Sewindu Pemetaan Bibliometrik Penelitian Teori Fraud Pentagon pada Financial Statement - Anggelina, Marhini; Rohmi, Nurul; Islami, Mauludatul; Rahma, Siti Aulia; Zuhdi, Rahmat (Studi Akuntansi, Keuangan, dan Manajemen, 2025)
Purpose: This study aims to conduct a bibliometric mapping of Google Scholar-indexed literature over the last eight years (2016–2024) related to the fraud pentagon theory and its application in analyzing fraudulent financial statements in Indonesia. Methodology/approach: This research employs a quantitative approach using a literature study design. Data were obtained using the Publish or Perish software, resulting in 347 relevant articles. These were further analyzed using the VOSviewer tool to visualize trends, clusters, and keyword frequencies in the existing literature. Results/findings: The findings indicate a significant growth in the number of publications on fraud pentagon theory, particularly in 2021 (65 articles) and 2022 (72 articles). Network visualization using VOSviewer shows five primary research clusters. Frequently used keywords include “Pentagon,” “Financial Statements,” and “Indonesia Stock Exchange,” reflecting mainstream research interests. In contrast, emerging topics such as “F-Score Model” and “Determinants” appear less frequently, highlighting research gaps. Influential factors commonly discussed include pressure, financial stability, and internal business conditions as key triggers of financial statement fraud. Conclusion: This bibliometric analysis confirms the central role of fraud pentagon theory in the Indonesian academic discourse on financial fraud. It also identifies underexplored areas that deserve more attention from future researchers, particularly quantitative indicators like the F-Score Model. Limitations: The study is limited to 347 articles deemed relevant from an initial pool of 450; 103 were excluded due to being non-academic (e.g., theses, reviews). Contribution: This research highlights gaps and future directions for forensic accounting studies, encouraging deeper exploration into predictive fraud detection models and underrepresented variables.