234
Views
0
CrossRef citations to date
0
Altmetric
Accounting, Corporate Governance & Business Ethics

Bibliometric insights into the intellectual dynamics of forensic accounting research

ORCID Icon, &
Article: 2344748 | Received 03 Jan 2024, Accepted 15 Apr 2024, Published online: 10 May 2024

References

  • Al Shbeil, S., Alshurafat, H., Taha, N., & Al Shbail, M. O. (2022). What do we know about forensic accounting? A ­literature review. European, Asian, Middle Eastern, North African conference on management & information systems (pp. 1–17). Springer International Publishing. https://doi.org/10.1007/978-3-031-17746-0_49
  • Aladayleh, K. J., Al Qudah, S. M. A., Bargues, J. L. F., & Gisbert, P. F. (2023). Global trends of the research on COVID-19 risks effect in sustainable facility management fields: A bibliometric analysis. Engineering Management in Production and Services, 15(1), 12–28. https://doi.org/10.2478/emj-2023-0002
  • Albrecht, C., Holland, D., Malagueño, R., Dolan, S., & Tzafrir, S. (2015a). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Journal of Business Ethics, 131(4), 803–813. https://doi.org/10.1007/s10551-013-2019-1
  • Albrecht, C., Holland, D., Malagueño, R., Dolan, S., & Tzafrir, S. (2015b). The role of power in financial statement fraud schemes. Journal of Business Ethics, 131(4), 803–813. https://doi.org/10.1007/s10551-013-2019-1
  • Alden, M. E. (2012). Application of machine learning methods to risk assessment of financial statement fraud: Evidence from China. Journal of Emerging Technologies in Accounting, 12(1), 1.00–1.00. https://doi.org/10.2308/jeta-50390
  • Al-Htaybat, K., von Alberti-Alhtaybat, L., & Alhatabat, Z. (2018). Educating digital natives for the future: Accounting educators’ evaluation of the accounting curriculum. Accounting Education, 27(4), 333–357. https://doi.org/10.1080/09639284.2018.1437758
  • Al-Qudah, L. A., Ahmad Qudah, H., Abu Hamour, A. M., Abu Huson, Y., & Al Qudah, M. Z. (2022). The effects of COVID-19 on conditional accounting conservatism in developing countries: Evidence from Jordan. Cogent Business & Management, 9(1), 2152156. https://doi.org/10.1080/23311975.2022.2152156
  • Alqudah, M., Ferruz, L., Martín, E., Qudah, H., & Hamdan, F. (2023). The sustainability of investing in cryptocurrencies: A bibliometric analysis of research trends. International Journal of Financial Studies, 11(3), 93. https://doi.org/10.3390/ijfs11030093
  • Alshurafat, H., Al Shbail, M. O., Hamdan, A., Al-Dmour, A., & Ensour, W. (2024). Factors affecting accounting students’ misuse of chatgpt: An application of the fraud triangle theory. Journal of Financial Reporting and Accounting, 22(2), 274–288. https://doi.org/10.1108/JFRA-04-2023-
  • Andiola, L. M., Masters, E., & Norman, C. (2020). Integrating technology and data analytic skills into the accounting curriculum: Accounting department leaders’ experiences and insights. Journal of Accounting Education, 50, 100655. https://doi.org/10.1016/j.jaccedu.2020.100655
  • Brooks, G., & Button, M. (2011). The police and fraud investigation and the case for a nationalised solution in the United Kingdom. The Police Journal, 84(4), 305–319. https://doi.org/10.1350/pojo.2011.84.4.559
  • Clarivate Analytics. (2018). InCites indicators handbook. ClarivateAnalytics
  • Craja, P., Kim, A., & Lessmann, S. (2020). Deep learning for detecting financial statement fraud. Decision Support Systems, 139, 113421. https://doi.org/10.1016/j.dss.2020.113421
  • Dzuranin, A. C., Jones, J. R., & Olvera, R. M. (2018). Infusing data analytics into the accounting curriculum: A framework and insights from faculty. Journal of Accounting Education, 43, 24–39. https://doi.org/10.1016/j.jaccedu.2018.03.004
  • Free, C. (2015). Looking through the fraud triangle: A review and call for new directions. Meditari Accountancy Research, 23(2), 175–196. https://doi.org/10.1108/MEDAR-02-2015-0009
