The Evolving Landscape of Artificial Intelligence and Audit Quality Research: A Bibliometric Analysis and Future Agenda
Keywords:
Artificial Intelligence; Audit Quality; Bibliometric Analysis; Co-Word Analysis; Conceptual Structure; Thematic EvolutionAbstract
The rapid adoption of Artificial Intelligence (AI) in the global economy necessitates a comprehensive understanding of its impact on the auditing profession, particularly concerning Audit Quality. This study aims to analyze the intellectual, social, and conceptual structure of research on AI and Audit Quality. Utilizing a Systematic Literature Review (SLR) combined with bibliometric analysis (Biblioshiny), we processed a final sample of 70 documents published between 1997 and 2025, extracted from the Scopus database. Key findings reveal a dramatic and exponential surge in scientific production since 2022, with an average annual growth rate of 12.34%, confirming the field's criticality. The social structure analysis shows high collaboration fragmentation among individual authors but strong centralization at the country level, dominated by the United States, China, and Western Europe, highlighting a significant global representation gap. The Conceptual Structure Map identifies four main clusters, including Audit Process, Judgment and Risk, and Firm Strategy. Crucially, the Thematic Evolution analysis points to a significant thematic shift: a decline in focus on individual Decision Making (judgmental focus) and the emergence of Accounting Firms (organizational/strategic adoption) as the new central research theme. This shift indicates the field's maturation, moving from mere technological capability exploration to addressing organizational governance and strategic consequences. Based on these findings, we propose a Future Research Agenda prioritizing ethical governance frameworks for algorithmic accountability, quantifying the necessary skill transformation in audit education, and encouraging empirical collaboration with developing economies to ensure global relevance.
References
Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100218. https://doi.org/10.1016/j.joitmc.2024.100218
Alawamleh, M., Shammas, N., Alawamleh, K., & Bani Ismail, L. (2024). Examining the limitations of AI in business and the need for human insights using Interpretive Structural Modelling. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100338. https://doi.org/10.1016/j.joitmc.2024.100338
Ariany, V. (2025). the Impact of Artificial Intelligence on Audit Quality and Auditor Judgement: a Multi Country Analysis. Accounting Studies and Tax Journal (COUNT), 2(3), 557–569. https://www.sciencedirect.com/science/article/pii/S1045235424000108
Ballantine, J., Boyce, G., & Stoner, G. (2024). A critical review of AI in accounting education: Threat and opportunity. Critical Perspectives on Accounting, 99(January), 102711. https://doi.org/10.1016/j.cpa.2024.102711
Beier, G., Ullrich, A., Niehoff, S., Reißig, M., & Habich, M. (2020). Industry 4.0: How it is defined from a sociotechnical perspective and how much sustainability it includes – A literature review. Journal of Cleaner Production, 259. https://doi.org/10.1016/j.jclepro.2020.120856
Celestino, M. S., Belluzzo, R. C. B., Albino, J. P., & Valente, V. C. P. N. (2024). Bibliometric analysis: literature review and proposal of a methodological framework in 12 steps. Revista ARACE, 6(4), 13421–13446. https://doi.org/https://doi.org/10.56238/arev6n4-146
Dong, M. M., Stratopoulos, T. C., & Wang, V. X. (2024). A scoping review of ChatGPT research in accounting and finance. International Journal of Accounting Information Systems, 55(December 2023), 100715. https://doi.org/10.1016/j.accinf.2024.100715
Esangbedo, C. O., Zhang, J., Esangbedo, M. O., Kone, S. D., & Xu, L. (2024). The role of industry-academia collaboration in enhancing educational opportunities and outcomes under the digital driven Industry 4.0. Journal of Infrastructure, Policy and Development, 8(1), 1–32. https://doi.org/10.24294/jipd.v8i1.2569
Guandalini, I. (2022). Sustainability through digital transformation: A systematic literature review for research guidance. Journal of Business Research, 148, 456–471. https://doi.org/10.1016/j.jbusres.2022.05.003
Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56, 1–22. https://doi.org/10.1016/j.accinf.2025.100734
Laine, J., Minkkinen, M., & Mäntymäki, M. (2024). Ethics-based AI auditing: A systematic literature review on conceptualizations of ethical principles and knowledge contributions to stakeholders. Information and Management, 61(5), 1–22. https://doi.org/10.1016/j.im.2024.103969
Lim, W. M., Kumar, S., & Donthu, N. (2024). How to combine and clean bibliometric data and use bibliometric tools synergistically: Guidelines using metaverse research. Journal of Business Research, 182(December 2023), 114760. https://doi.org/10.1016/j.jbusres.2024.114760
Maghsoudi, M., Mohammadi, N., & Bakhtiari, M. (2025). Artificial intelligence and sustainable development: Public concerns and governance in developed and developing nations. Cleaner Environmental Systems, 19(April), 100340. https://doi.org/10.1016/j.cesys.2025.100340
Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175(May 2023), 114542. https://doi.org/10.1016/j.jbusres.2024.114542
Murikah, W., Nthenge, J. K., & Musyoka, F. M. (2024). Bias and ethics of AI systems applied in auditing - A systematic review. Scientific African, 25, e02281. https://doi.org/10.1016/j.sciaf.2024.e02281
Murphy, B., Feeney, O., Rosati, P., & Lynn, T. (2024). Exploring accounting and AI using topic modelling. International Journal of Accounting Information Systems, 55, 100709. https://doi.org/10.1016/j.accinf.2024.100709
Obreja, D. M., Rughini?, R., & Rosner, D. (2024). Mapping the conceptual structure of innovation in artificial intelligence research: A bibliometric analysis and systematic literature review. Journal of Innovation and Knowledge, 9(1), 1–15. https://doi.org/10.1016/j.jik.2024.100465
Öztürk, O., Kocaman, R., & Kanbach, D. K. (2024). How to design bibliometric research: an overview and a framework proposal. Review of Managerial Science, 18(11), 3333–3361. https://doi.org/10.1007/s11846-024-00738-0
Schindler, S., Alami, I., DiCarlo, J., Jepson, N., Rolf, S., Bay?rba?, M. K., Cyuzuzo, L., DeBoom, M., Farahani, A. F., Liu, I. T., McNicol, H., Miao, J. T., Nock, P., Teri, G., Vila Seoane, M. F., Ward, K., Zajontz, T., & Zhao, Y. (2024). The Second Cold War: US-China Competition for Centrality in Infrastructure, Digital, Production, and Finance Networks. Geopolitics, 29(4), 1083–1120. https://doi.org/10.1080/14650045.2023.2253432
Usul, H., & Furkan Alpay, M. (2024). From Traditional Auditing To Information Technology Auditing: a Paradigm Shift in Practices. European Journal of Digital Economy Research, 5(1), 3–9. https://ejderhub.com/index.php/ejder/article/view/83/94
Vitali, S., & Giuliani, M. (2024). Emerging digital technologies and auditing firms: Opportunities and challenges. International Journal of Accounting Information Systems, 53(August 2023), 100676. https://doi.org/10.1016/j.accinf.2024.100676
Wassie, F. A., & Lakatos, L. P. (2025). Audit technology as a catalyst for improving non-financial performance in Ethiopian audit firms. Journal of Open Innovation: Technology, Market, and Complexity, 11(2), 1–8. https://doi.org/10.1016/j.joitmc.2025.100556
Wijaya, J. R. T., Prasetyo, I., Rahmatika, D. N., & Indriasih, D. (2025). Artificial Intelligence and Audit Quality: an Empirical Literature Review From Scopus Database. Fokus Ekonomi?: Jurnal Ilmiah Ekonomi, 20(1), 61–76. https://doi.org/10.34152/fe.20.1.61-76
Xiao, T., Geng, C., & Yuan, C. (2020). How audit effort affects audit quality: An audit process and audit output perspective. China Journal of Accounting Research, 13(1), 109–127. https://doi.org/10.1016/j.cjar.2020.02.002
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C. W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., … Zhang, J. (2021). Artificial intelligence: A powerful paradigm for scientific research. Innovation, 2(4), 1–20. https://doi.org/10.1016/j.xinn.2021.100179
Yang, J., Amrollahi, A., & Marrone, M. (2024). Harnessing the Potential of Artificial Intelligence: Affordances, Constraints, and Strategic Implications for Professional Services. Journal of Strategic Information Systems, 33(4), 101864. https://doi.org/10.1016/j.jsis.2024.101864
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel The Evolving Landscape of Artificial Intelligence and Audit Quality Research: A Bibliometric Analysis and Future Agenda
