Abstract

The application of computer-based technologies in academic education has at least three decades of history and experience. In some study fields, it has been present since the very beginning, while in others it has become a necessity only in recent years. The ongoing technological revolution is disrupting the traditional professions with fundamental changes and - in some cases - even with the threat of disappearance of jobs. The finance and accounting professions are expected to undergo a technological change in the near future. While the changes are visible at the corporate level, university education seems to lag one step behind. We conducted a study among the students and graduates of the finance and accounting line of studies at the Cracow University of Economics. Using regression analysis, we investigate the perception of the usefulness of courses providing knowledge on new technologies like Artificial Intelligence (AI). We use a unique Polish setting, which is a leader in terms of outsourcing services. Our findings show that both students and graduates are aware of the importance of technological change. The courses teaching basic subjects are essential, but the current expectations are much higher in terms of the application of new technology based on AI in finance and accounting.

Keywords: higher education, finance and accounting education, student preferences, university curricula, Artificial Intelligence

References

• Abd Rahman, S. F., Md Yunus, M., & Hashim, H. (2021). Applying UTAUT in predicting ESL lecturers intention to use flipped learning. Sustainability, 13(15), 8571. https://doi.org/10.3390/su13158571
• Alamin, A. A., Wilkin, C. L., Yeoh, W., & Warren, M. (2020). The impact of self-efficacy on accountants' behavioral intention to adopt and use accounting information systems. Journal of Information Systems, 34(3), 31-46. https://doi.org/10.2308/isys-52617
• Al-Htaybat, K., Alberti-Alhtaybat, L. von, & Alhatabat, Z. (2018). Educating digital natives for the future: accounting educators' evaluation of the accounting curriculum. Accounting Education, 27(4), 333-357. https://doi.org/10.1080/09639284.2018.1437758
• Apostolou, B., Dorminey, J. W., Hassell, J. M., & Hickey, A. (2019). Accounting education literature review (2018). Journal of Accounting Education, 47, 1-27. https://doi.org/10.1016/j.jaccedu.2019.02.001
• Arnold, V., & Sutton, S. G. (1998). The theory of technology dominance: Understanding the impact of intelligent decision aids on decision maker's judgments. Advances in Accounting Behavioral Research, 1, 175-194.
• Arnold, V., Collier, P. A., Leech, S. A., & Sutton, S.G. (2004). Impact of intelligent decision aids on expert and novice decision-makers' judgments. Accounting and Finance, 44(1), 1-26. https://doi.org/10.1111/j.1467-629x.2004.00099.x
• Ashok, M. L., Abhishek, N., & Divyashree, M. S. (2019). Emerging trends in accounting: An analysis of impact of robotics in accounting, reporting and auditing of business and financial information. International Journal of Business Analytics and Intelligence, 7(2), 28-34. https://doi.org/10.2139/ssrn.3524486
• Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279-1333. https://doi.org/10.1162/003355303322552801
• Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006
• Bonner, S. E., Libby, R., & Nelson, M. (1996). Using decision aids to improve auditors' conditional probability judgments. The Accounting Review, 71(2), 221-240. http://www.jstor.org/stable/248447
• Cho, S., Vasarhelyi, M. A., Sun, T., & Zhang, C. (2020). Learning from Machine Learning in accounting and assurance. Journal of Emerging Technologies in Accounting, 17(1), 1-10. https://doi.org/10.2308/jeta-10718
• Chukwudi, O., Echefu, S., Boniface, U., & Victoria, C. (2018). Effect of artificial intelligence on the performance of accounting operations among accounting firms in South East Nigeria. Asian Journal of Economics, Business and Accounting, 7(2), 1-11. https://doi.org/10.9734/AJEBA/2018/41641
• Das, S., Dey, A., Pal, A., & Roy, N. (2015). Applications of Artificial Intelligence in Machine Learning: Review and prospect. International Journal of Computer Applications, 115(9), 31-41. https://doi.org/10.5120/20182-2402
• Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
• European Commission. (2012). Rethinking education: Investing in skills for better socio-economic outcomes. Communication from the Commission to the European Parliament Communication from the Commission to the European Parliament, the Council the European Economic and Social Committee and the Committee of the Regions. European Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52012DC0669
• European Commission. (2018). Promoting the relevance of higher education. Main report. Publications Office of the European Union. https://op.europa.eu/en/publication-detail/-/publication/59d3a999-84b9-11e8-ac6a-01aa75ed71a1
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerization? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
