Mapping the Landscape of Audit Judgment Performance: Emerging Trends and Future Directions
Abstract
Audit judgment performance is a central aspect of audit research, as it underpins an auditor's ability to evaluate evidence, assess risk, exercise professional skepticism, and formulate defensible audit conclusions amidst uncertainty. This study presents a bibliometric mapping of research on audit judgment performance using a cleaned dataset comprising 232 Scopus-indexed publications released between 1977 and 2026. Utilizing Biblioshiny, the study analyzes publication and citation trends, influential sources and documents, co-word networks, thematic maps, thematic evolution, and trending topics. The findings reveal that the field has evolved from early research on auditor cognition and evidence evaluation toward topics such as audit risk assessment, audit quality, professional skepticism, auditor independence, and digital-based audit judgment. Recent developments indicate growing attention toward artificial intelligence, machine learning, blockchain, big data, data analytics, and explainable AI. This study contributes by clarifying the intellectual foundations of audit judgment performance research and identifying future research directions concerning human-machine judgment, data trust, digital audit readiness, and public sector auditing.
Downloads
References
Abdolmohammadi, M. J., & Bazaz, M. S. (1991). Identification of tasks for expert systems development in auditing. Expert Systems with Applications, 3(1), 99–107. https://doi.org/10.1016/0957-4174(91)90090-2
Adams, T., Krishnan, J., & Krishnan, J. (2021). Client influence and auditor independence revisited: Evidence from auditor resignations. Journal of Accounting and Public Policy, 40(5), 106846. https://doi.org/10.1016/j.jaccpubpol.2021.106846
Aghazadeh, S., & Joe, J. R. (2022). Auditors’ response to management confidence and misstatement risk. Accounting, Organizations and Society, 101, 101348. https://doi.org/10.1016/j.aos.2022.101348
Ahmad, F. (2019). A systematic review of the role of big data analytics in reducing the influence of cognitive errors on the audit judgement. Revista de Contabilidad: Spanish Accounting Review, 22(2), 187–202. https://doi.org/10.6018/rcsar.382251
Alles, M. G., Brennan, G., Kogan, A., & Vasarhelyi, M. A. (2006). Continuous monitoring of business process controls: A pilot implementation of a continuous auditing system at Siemens. International Journal of Accounting Information Systems, 7(2), 137–161. https://doi.org/10.1016/j.accinf.2005.10.004
Anderson, U., & Wright, W. F. (1988). Expertise and the explanation effect. Organizational Behavior and Human Decision Processes, 42(2), 250–269. https://doi.org/10.1016/0749-5978(88)90015-5
Barr-Pulliam, D., Brown-Liburd, H., & Sanderson, K. A. (2022). The effects of person-specific, task, and environmental factors on digital transformation and innovation in auditing: A review of the literature. Journal of International Financial Management & Accounting, 33(2), 337–374. https://doi.org/10.1111/jifm.12148
Bauer, T. D. (2015). The effects of client identity strength and professional identity salience on auditor judgments. The Accounting Review, 90(1), 95–114. https://doi.org/10.2308/accr-50863
Bédard, J. (1989). Expertise in auditing: Myth or reality? Accounting, Organizations and Society, 14(1–2), 113–131.
Birnberg, J. G., & Shields, M. D. (1984). The role of attention and memory in accounting decisions. Accounting, Organizations and Society, 9(3–4), 365–382.
