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A Review of AI and Its Impact on Management Accounting and Society

Author

Listed:
  • David Kerr

    (Turner School of Accountancy, Belk College of Business, University of North Carolina at Charlotte, Charlotte, NC 28223, USA)

  • Katherine Taken Smith

    (College of Business, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA)

  • Lawrence Murphy Smith

    (College of Business, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA)

  • Tian Xu

    (College of Business, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA)

Abstract

Past and current advances in artificial intelligence (AI) have resulted in a significant impact on business and accounting. Over time, AI has slowly transformed from the 1950s to today, from rule-based systems, also known as expert systems, to the deep learning architectures and sophisticated neural networks of modern generative AI. Early AI accounting applications of expert systems included a GAAP-based expert system to assess the appropriate accounting treatment for business combinations and an expert system to determine the proper type of audit report to issue. Recent accounting expert systems have been developed for document analysis, fraud detection, evaluating credit risk, and corporate default forecasting. The purpose of this study is to examine key events in the history of AI, current applications, and potential future effects pertaining to management accounting and society overall. In addition, the relationship of AI with economic and social factors will be evaluated. The study’s findings will be of interest to management accountants, businesspersons, academic researchers, and others who are concerned with artificial intelligence and its impact on management accounting and society overall.

Suggested Citation

  • David Kerr & Katherine Taken Smith & Lawrence Murphy Smith & Tian Xu, 2025. "A Review of AI and Its Impact on Management Accounting and Society," JRFM, MDPI, vol. 18(6), pages 1-22, June.
  • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:6:p:340-:d:1683189
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    References listed on IDEAS

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    1. Donald L. Ariail & Katherine Taken Smith & Lawrence Murphy Smith, 2024. "Human Trafficking and Gender Inequality: How Businesses Can Lower Risks and Costs," JRFM, MDPI, vol. 17(9), pages 1-21, September.
    2. Todd Broker & David Durr & Lawrence Murphy Smith, 2019. "Analysis of the global energy industry, climate change and financial matters: the need for effective corporate governance," International Journal of Corporate Governance, Inderscience Enterprises Ltd, vol. 10(3/4), pages 185-208.
    3. John Simon & Sharon Wardrop, 2002. "Australian Use of Information Technology and its Contribution to Growth," RBA Research Discussion Papers rdp2002-02, Reserve Bank of Australia.
    4. Ilkka Tuomi, 2018. "The Impact of Artificial Intelligence on Learning, Teaching, and Education," JRC Research Reports JRC113226, Joint Research Centre.
    5. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    6. Sandra E. Black & Lisa M. Lynch, 2001. "How To Compete: The Impact Of Workplace Practices And Information Technology On Productivity," The Review of Economics and Statistics, MIT Press, vol. 83(3), pages 434-445, August.
    7. John A. DeLeon & Lawrence Murphy Smith & Rabih Zeidan, 2024. "Relationship of Internet Activity to Income Inequality and Life Satisfaction," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 25(1), pages 45-72, January.
    8. Hannigan, Timothy R. & McCarthy, Ian P. & Spicer, André, 2024. "Beware of botshit: How to manage the epistemic risks of generative chatbots," Business Horizons, Elsevier, vol. 67(5), pages 471-486.
    9. Alexander Kopka & Dirk Fornahl, 2024. "Artificial intelligence and firm growth — catch-up processes of SMEs through integrating AI into their knowledge bases," Small Business Economics, Springer, vol. 62(1), pages 63-85, January.
    10. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
    11. Erik Brynjolfsson, 1996. "The Contribution of Information Technology to Consumer Welfare," Information Systems Research, INFORMS, vol. 7(3), pages 281-300, September.
    12. Bresnahan, Timothy F, 1999. "Computerisation and Wage Dispersion: An Analytical Reinterpretation," Economic Journal, Royal Economic Society, vol. 109(456), pages 390-415, June.
    13. R. Steve McDuffie & L. Murphy Smith, 2006. "Impact of an audit reporting expert system on learning performance: A teaching note," Accounting Education, Taylor & Francis Journals, vol. 15(1), pages 89-102.
    14. Hai Lan & Xiaofei Tang & Yong Ye & Huiqin Zhang, 2024. "Abstract or concrete? The effects of language style and service context on continuous usage intention for AI voice assistants," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    15. Martin Hilbert, 2016. "Big Data for Development: A Review of Promises and Challenges," Development Policy Review, Overseas Development Institute, vol. 34(1), pages 135-174, January.
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