A Study on Ethical Implications, Trust, and Audit Reliability in AI Assisted Accounting: An Extended UTAUT Perspective in Emerging Economies
Main Article Content
Abstract
Purpose: This paper examines how ethical perception and trust shape the adoption and reliability of artificial intelligence systems in accounting and auditing, particularly within emerging economies. It extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to incorporate ethical and trust-based dimensions that influence professional acceptance beyond functional factors.
Design/methodology/approach: A conceptual framework is proposed by integrating ethical perception and trust into the traditional UTAUT model. The framework highlights their moderating and mediating roles in linking performance expectancy, behavioural intention, and audit reliability. Insights from existing literature and theoretical reasoning support the development of this extended model.
Findings: The study argues that AI adoption in professional accounting contexts is both a technological and moral process. Ethical perception strengthens trust, and together they enhance responsible adoption, critical engagement, and audit reliability. The framework positions moral legitimacy and institutional trust as essential conditions for sustainable AI integration.
Practical implications: For practitioners and audit firms, embedding ethical design principles, algorithmic transparency, and human oversight in AI systems can foster greater trust and accountability. For regulators, establishing AI audit assurance frameworks and updating ethical codes can help manage risks associated with automation in professional judgment.
Social implications: The framework emphasises the need for ethical governance and professional readiness in emerging economies, where rapid technological adoption can outpace institutional safeguards. Promoting ethical AI awareness can strengthen confidence in technology and enhance the integrity of financial reporting.
Originality/value: This paper contributes to technology adoption literature by reconceptualising AI use in accounting as an ethically anchored process. It shifts the discussion from efficiency and usability to accountability, transparency, and professional trust, offering a foundation for future empirical studies in emerging markets.
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
1. Adel, N. (2024). The impact of digital literacy and technology adoption on financial inclusion in Africa, Asia, and Latin America. PLOS ONE. https://doi.org/10.1371/journal.pone.0302627
2. Alles, M., & Gray, G. L. (2023). Artificial intelligence and audit reliability: Challenges for professional scepticism. International Journal of Accounting Information Systems, 52, 100642.
3. Celestin, M. (2024). Adopting AI in emerging markets: Challenges and opportunities in the accounting sector (Conference paper). Indo American Journal of Multidisciplinary Research and Review. Retrieved from https://www.researchgate.net/publication/ 390179035_ADOPTING_AI_IN_EMERGING_MARKETS_CHALLENGES_AND_OPPORTUNITIES_IN_THE_ACCOUNTING_SECTOR
4. Cheong, B. C. (2024). Transparency and accountability in artificial intelligence systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6, 1421273. https://www.frontiersin.org/articles/ 10.3389/fhumd.2024.1421273/full
5. Comptroller and Auditor General of India (CAG). (2024, July). Application of machine learning in auditing. The Comptroller and Auditor General Journal. Retrieved October 17, 2025, from https://cag.gov.in/uploads/journal/journal-en-july-2024/application_machine_learning.html
6. Ethics Based Auditing. (2021). Minds and Machines, 31(4), 699–722.
7. Ethical Considerations in the Use of AI for Auditing: Balancing Innovation and Integrity. (2022). European Journal of Accounting, Auditing and Finance Research, 10(12). https://eajournals.org/ejaafr/vol10 issue12 2022/ethical considerations in the use of ai for auditing balancing innovation and integrity/
8. Floridi, L., & Cowls, J. (2022). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review. https://hdsr.mitpress.mit.edu/pub/l0jsh9d1/release/5.
9. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627 660.
10. Hamadeh, A., Salameh, M., & Dabbous, A. (2025). Auditors’ intentions to use blockchain and AI technologies: The role of trust and ethics. International Journal of Accounting and Finance, 47(1), 78–95.
11. Hasnain, F., & Khalid, S. (2025). AI assisted auditing: Navigating ethical risks and professional standards. Contemporary Management Studies Review, 5(2), 22–39.