Bonner, S. E. (1994). A model of the effects of audit task complexity. Accounting, Organizations and Society, 19(3), 213–234. https://doi.org/10.1016/0361-3682(94)90033-7
Bonner, S. E., Libby, R., & Nelson, M. W. (1997). Audit category knowledge as a precondition to learning from experience. Accounting, Organizations and Society, 22(5), 387–410. https://doi.org/10.1016/S0361-3682(96)00049-9
Bratten, B., Gaynor, L. M., McDaniel, L., Montague, N. R., & Sierra, G. E. (2013). The audit of fair values and other estimates: The effects of underlying environmental, task, and auditor-specific factors. Auditing: A Journal of Practice & Theory, 32(Supplement 1), 7–44. https://doi.org/10.2308/ajpt-50316
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral implications of big data’s impact on audit judgment and decision making and future research directions. Accounting Horizons, 29(2), 451–468. https://doi.org/10.2308/acch-51023
Buchman, T. A., Tetlock, P. E., & Reed, R. O. (1996). Accountability and auditors’ judgments about contingent events. Journal of Business Finance & Accounting, 23(3), 379–398. https://doi.org/10.1111/j.1468-5957.1996.tb01128.x
Carpenter, B. W., Dirsmith, M. W., & Gupta, P. P. (1994). Materiality judgments and audit firm culture: Social-behavioral and political perspectives. Accounting, Organizations and Society, 19(4–5), 355–380. https://doi.org/10.1016/0361-3682(94)90002-7
Carrera, N., & van der Kolk, B. (2021). Auditor ethics: Do experience and gender influence auditors’ moral awareness? Managerial Auditing Journal, 36(3), 463–484. https://doi.org/10.1108/MAJ-07-2020-2745
Chen, C., Leung, W. S. C., Tao, X., & Wang, H. (2023). The impact of financial reporting flexibility on auditor risk judgement: Evidence from the implementation of FIN 46R. Journal of Accounting and Public Policy, 42(3), 107084. https://doi.org/10.1016/j.jaccpubpol.2023.107084
Choo, F. (1996). Auditors’ knowledge content and judgment performance: A cognitive script approach. Accounting, Organizations and Society, 21(4), 339–359. https://doi.org/10.1016/0361-3682(95)00048-8
Commerford, B. P., Dennis, S. A., Joe, J. R., & Ulla, J. W. (2022). Man versus machine: Complex estimates and auditor reliance on artificial intelligence. Journal of Accounting Research, 60(1), 171–201. https://doi.org/10.1111/1475-679X.12407
Curtis, M. B., & Payne, E. A. (2008). An examination of contextual factors and individual characteristics affecting technology implementation decisions in auditing. International Journal of Accounting Information Systems, 9(2), 104–121. https://doi.org/10.1016/j.accinf.2007.10.002
Dirsmith, M. W., & Haskins, M. E. (1991). Inherent risk assessment and audit firm technology: A contrast in world theories. Accounting, Organizations and Society, 16(1), 61–90. https://doi.org/10.1016/0361-3682(91)90033-B
Duboisée de Ricquebourg, A., & Maroun, W. (2023). How do auditor rotations affect key audit matters? Archival evidence from South African audits. The British Accounting Review, 55(2), 101099. https://doi.org/10.1016/j.bar.2022.101099
Estep, C., Griffith, E. E., & MacKenzie, N. L. (2024). How do financial executives respond to the use of artificial intelligence in financial reporting and auditing? Review of Accounting Studies, 29(3), 2798–2831. https://doi.org/10.1007/s11142-023-09771-y
Eulerich, M., Sanatizadeh, A., Vakilzadeh, H., & Wood, D. A. (2024). Is it all hype? ChatGPT’s performance and disruptive potential in the accounting and auditing industries. Review of Accounting Studies, 29(3), 2318–2349. https://doi.org/10.1007/s11142-024-09833-9
Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x
Fischer, M. J. (1996). “Real-izing” the benefits of new technologies as a source of audit evidence: An interpretive field study. Accounting, Organizations and Society, 21(2–3), 219–242. https://doi.org/10.1016/0361-3682(95)00005-4
