L. E. (1981). Auditor size and audit quality. Journal of Accounting and Economics, 3(3), 183–199. Deloitte. (2025). Global audit technology survey report. Deloitte Insights. Earley, C. E. (2023). Data analytics in auditing: Expanding audit coverage and assurance. The Accounting Review. IFAC. (2023). Code of ethics for professional accountants. International Federation of Accountants. IAASB. (2024). International standards on auditing: Framework and updates. International Auditing and Assurance Standards Board. Issa, H., Sun, T., & Vasarhelyi, M. (2024). Artificial intelligence in auditing: Efficiency and effectiveness implications. Journal of Information Systems, 38(2), 45–62. KPMG. (2024). Digital audit transformation report. KPMG Global Insights. Maidarasu, S., Ahmed, R., & Kumar, P. (2025). Artificial intelligence and audit performance: Evidence from professional auditors. International Journal of Auditing Studies. Nwayen, A., Ukpong, E. G., & Uwah, E. (2024). Impact of financial technology (Fintech) on the profitability of listed deposit money banks in Nigeria. Journal of Accounting and Financial Management, 10(9), 187–199. PwC. (2025). Global audit innovation report. PricewaterhouseCoopers. Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374. Ukpong, E. G., & Otung, A. U. (n.d.). Sustainability reporting and investors' behaviour in the oil and gas firms listed in the Nigerian capital market.International Journal of Banking and Finance 10 (8), 100-126

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