Too Big to Ignore: Lato Sensu Business Students’ Perceptions on an Accounting Big Data Case

Authors

DOI:

https://doi.org/10.24023/FutureJournal/2175-5825/2019.v11i3.464

Keywords:

Big Data, Educational Case, Business Students, Accounting Education, Survey.

Abstract

The objective of this study was to analyze the perceptions of lato sensu business students on an accounting big data case. The case was developed by Hoelscher and Mortimer (2018) and was designed to be used with the Tableau software. It was implemented in two distinct accounting courses from two higher education institutions located in the South region of Brazil. Right after the end of the case’s development, surveys were administered to collect data from the 41 participants. Survey questions were divided into three sections: (i) student’s demographic information; (ii) student’s proficiency level in Excel, Tableau, and Statistical software packages; and (iii) ten case-related and Tableau-related questions that were extracted from prior literature. According to the descriptive statistics, most of the students had low proficiency levels in Tableau before the case’s implementation. After the case, students’ perceptions changed from “none” to “basic” regarding their proficiency level. Also, students found the case interesting, engaging, and useful. It helped them to gain an understanding of how data analytics can be used to answer crucial business questions. Results from the association tests indicated that the students’ demographic and academic information is not significantly associated with the case-related and Tableau-related questions. It means that students who majored in accounting and non-accounting areas had similar perceptions. Then, the case has shown to be a productive activity even for those whose academic background is not directly related to the accounting field. Finally, students’ comments reinforced the positive experience with the case.

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Author Biographies

Vitor Hideo Nasu, University of São Paulo

PhD candidate in Accounting at the University of São Paulo.

Daniel Ramos Nogueira, State University of Londrina.

PhD in Accounting from FEA/USP. Adjunct professor of the Department of Accounting of the State University of Londrina.

Elvis Araujo Albertin, State University of Maringá

Master student in Accounting at the State University of Maringá

Claudio Marques, State University of Maringá

PhD in Accounting from FEA/USP. Adjunct professor of the Department of Accounting of the State University of Maringá.

References

Alles, M., & Gray, G. L. (2016). Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors. International Journal of Accounting Information Systems, 22, 44–59. https://doi.org/10.1016/j.accinf.2016.07.004

Beaver, D., Kumar, S., Li, H. C., Sobel, J., & Vajgel, P. (2010). Finding a needle in Haystack: Facebook’ s photo storage. Proceedings of the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI’10), (October), 1–8. Retrieved from http://www.usenix.org/events/osdi/tech/full_papers/Beaver.pdf

Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59. https://doi.org/10.1016/j.inffus.2015.08.005

Blazquez, D., & Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 130(September 2017), 99–113. https://doi.org/10.1016/j.techfore.2017.07.027

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

Cao, M., Chychyla, R., & Stewart, T. (2015). Big Data Analytics in Financial Statement Audits. Accounting Horizons, 29(2), 423–429. https://doi.org/10.2308/acch-51068

Coyne, E. M., Coyne, J. G., & Walker, K. B. (2018). Big Data information governance by accountants. International Journal of Accounting & Information Management, 26(1), 153–170. https://doi.org/10.1108/IJAIM-01-2017-0006

Earley, C. E. (2015). Data analytics in auditing: Opportunities and challenges. Business Horizons, 58(5), 493–500. https://doi.org/10.1016/j.bushor.2015.05.002

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191–209. https://doi.org/10.1016/j.jsis.2017.07.003

Hoelscher, J., & Mortimer, A. (2018). Using Tableau to visualize data and drive decision-making. Journal of Accounting Education, 44(October 2017), 49–59. https://doi.org/10.1016/j.jaccedu.2018.05.002

Igou, A., & Coe, M. (2016). Vistabeans coffee shop data analytics teaching case. Journal of Accounting Education, 36, 75–86. https://doi.org/10.1016/j.jaccedu.2016.05.004

Janvrin, D. J., Raschke, R. L., & Dilla, W. N. (2014). Making sense of complex data using interactive data visualization. Journal of Accounting Education, 32(4), 31–48. https://doi.org/10.1016/j.jaccedu.2014.09.003

Janvrin, D. J., & Watson, M. W. (2017). “Big Data”: A new twist to accounting. Journal of Accounting Education, 38, 3–8. https://doi.org/10.1016/j.jaccedu.2016.12.009

Kokina, J., Pachamanova, D., & Corbett, A. (2017). The role of data visualization and analytics in performance management: Guiding entrepreneurial growth decisions. Journal of Accounting Education, 38, 50–62. https://doi.org/10.1016/j.jaccedu.2016.12.005

Krahel, J. P., & Titera, W. R. (2015). Consequences of Big Data and Formalization on Accounting and Auditing Standards. Accounting Horizons, 29(2), 409–422. https://doi.org/10.2308/acch-51065

Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70), 1. Retrieved from https://www.bibsonomy.org/bibtex/742811cb00b303261f79a98e9b80bf49

Lindell, J. (2018). Analytics and Big Data for Accountants. New York, NY: American Institute of Certified Public Accountants, Inc. https://doi.org/10.1002/9781119512356

Pathways Commission. (2012). The Pathways Commission: Charting a national strategy for the next generation of accountants. Retrieved from http://commons.aaahq.org/posts/a3470e7ffa

Pincus, K. V., Stout, D. E., Sorensen, J. E., Stocks, K. D., & Lawson, R. A. (2017). Forces for change in higher education and implications for the accounting academy. Journal of Accounting Education, 40, 1–18. https://doi.org/10.1016/j.jaccedu.2017.06.001

Richins, G., Stapleton, A., Stratopoulos, T. C., & Wong, C. (2017). Big Data Analytics: Opportunity or Threat for the Accounting Profession? Journal of Information Systems, 31(3), 63–79. https://doi.org/10.2308/isys-51805

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

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009

Warren, J. D., Moffitt, K. C., & Byrnes, P. (2015). How Big Data Will Change Accounting. Accounting Horizons, 29(2), 397–407. https://doi.org/10.2308/acch-51069

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

Zhang, J., Yang, X., & Appelbaum, D. (2015). Toward Effective Big Data Analysis in Continuous Auditing. Accounting Horizons, 29(2), 469–476. https://doi.org/10.2308/acch-51070

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Published

2019-12-27

How to Cite

Nasu, V. H., Nogueira, D. R., Albertin, E. A., & Marques, C. (2019). Too Big to Ignore: Lato Sensu Business Students’ Perceptions on an Accounting Big Data Case. Future Studies Research Journal: Trends and Strategies, 11(3), 305–329. https://doi.org/10.24023/FutureJournal/2175-5825/2019.v11i3.464

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Artigos / Articles