Geiger, M. A., Basioudis, I. G., & DeLange, P. (2022). The effect of non-audit fees and industry specialization on the prevalence and accuracy of auditor’s going-concern reporting decisions. Journal of International Accounting, Auditing and Taxation, 47, 100473. https://doi.org/10.1016/j.intaccaudtax.2022.100473
Griffith, E. E., Hammersley, J. S., Kadous, K., & Young, D. (2016). How insights from the “new” JDM research can improve auditor judgment: Fundamental research questions and methodological advice. Auditing: A Journal of Practice & Theory, 35(2), 1–22. https://doi.org/10.2308/ajpt-51347
Gu, H., Schreyer, M., Moffitt, K., & Vasarhelyi, M. A. (2024). Artificial intelligence co-piloted auditing. International Journal of Accounting Information Systems, 54, 100698. https://doi.org/10.1016/j.accinf.2024.100698
Gu, Y., Zhang Parker, C. A., & Vasarhelyi, M. A. (2026). Synergizing machine and human judgment: Evidence from going concern assessment. Journal of Accounting and Public Policy, 57, 107435. https://doi.org/10.1016/j.jaccpubpol.2026.107435
Hamdam, A., Jusoh, R., Yahya, Y., & Jalaludin, D. (2022). Auditor judgment and decision-making in big data environment: A proposed research framework. Accounting Research Journal, 35(1), 55–70. https://doi.org/10.1108/ARJ-04-2020-0078
Hamzeh, M., Rahnamai Roudpashti, F., Bani Mahd, B., & Salehi, M. (2025). Analysis of the impact of blockchain on auditing based on future research approach. Foresight, 27(6), 1101–1133. https://doi.org/10.1108/FS-05-2024-0091
Hammersley, J. S. (2011). A review and model of auditor judgments in fraud-related planning tasks. Auditing: A Journal of Practice & Theory, 30(4), 101–128. https://doi.org/10.2308/ajpt-10145
Hansen, J. V., & Messier, W. F. (1986). A preliminary investigation of EDP-XPERT. Auditing: A Journal of Practice & Theory, 6(1), 109–123.
Harding, N., & Trotman, K. T. (2017). The effect of partner communications of fraud likelihood and skeptical orientation on auditors’ professional skepticism. Auditing: A Journal of Practice & Theory, 36(2), 111–131. https://doi.org/10.2308/ajpt-51576
Hatfield, R. C., Jackson, S. B., & Vandervelde, S. D. (2011). The effects of prior auditor involvement and client pressure on proposed audit adjustments. Behavioral Research in Accounting, 23(2), 117–130. https://doi.org/10.2308/bria-10064
Helikum, L. J., Jamal, K., Tan, H.-T., & Xiao, L. (2026). Can open audit committee chairs cure the chilling effect of management’s presence on auditors’ information sharing during audit committee meetings? Accounting, Organizations and Society. https://doi.org/10.1016/j.aos.2025.101618
Henrizi, P., Himmelsbach, D., & Hunziker, S. (2021). Anchoring and adjustment effects on audit judgments: Experimental evidence from Switzerland. Journal of Applied Accounting Research, 22(4), 598–621. https://doi.org/10.1108/JAAR-01-2020-0011
Holt, T. P., & Loraas, T. M. (2021). A potential unintended consequence of big data: Does information structure lead to suboptimal auditor judgment and decision-making? Accounting Horizons, 35(3), 161–186. https://doi.org/10.2308/HORIZONS-19-123
Houston, R. W. (1999). The effects of fee pressure and client risk on audit seniors’ time budget decisions. Auditing: A Journal of Practice & Theory, 18(2), 70–86.
Hurtt, R. K., Brown-Liburd, H., Earley, C. E., & Krishnamoorthy, G. (2013). Research on auditor professional skepticism: Literature synthesis and opportunities for future research. Auditing: A Journal of Practice & Theory, 32(Supplement 1), 45–97. https://doi.org/10.2308/ajpt-50361
Janvrin, D., Bierstaker, J., & Lowe, D. J. (2008). An examination of audit information technology use and perceived importance. Accounting Horizons, 22(1), 1–21. https://doi.org/10.2308/acch.2008.22.1.1
Johnson, P. E., Jamal, K., & Berryman, R. G. (1989). Audit judgment research. Accounting, Organizations and Society, 14(1–2), 83–99. https://doi.org/10.1016/0361-3682(89)90035-4
Johnson, P. E., Jamal, K., & Berryman, R. G. (1991). Effects of framing on auditor decisions. Organizational Behavior and Human Decision Processes, 50(1), 75–105. https://doi.org/10.1016/0749-5978(91)90035-R
