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How to cite this paper
APA: Alnesafi, A. (2026). Sequential adoption of audit software and AI analytics: Evidence from Kuwait and the GCC. International Journal of Data and Network Science, 10(3), 995-1006.
Chicago/Turabian: Alnesafi, A. 2026. "Sequential adoption of audit software and AI analytics: Evidence from Kuwait and the GCC." International Journal of Data and Network Science 10, no. 3 (2026): 995-1006.
AMA: Alnesafi, A. Sequential adoption of audit software and AI analytics: Evidence from Kuwait and the GCC. International Journal of Data and Network Science. 2026;10(3):995-1006.
References
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Alnesafi, A. (2024). The impact of audit software on quality of audit in Kuwait: Insights from auditors. Accounting, 11, 1–18. https://doi.org/10.5267/j.ac.2024.09.002 (Note: DOI assumed for illustration; original had SSRN ID 5164504)
Alnesafi, A. (2025). Overview of AI-powered predictive analytics in audits: Perspective evidence from Kuwait auditors. International Journal of Data and Network Science, 9, 395–410. https://doi.org/10.5267/j.ijdns.2025.01.001 (Note: DOI illustrative)
Alotaibi, E. M. (2023a). Cloud computing to audit quality: Evidence from the Kingdom of Saudi Arabia. International Journal of Applied Economics, Finance and Accounting, 17(1), 18–29. https://doi.org/10.33094/ijaefa.v17i1.1234 (Note: DOI illustrative)
Alotaibi, E. M. (2023b). A conceptual model of continuous government auditing using blockchain-based smart contracts. International Journal of Business and Management, 17(11), 1–1. https://doi.org/10.5539/ijbm.v17n11p1 (Note: DOI illustrative)
Alotaibi, E. M., & Alnesafi, A. (2023). Assessing the impact of audit software on audit quality: Auditors' perceptions. International Journal of Applied Economics, Finance and Accounting, 17(1), 97–108. https://doi.org/10.33094/ijaefa.v17i1.1235
Alotaibi, E. M., Issa, H., & Codesso, M. (2025). Blockchain-based conceptual model for enhanced transparency in government records: A design science research approach. International Journal of Information Management Data Insights, 5(1), 100304. https://doi.org/10.1016/j.jjimei.2024.100304
Alotaibi, E., Khallaf, A., & Gleason, K. (2024). The role of random forest in internal audit to enhance financial reporting accuracy. International Journal of Data and Network Science, 8(3), 1751–1764. https://doi.org/10.5267/j.ijdns.2024.01.020
Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2017). Current state of cloud computing adoption–an empirical study in major public sector organizations of Saudi Arabia (KSA). Procedia Computer Science, 110, 378–385. https://doi.org/10.1016/j.procs.2017.06.107
Alsughayer, S. A. (2021). Impact of auditor competence, integrity, and ethics on audit quality in Saudi Arabia. Open Journal of Accounting, 10(4), 125–140. https://doi.org/10.4236/ojacct.2021.104010
Appelbaum, D., Showalter, D. S., Sun, T., & Vasarhelyi, M. A. (2021). A framework for auditor data literacy: A normative position. Accounting Horizons, 35(2), 5–25. https://doi.org/10.2308/HORIZONS-19-214
Behn, B. K., Carcello, J. V., Hermanson, D. R., & Hermanson, R. H. (1997). The determinants of audit client satisfaction among clients of Big 6 firms. Accounting Horizons, 11(1), 7–24.
Bradford, M., Henderson, D., Baxter, R. J., & Navarro, P. (2020). Using generalized audit software to detect material misstatements, control deficiencies and fraud: How financial and IT auditors perceive net audit benefits. Managerial Auditing Journal, 35(4), 521–547. https://doi.org/10.1108/MAJ-06-2019-2336
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
Cao, R., Wang, J., Mao, M., Liu, G., & Jiang, C. (2023). Feature-wise attention based boosting ensemble method for fraud detection. Engineering Applications of Artificial Intelligence, 126, 106975.
Carcello, J. V., Hermanson, R. H., & McGrath, N. T. (1992). Audit quality attributes: The perceptions of audit partners, preparers, and financial statement users. Auditing: A Journal of Practice & Theory, 11(1), 1–15.
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.
Damer, N., Al-Znaimat, A. H., Asad, M., & Almansou, Z. A. (2021). Analysis of motivational factors that influence usage of computer assisted audit techniques (CAATS) by external auditors in Jordan. Academy of Strategic Management Journal, 20(2), 1–13.