  • Gepp, A. (2021). Accounting fraud, auditing, and the role of government sanctions in China. Accounting & Finance, 13(1), 4.33–2.25. https://doi.org/10.1111/acfi.12742
  • Ghafoor, A. (2019). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Journal of Business Ethics, 19(1), 3.80–1.61. https://doi.org/10.1007/s10551-018-3877-3
  • Hogan, C. E., Rezaee, Z., Riley, R. A., Jr. & Velury, U. K. (2008). Financial statement fraud: Insights from the academic literature. AUDITING: A Journal of Practice & Theory, 27(2), 231–252. https://doi.org/10.2308/aud.2008.27.2.205
  • Hoogs, B., Kiehl, T., Lacomb, C., & Senturk, D. (2017). A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud. Intelligent systems in accounting. Finance & Management: International Journal, 24(1–2), 23–47. https://doi.org/10.1002/isaf.1426
  • Howieson, B. (2018). A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud. Pacific Accounting Review, 13(1), 2.17–1.34. https://doi.org/10.1108/PAR-01-2017-0005
  • Kagias, P., Cheliatsidou, A., Garefalakis, A., Azibi, J., & Sariannidis, N. (2022). The fraud triangle – an alternative approach. Journal of Financial Crime, 29(3), 908–924. https://doi.org/10.1108/JFC-07-2021-0159
  • Kamwani, S. S., Vieira, E., Madaleno, M., & do Carmo Azevedo, G. M. (2022). Significance of forensic accounting techniques in corporate governance: Bibliometric analysis. Handbook of research on the significance of forensic accounting techniques in corporate governance (pp. 22–40). IGI Global. https://doi.org/10.4018/978-1-7998-8754-6.ch002
  • Khan, A., Hassan, M. K., Paltrinieri, A., Dreassi, A., & Bahoo, S. (2020). A bibliometric review of takaful literature. International Review of Economics & Finance, 69, 389–405. https://doi.org/10.1016/j.iref.2020.05.013
  • King, M. (2021). Financial fraud investigative interviewing – corporate investigators’ beliefs and practices: A qualitative inquiry. Journal of Financial Crime, 28(2), 345–358. https://doi.org/10.1108/JFC-08-2020-0158
  • Lisic, L. L., Silveri, S. D., Song, Y., & Wang, K. (2011). Accounting fraud, auditing, and the role of government sanctions in China. Journal of Business Research, 64(3), 322–331. https://doi.org/10.1016/j.jbusres.2010.01.016
  • Mandal, A., & Shanmugam, A. (2023). Fathoming fraud: Unveiling theories, investigating pathways and combating fraud. Journal of Financial Crime. Ahead-of-print. https://doi.org/10.1108/JFC-06-2023-0153
  • Momani, M. A. K. A., Alharahasheh, K. A., & Alqudah, M. (2023). Digital learning in Sciences education: A literature review. Cogent Education, 10(2), 2277007. https://doi.org/10.1080/2331186X.2023.2277007
  • Morales, J., Gendron, Y., & Guénin-Paracini, H. (2017). The construction of the risky individual and vigilant organization: A genealogy of the fraud triangle. Accounting, Organizations and Society, 60, 62–81. https://doi.org/10.1016/j.aos.2017.07.003
  • Mui, G. (2015). Finding needles in a haystack: Using data analytics to improve fraud prediction. Accounting Research Journal, 18(1), 2.00–0.50. https://doi.org/10.1108/ARJ-10-2014-0092
  • Nasir, N., & Nabm. (2018). Financial statement fraud: Insights from the academic literature. International Journal of Accounting Information Management, 23(2), 3.83–2.38. https://doi.org/10.1108/IJAIM-03-2017-0039
  • Nasrallah, N. H., El Khoury, R., & Harb, E. (2022). Forensic accounting in a digital environment: A New proposed model. Handbook of research on the significance of forensic accounting techniques in corporate governance (pp. 128–149). IGI Global. https://doi.org/10.4018/978-1-7998-8754-6.ch007
  • Paltrinieri, A., Hassan, M. K., Bahoo, S., & Khan, A. (2019). A bibliometric review of sukuk literature. International Review of Economics & Finance, 86, 897–918. https://doi.org/10.1016/j.iref.2019.04.004
  • Perols, J. (2012). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 31(1), 1–32. https://doi.org/10.2308/ajpt-10209
  • Perols, J. L., & Lougee, B. A. (2012). The relation between earnings management and financial statement fraud. Advances in Accounting, 27(1), 39–53. https://doi.org/10.1016/j.adiac.2012.01.004
