Fraud risk judgments performance: The role of audit technology, task structure, and auditor competence

Dadek Nandemar; Haliah; Syarifuddin; Nirwana

doi:10.31893/multiscience.2024180

Keywords:

audit technology

task structure

auditor competence

fraud risk judgments performance

Abstract

This study aims to examine the factors affecting auditor performance in fraud risk judgment, specifically audit technology, task structure, and auditor competence. It explores the interaction effects of audit technology, task structure, and auditor competence on fraud risk judgments performance. Employing a survey method and partial least square analysis responses from 59 state auditors at the Supreme Audit Agency (BPK) Representative Office in Southeast Sulawesi Province. The results indicate that audit technology, task structure, and auditor competence each have a positive and significant impact on fraud risk judgment performance. However, the combined interaction effect of audit technology, task structure, and auditor competence does not significantly influence fraud risk judgments performance. Given these findings, further investigation is warranted to enhance our understanding of these tasks and to refine audit technology specifications for improved fraud risk evaluation.
References
1. Afsar, B., & Umrani, W. A. (2020). Transformational leadership and innovative work behavior. European Journal of Innovation Management, 23(3), 402–428. https://doi.org/10.1108/EJIM-12-2018-0257
2. Aslan, L. (2021). The Evolving Competencies of the Public Auditor and the Future of Public Sector Auditing. In S. Grima & E. Boztepe (Eds.), Contemporary Issues in Public Sector Accounting and Auditing 105, 113–129. Emerald Publishing Limited. https://doi.org/10.1108/S1569-375920200000105008
3. Badan Pemeriksa Keuangan Republik Indonesia. (2017). Standar Pemeriksaan Keuangan Negara (SPKN) Tahun 2017.
4. Boyle, D. M., DeZoort, F. T., & Hermanson, D. R. (2015a). The effect of alternative fraud model use on auditors’ fraud risk judgments. Journal of Accounting and Public Policy, 34(6), 578–596.
5. Boyle, D. M., DeZoort, F. T., & Hermanson, D. R. (2015b). The effect of alternative fraud model use on auditors’ fraud risk judgments. Journal of Accounting and Public Policy, 34(6), 578–596. https://doi.org/https://doi.org/10.1016/j.jaccpubpol.2015.05.006
6. 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.
7. Brown-Liburd, H., & Vasarhelyi, M. A. (2015). Big Data and Audit Evidence. Journal of Emerging Technologies in Accounting, 12(1), 1–16. https://doi.org/10.2308/jeta-10468
8. Byrnes, P. E., Al-Awadhi, A., Gullvist, B., Brown-Liburd, H., Teeter, R., Warren, J. D., & Vasarhelyi, M. (2018). Evolution of Auditing: From the Traditional Approach to the Future Audit. In D. Y. Chan, V. Chiu, & M. A. Vasarhelyi (Eds.), Continuous Auditing, 285–297. Emerald Publishing Limited. https://doi.org/10.1108/978-1-78743-413-420181014
9. Clements, L. H. (2020). Personality traits common to fraud investigators. Journal of Financial Crime, 27(1), 119–129. https://doi.org/10.1108/JFC-03-2019-0036
10. Dai, J., & Vasarhelyi, M. A. (2020). Continuous audit intelligence as a service (CAIaaS) and intelligent app recommendations. In Journal of Emerging Technologies in Accounting,17(2), 1–15. American Accounting Association.
11. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
12. Frare, A. B., & Beuren, I. M. (2021). The role of green process innovation translating green entrepreneurial orientation and proactive sustainability strategy into environmental performance. Journal of Small Business and Enterprise Development, 29(5), 789–806.
13. Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: an introduction (8. utg.). AE Burvikovs, Red.) USA: Pearson.
14. Griffith, E. E., Kadous, K., & Young, D. (2021). Improving Complex Audit Judgments: A Framework and Evidence*†. Contemporary Accounting Research, 38(3), 2071–2104. https://doi.org/https://doi.org/10.1111/1911-3846.12658
15. Guiral, A., Rodgers, W., Ruiz, E., & Gonzalo-Angulo, J. A. (2015). Can expertise mitigate auditors’ unintentional biases? Journal of International Accounting, Auditing and Taxation, 24, 105–117. https://doi.org/https://doi.org/10.1016/j.intaccaudtax.2014.11.002
16. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
17. Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling.
18. 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.
19. Hassink, H., Meuwissen, R., & Bollen, L. (2010). Fraud detection, redress and reporting by auditors. Managerial Auditing Journal, 25(9), 861–881. https://doi.org/10.1108/02686901011080044
