Analysis of the Influence of Fraud Diamond Dimensions on Fraudulent Behavior of Accounting Students at Diponegoro University
Article Sidebar
-
Academic Cheating, Fraudulent Behavior, Froud Diamond
Abstract
This study aims to determine the effect of the fraud diamond dimension on the fraudulent behavior of accounting students at Diponegoro University. Cheating is a fraudulent act committed by someone to gain profit for himself by taking advantage of other people. The data analysis technique in this study was multiple linear regression analysis using data from Diponegoro University accounting student respondents in the 2019 and 2020 batches. The results showed that pressure and ability had an effect on academic cheating, while opportunity and rationalization had no effect on academic cheating. The results of the model feasibility test show that pressure, opportunity, rationalization and ability simultaneously influence the academic fraud of accounting students at Diponegoro University.
Full text article
References
Al Serhan, O., Houjeir, R., & Aldhaheri, M. (2022). Academic Dishonesty and the Diamond Fraud: Attitudes of UAE Undergraduate Business Students during the COVID-19 Pandemic. International Journal of Learning, Teaching and Educational Research, 21(10), 88–108. https://doi.org/10.26803/ijlter.21.10.5
Arya, M., & Sastry G, H. (2020). DEAL – ‘Deep Ensemble ALgorithm’ Framework for Credit Card Fraud Detection in Real-Time Data Stream with Google TensorFlow. Smart Science, 8(2), 71–83. https://doi.org/10.1080/23080477.2020.1783491
Avortri, C., & Agbanyo, R. (2020). Determinants of management fraud in the banking sector of Ghana: The perspective of the diamond fraud theory. Journal of Financial Crime, 28(1), 142–155. https://doi.org/10.1108/JFC-06-2020-0102
Awang, Y., Abdul Rahman, A. R., & Ismail, S. (2019). The influences of attitude, subjective norm and adherence to Islamic professional ethics on fraud intention in financial reporting. Journal of Islamic Accounting and Business Research, 10(5), 710–725. https://doi.org/10.1108/JIABR-07-2016-0085
Bauder, R. A., & Khoshgoftaar, T. M. (2018). The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data. Health Information Science and Systems, 6(1), 9. https://doi.org/10.1007/s13755-018-0051-3
Behzadi, G., O’Sullivan, M. J., Olsen, T. L., & Zhang, A. (2018). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, 79, 21–42. https://doi.org/10.1016/j.omega.2017.07.005
Chandler, J., Sisso, I., & Shapiro, D. (2020). Participant carelessness and fraud: Consequences for clinical research and potential solutions. Journal of Abnormal Psychology, 129(1), 49–55. https://doi.org/10.1037/abn0000479
Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., & Vreeland, T. J. (2020). Using Technology to Maintain the Education of Residents During the COVID-19 Pandemic. Journal of Surgical Education, 77(4), 729–732. https://doi.org/10.1016/j.jsurg.2020.03.018
Darwish, S. M. (2020). A bio-inspired credit card fraud detection model based on user behavior analysis suitable for business management in electronic banking. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4873–4887. https://doi.org/10.1007/s12652-020-01759-9
de Souza Vasconcelos, A. L. F., Segura, L. C., Serbonchini, M. A., dos Santos Silva, N. K., Chagas, P. A. M., & Naser, M. A. (2023). Adherence of Fraud Pentagon Dimensions in Cases Reported by Security Exchange Commission in United States Between 2018 and 2019. Dalam M. A. Naser (Ed.), New Approaches to CSR, Sustainability and Accountability, Volume IV (hlm. 107–125). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9499-8_6
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285–307. https://doi.org/10.1016/j.tre.2019.03.001
Juan, L. X., Tao, W. Y., Veloo, P. K., & Supramaniam, M. (2022). Using Extended TPB Models to Predict Dishonest Academic Behaviors of Undergraduates in a Chinese Public University. SAGE Open, 12(4), 215824402211403. https://doi.org/10.1177/21582440221140391
Kazemian, S., Said, J., Hady Nia, E., & Vakilifard, H. (2019). Examining fraud risk factors on asset misappropriation: Evidence from the Iranian banking industry. Journal of Financial Crime, 26(2), 447–463. https://doi.org/10.1108/JFC-01-2018-0008
Li, Q., & Xie, Y. (2019). A Behavior-cluster Based Imbalanced Classification Method for Credit Card Fraud Detection. Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, 134–139. https://doi.org/10.1145/3352411.3352433
