The Prevention of Bribery in Government Agencies: The Role of Big Data and Whistle-blowing Systems
DOI:
https://doi.org/10.23887/jia.v7i2.41356Keywords:
big data, prevention of bribery, whistle-blowing systemAbstract
Bribery has become the kind of corruption with the highest annual incidence rate. Therefore, the hunt for solutions to these problems must continue. Consequently, this research must be conducted. The objective is to examine how big data and the whistle-blowing system affect the prevention of bribery. This investigation was conducted utilizing a quantitative methodology. 191 auditors from the Financial and Development Supervisory Agency (BPKP), the Supreme Audit Agency (BPK), and the Indonesian Government Inspectorate were surveyed via distributing questionnaires. This research employs structural equation modeling (SEM) with the assistance of the smartPLS application for statistical testing. Both big data and the whistle-blowing system have proven to have a positive impact on bribery prevention, according to the findings of this study.References
ACFE. (2020). 2020 Report to The Nations. https://acfepublic.s3-us-west-2.amazonaws.com/2020-Report-to-the-Nations.pdf
Agusyani, N. K. S., Sujana, E., & Wahyuni, M. A. (2016). Pengaruh Whistleblowing System dan Kompetensi Sumber Daya Manusia terhadap Pencegahan Fraud pada Pengelolaan Keuangan Penerimaan Pendapatan Asli Daerah (Studi Pada Dinas Pendapatan Daerah Kabupaten Buleleng). JIMAT (Jurnal Ilmiah Mahasiswa Akuntansi) Undiksha, 6(3).
Ahmed, W., & Ameen, K. (2017). Defining Big Data and Measuring Its Associated Trends in the Field of Information and Library Management. Library Hi Tech News, 9, 21–24.
Al-Haidar, F. (2018). Whistleblowing in Kuwait and UK Against Corruption and Misconduct. International Journal of Law and Management, 60(4), 1020–1033.
Ali, C. Ben. (2020). Agency Theory and Fraud. In H. K. Baker, L. Purda-Heeler, & S. Saadi (Eds.), Corporate Fraud Exposed (pp. 149–167). Emerald Group Publishing Limited.
Andresen, M. S., & Button, M. (2019). The Profile and Detection of Bribery in Norway and England & Wales: A Comparative Study. European Journal of Criminology, 16(1), 1–23.
Anugerah, R. (2014). Peranan Good Corporate Governance dalam Pencegahan Fraud. Jurnal Akuntansi, 3(1), 101–113.
Atmadja, A. T., Saputra, K. A. K., & Manurung, D. T. H. (2019). Proactive Fraud Audit, Whistleblowing and Cultural Implementation of Tri Hita Karana for Fraud Prevention. European Research Studies Journal, 22(3), 2019.
Balasupramanian, N., Ephrem, B. G., & Al-Barwani, I. S. (2018). User Pattern Based Online Fraud Detection and Prevention using Big Data Analytics and Self oOganizing Maps. 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 691–694.
Bănărescu, A. (2015). Detecting and Preventing Fraud with Data Analytics. Procedia Economics and Finance, 32(15), 1827–1836.
Bendickson, J., Muldoon, J., Liguori, E., & Davis, P. E. (2016). Agency Theory: the times, they are a-changin’. Management Decision, 54(1), 174–193.
Boyle, D. M., DeZoort, F. T., & Hermanson, D. R. (2015). The Effect of Alternatove Fraud Model Use on Auditor’s Fraud Risk Judgments. Journal of Accounting and Public Policy, 34(6), 578–596.
Chen, J., Tao, Y., Wang, H., & Chen, T. (2015). Big Data Based Fraud Risk Management at Alibaba. The Journal of Finance and Data Science, 1(1), 1–10.
Chin, W. W. (1988). The Partial Least Square Approach for Structural Equation Modeling. in G.A. Marcoulides (Ed.), Modern Methods for Businnes Research. Lawrence Erlbaum Associates.
Cressey, D. (1953). Other People’s Money: A Study in the Social Psychology of Embezzlement. FreePress.