12. IFAC. (2023). Ethical guidance for AI use in audit and assurance. International Federation of Accountants.
13. Ifada, L. M., Mutoharoh, M., Indriastuti, M., & Indarti, K. (2025). The digital frontier of accounting: Unraveling the impact of digital literacy and technology adoption. Jurnal Economia, 21(1), 34–51. https://doi.org/10.21831/economia.v21i1.68723
14. Köhler, A., & Schaffner, J. (2022). Ethics by design: A framework for responsible AI in financial services. Journal of Business Ethics, 179(3), 611–627.
15. Kokina, J., & Davenport, T. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting.
16. Kokina, J., Mancha, R., & Pachamanova, D. (2021). The role of artificial intelligence in the audit process: Enhancing reliability through explainability. International Journal of Accounting Information Systems, 40, 100501. https://doi.org/10.1016/j.accinf.2021.100501
17. Kwahk, K. Y., & Park, D. H. (2023). Extending UTAUT: The roles of trust and ethical perception in AI adoption. Computers in Human Behavior, 141, 107698.
18. Laine, T., Suomala, P., & Partanen, V. (2024). Ethics based AI auditing: Conceptualizing ethical principles for trustworthy accounting technologies. Accounting, Organizations and Society, 112, 101518.
19. Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629 650.
20. Manheim, K., Matzner, T., & Weller, A. (2024). Auditing AI: Legal, ethical, and technical perspectives. Digital Society, 2(1), 1–17.
21. Mogaji, E., Jain, V., & Kaur, H. (2024). More trust or more risk? User acceptance of artificial intelligence in professional settings. Human Factors and Ergonomics in Manufacturing & Service Industries, 34(2), 223–241.
22. Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. Journal of Strategic Information Systems. Retrieved from https://www.sintef.no/en/publications/publication/2339854/
23. Pérez-Calderón, E., Alrahamneh, S. A., & Milanés Montero, P. (2025). Impact of artificial intelligence on auditing: An evaluation from the profession in Jordan. Discover Sustainability, 6, Article 251. https://doi.org/10.1007/s43621-025-01058-3
24. Permata Suyono, T., Rahman, A., & Wirawan, P. (2024). Artificial intelligence in auditing: Tools, applications, and challenges. Review of Information Governance and Systems, 12(3), 56–72.
25. Ribeiro, D., Rocha, T., Pinto, G., Cartaxo, B., Amaral, M., & Davila, N. (2025). Toward effective AI governance: A review of principles. arXiv. https://arxiv.org/abs/2505.23417
26. Schiff, D. S., Kelley, S., & Camacho Ibáñez, J. (2024). The emergence of artificial intelligence ethics auditing. Big Data & Society, 11(4). https://doi.org/10.1177/20539517241299732
27. Suyono, T., Ardiansyah, F., & Wibowo, A. (2024). Bias and ethics of AI systems applied in auditing: A systematic review. Scientific African, 16, e01226.
28. World Bank. (2023). TaxTech: Leveraging digitalization to improve tax compliance and revenue mobilization (Report No. P179405). World Bank. Retrieved from https://documents1.worldbank.org/curated/en/099061225023042590/pdf/P179405-6877e00c-7f93-46b1-8d09-d72ad0afd311.pdf
29. The Economic Times. (2024, August 23). Revolutionizing auditing: CAG emphasizes AI/ML’s role in detecting fraud and enhancing remote audits. ET Enterprise AI. Retrieved October 17, 2025, from https://enterpriseai.economictimes.indiatimes.com/news/industry/revolutionizing-auditing-cag-emphasizes-aimls-role-in-detecting-fraud-and-enhancing-remote-audits/124011119
30. Vasarhelyi, M. A., Alles, M., & Kogan, A. (2023). Artificial intelligence and the transformation of auditing. Accounting Horizons, 37(1), 45–60.
31. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
32. Zhang, X., Wang, Y., & Yu, P. (2023). Trust, transparency, and reliability in AI auditing systems. Journal of Accounting and Information Systems.