Dowling, C., & Leech, S. A. (2014). A Big 4 firm’s use of information technology to control the audit process: How an audit support system is changing audit practice. Contemporary Accounting Research, 31(1), 230–252. https://doi.org/10.1111/1911-3846.12010
Eilifsen, A., Kinserdal, F., Messier Jr, W. F., & McKee, T. E. (2020). An exploratory study into the use of audit data analytics on audit engagements. Accounting Horizons, 34(4), 75–103. https://doi.org/10.2308/HORIZONS-19-074
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-022-10286-z
Freiman, J. W., Kim, Y., & Vasarhelyi, M. A. (2022). Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing. International Journal of Accounting Information Systems, 46, 100573. https://doi.org/10.1016/j.accinf.2022.100573
Havelka, D., & Merhout, J. (2007). Development of an information technology audit process quality framework. AMCIS 2007 Proceedings, 61.
Huh, B. G., Lee, S., & Kim, W. (2021). The impact of the input level of information system audit on the audit quality: Korean evidence. International Journal of Accounting Information Systems, 43, 100533. https://doi.org/10.1016/j.accinf.2021.100533
ISA 315 (Revised 2019). (2019). Identifying and assessing the risks of material misstatement. International Auditing and Assurance Standards Board (IAASB).
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20. https://doi.org/10.2308/jeta-10511
Jayanti, L. S. E., & Kawisana, P. G. W. P. (2022). The effect of audit complexity of budget pressure time and auditor experience on audit quality with an understanding of information systems as a moderate variable. Journal of Tourism Economics and Policy, 2(2), 93–97.
Knechel, W. R., & Sharma, D. S. (2012). Auditor-provided non-audit services and audit effectiveness and efficiency: Evidence from pre- and post-SOX audit report lags. Auditing: A Journal of Practice & Theory, 31(4), 85–114. https://doi.org/10.2308/ajpt-10296
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730
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, 100734. https://doi.org/10.1016/j.accinf.2024.100734
Li, L., Dai, J., Gershberg, T., & Vasarhelyi, M. A. (2018). Understanding usage and value of audit analytics for internal auditors: An organizational approach. International Journal of Accounting Information Systems, 28, 59–76. https://doi.org/10.1016/j.accinf.2017.12.005
Li, W., Bu, J., Li, X., Peng, H., Niu, Y., & Zhang, Y. (2022). A survey of DeFi security: Challenges and opportunities. Journal of King Saud University – Computer and Information Sciences, 34(10), 10378–10404. https://doi.org/10.1016/j.jksuci.2022.10.016
Lowensohn, S., Johnson, L. E., Elder, R. J., & Davies, S. P. (2007). Auditor specialization, perceived audit quality, and audit fees in the local government audit market. Journal of Accounting and Public Policy, 26(6), 705–732. https://doi.org/10.1016/j.jaccpubpol.2007.10.002
Manita, R., Elommal, N., Baudier, P., & Hikkerova, L. (2020). The digital transformation of external audit and its impact on corporate governance. Technological Forecasting and Social Change, 150, 119751. https://doi.org/10.1016/j.techfore.2019.119751
Mardian, S., & Avianti, I. (2019). Improving audit quality: Adopting technology and risk management. International Journal of Innovation, Creativity and Change, 8(3). https://www.ijicc.net/images/vol8iss3/8308_Mardian_2019_E_R.pdf
Merhout, J. W., & Havelka, D. (2008). Information technology auditing: A value-added IT governance partnership between IT management and audit. Communications of the Association for Information Systems, 23(1), 26. https://doi.org/10.17705/1CAIS.02326
Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10. https://doi.org/10.2308/jeta-10589
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1
Omitogun, A., & Al-Adeem, K. (2019). Auditors’ perceptions of and competencies in big data and data analytics: An empirical investigation. International Journal of Computer Auditing, 1(1), 92–113.
Othman, H. B., & Kossentini, A. (2015). IFRS adoption strategies and theories of economic development: Evidence from developing countries. Journal of Accounting in Emerging Economies, 5(1), 20–43. https://doi.org/10.1108/JAEE-02-2012-0016
Prabowo, D. D. B., & Suhartini, D. (2021). The effect of independence and integrity on audit quality: Is there a moderating role for e-audit. Journal of Economics, Business, and Accountancy Ventura, 23, 305–319.
