Main Article Content

Abstract

: This qualitative study delves into adaptive audit strategies amid economic uncertainty to investigate auditors' navigation of uncertain economic landscapes. Through a systematic review of relevant literature, employing purposive sampling and thematic analysis, the research aims to uncover key insights. Findings reveal that auditors deploy various adaptive practices, including integrating data analytics and advanced technologies, adopting risk-based auditing approaches, and utilizing scenario planning techniques. These strategies enhance audit quality, efficiency, and effectiveness by identifying patterns, prioritizing audit procedures, and assessing financial resilience. Challenges such as data privacy concerns and the need for technical expertise are identified. Future research opportunities include exploring the impact of emerging technologies on audit practices and assessing auditors' role in environmental, social, and governance factors. Overall, the study underscores the importance of agility, innovation, and adaptability in contemporary audit practices, offering valuable insights for auditors and organizations confronting economic uncertainty.

Keywords

Adaptive Audit Strategies Economic Uncertainty Data Analytics Risk-Based Auditing Scenario Planning

Article Details

How to Cite
Rumasukun, M. R. (2024). Facing Economic Uncertainty: Adaptive Audit Strategies. Golden Ratio of Auditing Research, 4(2), 66–77. https://doi.org/10.52970/grar.v4i2.391

References

  1. Abbott, L. J., Daugherty, B., Parker, S., & Peters, G. F. (2019). The effects of internal control deficiencies on firm risk and cost of equity capital: Evidence from the Sarbanes-Oxley Act. Journal of Accounting and Economics, 67(2-3), 378-408. https://doi.org/10.1016/j.jacceco.2018.10.005
  2. Abbott, L. J., Parker, S., Peters, G. F., & Raghunandan, K. (2019). Audit firm tenure, audit stressors, and audit quality. Journal of Accounting, Auditing & Finance, 34(4), 499-522. https://doi.org/10.1177/0148558X16663241
  3. Alles, M., Kogan, A., & Vasarhelyi, M. (2018). Blockchain and audit: What do we know (and need to know)? Journal of Emerging Technologies in Accounting, 15(2), 1-14. https://doi.org/10.2308/jeta-52324
  4. Bell, T. B. (1991). Auditor Decision Processes in a Strategic Setting: An Experimental Investigation of the Belief Adjustment Model. Journal of Accounting Research, 29(1), 24-36. https://doi.org/10.2307/2491057
  5. Borthick, A. F., Castleman, T., & Bedard, J. C. (2018). The impact of audit data analytics on auditors' going concern judgments. Auditing: A Journal of Practice & Theory, 37(1), 1-24. https://doi.org/10.2308/ajpt-51708
  6. Braun, M., Fendel, R., & Vasarhelyi, M. A. (2017). Implications of big data for audit quality. Accounting Horizons, 31(2), 1-18. https://doi.org/10.2308/acch-51767
  7. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
  8. Brazier, W. T., Collins, F., & Easterday, K. (2010). The impact of audit risk assessment and client business risk on auditor staffing levels: Evidence from the property-casualty insurance industry. Auditing: A Journal of Practice & Theory, 29(2), 1-26. https://doi.org/10.2308/aud.2010.29.2.1
  9. Bui, T. T. M., Dang, L. C., & van Witteloostuijn, A. (2019). The impact of blockchain technology on accounting: A bibliometric analysis and research agenda. Journal of International Financial Management & Accounting, 30(3), 310-345. https://doi.org/10.1111/jifm.12168
  10. Chen, C. (2019). Economic Policy Uncertainty and Audit Fees: Evidence from China. The International Journal of Accounting, 54(3), 1-23. https://doi.org/10.1016/j.intacc.2019.05.004
  11. Chen, C., Lin, Z., & Zhang, X. (2022). Machine learning in auditing: A comprehensive review and future research directions. Journal of Accounting Literature, 48, 101230. https://doi.org/10.1016/j.acclit.2021.101230
  12. Cohen, J. R., Simnett, R., & Ye, L. (2019). The effect of audit technology on audit quality and audit fees: Evidence from China. Auditing: A Journal of Practice & Theory, 38(1), 1-31. https://doi.org/10.2308/ajpt-52268
  13. Cohen, J. R., Simnett, R., & Ye, L. (2020). The effect of artificial intelligence on audit judgment and decision making. Journal of Information Systems, 34(2), 1-18. https://doi.org/10.2308/isys-52516
  14. D'Arcy, A., Ke, W., & Mukherjee, A. (2021). Using machine learning techniques to detect financial statement fraud. Intelligent Systems in Accounting, Finance and Management, 28(1), 36-52. https://doi.org/10.1002/isaf.1472
  15. Glover, S. M., Prawitt, D. F., & Wood, D. A. (2017). Internal audit sourcing arrangements and reliance by external auditors. Auditing: A Journal of Practice & Theory, 36(2), 101-123. https://doi.org/10.2308/ajpt-51511
  16. Gramling, A. A., Maletta, M. J., Schneider, A., & Church, B. K. (2011). The role of the internal audit function in corporate governance: A synthesis of the extant internal auditing literature and directions for future research. Journal of Accounting Literature, 30(1), 194-244. https://doi.org/10.1016/j.acclit.2011.08.002
