Automation and AI in Accounting: Comparative Impact of Chat-bots and Deep Learning Machine of Accounting Officer's Work Effectiveness in Business Organizations in Anambra State

Authors

  • Uchenna Sophia Nweke Department of Vocational Education, Faculty of Education, Chukwuemeka Odumuegwu Ojukwu University, Igbariam Campus, Anambra state, Nigeria
  • Anthonia Ngozi Umezulike Department of Vocational Education, Faculty of Education, Chukwuemeka Odumuegwu Ojukwu University, Igbariam Campus, Anambra state, Nigeria

DOI:

https://doi.org/10.38035/sijet.v3i1.268

Keywords:

Artificial Intelligence, Chat-bots, Deep Learning Machine, Work Effectiveness, Accounting Officers

Abstract

This study investigated the comparative impact of chat-bots and deep learning of accounting officers' work effectiveness in Anambra State. A correlation research design was adopted, and a structured questionnaire was administered using Artificial Intelligence Questionnaire (AIQ) and Work Effectiveness Questionnaire (WEQ) to 221 accountants in business organizations. The study found a moderate positive relationship between automated chat-bots and work effectiveness (r = .465, N = 221) and a low positive relationship between deep learning machines and work effectiveness (r = .305, N = 221). The study also revealed significant relationships between both automated chat-bots and deep learning machines with work effectiveness. The findings have implications for business organizations, highlighting the need to adopt AI technologies to enhance financial management capabilities, productivity, and efficiency. The study recommends that business organizations adopt automated chat-bots and deep learning machines to improve accounting practices and provide training and development opportunities for accounting officers. This study contributes to knowledge theoretically, practically, and methodologically, providing insights into the relationship between AI technologies and work effectiveness in accounting.

References

Accenture. (2018). Future workforce survey. https://www.accenture.com/acnmedia/PDF-73/Accentue-Future-Workforce-Survey.pdf#zoom=50

Aguirre, S., & Rodriguez, A. (2017). Automation of a business process using robotic process automation (RPA): A case study. In Figueroa-García, J. C., López-Santana, E. R., Villa-Ramírez, J. L., & Ferro-Escobar, R. (Eds.), Applied computer sciences in engineering (Vol. 742, pp. 65–71). Springer. https://doi.org/10.1007/978-3-319-66963-2_7

Armstrong, M., & Taylor, S. (2018). Armstrong’s handbook of human resource management practice. Kogan Page.

Basiru, J. O., Ejiofor, C. L., Onukwulu, E. C., & Attah, R. (2023). Enhancing financial reporting systems: A conceptual framework for integrating data analytics in business decision-making. IRE Journals,[online], 7(4), 587-606.

Chen, L., & Zhang, W. (2020). The role of AI-driven technologies in financial decision-making among Chinese manufacturing firms. Asian Journal of Finance and Accounting, 12(1), 67–83. https://doi.org/10.5296/ajfa.v12i1.16785

Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education. Routledge.

Deloitte. (2019). AI-powered in accounting: A guide to getting started.

Frisk, J. E., & Bannister, F. (2017). Improving the use of analytics and big data by changing the decision-making culture: A design approach. Management Decision, 55(10), 2074–2088. http://dx.doi.org/10.1108/md-07-2016-0460

Greenman, C. (2017). Exploring the impact of artificial intelligence on the accounting profession. Journal of Research in Business, Economics and Management, 8(3), 1451–1454. http://scitecresearch.com/journals/index.php/jrbem/article/view/1063

Hindle, J., Robinson, J., & McCarteny, S. (2018). The impact of artificial intelligence on accounting. Journal of Applied Accounting Research, 19(2), 147–155.

Keith, D. (2022). A brief history of artificial intelligence.

KPMG. (2017). The future of accounting: How AI and automation will change the profession.

Kumar, P. S., Rao, C. R., & Kumar, P. (2020). Machine learning applications in accounting: A review. Journal of Accounting and Finance, 20(1), 1–15.

Lewis, T. (2014, February 26). The AI revolution: How artificially intelligent computers are changing everything. LiveScience. https://www.livescience.com/49007-history-of-artificial-intelligence.html

Manyika, J., Chui, M., Bisson, P., Woetzel, J., Stolyar, K., Thiel, V., & Stolyar, A. (2017). A future that works: Automation, employment, and productivity. McKinsey Global Institute.

Mbuba, F. (2016). Organizational conflict and business growth: An appraisal. COOU Journal of Public Administration, 1(2).

Mbuba, F. (2018). Nigerian Broadcasting Commission and the regulation of broadcasting media in Nigeria: A study of broadcast media in Anambra State. Journal of Social Sciences and Public Policy, 10(3).

Mbuba, F. (2021). Federal character principle and Nigerian federalism: An overview. Journal of Social Sciences and Public Policy, 13(1), 17–39.

Mbuba, F. (2022). The bravo of human capital development and employee productivity in the Ministry of Information, Awka. International Journal of Academic Information Systems Research (IJAISR), 5(2), 82–92.

McKinsey. (2018). Artificial intelligence: Implications for the accounting profession.

Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. http://dx.doi.org/10.1016/j.artint.2018.07.007

Omar, A., Musa, K., & Bello, T. (2017). Artificial intelligence applications in accounting: A review. Journal of Business and Accounting Research, 12(3), 45–59.

Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Prentice Hall.

Smith, C., Cong, L., Han, M., Hou, Q., & Yang, B. (2020). Blockchain architecture for auditing automation and trust-building in public markets. Computer, 53(7), 44–53. https://doi.org/10.1109/MC.2020.2995151

Sutton, S. G., Holt, M., & Arnold, V. (2016). “The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting. International Journal of Accounting Information Systems, 22(September), 60–73. http://dx.doi.org/10.1016/j.accinf.2016.07.005

Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics. Pearson Education.

Zafar, Q. A., Ali, A., & Audi, M. (2025). Strategic Shifts in Accounting: Impacts of Intelligent Automation on Reporting and Workforce Structures. Policy Journal of Social Science Review, 3(3), 310-334.

Zhang, Y., Li, Z., & Liu, J. (2020). Deep learning for financial forecasting: A survey. Journal of Forecasting, 39(3), 461–477.

Downloads

Published

2025-08-04

How to Cite

Nweke, U. S., & Umezulike, A. N. (2025). Automation and AI in Accounting: Comparative Impact of Chat-bots and Deep Learning Machine of Accounting Officer’s Work Effectiveness in Business Organizations in Anambra State. Siber International Journal of Education Technology (SIJET), 3(1), 178–189. https://doi.org/10.38035/sijet.v3i1.268

Issue

Vol. 3 No. 1 (2025): Siber International Journal of Education Technology (July - September 2025)

Section

Articles

License

Copyright (c) 2025 Uchenna Sophia Nweke, Anthonia Ngozi Umezulike

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright :

Authors who publish their manuscripts in this journal agree to the following conditions:

  1. Copyright in each article belongs to the author.
  2. The author acknowledges that the Siber International Journal of Education Technology (SIJET) has the right to be the first to publish under a Creative Commons Attribution 4.0 International license (Attribution 4.0 International CC BY 4.0).
  3. Authors can submit articles separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal to other versions (for example, sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time at Siber International Journal of Education Technology (SIJET).