Main Article Content
Abstract
Purpose: Penelitian ini bertujuan untuk memberikan pemetaan menyeluruh terhadap tren dan struktur pengetahuan dalam studi mengenai financial statements dengan mengidentifikasi arah perkembangan, kolaborasi, serta potensi riset di masa depan.
Research Method: Studi ini menggunakan pendekatan bibliometrik dengan data yang dikumpulkan dari database Scopus. Teknik analisis dilakukan menggunakan perangkat lunak VOSViewer untuk memvisualisasikan kata kunci, penulis, afiliasi negara, dan jumlah kutipan. Penelitian ini menekankan dua tujuan utama: mengevaluasi kinerja riset dan memetakan struktur intelektual dalam bidang laporan keuangan.
Results and Discussion: Hasil analisis mengungkap lima cluster utama seperti profitabilitas, performa, inovasi, biaya, dan aset tidak berwujud, serta isu terkait deteksi kecurangan dengan teknologi seperti machine learning. Negara-negara dominan dalam publikasi termasuk Amerika Serikat, Indonesia, dan Inggris, dengan tren kolaboratif yang semakin meluas. Topik audit, pengungkapan informasi, dan keberlanjutan menjadi perhatian utama pascapandemi.
Implications: Penelitian ini memberikan kontribusi terhadap pengembangan ilmu akuntansi dengan mengidentifikasi celah riset, khususnya dalam analisis teks dan pemanfaatan teknologi informasi. Hasil studi ini dapat menjadi rujukan strategis bagi peneliti dan praktisi untuk mengarahkan fokus riset di masa mendatang.
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References
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- Birkner, P. (2019). A bibliometric analysis of the financial accounting literature from 1969 to 2017. International Journal of Accounting, 54(3), 323-348.
- Chen, M., & Lin, Y. (2022). Sustainability and financial reporting: A bibliometric analysis of emerging trends and future research directions. Journal of Business Ethics, 169(4), 637-654. https://doi.org/10.1007/s10551-020-04423-4
- Huang, Y., & Lai, F. (2021). Machine learning applications in financial statement fraud detection: A review. Expert Systems with Applications, 175, 114745. https://doi.org/10.1016/j.eswa.2021.114745
- Li, Y., & Lu, Y. (2020). Research trends in financial statement analysis: A bibliometric study of published papers from 1990 to 2018. Journal of Accounting Literature, 43, 1-20. https://doi.org/10.1016/j.acclit.2020.01.001
- Milojević, S. (2014). Bibliometric mapping of the intellectual structure of the social sciences: Analysis of citation patterns in sociology. Scientometrics, 101(3), 1519-1544. https://doi.org/10.1007/s11192-014-1312-4
- Rouse, P., & Rouse, M. (2018). Fraud detection in financial statements using machine learning techniques. Journal of Financial Crime, 25(3), 720-734. https://doi.org/10.1108/JFC- 04-2017-0037
- van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring scholarly impact (pp. 285-320). Springer. https://doi.org/10.1007/978-3-319-10377- 8_15
- van Raan, A. F. J. (2005). Fatal attraction: Conceptual and methodological problems in the ranking of universities by bibliometric methods. Scientometrics, 62(2), 179-197. https://doi.org/10.1007/s11192-005-0008-2
- Zhao, D., & Strotmann, A. (2015). Visualizing the evolution of research topics in library and information science: A bibliometric analysis of a citation network. Journal of Informetrics, 9(1), 195-209. https://doi.org/10.1016/j.joi.2014.12.004
References
Acedo, F. J., Barroso, C., Casillas, J. C., & Galán, J. L. (2006). The knowledge-based view: A review of the theoretical contributions. Academy of Management Review, 31(2), 426-448. https://doi.org/10.5465/amr.2006.20208690
Birkner, P. (2019). A bibliometric analysis of the financial accounting literature from 1969 to 2017. International Journal of Accounting, 54(3), 323-348.
Chen, M., & Lin, Y. (2022). Sustainability and financial reporting: A bibliometric analysis of emerging trends and future research directions. Journal of Business Ethics, 169(4), 637-654. https://doi.org/10.1007/s10551-020-04423-4
Huang, Y., & Lai, F. (2021). Machine learning applications in financial statement fraud detection: A review. Expert Systems with Applications, 175, 114745. https://doi.org/10.1016/j.eswa.2021.114745
Li, Y., & Lu, Y. (2020). Research trends in financial statement analysis: A bibliometric study of published papers from 1990 to 2018. Journal of Accounting Literature, 43, 1-20. https://doi.org/10.1016/j.acclit.2020.01.001
Milojević, S. (2014). Bibliometric mapping of the intellectual structure of the social sciences: Analysis of citation patterns in sociology. Scientometrics, 101(3), 1519-1544. https://doi.org/10.1007/s11192-014-1312-4
Rouse, P., & Rouse, M. (2018). Fraud detection in financial statements using machine learning techniques. Journal of Financial Crime, 25(3), 720-734. https://doi.org/10.1108/JFC- 04-2017-0037
van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring scholarly impact (pp. 285-320). Springer. https://doi.org/10.1007/978-3-319-10377- 8_15
van Raan, A. F. J. (2005). Fatal attraction: Conceptual and methodological problems in the ranking of universities by bibliometric methods. Scientometrics, 62(2), 179-197. https://doi.org/10.1007/s11192-005-0008-2
Zhao, D., & Strotmann, A. (2015). Visualizing the evolution of research topics in library and information science: A bibliometric analysis of a citation network. Journal of Informetrics, 9(1), 195-209. https://doi.org/10.1016/j.joi.2014.12.004