ARTIFICIAL INTELLIGENCE AND ACCOUNTANTS’ WORK EFFICIENCY: A GLOBAL BIBLIOMETRIC AND SCIENCE-MAPPING ANALYSIS OF ACCOUNTING RESEARCH

Authors

  • Noldin Jerry Tumbel Universitas Klabat

DOI:

https://doi.org/10.54314/jssr.v9i3.6820

Keywords:

artificial intelligence, accounting, work efficiency, bibliometric analysis, science mapping

Abstract

Abstract: This study maps the intellectual, conceptual, and temporal structure of research on artificial intelligence (AI) and accountants’ work efficiency. Design/methodology/approach — A Scopus search identified 1,418 English-language, open-access journal articles published from 2020 to 2026. Because the term “account” generated finance- and banking-related noise, title and author-keyword screening retained 185 accounting- and audit-relevant articles. Performance analysis was combined with keyword co-occurrence, network, overlay, and density mapping using VOSviewer-compatible files. Findings — Annual output increased from 3 articles in 2020 to 67 in 2025, with 39 additional articles indexed during the partial 2026 period. The corpus comprised 91 sources and 520 authors, with a mean of 14.0 citations per article and a median of 3. The keyword network contained 35 terms, 70 links, and five clusters covering audit and risk analytics; professional automation and ethics; generative AI, financial reporting, and governance; digital infrastructure; and education and technology acceptance. The thematic focus shifted from RPA, blockchain, and IoT toward generative AI, large language models, explainable AI, corporate governance, and financial reporting. Originality/value — This study provides an outcome-centered and reproducible map of AI-enabled accounting efficiency, distinguishes established automation themes from emerging generative-AI and governance research, and documents the screening required to address query contamination.

Keywords: artificial intelligence; accounting; work efficiency; bibliometric analysis; science mapping.

 

Abstrak: Studi ini memetakan struktur intelektual, konseptual, dan temporal dari penelitian mengenai kecerdasan buatan (AI) dan efisiensi kerja akuntan. Desain/metodologi/pendekatan — Pencarian di Scopus mengidentifikasi 1.418 artikel jurnal akses terbuka berbahasa Inggris yang diterbitkan antara tahun 2020 dan 2026. Karena istilah "account" menghasilkan data yang tidak relevan (noise) terkait keuangan dan perbankan, penyaringan berdasarkan judul dan kata kunci penulis menyisakan 185 artikel yang relevan dengan bidang akuntansi dan audit. Analisis kinerja digabungkan dengan pemetaan ko-okurensi kata kunci, jaringan, overlay, dan kepadatan menggunakan berkas yang kompatibel dengan VOSviewer. Temuan — Jumlah publikasi tahunan meningkat dari 3 artikel pada tahun 2020 menjadi 67 artikel pada tahun 2025, dengan tambahan 39 artikel terindeks selama periode parsial tahun 2026. Korpus data mencakup 91 sumber dan 520 penulis, dengan rata-rata 14,0 sitasi per artikel dan nilai tengah (median) sebesar 3. Jaringan kata kunci memuat 35 istilah, 70 tautan, dan lima klaster yang mencakup analitik audit dan risiko; otomatisasi profesional dan etika; AI generatif, pelaporan keuangan, dan tata kelola; infrastruktur digital; serta pendidikan dan penerimaan teknologi. Fokus tematik bergeser dari RPA, blockchain, dan IoT menuju AI generatif, model bahasa besar (LLM), AI yang dapat dijelaskan (explainable AI), tata kelola perusahaan, dan pelaporan keuangan. Orisinalitas/nilai — Studi ini menyajikan peta efisiensi akuntansi berbasis AI yang berorientasi pada hasil dan dapat direplikasi, membedakan tema otomatisasi yang sudah mapan dari penelitian baru mengenai AI generatif dan tata kelola, serta mendokumentasikan proses penyaringan yang diperlukan untuk mengatasi kontaminasi hasil pencarian.

Kata kunci: kecerdasan buatan; akuntansi; efisiensi kerja; analisis bibliometrik; pemetaan sains.