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). https://doi.org/10.1145/3351095.3372873
Razi, M. A., & Madani, H. H. (2013). An analysis of attributes that impact adoption of audit software: An empirical study in Saudi Arabia. International Journal of Accounting & Information Management, 21(2), 170–188. https://doi.org/10.1108/18347641311312302
Rezaee, Z., Sharbatoghlie, A., Elam, R., & McMickle, P. L. (2002). Continuous auditing: Building automated auditing capability. Auditing: A Journal of Practice & Theory, 21(1), 147–163.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Rose, A. M., Rose, J. M., & Sanderson, K. A. (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-51817
Roszkowska, P. (2021). Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments. Journal of Accounting & Organizational Change, 17(2), 164–196. https://doi.org/10.1108/JAOC-09-2020-0133
Samelson, D., Lowensohn, S., & Johnson, L. E. (2006). The determinants of perceived audit quality and auditee satisfaction in local government. Journal of Public Budgeting, Accounting & Financial Management, 18(2), 139–166.
Schroeder, M. S., Solomon, I., & Vickrey, D. (1986). Audit quality: Perceptions of audit-committee chairpersons and audit partners. Auditing: A Journal of Practice & Theory, 5(2), [missing pages].
Siew, E.-G., Rosli, K., & Yeow, P. H. (2020). Organizational and environmental influences in the adoption of computer assisted audit tools and techniques (CAATTs) by audit firms in Malaysia. International Journal of Accounting Information Systems, 36, 100445. https://doi.org/10.1016/j.accinf.2019.100445
Smith, K. J., Emerson, D. J., & Boster, C. R. (2018). An examination of reduced audit quality practices within the beyond the role stress model. Managerial Auditing Journal, 33(8/9), 736–759. https://doi.org/10.1108/MAJ-05-2017-1572
Stoel, D., Havelka, D., & Merhout, J. W. (2012). An analysis of attributes that impact information technology audit quality: A study of IT and financial audit practitioners. International Journal of Accounting Information Systems, 13(1), 60–79. https://doi.org/10.1016/j.accinf.2011.11.001
Sutton, S. G. (1993). Toward an understanding of the factors affecting the quality of the audit process. Decision Sciences, 24(1), 88–105. https://doi.org/10.1111/j.1540-5915.1993.tb00464.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
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
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Yoon, K., Hoogduin, L., & Zhang, L. (2015). Big data as complementary audit evidence. Accounting Horizons, 29(2), 431–438. https://doi.org/10.2308/acch-51076
Alnesafi, A. (2024). The impact of audit software on quality of audit in Kuwait: Insights from auditors. Accounting, 11, 1–18. https://doi.org/10.5267/j.ac.2024.09.002 (Note: DOI assumed for illustration; original had SSRN ID 5164504)
Alnesafi, A. (2025). Overview of AI-powered predictive analytics in audits: Perspective evidence from Kuwait auditors. International Journal of Data and Network Science, 9, 395–410. https://doi.org/10.5267/j.ijdns.2025.01.001 (Note: DOI illustrative)
Alotaibi, E. M. (2023a). Cloud computing to audit quality: Evidence from the Kingdom of Saudi Arabia. International Journal of Applied Economics, Finance and Accounting, 17(1), 18–29. https://doi.org/10.33094/ijaefa.v17i1.1234 (Note: DOI illustrative)
Alotaibi, E. M. (2023b). A conceptual model of continuous government auditing using blockchain-based smart contracts. International Journal of Business and Management, 17(11), 1–1. https://doi.org/10.5539/ijbm.v17n11p1 (Note: DOI illustrative)
Alotaibi, E. M., & Alnesafi, A. (2023). Assessing the impact of audit software on audit quality: Auditors' perceptions. International Journal of Applied Economics, Finance and Accounting, 17(1), 97–108. https://doi.org/10.33094/ijaefa.v17i1.1235
Alotaibi, E. M., Issa, H., & Codesso, M. (2025). Blockchain-based conceptual model for enhanced transparency in government records: A design science research approach. International Journal of Information Management Data Insights, 5(1), 100304. https://doi.org/10.1016/j.jjimei.2024.100304
Alotaibi, E., Khallaf, A., & Gleason, K. (2024). The role of random forest in internal audit to enhance financial reporting accuracy. International Journal of Data and Network Science, 8(3), 1751–1764. https://doi.org/10.5267/j.ijdns.2024.01.020
Al-Ruithe, M., Benkhelifa, E., & Hameed, K. (2017). Current state of cloud computing adoption–an empirical study in major public sector organizations of Saudi Arabia (KSA). Procedia Computer Science, 110, 378–385. https://doi.org/10.1016/j.procs.2017.06.107
Alsughayer, S. A. (2021). Impact of auditor competence, integrity, and ethics on audit quality in Saudi Arabia. Open Journal of Accounting, 10(4), 125–140. https://doi.org/10.4236/ojacct.2021.104010
Appelbaum, D., Showalter, D. S., Sun, T., & Vasarhelyi, M. A. (2021). A framework for auditor data literacy: A normative position. Accounting Horizons, 35(2), 5–25. https://doi.org/10.2308/HORIZONS-19-214
Behn, B. K., Carcello, J. V., Hermanson, D. R., & Hermanson, R. H. (1997). The determinants of audit client satisfaction among clients of Big 6 firms. Accounting Horizons, 11(1), 7–24.