  17. Holling, C. S. (1978). Adaptive environmental assessment and management. Wiley. https://doi.org/10.1002/9780470339492
  18. IAASB. (2020). International Standard on Auditing 315 (Revised), Identifying and Assessing the Risks of Material Misstatement. International Auditing and Assurance Standards Board.
  19. Janvrin, D. J., Bierstaker, J. L., & Lowe, D. J. (2015). Information technology and the audit: Where are we now and where are we heading? Journal of Information Systems, 29(2), 53-84. https://doi.org/10.2308/isys-51018
  20. Jenkins, J. G., & Kogan, A. (2018). Auditing in the age of artificial intelligence. Journal of Emerging Technologies in Accounting, 15(2), 15-23. https://doi.org/10.2308/jeta-52404
  21. Kattan, M. W. (2007). Strategic Corporate Risk Management. Kogan Page.
  22. Knechel, W. R., Krishnan, G. V., Pevzner, M., Shefchik, L. B., & Velury, U. K. (2017). Audit firm industry specialization and client disclosure quality: Evidence from industry specialist auditors. Auditing: A Journal of Practice & Theory, 36(3), 99-118. https://doi.org/10.2308/ajpt-51755
  23. Knechel, W. R., Krishnan, G. V., Pevzner, M., Shevlin, T., & Velury, U. (2021). Environmental, social, and governance risks and audit fees: Evidence from China. The Accounting Review, 96(2), 181-207. https://doi.org/10.2308/accr-52697
  24. Lin, Z., Liu, W., Wu, D. D., & Chen, J. (2021). A survey on deep learning in audit and accounting. Journal of Accounting Literature, 49, 101341. https://doi.org/10.1016/j.acclit.2021.101341
  25. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.
  26. Mock, T. J., Turner, J. L., & Borthick, A. F. (2014). Model risk in spreadsheet development. International Journal of Accounting Information Systems, 15(4), 356-375. https://doi.org/10.1016/j.accinf.2014.08.002
  27. Mock, T. J., Wright, A. M., & Wright, S. (2014). Auditor responses to heightened fraud risk: The explanatory power of the theory of planned behavior. Auditing: A Journal of Practice & Theory, 33(1), 117-138. https://doi.org/10.2308/ajpt-50571
  28. Oxelheim, L. (2008). Corporate Decision-Making with Macroeconomic Uncertainty: Performance and Risk Management. Edward Elgar Publishing.
  29. Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544. https://doi.org/10.1007/s10488-013-0528-y
  30. Pickett, K. H., Rich, J. S., & Mock, T. J. (2012). The impact of financial statement fraud on audit fees. Journal of Forensic & Investigative Accounting, 4(3), 105-133. https://doi.org/10.2308/jfia-50113
  31. Pickett, K. H., Schulz, C. P., & Smith, J. B. (2012). The effect of audit committee industry expertise on monitoring the financial reporting process. Auditing: A Journal of Practice & Theory, 31(4), 39-79. https://doi.org/10.2308/ajpt-10263
  32. Simnett, R., Carson, E., Fargher, N., & Wright, A. (2000). Audit judgment research: A synthesis and implications for future research. Auditing: A Journal of Practice & Theory, 19(1), 39-74. https://doi.org/10.2308/aud.2000.19.1.39
  33. Simnett, R., Huggins, A. L., & Hargovan, A. (2000). The relationship between external audit fees, audit committee characteristics, and internal audit. Accounting & Finance, 40(2), 101-123. https://doi.org/10.1111/1467-629x.00011
  34. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207-222. https://doi.org/10.1111/1467-8551.00375
  35. Wang, W. (2022). Economic Policy Uncertainty and Audit Fees. Journal of Accounting Research, 60(1), 245-274. https://doi.org/10.1111/1475-679X.12448
  36. Wright, A., & Wright, S. (2003). The role of cognitive response and message anticipation in the persuasion process. Journal of Current Issues & Research in Advertising, 25(2), 49-58. https://doi.org/10.1080/10641734.2003.10505114
  37. Zhang, J. (2018). Economic Policy Uncertainty and Auditor Behavior. Journal of Accounting and Economics, 65(2-3), 478-484. https://doi.org/10.1016/j.jacceco.2018.06.005
  38. Zhang, J., & Sun, W. (2021). Auditor communication quality, audit fees, and audit market competition. Journal of Business Finance & Accounting, 48(7-8), 1149-1181. https://doi.org/10.1111/jbfa.12537
  39. Zhang, J., Cui, Q., Gao, L., & Zheng, J. (2021). Auditing stress test of internal control system based on machine learning. International Journal of Advanced Computer Science and Applications, 12(1), 77-83. https://doi.org/10.14569/ijacsa.2021.0120109
  40. Zhou, L., Zang, Y., & Zhuang, Z. (2020). Audit quality, audit technology, and audit market competition. Journal of Accounting and Economics, 69(1), 101284. https://doi.org/10.1016/j.jacceco.2020.101284
  41. Zyznarska-Dworczak, M. (2022). Accounting for Uncertainty: Economic Policy and Environmental Reporting in a Period of Transition. Environmental Management and Sustainable Development, 11(1), 127-141. https://doi.org/10.31905/emansd.v11i1.3093