ACCT 332 Week 2 Research Paper: Topic, Abstract and Annotated Bibliography Example

Continuous Monitoring and Continuous Auditing of Accounting Information Systems: Topic, Abstract and Annotated Bibliography

Research Topic

Topic: Continuous monitoring and continuous auditing of accounting information systems. Research question: How far have continuous monitoring by management and continuous auditing by auditors moved from theory into practice, and what technical and organizational factors limit their use? I chose this topic because my internship in the finance office of a regional distributor showed me how much of month-end review is spent finding errors that a system could have flagged the day they happened.

Abstract

This paper examines continuous monitoring and continuous auditing, which use automated tests to review accounting transactions and controls as they occur. It reviews their development, evidence on benefits for internal control and audit efficiency, and implementation barriers, and recommends how accountants can introduce continuous techniques in organizations of modest size.

Annotated Bibliography

Alles, M. G., Kogan, A., & Vasarhelyi, M. A. (2008). Putting continuous auditing theory into practice: Lessons from two pilot implementations. Journal of Information Systems, 22(2), 195-214. https://doi.org/10.2308/jis.2008.22.2.195

Alles et al. (2008) look back on almost twenty years of continuous auditing research and report lessons from pilot projects they carried out with two major firms. They describe how the concept moved from academic proposals toward practice and note survey evidence that about half of responding firms were already using some continuous auditing techniques. The article is a practitioner-oriented report by the field's leading researchers rather than a statistical study, so its conclusions rest on experience in a small number of settings. It will anchor the history section of my paper and supply concrete examples of what implementation involved in real organizations.

Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1-27. https://doi.org/10.2308/ajpt-51684

Appelbaum et al. (2017) argue that clients' use of big data, cloud systems and complex analytics creates both the opportunity and the urgency for external auditors to adopt advanced analytics. They compare existing standards on audit evidence and analytical procedures with the new data environment and propose six research questions, including how to measure and report the results of analytics. As a review and research agenda, it does not test hypotheses, but it is published in a leading audit journal and is widely cited. I will use it to explain why continuous techniques matter more as data volumes grow and where standards have not kept pace.

Brown, C. E., Wong, J. A., & Baldwin, A. A. (2007). A review and analysis of the existing research streams in continuous auditing. Journal of Emerging Technologies in Accounting, 4(1), 1-28. https://doi.org/10.2308/jeta.2007.4.1.1

This literature review identifies more than 80 papers on continuous auditing and classifies them into five research streams: demand factors, theory and guidance, enabling technologies, applications and impacts. Brown et al. (2007) intended it as a background for researchers and practitioners and point to areas, especially impacts, where little evidence existed at the time. Its age is a limitation, since much of the empirical work came later, but its framework is still useful. I will use its five categories to organize my paper's discussion and to show how the field's emphasis has shifted from technology toward evidence on outcomes.

Chan, D. Y., & Vasarhelyi, M. A. (2011). Innovation and practice of continuous auditing. International Journal of Accounting Information Systems, 12(2), 152-160. https://doi.org/10.1016/j.accinf.2011.01.001

Chan and Vasarhelyi (2011) contrast the traditional periodic audit with continuous auditing and explain how the continuous approach changes when testing happens, how much of it is automated, how much of the population can be examined and what the auditor's work consists of. The article is conceptual and short, but it gives one of the clearest descriptions of what makes continuous auditing different rather than simply faster. I will use it in the definitions section to separate continuous auditing from continuous monitoring, and to explain why the shift requires auditors to change their procedures instead of automating old ones.

Hardy, C. A., & Laslett, G. (2015). Continuous auditing and monitoring in practice: Lessons from Metcash's business assurance group. Journal of Information Systems, 29(2), 183-194. https://doi.org/10.2308/isys-50969

This case study describes how Metcash, a wholesale distribution and marketing company in Australia, interpreted and implemented continuous auditing and monitoring. Hardy and Laslett (2015) report more than 100 automated tests run daily, a fully integrated exception management system, a move from descriptive data toward predictive analytics and the use of visualization in reporting, along with the challenges the group faced along the way. As a single case it cannot show how common these results are, but it is unusually detailed. Because Metcash is a distributor like my internship employer, I will use it as the main practical example in my implementation section.

Huang, F., No, W. G., Vasarhelyi, M. A., & Yan, Z. (2022). Audit data analytics, machine learning, and full population testing. The Journal of Finance and Data Science, 8, 138-144. https://doi.org/10.1016/j.jfds.2022.05.002

Huang et al. (2022) argue that audit sampling, which examines only a small snapshot of transactions, loses much of its meaning when complete data are available, and they present an approach that uses audit data analytics and machine learning to test entire populations. They also discuss the challenges of doing so, such as handling the many exceptions full-population tests produce. The paper is short and proposes an approach rather than testing it on a large sample, but it is recent and connects continuous auditing to current analytics. I will use it in the section on where continuous techniques are heading.

Kogan, A., Alles, M. G., Vasarhelyi, M. A., & Wu, J. (2014). Design and evaluation of a continuous data level auditing system. Auditing: A Journal of Practice & Theory, 33(4), 221-245. https://doi.org/10.2308/ajpt-50844

Kogan et al. (2014) design a two-layer continuous auditing system, in which the first layer checks compliance with fixed business process rules and the second applies analytical models to detect anomalies, and simulate it on a large sample of procurement data from a major health care provider. Their central result is conditional: testing every record pays off only when auditors also change how they work. They find that auditing complete data continuously can produce superior results, but only when audit practices change to use the new availability of data. The design is rigorous and uses real data, though the data are proprietary and come from one organization. This will be my main source on how a continuous system is actually built.

Masli, A., Peters, G. F., Richardson, V. J., & Sanchez, J. M. (2010). Examining the potential benefits of internal control monitoring technology. The Accounting Review, 85(3), 1001-1034. https://doi.org/10.2308/accr.2010.85.3.1001

This empirical study identifies firms that implemented technology to monitor the effectiveness of their internal controls in response to the Sarbanes-Oxley Act. Masli et al. (2010) find that implementation was associated with a lower likelihood of material weaknesses, smaller increases in audit fees and smaller increases in audit delays after SOX. Published in a top journal with a large sample, it provides the strongest evidence in my bibliography that continuous monitoring by management produces measurable benefits, although an association does not prove that the technology caused them. It will be central to my evidence section, and one of its authors also wrote our course textbook.

Closing Note

These sources cover each part of the planned paper. Brown et al. (2007), Alles et al. (2008) and Chan and Vasarhelyi (2011) supply history and definitions; Masli et al. (2010) supply the evidence on monitoring benefits; Kogan et al. (2014) and Hardy and Laslett (2015) show how systems are built and run; and Appelbaum et al. (2017) and Huang et al. (2022) point to the future of analytics-based assurance. I will add recent empirical studies before the final draft.