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References

Abbas, K. (2026). Management accounting and artificial intelligence: A comprehensive literature review and recommendations for future research. The British Accounting Review, 58(2), 101551. https://doi.org/10.1016/j.bar.2025.101551

Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100218. https://doi.org/10.1016/j.joitmc.2024.100218

Alassuli, A. (2025). Impact of artificial intelligence using the robotic process automation system on the efficiency of internal audit operations at Jordanian commercial banks. Banks and Bank Systems, 20(1), 122–135. https://doi.org/10.21511/bbs.20(1).2025.11

Almaqtari, F. A. (2024). The role of IT governance in the integration of AI in accounting and auditing operations. Economies, 12(8), 199. https://doi.org/10.3390/economies12080199

Alruwaili, T. F., & Mgammal, M. H. (2025). The impact of artificial intelligence on accounting practices: An academic perspective. Humanities and Social Sciences Communications, 12(1), 1197. https://doi.org/10.1057/s41599-025-05004-6

Anh, N. T. M., Hoa, L. T. K., Thao, L. P., Nhi, D. A., Long, N. T., Truc, N. T., & Ngoc Xuan, V. (2024). The effect of technology readiness on adopting artificial intelligence in accounting and auditing in Vietnam. Journal of Risk and Financial Management, 17(1), 27. https://doi.org/10.3390/jrfm17010027

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Atayah, O. F., & Alshater, M. M. (2021). Audit and tax in the context of emerging technologies: A retrospective analysis, current trends, and future opportunities. International Journal of Digital Accounting Research, 21, 95–128. https://doi.org/10.4192/1577-8517-v21_4

Ballantine, J., Boyce, G., & Stoner, G. (2024). A critical review of AI in accounting education: Threat and opportunity. Critical Perspectives on Accounting, 99, 102711. https://doi.org/10.1016/j.cpa.2024.102711

Bhattacharya, I., & Mickovic, A. (2024). Accounting fraud detection using contextual language learning. International Journal of Accounting Information Systems, 53, 100682. https://doi.org/10.1016/j.accinf.2024.100682

Bin-Nashwan, S. A., Li, J. Z., Jiang, H., Bajary, A. R., & Ma’aji, M. M. (2025). Does AI adoption redefine financial reporting accuracy, auditing efficiency, and information asymmetry? An integrated model of TOE-TAM-RDT and big data governance. Computers in Human Behavior Reports, 17, 100572. https://doi.org/10.1016/j.chbr.2024.100572

Blankespoor, E., DeHaan, E., & Li, Q. (2026). Generative AI in financial reporting. Journal of Accounting

Research, 64(3), 1189–1232. https://doi.org/10.1111/1475-679X.70050

Bochkay, K., Brown, S. V., Leone, A. J., & Tucker, J. W. (2023). Textual analysis in accounting: What’s next? Contemporary Accounting Research, 40(2), 765–805. https://doi.org/10.1111/1911-3846.12825

Dahabiyeh, L., & Mowafi, O. (2023). Challenges of using RPA in auditing: A socio-technical systems approach. Intelligent Systems in Accounting, Finance and Management, 30(2), 76–86. https://doi.org/10.1002/isaf.1537

Dahiyat, A. (2022). Robotic process automation and audit quality. Corporate Governance and Organizational Behavior Review, 6(1), 160–167. https://doi.org/10.22495/cgobrv6i1p12

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

Epelbaum, N., & Farrell, P. J. (2026). Artificial intelligence and auditing: A bibliometric study. Accounting Perspectives, 25(2), 289–311. https://doi.org/10.1111/1911-3838.70008

Fülöp, M. T., Ionescu, C. A., Măgdaș, N., Stefan, M. C., & Topor, D. I. (2025). Digital transformation of the accounting profession at the intersection of artificial intelligence and ethics. Economics, 19(1), 20250155. https://doi.org/10.1515/econ-2025-0155

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Published

2026-06-30

Issue

Vol. 9 No. 3 (2026): June 2026

Section

Artikel

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Copyright (c) 2026 Noldin Jerry Tumbel

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How to Cite

ARTIFICIAL INTELLIGENCE AND ACCOUNTANTS’ WORK EFFICIENCY: A GLOBAL BIBLIOMETRIC AND SCIENCE-MAPPING ANALYSIS OF ACCOUNTING RESEARCH. (2026). JOURNAL OF SCIENCE AND SOCIAL RESEARCH, 9(3), 5244-5258. https://doi.org/10.54314/jssr.v9i3.6820