  • Perols, J. L., Bowen, R. M., Zimmermann, C., & Samba, B. (2017). Finding needles in a haystack: Using data analytics to improve fraud prediction. The Accounting Review, 92(2), 221–245. https://doi.org/10.2308/accr-51727
  • Qudah, H., Malahim, S., Airout, R., Alomari, M., Hamour, A. A., & Alqudah, M. (2023). Islamic finance in the era of financial technology: A bibliometric review of future trends. International Journal of Financial Studies, 11(2), 76. https://doi.org/10.3390/ijfs11020076
  • Ramachandran Rackliffe, U., & Ragland, L. (2016). Excel in the accounting curriculum: Perceptions from accounting professors. Accounting Education, 25(2), 139–166. https://doi.org/10.1080/09639284.2015.1126791
  • Richardson, V. J., & Shan, Y. (2019). Data analytics in the accounting curriculum. In T. G. Calderon (Ed.), Advances in accounting education: Teaching and curriculum innovations (Advances in accounting education (Vol. 23, pp. 67–79). Emerald Publishing Limited. https://doi.org/10.1108/S1085-462220190000023004
  • Sánchez-Aguayo, M., Urquiza-Aguiar, L., & Estrada-Jiménez, J. (2021). Fraud detection using the fraud triangle theory and data mining techniques: A literature review. Computers, 10(10), 121. https://doi.org/10.3390/computers10100121
  • Sharma, R., Mehta, K., & Vyas, V. (2023). Investigating academic dishonesty among business school students using fraud triangle theory and role of technology. Journal of Education for Business, 99(2), 69–78. https://doi.org/10.1080/08832323.2023.2260925
  • Smaili, N., & Arroyo, P. (2019). The construction of the risky individual and vigilant organization: A genealogy of the fraud triangle. Journal of Business Ethics, 157(1), 95–117. https://doi.org/10.1007/s10551-017-3663-7
  • Song, X. P., Hu, Z. H., Du, J. G., & Sheng, Z. H. (2011). Application of machine learning methods to risk assessment of financial statement fraud: Evidence from China. Journal of Forecasting, 30(3), 293–317. https://doi.org/10.1002/for.1203
  • Suh, I., Sweeney, J. T., Linke, K., & Wall, J. M. (2020). The relation between earnings management and financial statement fraud. Journal of Business Ethics, 162(3), 645–673. https://doi.org/10.1007/s10551-018-3982-3
  • Troy, C., Smith, K. G., & Domino, M. A. (2015). CEO demographics and accounting fraud: Who is more likely to rationalize illegal acts? Strategic Organization, 13(1), 1–29. https://doi.org/10.1177/1476127014549441
  • Tunger, D., & Eulerich, M. (2018). Bibliometric analysis of corporate governance research in German- speaking countries: Applying bibliometrics to business research using a custom-made database. Scientometrics, 117(3), 2041–2059. https://doi.org/10.1007/s11192-018-2919-z
  • Van Eck, N. J., & Waltman, L. (2013). VOSviewer manual (Vol. 1, pp. 1–53). Univeristeit Leiden. https://doi.org/10.13140/RG.2.1.1093.7121
  • Van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of articles using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070. https://doi.org/10.1007/s11192-017-2300-7
  • Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  • Waltman, L., & Yan, E. (2014). PageRank-related methods for analyzing citation networks. Measuring Scholarly Impact: Methods and Practice, 83–100. arXiv preprint arXiv:1109.2058. https://doi.org/10.1007/978-3-319-10377-8_4
  • Wang, Y. (2022). CEO demographics and accounting fraud: Who is more likely to rationalize illegal acts? Journal of Accounting and Public Policy, 16(1), 8.00–2.86. https://doi.org/10.1016/j.jaccpubpol.2021.106903
  • Young, S. D. (2020). Financial statement fraud: Motivation, methods, and detection. In H. K. Baker, L. Purda- Heeler, and S. Saadi (Eds.), Corporate fraud exposed (pp. 321–339). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78973-417-120201021
  • Zhou, W., & Kapoor, G. (2011). Detecting evolutionary financial statement fraud. Decision Support Systems, 50(3), 570–575. https://doi.org/10.1016/j.dss.2010.08.007