Analysis of Actor Strategies and Conflicts in the Implementation of Machine Learning in Accounting Management and Organizational Growth
Keywords:
Machine Learning, Accounting Management, Actor Strategy, Conflict Analysis, MACTOR MethodologyAbstract
This study analyzes actor strategies and conflicts in the implementation of machine learning (ML) within accounting management and organizational growth. The research is grounded in a qualitative exploratory–descriptive approach involving expert interviews and systematic literature review. A total of 12 experts from accounting, artificial intelligence, and organizational management fields were selected using purposive sampling. The MACTOR (Matrix of Alliances and Conflicts: Tactics, Objectives, Recommendations) methodology was applied to identify actor relationships, strategic objectives, influence structures, and convergence–divergence patterns among stakeholders. The findings reveal that ML adoption in accounting generates both strategic alignment and conflict among actors. Dominant actors, such as business executives and financial managers, play a key role in driving digital transformation, while accountants and auditors tend to resist due to concerns about job displacement and algorithm reliability. Technology developers and regulators act as linkage actors facilitating interaction among stakeholder groups. The analysis also shows strong convergence toward efficiency and automation objectives, while significant divergence occurs in relation to human role preservation and regulatory concerns. Overall, the study highlights that ML implementation in accounting is shaped by complex power dynamics and competing interests among organizational actors.
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Bendahan, S., Camponovo, G., & Pigneur, Y. (2004). Multi-issue actor analysis: Tools and models for assessing technology environments. Journal of Decision Systems, 13(2), 223–253.
Bertomeu, J. (2020). Machine learning improves accounting: Discussion, implementation and research opportunities. Review of Accounting Studies, 25(3), 1135–1155. https://doi.org/10.1007/s11142-020-09554-9
Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519. https://doi.org/10.1007/s11142-020-09563-8
Bhimani, A. (2020). Digital data and management accounting: Why we need to rethink research methods. Journal of Management Control, 31(1), 9–23.
Cai, C., Linnenluecke, M., Marrone, M., & Singh, A. (2019). Machine learning and expert judgement: Analyzing emerging topics in accounting and finance research in the Asia–Pacific. Abacus, 55(4), 709–733.
Chae, H. (2024). In search of gazelles: Machine learning prediction for Korean high-growth firms. Small Business Economics, 62(1), 243–284.
Chakri, P., Pratap, S., & Kumar, S. (2023). An exploratory data analysis approach for analyzing financial accounting data using machine learning. Decision Analytics Journal, 7, Article 100212. https://doi.org/10.1016/j.dajour.2023.100212
Chowdhury, E. (2023). Integration of artificial intelligence technology in management accounting information system: An empirical study. In Novel financial applications of machine learning and deep learning (pp. 35–46). Springer.
Ding, K., Lev, B., Peng, X., Sun, T., & Vasarhelyi, M. (2020). Machine learning improves accounting estimates: Evidence from insurance payments. Review of Accounting Studies, 25(3), 1098–1134.
Duan, H., Vasarhelyi, M., Codesso, M., & Alzamil, Z. (2023). Enhancing government accounting information systems using social media information: An application of text mining and machine learning. International Journal of Accounting Information Systems, 48, 100600.
Elmsalmi, M., & Hachicha, W. (2014). Risk mitigation strategies according to supply actors' objectives through MACTOR method. In 2014 International Conference on Advanced Logistics and Transport (ICALT) (pp. 362–367). IEEE.
Fieberg, C., Hesse, M., Loy, T., & Metko, D. (2022). Machine learning in accounting research. In Diginomics research perspectives: The role of digitalization in business and society (pp. 105–124). Springer.
Gomber, P., Kauffman, R., Parker, C., & Weber, B. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220–265.
Grossi, G., & Argento, D. (2022). The fate of accounting for public governance development. Accounting, Auditing & Accountability Journal, 35(9), 272–303.
Grosu, V., Cosmulese, C., Socoliuc, M., Ciubotariu, M., & Mihaila, S. (2023). Testing accountants' perceptions of digitization of the profession and profiling the future professional. Technological Forecasting and Social Change, 193, 122630.
Hasan, A. (2021). Artificial intelligence (AI) in accounting & auditing: A literature review. Open Journal of Business and Management, 10(1), 440–465.
Herrera, J. (2017). La investigación cualitativa. UDGVirtual.
Ionescu, L. (2022). Machine learning-based decision-making systems in accounting and auditing. Economics, Management, and Financial Markets, 17(4), 9–26.
Kamuangu, P. (2024). A review on financial fraud detection using AI and machine learning. Journal of Economics, Finance and Accounting Studies, 6(1), 67–77.
Lei, X., Mohamad, U., Sarlan, A., Shutaywi, M., Daradkeh, Y., & Mohammed, H. (2022). Development of an intelligent information system for financial analysis using supervised machine learning. Information Processing & Management, 59(5), 103036.
Liaras, E., Nerantzidis, M., & Alexandridis, A. (2024). Machine learning in accounting and finance research: A literature review. Review of Quantitative Finance and Accounting, 63(4), 1431–1471.
Makmun, M., Fahmid, I., Ali, M., Saud, Y., & Rahmadanih, R. (2024). Power relations among actors in laying hen business in Indonesia: A MACTOR analysis. Open Agriculture, 9(1), 20220334.
Nielsen, S. (2022). Management accounting and exploratory data analysis using machine learning. Journal of Accounting & Organizational Change, 18(5), 811–853.
Obeng, S., Iyelolu, T., Akinsulire, A., & Idemudia, C. (2024). The transformative impact of fintech on regulatory compliance in banking. World Journal of Advanced Research and Reviews, 23(1), 2008–2018.
Pozo-Antúnez, J., Molina-Sánchez, H., Ariza-Montes, A., & Fernández-Navarro, F. (2021). Promoting work engagement in accounting profession: A machine learning approach. Social Indicators Research, 157(2), 653–670.
Quinteros, J., & Hamann, A. (2017). Planeamiento estratégico prospectivo. Ecoe Ediciones.
Ranta, M., Ylinen, M., & Järvenpää, M. (2023). Machine learning in management accounting research: Literature review and future pathways. European Accounting Review, 32(3), 607–636.
Riadh, H. (2022). Intelligent tourism system using MACTOR methodology. Current Issues in Tourism, 25(9), 1376–1398.
Tuzcuoğlu, T. (2023). Machine learning use case discovery in finance and accounting. Florya Chronicles of Political Economy, 9(2), 89–106.
Van den Bogaerd, M., & Aerts, W. (2011). Applying machine learning in accounting research. Expert Systems with Applications, 38(10), 13414–13424.
Yi, Z., Cao, X., Chen, Z., & Li, S. (2023). Artificial intelligence in accounting and finance: Challenges and opportunities. IEEE Access, 11, 129100–129123.
