Too Big to Ignore: Lato Sensu Business Students’ Perceptions on an Accounting Big Data Case
DOI:
https://doi.org/10.24023/FutureJournal/2175-5825/2019.v11i3.464Keywords:
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.Downloads
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
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
1. Authors who publish in this journal agree to the following terms: the author(s) authorize(s) the publication of the text in the journal;
2. The author(s) ensure(s) that the contribution is original and unpublished and that it is not in the process of evaluation by another journal;
3. The journal is not responsible for the views, ideas and concepts presented in articles, and these are the sole responsibility of the author(s);
4. The publishers reserve the right to make textual adjustments and adapt texts to meet with publication standards.
5. Authors retain copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Atribuição NãoComercial 4.0 internacional, which allows the work to be shared with recognized authorship and initial publication in this journal.
6. Authors are allowed to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (e.g. publish in institutional repository or as a book chapter), with recognition of authorship and initial publication in this journal.
7. Authors are allowed and are encouraged to publish and distribute their work online (e.g. in institutional repositories or on a personal web page) at any point before or during the editorial process, as this can generate positive effects, as well as increase the impact and citations of the published work (see the effect of Free Access) at http://opcit.eprints.org/oacitation-biblio.html
• 8. Authors are able to use ORCID is a system of identification for authors. An ORCID identifier is unique to an individual and acts as a persistent digital identifier to ensure that authors (particularly those with relatively common names) can be distinguished and their work properly attributed.