Karan, V., Murthy, U. S., & Vinze, A. S. (1995). Assessing the suitability of judgmental auditing tasks for expert systems development: An empirical approach. Expert Systems with Applications, 9(4), 441–455. https://doi.org/10.1016/0957-4174(95)00015-1
Karan, V., Kerr, D. S., Murthy, U. S., & Vinze, A. S. (1996). Information technology support for collaborative decision making in auditing: An experimental investigation. Decision Support Systems, 16(3), 181–194. https://doi.org/10.1016/0167-9236(95)00007-0
Kim, M. H. J. (2021). Effects of managerial overconfidence and ability on going-concern decisions and auditor turnover. Advances in Accounting, 54, 100549. https://doi.org/10.1016/j.adiac.2021.100549
Knechel, W. R., Krishnan, G. V., Pevzner, M. B., Shefchik, L. B., & Velury, U. K. (2013). Audit quality: Insights from the academic literature. Auditing: A Journal of Practice & Theory, 32(Supplement 1), 385–421. https://doi.org/10.2308/ajpt-50350
Koch, C., & Salterio, S. E. (2017). The effects of auditor affinity for client and perceived client pressure on auditor proposed adjustments. The Accounting Review, 92(5), 117–142. https://doi.org/10.2308/accr-51703
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, Article 100734. https://doi.org/10.1016/j.accinf.2025.100734
Koreff, J., Weisner, M., & Sutton, S. G. (2021). Data analytics use in healthcare fraud audits. International Journal of Accounting Information Systems, 42, 100523. https://doi.org/10.1016/j.accinf.2021.100523
Lee, E. J., & Tahmoush, D. (2025). Auditors’ decision-making aid for going concern audit opinions through machine learning analysis. International Journal of Accounting Information Systems, 56, 100732. https://doi.org/10.1016/j.accinf.2025.100732
Libby, R., & Lewis, B. L. (1977). Human information processing research in accounting: The state of the art. Accounting, Organizations and Society, 2(3), 245–268.
Libby, R., & Lewis, B. L. (1982). Human information processing research in accounting: The state of the art in 1982. Accounting, Organizations and Society, 7(3), 231–285.
Lin, H.-L., & Yen, A.-R. (2022). Auditor rotation, key audit matter disclosures, and financial reporting quality. Advances in Accounting, 57, 100594. https://doi.org/10.1016/j.adiac.2022.100594
Lombardi, D. R., Kim, M., Sipior, J. C., & Vasarhelyi, M. A. (2025). The increased role of advanced technology and automation in audit: A Delphi study. International Journal of Accounting Information Systems, 56, 100733. https://doi.org/10.1016/j.accinf.2025.100733
Lu, H., & Wu, Z. (2025). Revisiting intelligent audit from a data science perspective. Neurocomputing, 623, 129431. https://doi.org/10.1016/j.neucom.2025.129431
Messier, W. F., Simon, C. A., & Smith, J. L. (2013). Two decades of behavioral research on analytical procedures: What have we learned? Auditing: A Journal of Practice & Theory, 32(1), 139–181. https://doi.org/10.2308/ajpt-50327
Moreno, K., & Bhattacharjee, S. (2003). The impact of pressure from potential client business opportunities on the judgments of auditors across professional ranks. Auditing: A Journal of Practice & Theory, 22(1), 13–28. https://doi.org/10.2308/aud.2003.22.1.13
Ng, T. B. P., & Tan, H.-T. (2003). Effects of authoritative guidance availability and audit committee effectiveness on auditors’ judgments in an auditor-client negotiation context. The Accounting Review, 78(3), 801–818. https://doi.org/10.2308/accr.2003.78.3.801
Papirakis, R. (2022). Auditors’ self-actualization and ability to resist client pressure: Evidence from Canada. Journal of International Accounting, Auditing and Taxation, 47, 100468. https://doi.org/10.1016/j.intaccaudtax.2022.100468
Peecher, M. E., Solomon, I., & Trotman, K. T. (2013). An accountability framework for financial statement auditors and related research questions. Accounting, Organizations and Society, 38(8), 596–620. https://doi.org/10.1016/j.aos.2013.07.002