20. IAASB. (2013). International standard on assurance engagements 3000 (revised): Assurance engagements other than audits or reviews of historical financial information. https://doi.org/Available at: https://www.ifac.org/publicationsresources/international-standard-assurance-engagements-isae-3000-revised-assurance-enga. Accessed 21 Aug 2023.
21. Krejcie, R. V, & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308
22. Kroon, N., do Céu Alves, M., & Martins, I. (2021). The impacts of emerging technologies on accountants’ role and skills: Connecting to open innovation—a systematic literature review. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 163.
23. Libby, R., & Tan, H. T. (1995). The role of knowledge and memory in audit judgment. Judgment and Decision-Making Research in Accounting and Auditing, 1, 176–206.
24. Lugli, E., & Bertacchini, F. (2023). Audit quality and digitalization: some insights from theItalian context. Meditari Accountancy Research, 31(4), 841–860. https://doi.org/10.1108/MEDAR-08-2021-1399
25. Mansour, A. Z., Ahmi, A., & Popoola, O. M. J. (2020). The Personality Factor of Conscientiousness on Skills Requirement and Fraud Risk Assessment Performance. International Journal of Financial Research, 11, 405. https://api.semanticscholar.org/CorpusID:216468239
26. Mat Ridzuan, N. I., Said, J., Razali, F. M., Abdul Manan, D. I., & Sulaiman, N. (2022). Examining the Role of Personality Traits, Digital Technology Skills and Competency on the Effectiveness of Fraud Risk Assessment among External Auditors. Journal of Risk and Financial Management, 15(11), 536.
27. Maynard, D. C., & Hakel, M. D. (1997). Effects of Objective and Subjective Task Complexity on Performance. Human Performance, 10(4), 303–330. https://doi.org/10.1207/s15327043hup1004_1
28. Nelson, M., & Tan, H. (2005). Judgment and Decision Making Research in Auditing: A Task, Person, and Interpersonal Interaction Perspective. AUDITING: A Journal of Practice & Theory, 24(s-1), 41–71. https://doi.org/10.2308/aud.2005.24.s-1.41
29. Parker, S. K., Andrei, D. M., & Van den Broeck, A. (2019). Poor work design begets poor work design: Capacity and willingness antecedents of individual work design behavior. Journal of Applied Psychology, 104(7), 907.
30. Pramano, A. J., Razali, F. M., Said, J., Khalid, N., & Suffian, M. T. M. (2023). Digital Technology Skills And Fraud Risk Judgement Performance With Auditors’ Competencies Mediation. European Proceedings of Social and Behavioural Sciences. https://doi.org/10.15405/epsbs.2023.11.70
31. Rezaee, Z., Sharbatoghlie, A., Elam, R., & McMickle, P. L. (2018). Continuous auditing: Building automated auditing capability. Chan, DY, Chiu, V. & Vasarhelyi, MA (Ed.) Continuous auditing (Rutgers studies in accounting analytics). Emerald Publishing Limited.
32. Rikhardsson, P., & Dull, R. (2016). An exploratory study of the adoption, application and impacts of continuous auditing technologies in small businesses. International Journal of Accounting Information Systems, 20, 26–37. https://doi.org/https://doi.org/10.1016/j.accinf.2016.01.003
33. Rose, A. M., Rose, J. M., Sanderson, K.-A., & Thibodeau, J. C. (2017). When Should Audit Firms Introduce Analyses of Big Data Into the Audit Process? Journal of Information Systems, 31(3), 81–99. https://doi.org/10.2308/isys-51837
34. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial Least Squares Structural Equation Modeling BT - Handbook of Market Research (C. Homburg, M. Klarmann, & A. Vomberg (eds.); 1–40. Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-1
35. Schafer, B. A., & Schafer, J. K. (2019). Interpersonal Affect, Accountability and Experience in Auditor Fraud Risk Judgments and the Processing of Fraud Cues. In Advances in Accounting Behavioral Research 22, 43–65. Emerald Publishing Limited. https://doi.org/10.1108/S1475-148820190000022004
36. Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
37. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing.
38. Shonhadji, N., & Maulidi, A. (2021). The roles of whistleblowing system and fraud awareness as financial statement fraud deterrent. International Journal of Ethics and Systems, 37(3), 370–389. https://doi.org/10.1108/IJOES-09-2020-0140
39. Siriwardane, H. P., Kin Hoi Hu, B., & Low, K. Y. (2014). Skills, Knowledge, and Attitudes Important for Present-Day Auditors. International Journal of Auditing, 18(3), 193–205. https://doi.org/https://doi.org/10.1111/ijau.12023
40. Zaghloul, S. A. L. I. M. (2020). The Impact of Technological Development on Audit Evidence.
41. Zahmatkesh, S., & Rezazadeh, J. (2017). The effect of auditor features on audit quality. Tékhne, 15(2), 79–87. https://doi.org/https://doi.org/10.1016/j.tekhne.2017.09.003