• Goldstein, P. J. (2005, December). Academic analytics: The uses of management information and technology in higher education. Key findings. Educause Center for Applied Research. https://er.educause.edu
• Hawksworth, J., Berriman, R., & Goel, S. (2018). Will robots really steal our jobs? An international analysis of the potential long term impact of automation. PricewaterhouseCoopers (PwC). https://www.pwc.co.uk/economic-services/assets/international-impact-of-automation-feb-2018.pdf
• GUS. (2021, June 15). Szkolnictwo wyższe w roku akademickim 2020/21 (wyniki wstępne). https://stat.gov.pl/files/gfx/portalinformacyjny/pl/defaultaktualnosci/5488/8/7/1/szkolnictwo_wyzsze_w_roku_akademickim_2020-2021.pdf
• Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1(1), 1-5. https://doi.org/10.1016/j.caeai.2020.100001
• Janvrin, D. J., & Weidenmier Watson, M. (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
• Krahel, J. P., & Vasarhelyi, M. A. (2014). AIS as facilitator of accounting change: technology, practice, and education. Journal of Information Systems, 28(2), 1-15. https://doi.org/10.2308/isys-10412
• Łada M., Konieczny A., & Wolak, J. (2020). A career in BPO and SSC Accounting Service Centers in Poland - the similarities and differences in selected aspects. The Theoretical Journal of Accounting, 106(162), 191-211.
• Maj-Waśniowska, K., & Jedynak, T. (2020). The issues and challenges of local government units in the era of population ageing. Administrative Sciences, 10(2), 1-22. https://doi.org/10.3390/admsci10020036
• Noga, T., & Arnold, V. (2002). Do tax decision support systems affect the accuracy of tax compliance decisions? International Journal of Accounting Information Systems, 3(3), 125-144. https://doi.org/10.1016/S1467-0895(02)00034-9
• OECD. (2017). In-depth analysis of the labour market relevance and outcomes of higher education systems: Analytical framework and country practices report. Enhancing higher education system performance. https://www.oecd.org/education/skills-beyond-school/LMRO%20Report.pdf
• Patrick, B. (2020, February 1). What is artificial intelligence? Journal of Accountancy. https://www.journalofaccountancy.com/issues/2020/feb/what-is-artificial-intelligence.html
• Petkov, R. (2020). Artificial Intelligence (AI) and the Accounting Function - A revisit and a new perspective for developing framework. Journal of Emerging Technologies in Accounting, 17(1), 99-105. https://doi.org/10.2308/jeta-52648
• Powell, W. W., & Colyvas, J. A. (2008). Microfoundations of institutional theory. In R. Greenwood, C. Oliver, R. Suddaby, & K. Sahlin (Eds.), The Sage Handbook of Organizational Institutionalism (pp. 276-298). SAGE Publications. https://doi.org/10.4135/9781849200387.n11
• Qu, Y., Quan, P., Lei, M., & Shi, Y. (2019). Review of bankruptcy prediction using machine learning and deep learning techniques. Procedia Computer Science, 162, 895-899. https://doi.org/10.1016/j.procs.2019.12.065
• Rose, A. M., & Rose, J. M. (2003). The effects of fraud risk assessments and a risk analysis decision aid on auditors' evaluation of evidence and judgment. Accounting Forum, 27(3), 312-338.
• Seasongood, S. (2016). Not just for the asembly line: A case for robotics in accounting and finance. Financial Executive, 32(1), 31-39. http://ksuweb.kennesaw.edu/~snorth/Robots/Articles/article4.pdf
• Sledgianowski, D., Gomaa, M., & Tan, C. (2017). Toward integration of Big Data, technology and information systems competencies into the accounting curriculum. Journal of Accounting Education, 38, 81-93. https://doi.org/10.1016/j.jaccedu.2016.12.008
• Squicciarini, M., & Nachtigall, H. (2021). Demand for AI skills in jobs: Evidence from online job postings. OECD Science, Technology and Industry Working Papers. OECD. https://doi.org/10.1787/3ed32d94-en
• Suki, N. M [Norbayah Mohd], & Suki, N. M [Norazah Mohd]. (2017). Determining students' behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3), 528-538. https://doi.org/10.1016/j.ijme.2017.10.002
• Triki, A., & Weisner, M. M. (2014). Lessons from the literature on the theory of technology dominance: Possibilities for an extended research framework. Journal of Emerging Technologies in Accounting, 11(1), 41-69. https://doi.org/10.2308/jeta-51078
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
• Vetter, A. (2021, January 20). Embracing the AI wave. Accounting Today. https://www.accountingtoday.com/opinion/embracing-the-ai-wave
• Wan, K., Cheung, G., & Chan, K. (2017). Prediction of students' use and acceptance of clickers by learning approaches: A cross-sectional observational study. Education Sciences, 7(4), 91. https://doi.org/10.3390/educsci7040091
• Yap, A. Y., & Drye, S. L. (2018). The challenges of teaching business analytics: Finding real Big Data for business students. Information System Education Journal, 16(2), 41-50.
• Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education - where are the educators? International Journal of Educational Technology in Higher Education, 16 (39), 1-27. https://doi.org/10.1186/s41239-019-0171-0
• 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