Artificial Intelligence and Big Data Analytics in Accounting: A Systematic Review of Research Trends, Theoretical Perspectives, and Future Agenda
DOI:
https://doi.org/10.38035/dijemss.v7i3.6018Keywords:
Artificial Intelligence, Big Data Analytics, AccountingAbstract
This study aims to map the intellectual developments, thematic trends, and methodological directions of research on BDA and AI in accounting during the 2015–2025 period using a Systematic Literature Review (SLR) approach based on PRISMA guidelines combined with bibliometric analysis of 74 articles. The research process includes identification, screening, eligibility, and inclusion stages, accompanied by a quality assessment using the CASP instrument to ensure methodological validity. The analysis results show that publications on BDA and AI in accounting have increased significantly since 2019, dominated by contributions from developed countries such as the United Kingdom, the United States, and Australia, while developing countries such as Indonesia and Malaysia show a trend of rapid research growth. Methodologically, quantitative research still dominates, followed by qualitative and mixed methods approaches. The bibliometric analysis results place The British Accounting Review, Journal of Business Ethics, and Technological Forecasting and Social Change as the most productive journals, with prominent authors such as Moll, Munoko, and Dwivedi as key contributors to the global citation network. The keyword co-occurrence map reveals two major orientations in the literature: technology-driven accounting research, which focuses on automation, predictive auditing, and machine learning, and value-driven research, which emphasizes transparency, sustainability reporting, and institutional legitimacy. These findings reinforce the relevance of Decision Usefulness, Legitimacy, and Agency Theory in explaining the dynamics of technology adoption in accounting. Theoretically, this study broadens the understanding of the digital accounting knowledge ecosystem and future research directions related to ethics, data governance, and the integration of AI in auditing and sustainability reporting. Practically, the results provide implications for educators, practitioners, and regulators to strengthen analytical competencies, AI governance policies, and the development of technology-based accounting curricula.
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