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Nandemar, D., Haliah, Syarifuddin, & Nirwana. (2024). Fraud risk judgments performance: The role of audit technology, task structure, and auditor competence. Multidisciplinary Science Journal, 6(9), 2024180. https://doi.org/10.31893/multiscience.2024180

[1] Afsar, B., & Umrani, W. A. (2020). Transformational leadership and innovative work behavior. European Journal of Innovation Management, 23(3), 402–428. https://doi.org/10.1108/EJIM-12-2018-0257

[2] Aslan, L. (2021). The Evolving Competencies of the Public Auditor and the Future of Public Sector Auditing. In S. Grima & E. Boztepe (Eds.), Contemporary Issues in Public Sector Accounting and Auditing 105, 113–129. Emerald Publishing Limited. https://doi.org/10.1108/S1569-375920200000105008

[3] Badan Pemeriksa Keuangan Republik Indonesia. (2017). Standar Pemeriksaan Keuangan Negara (SPKN) Tahun 2017.

[4] Boyle, D. M., DeZoort, F. T., & Hermanson, D. R. (2015a). The effect of alternative fraud model use on auditors’ fraud risk judgments. Journal of Accounting and Public Policy, 34(6), 578–596.

[5] Boyle, D. M., DeZoort, F. T., & Hermanson, D. R. (2015b). The effect of alternative fraud model use on auditors’ fraud risk judgments. Journal of Accounting and Public Policy, 34(6), 578–596. https://doi.org/https://doi.org/10.1016/j.jaccpubpol.2015.05.006

[6] 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.

[7] Brown-Liburd, H., & Vasarhelyi, M. A. (2015). Big Data and Audit Evidence. Journal of Emerging Technologies in Accounting, 12(1), 1–16. https://doi.org/10.2308/jeta-10468

[8] Byrnes, P. E., Al-Awadhi, A., Gullvist, B., Brown-Liburd, H., Teeter, R., Warren, J. D., & Vasarhelyi, M. (2018). Evolution of Auditing: From the Traditional Approach to the Future Audit. In D. Y. Chan, V. Chiu, & M. A. Vasarhelyi (Eds.), Continuous Auditing, 285–297. Emerald Publishing Limited. https://doi.org/10.1108/978-1-78743-413-420181014

[9] Clements, L. H. (2020). Personality traits common to fraud investigators. Journal of Financial Crime, 27(1), 119–129. https://doi.org/10.1108/JFC-03-2019-0036

[10] Dai, J., & Vasarhelyi, M. A. (2020). Continuous audit intelligence as a service (CAIaaS) and intelligent app recommendations. In Journal of Emerging Technologies in Accounting,17(2), 1–15. American Accounting Association.

[11] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

[12] Frare, A. B., & Beuren, I. M. (2021). The role of green process innovation translating green entrepreneurial orientation and proactive sustainability strategy into environmental performance. Journal of Small Business and Enterprise Development, 29(5), 789–806.

[13] Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: an introduction (8. utg.). AE Burvikovs, Red.) USA: Pearson.

[14] Griffith, E. E., Kadous, K., & Young, D. (2021). Improving Complex Audit Judgments: A Framework and Evidence*†. Contemporary Accounting Research, 38(3), 2071–2104. https://doi.org/https://doi.org/10.1111/1911-3846.12658

[15] Guiral, A., Rodgers, W., Ruiz, E., & Gonzalo-Angulo, J. A. (2015). Can expertise mitigate auditors’ unintentional biases? Journal of International Accounting, Auditing and Taxation, 24, 105–117. https://doi.org/https://doi.org/10.1016/j.intaccaudtax.2014.11.002

[16] Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.

[17] Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling.

[18] 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.

[19] Hassink, H., Meuwissen, R., & Bollen, L. (2010). Fraud detection, redress and reporting by auditors. Managerial Auditing Journal, 25(9), 861–881. https://doi.org/10.1108/02686901011080044

[20] IAASB. (2013). International standard on assurance engagements 3000 (revised): Assurance engagements other than audits or reviews of historical financial information. https://doi.org/Available at: https://www.ifac.org/publicationsresources/international-standard-assurance-engagements-isae-3000-revised-assurance-enga. Accessed 21 Aug 2023.