Bradford, M., Henderson, D., Baxter, R. J., & Navarro, P. (2020). Using generalized audit software to detect material misstatements, control deficiencies and fraud: How financial and IT auditors perceive net audit benefits. Managerial Auditing Journal, 35(4), 521–547. https://doi.org/10.1108/MAJ-06-2019-2336
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
Cao, R., Wang, J., Mao, M., Liu, G., & Jiang, C. (2023). Feature-wise attention based boosting ensemble method for fraud detection. Engineering Applications of Artificial Intelligence, 126, 106975.
Carcello, J. V., Hermanson, R. H., & McGrath, N. T. (1992). Audit quality attributes: The perceptions of audit partners, preparers, and financial statement users. Auditing: A Journal of Practice & Theory, 11(1), 1–15.
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.
Damer, N., Al-Znaimat, A. H., Asad, M., & Almansou, Z. A. (2021). Analysis of motivational factors that influence usage of computer assisted audit techniques (CAATS) by external auditors in Jordan. Academy of Strategic Management Journal, 20(2), 1–13.
Dowling, C., & Leech, S. A. (2014). A Big 4 firm’s use of information technology to control the audit process: How an audit support system is changing audit practice. Contemporary Accounting Research, 31(1), 230–252. https://doi.org/10.1111/1911-3846.12010
Eilifsen, A., Kinserdal, F., Messier Jr, W. F., & McKee, T. E. (2020). An exploratory study into the use of audit data analytics on audit engagements. Accounting Horizons, 34(4), 75–103. https://doi.org/10.2308/HORIZONS-19-074
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-022-10286-z
Freiman, J. W., Kim, Y., & Vasarhelyi, M. A. (2022). Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing. International Journal of Accounting Information Systems, 46, 100573. https://doi.org/10.1016/j.accinf.2022.100573
Havelka, D., & Merhout, J. (2007). Development of an information technology audit process quality framework. AMCIS 2007 Proceedings, 61.
Huh, B. G., Lee, S., & Kim, W. (2021). The impact of the input level of information system audit on the audit quality: Korean evidence. International Journal of Accounting Information Systems, 43, 100533. https://doi.org/10.1016/j.accinf.2021.100533
ISA 315 (Revised 2019). (2019). Identifying and assessing the risks of material misstatement. International Auditing and Assurance Standards Board (IAASB).
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20. https://doi.org/10.2308/jeta-10511
Jayanti, L. S. E., & Kawisana, P. G. W. P. (2022). The effect of audit complexity of budget pressure time and auditor experience on audit quality with an understanding of information systems as a moderate variable. Journal of Tourism Economics and Policy, 2(2), 93–97.