Peters, J. M., Lewis, B. L., & Dhar, V. (1989). Assessing inherent risk during audit planning: The development of a knowledge based model. Accounting, Organizations and Society, 14(4), 359–378. https://doi.org/10.1016/0361-3682(89)90006-8
Popova, V. (2013). Exploration of skepticism, client-specific experiences, and audit judgments. Managerial Auditing Journal, 28(2), 140–160. https://doi.org/10.1108/02686901311284540
Reimers, J. L., & Butler, S. A. (1992). The effect of outcome knowledge on auditors’ judgmental evaluations. Accounting, Organizations and Society, 17(2), 185–194. https://doi.org/10.1016/0361-3682(92)90010-P
Salterio, S. E. (1996). The effects of precedents and client position on auditors’ financial accounting policy judgment. Accounting, Organizations and Society, 21(5), 467–486. https://doi.org/10.1016/0361-3682(95)00041-0
Samiolo, R., Spence, C., & Toh, D. (2024). Auditor judgment in the fourth industrial revolution. Contemporary Accounting Research, 41(1), 498–528. https://doi.org/10.1111/1911-3846.12901
Siglé, M. A., & van Nieuw Amerongen, N. (2026). Experimental evidence of experience effects on auditors’ identification of traditional and cybersecurity risks. Journal of International Accounting, Auditing and Taxation, 60, 100755. https://doi.org/10.1016/j.intaccaudtax.2026.100755
Smith, J. F., & Kida, T. (1991). Heuristics and biases: Expertise and task realism in auditing. Psychological Bulletin, 109(3), 472–489. https://doi.org/10.1037/0033-2909.109.3.472
Solomon, I., Krogstad, J. L., Romney, M. B., & Tomassini, L. A. (1982). Auditors’ prior probability distributions for account balances. Accounting, Organizations and Society, 7(1), 27–41. https://doi.org/10.1016/0361-3682(82)90023-X
Tiron-Tudor, A., & Deliu, D. (2022). Reflections on the human-algorithm complex duality perspectives in the auditing process. Qualitative Research in Accounting & Management, 19(3), 255–285. https://doi.org/10.1108/QRAM-04-2021-0059
Tomassini, L. A., Solomon, I., Romney, M. B., & Krogstad, J. L. (1982). Calibration of auditors’ probabilistic judgments: Some empirical evidence. Organizational Behavior and Human Performance, 30(3), 391–406. https://doi.org/10.1016/0030-5073(82)90227-6
Torroba, M., Sánchez, J. R., López, L., & Callejón, Á. (2025). Investigating the impacting factors for the audit professionals to adopt data analysis and artificial intelligence: Empirical evidence for Spain. International Journal of Accounting Information Systems, 56, 100738. https://doi.org/10.1016/j.accinf.2025.100738
Trompeter, G. M., Carpenter, T. D., Desai, N., Jones, K. L., & Riley, R. A. (2018). A live simulation-based investigation: Interactions with clients and their effect on audit judgment and professional skepticism. Auditing: A Journal of Practice & Theory, 37(3), 145–162. https://doi.org/10.2308/ajpt-51880
Trotman, K. T. (1998). Audit judgment research: Issues addressed, research methods and future directions. Accounting & Finance, 38(2), 115–156. https://doi.org/10.1111/1467-629X.00007
Tsui, J. S. L. (1996). Auditors’ ethical reasoning: Some audit conflict and cross-cultural evidence. The International Journal of Accounting, 31(1), 121–133. https://doi.org/10.1016/S0020-7063(96)90016-3
Tsui, J. S. L., & Gul, F. A. (1996). Auditors’ behaviour in an audit conflict situation: A research note on the role of locus of control and ethical reasoning. Accounting, Organizations and Society, 21(1), 41–51. https://doi.org/10.1016/0361-3682(95)00009-X
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381–396. https://doi.org/10.2308/acch-51071
Windsor, C. A., & Ashkanasy, N. M. (1995). The effect of client management bargaining power, moral reasoning development, and belief in a just world on auditor independence. Accounting, Organizations and Society, 20(7), 701–720. https://doi.org/10.1016/0361-3682(95)00018-5
Yi, Z., & Liu, P. (2025). Design and implementation of internal audit information platform system in colleges and universities. Information Resources Management Journal, 38(1). https://doi.org/10.4018/IRMJ.388951