[21] Krejcie, R. V, & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308

[22] Kroon, N., do Céu Alves, M., & Martins, I. (2021). The impacts of emerging technologies on accountants’ role and skills: Connecting to open innovation—a systematic literature review. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 163.

[23] Libby, R., & Tan, H. T. (1995). The role of knowledge and memory in audit judgment. Judgment and Decision-Making Research in Accounting and Auditing, 1, 176–206.

[24] Lugli, E., & Bertacchini, F. (2023). Audit quality and digitalization: some insights from theItalian context. Meditari Accountancy Research, 31(4), 841–860. https://doi.org/10.1108/MEDAR-08-2021-1399

[25] Mansour, A. Z., Ahmi, A., & Popoola, O. M. J. (2020). The Personality Factor of Conscientiousness on Skills Requirement and Fraud Risk Assessment Performance. International Journal of Financial Research, 11, 405. https://api.semanticscholar.org/CorpusID:216468239

[26] Mat Ridzuan, N. I., Said, J., Razali, F. M., Abdul Manan, D. I., & Sulaiman, N. (2022). Examining the Role of Personality Traits, Digital Technology Skills and Competency on the Effectiveness of Fraud Risk Assessment among External Auditors. Journal of Risk and Financial Management, 15(11), 536.

[27] Maynard, D. C., & Hakel, M. D. (1997). Effects of Objective and Subjective Task Complexity on Performance. Human Performance, 10(4), 303–330. https://doi.org/10.1207/s15327043hup1004_1

[28] Nelson, M., & Tan, H. (2005). Judgment and Decision Making Research in Auditing: A Task, Person, and Interpersonal Interaction Perspective. AUDITING: A Journal of Practice & Theory, 24(s-1), 41–71. https://doi.org/10.2308/aud.2005.24.s-1.41

[29] Parker, S. K., Andrei, D. M., & Van den Broeck, A. (2019). Poor work design begets poor work design: Capacity and willingness antecedents of individual work design behavior. Journal of Applied Psychology, 104(7), 907.

[30] Pramano, A. J., Razali, F. M., Said, J., Khalid, N., & Suffian, M. T. M. (2023). Digital Technology Skills And Fraud Risk Judgement Performance With Auditors’ Competencies Mediation. European Proceedings of Social and Behavioural Sciences. https://doi.org/10.15405/epsbs.2023.11.70

[31] Rezaee, Z., Sharbatoghlie, A., Elam, R., & McMickle, P. L. (2018). Continuous auditing: Building automated auditing capability. Chan, DY, Chiu, V. & Vasarhelyi, MA (Ed.) Continuous auditing (Rutgers studies in accounting analytics). Emerald Publishing Limited.

[32] Rikhardsson, P., & Dull, R. (2016). An exploratory study of the adoption, application and impacts of continuous auditing technologies in small businesses. International Journal of Accounting Information Systems, 20, 26–37. https://doi.org/https://doi.org/10.1016/j.accinf.2016.01.003

[33] Rose, A. M., Rose, J. M., Sanderson, K.-A., & Thibodeau, J. C. (2017). When Should Audit Firms Introduce Analyses of Big Data Into the Audit Process? Journal of Information Systems, 31(3), 81–99. https://doi.org/10.2308/isys-51837

[34] Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial Least Squares Structural Equation Modeling BT - Handbook of Market Research (C. Homburg, M. Klarmann, & A. Vomberg (eds.); 1–40. Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-1

[35] Schafer, B. A., & Schafer, J. K. (2019). Interpersonal Affect, Accountability and Experience in Auditor Fraud Risk Judgments and the Processing of Fraud Cues. In Advances in Accounting Behavioral Research 22, 43–65. Emerald Publishing Limited. https://doi.org/10.1108/S1475-148820190000022004

[36] Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.

[37] Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing.

[38] Shonhadji, N., & Maulidi, A. (2021). The roles of whistleblowing system and fraud awareness as financial statement fraud deterrent. International Journal of Ethics and Systems, 37(3), 370–389. https://doi.org/10.1108/IJOES-09-2020-0140

[39] Siriwardane, H. P., Kin Hoi Hu, B., & Low, K. Y. (2014). Skills, Knowledge, and Attitudes Important for Present-Day Auditors. International Journal of Auditing, 18(3), 193–205. https://doi.org/https://doi.org/10.1111/ijau.12023

[40] Zaghloul, S. A. L. I. M. (2020). The Impact of Technological Development on Audit Evidence.

[41] Zahmatkesh, S., & Rezazadeh, J. (2017). The effect of auditor features on audit quality. Tékhne, 15(2), 79–87. https://doi.org/https://doi.org/10.1016/j.tekhne.2017.09.003

Abstract

References

How to cite