Liu, G., Guo, J., Zuo, Y., Wu, J., & Guo, R. (2020). Fraud detection via behavioral sequence embedding. Knowledge and Information Systems, 62(7), 2685–2708. https://doi.org/10.1007/s10115-019-01433-3
Liu, S., Hooi, B., & Faloutsos, C. (2019). A Contrast Metric for Fraud Detection in Rich Graphs. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2235–2248. https://doi.org/10.1109/TKDE.2018.2876531
Malesky, A., Grist, C., Poovey, K., & Dennis, N. (2022). The Effects of Peer Influence, Honor Codes, and Personality Traits on Cheating Behavior in a University Setting. Ethics & Behavior, 32(1), 12–21. https://doi.org/10.1080/10508422.2020.1869006
Marques, T., Ferreira, M. P., & Gomes, J. F. S. (2019). Understanding cheating behaviours: Proactive and reactive intentions. Ethics and Education, 14(4), 415–429. https://doi.org/10.1080/17449642.2019.1669310
Omukaga, K. O. (2021). Is the fraud diamond perspective valid in Kenya? Journal of Financial Crime, 28(3), 810–840. https://doi.org/10.1108/JFC-11-2019-0141
Ozcelik, H. (2020). An Analysis of Fraudulent Financial Reporting Using the Fraud Diamond Theory Perspective: An Empirical Study on the Manufacturing Sector Companies Listed on the Borsa Istanbul. Dalam S. Grima, E. Boztepe, & P. J. Baldacchino (Ed.), Contemporary Studies in Economic and Financial Analysis (hlm. 131–153). Emerald Publishing Limited. https://doi.org/10.1108/S1569-375920200000102012
Park, Y., Depeursinge, C., & Popescu, G. (2018). Quantitative phase imaging in biomedicine. Nature Photonics, 12(10), 578–589. https://doi.org/10.1038/s41566-018-0253-x
Pulfrey, C. J., Vansteenkiste, M., & Michou, A. (2019). Under Pressure to Achieve? The Impact of Type and Style of Task Instructions on Student Cheating. Frontiers in Psychology, 10, 1624. https://doi.org/10.3389/fpsyg.2019.01624
Ratmono, D. & Frendy. (2022). Examining the fraud diamond theory through ethical culture variables: A study of regional development banks in Indonesia. Cogent Business & Management, 9(1), 2117161. https://doi.org/10.1080/23311975.2022.2117161
Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational Data Mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701–1717. https://doi.org/10.1016/j.tele.2018.04.015
Rustiarini, N. W., T., S., Nurkholis, N., & Andayani, W. (2019). Why people commit public procurement fraud? The fraud diamond view. Journal of Public Procurement, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JOPP-02-2019-0012
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Sharma, S., Singh, G., Gaur, L., & Sharma, R. (2022). Does psychological distance and religiosity influence fraudulent customer behaviour? International Journal of Consumer Studies, 46(4), 1468–1487. https://doi.org/10.1111/ijcs.12773
Siev, S., & Kliger, D. (2019). Cheating in academic exams: A field study. Dalam Dishonesty in Behavioral Economics (hlm. 111–140). Elsevier. https://doi.org/10.1016/B978-0-12-815857-9.00008-X
Taber, K. S. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
Vousinas, G. L. (2019). Advancing theory of fraud: The S.C.O.R.E. model. Journal of Financial Crime, 26(1), 372–381. https://doi.org/10.1108/JFC-12-2017-0128
Wang, X., Wu, H., & Yi, Z. (2018). Research on Bank Anti-Fraud Model Based on K-Means and Hidden Markov Model. 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 780–784. https://doi.org/10.1109/ICIVC.2018.8492795
Wenzel, K., & Reinhard, M.-A. (2020). Tests and academic cheating: Do learning tasks influence cheating by way of negative evaluations? Social Psychology of Education, 23(3), 721–753. https://doi.org/10.1007/s11218-020-09556-0
Yachison, S., Okoshken, J., & Talwar, V. (2018). Students’ reactions to a peer’s cheating behavior. Journal of Educational Psychology, 110(6), 747–763. https://doi.org/10.1037/edu0000227
Yusliza, M. Y., Saputra, J., Fawehinmi, O., Mat, N. H. N., & Mohamed, M. (2020). The mediating role of justification on the relationship of subjective norms, perceived behavioral control and attitude on intention to cheat among students. Management Science Letters, 3767–3776. https://doi.org/10.5267/j.msl.2020.7.035
Zhou, H., Sun, G., Fu, S., Fan, X., Jiang, W., Hu, S., & Li, L. (2020). A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain. Computers, Materials & Continua, 64(2), 1091–1105. https://doi.org/10.32604/cmc.2020.09834
Authors
Copyright (c) 2023 Andrean Seto Nurdiansyah, NPMA Durya, Fusi Rachele
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
