RECONFIGURATION OF ACCOUNTING AND TAX KNOWLEDGE IN THE DATA-DRIVEN ECONOMY: FROM NORMATIVE RATIONALITY TO ALGORITHMIC EPISTEMOLOGY
Abstract
The purpose of the article is to develop a comprehensive epistemological framework for understanding the transformation of accounting and tax knowledge in the context of the data-driven economy, with a focus on the transition from normative rationality to algorithmic epistemology. Methods. The study is based on an integrated methodological approach combining epistemological analysis, system analysis, institutional theory, and interdisciplinary synthesis. The DIKW hierarchy and SECI model are used to conceptualize the transformation of knowledge, while the data-driven paradigm provides a basis for analyzing algorithmic processes in accounting and taxation. Results. The research demonstrates that accounting and tax knowledge is undergoing a structural transformation characterized by the integration of normative, interpretative, and algorithmic dimensions. It is shown that traditional accounting systems evolve into dynamic socio-technical systems capable of real-time data processing, predictive analytics, and automated decision-making. Specifically, the integration of big data and artificial intelligence shifts the focus of accounting from retrospective recording to proactive predictive modeling. Scientific novelty. The scientific novelty lies in the development of an original conceptual model of knowledge reconfiguration that integrates multiple epistemological dimensions and explains the emergence of algorithmic epistemology as a new stage in the evolution of accounting science. Practical significance. The proposed approach provides a theoretical basis for improving accounting and tax systems in digital environments, enhancing decision-making processes, and supporting the implementation of data-driven governance and intelligent tax administration. Furthermore, the findings highlight the critical need to address algorithmic bias and the "black box" problem to ensure the reliability of automated financial assessments. Conclusions. The study concludes that the transformation of accounting and tax knowledge reflects a broader shift toward hybrid epistemological systems, where human expertise and algorithmic intelligence coexist, creating new opportunities and challenges for transparency, accountability, and trust. Consequently, professionals and regulators must develop new institutional frameworks that balance technological efficiency with rigorous ethical oversight.
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(2024). Using big data and analytics in accounting for strategic decision-making. Economics, Finance and Prospects, no. 6. https://doi.org/10.37634/efp.2024.6.20
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