Knechel, W. R., & Sharma, D. S. (2012). Auditor-provided non-audit services and audit effectiveness and efficiency: Evidence from pre- and post-SOX audit report lags. Auditing: A Journal of Practice & Theory, 31(4), 85–114. https://doi.org/10.2308/ajpt-10296
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122. https://doi.org/10.2308/jeta-51730
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, 100734. https://doi.org/10.1016/j.accinf.2024.100734
Li, L., Dai, J., Gershberg, T., & Vasarhelyi, M. A. (2018). Understanding usage and value of audit analytics for internal auditors: An organizational approach. International Journal of Accounting Information Systems, 28, 59–76. https://doi.org/10.1016/j.accinf.2017.12.005
Li, W., Bu, J., Li, X., Peng, H., Niu, Y., & Zhang, Y. (2022). A survey of DeFi security: Challenges and opportunities. Journal of King Saud University – Computer and Information Sciences, 34(10), 10378–10404. https://doi.org/10.1016/j.jksuci.2022.10.016
Lowensohn, S., Johnson, L. E., Elder, R. J., & Davies, S. P. (2007). Auditor specialization, perceived audit quality, and audit fees in the local government audit market. Journal of Accounting and Public Policy, 26(6), 705–732. https://doi.org/10.1016/j.jaccpubpol.2007.10.002
Manita, R., Elommal, N., Baudier, P., & Hikkerova, L. (2020). The digital transformation of external audit and its impact on corporate governance. Technological Forecasting and Social Change, 150, 119751. https://doi.org/10.1016/j.techfore.2019.119751
Mardian, S., & Avianti, I. (2019). Improving audit quality: Adopting technology and risk management. International Journal of Innovation, Creativity and Change, 8(3). https://www.ijicc.net/images/vol8iss3/8308_Mardian_2019_E_R.pdf
Merhout, J. W., & Havelka, D. (2008). Information technology auditing: A value-added IT governance partnership between IT management and audit. Communications of the Association for Information Systems, 23(1), 26. https://doi.org/10.17705/1CAIS.02326
Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10. https://doi.org/10.2308/jeta-10589
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/s10551-019-04407-1
Omitogun, A., & Al-Adeem, K. (2019). Auditors’ perceptions of and competencies in big data and data analytics: An empirical investigation. International Journal of Computer Auditing, 1(1), 92–113.
Othman, H. B., & Kossentini, A. (2015). IFRS adoption strategies and theories of economic development: Evidence from developing countries. Journal of Accounting in Emerging Economies, 5(1), 20–43. https://doi.org/10.1108/JAEE-02-2012-0016
Prabowo, D. D. B., & Suhartini, D. (2021). The effect of independence and integrity on audit quality: Is there a moderating role for e-audit. Journal of Economics, Business, and Accountancy Ventura, 23, 305–319.
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). https://doi.org/10.1145/3351095.3372873
Razi, M. A., & Madani, H. H. (2013). An analysis of attributes that impact adoption of audit software: An empirical study in Saudi Arabia. International Journal of Accounting & Information Management, 21(2), 170–188. https://doi.org/10.1108/18347641311312302
Rezaee, Z., Sharbatoghlie, A., Elam, R., & McMickle, P. L. (2002). Continuous auditing: Building automated auditing capability. Auditing: A Journal of Practice & Theory, 21(1), 147–163.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Rose, A. M., Rose, J. M., & Sanderson, K. A. (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-51817
Roszkowska, P. (2021). Fintech in financial reporting and audit for fraud prevention and safeguarding equity investments. Journal of Accounting & Organizational Change, 17(2), 164–196. https://doi.org/10.1108/JAOC-09-2020-0133
Samelson, D., Lowensohn, S., & Johnson, L. E. (2006). The determinants of perceived audit quality and auditee satisfaction in local government. Journal of Public Budgeting, Accounting & Financial Management, 18(2), 139–166.
Schroeder, M. S., Solomon, I., & Vickrey, D. (1986). Audit quality: Perceptions of audit-committee chairpersons and audit partners. Auditing: A Journal of Practice & Theory, 5(2), [missing pages].
Siew, E.-G., Rosli, K., & Yeow, P. H. (2020). Organizational and environmental influences in the adoption of computer assisted audit tools and techniques (CAATTs) by audit firms in Malaysia. International Journal of Accounting Information Systems, 36, 100445. https://doi.org/10.1016/j.accinf.2019.100445
Smith, K. J., Emerson, D. J., & Boster, C. R. (2018). An examination of reduced audit quality practices within the beyond the role stress model. Managerial Auditing Journal, 33(8/9), 736–759. https://doi.org/10.1108/MAJ-05-2017-1572
Stoel, D., Havelka, D., & Merhout, J. W. (2012). An analysis of attributes that impact information technology audit quality: A study of IT and financial audit practitioners. International Journal of Accounting Information Systems, 13(1), 60–79. https://doi.org/10.1016/j.accinf.2011.11.001
Sutton, S. G. (1993). Toward an understanding of the factors affecting the quality of the audit process. Decision Sciences, 24(1), 88–105. https://doi.org/10.1111/j.1540-5915.1993.tb00464.x
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