Crowe, H. (2011). Why the Fraud Triangle is no Longer Enough. In Horwath, Crowe LLP.
Desai, N. (2020). Understanding the Theoretical Underpinnings of Corporate Fraud. The Journal for Decision Makers, 45(1), 22–31.
Elias, R. Z. (2008). Auditing Students’ Professional Commitment and Anticipatory Socialization and Their Relationship to Whistleblowing. Managerial Auditing Journal, 23(3), 283–294.
Ernst & Young. (2014). Global EY FIDS Forensic Data Analytics Survey 2014: Big Risks Require Big Data Thinking. 2014 EYGM Limited, SCORE no. DQ0037.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50.
Francis, R. D., & Armstrong, A. (2011). Corruption and Whistleblowing in International Humanitarian Aid Agencies. Journal of Financial Crime, 18(4), 319–335.
Fredriksson, C., Mubarak, F., Tuohimaa, M., & Zhan, M. (2017). Big Data in The Public Sector: A Systematic Literature Review. Scandinavian Journal of Public Administration, 21(3), 39–61.
Fuller, L. R., & Shawver, T. J. (2020). Will Cognitive Style Impact Whistleblowing Intentions? In Research on Professional Responsibility and Ethics in Accounting (Vol. 23, pp. 47–62). Emerald Publishing Limited.
Gonzales, G. C., & Hoffman, V. B. (2018). Continuous Auditing’s Effectiveness as a Fraud Deterrent. AUDITING: A Journal of Practice and Theory, 37(2), 225–247.
Hartono, J. (2019). Kajian Topik-Topik Mutakhir dan Agenda Riset ke Depan (1st ed.). Penerbit Andi.
Hipgrave, S. (2013). Smarter Fraud Investigations with Big Data Analytics. Network Security, 12, 7–9.
Imtikhani, L., & Sukirman. (2021). Determinan Fradulent Financial Statemen melalui Perpektif Fraud Hexagon Theory pada Perusahaan Pertambangan. Jurnal Akuntansi Bisnis, 19(1), 2541–5204.
Jain, R. (2020). Briibery and Firm Performance in India: A Political Economy Perpective. Journal of Asian Economics, 68, 1–13.
Jha, B. K., Sivasankari, G. G., & Venugopal, K. R. (2020). Fraud Detection and Prevention by Using Big Data Analytics. Proceedings of the 4th International Conference on Computing Methodologies and Communication (ICCMC) 2020, 267–274.
Johansson, E., & Carey, P. (2016). Detecting Fraud: The Role of the Anonymous Reporting Channel. Journal Business Ethics, 139, 391–409.
Kılıç, B. İ. (2020). The Effect of Big Data on Forensic Accounting Practices and Education. In S. Grima, E. Boztepe, & P. J. Baldacchino (Eds.), Contemporary Studies in Economic and Financial Analysis (Vol. 102, pp. 11–26). Emerald Publishing Limited.
Kompas. (2020). Catatan ICW, Tren Penindakan Korupsi Turun 271 Kasus. Kompas. https://nasional.kompas.com/read/2020/02/18/16532131/catatan-icw-tren-penindakan-korupsi-turun-jadi-271-kasus
Kompas. (2021a). Data ICW 2020: Kerugian Negara Rp 56,7 Triliun, Uang Pengganti dari Koruptor Rp 8,9 Trilliun. Kompas. https://nasional.kompas.com/read/2021/03/22/19301891/data-icw-2020-kerugian-negara-rp-567-triliun-uang-pengganti-dari-koruptor-rp
Kompas. (2021b). ICW: Sepanjang 2020 Ada 1.298 Terdakwa Kasus Korupsi, Kerugian Negara Rp 56,7 Triliun. Kompas. https://nasional.kompas.com/read/2021/04/09/18483491/icw-sepanjang-2020-ada-1298-terdakwa-kasus-korupsi-kerugian-negara-rp-567
KPK. (2021). Tindak Pidana Korupsi Berdasarkan Jenis Perkara. Komisi Pemberantasan Korupsi (KPK).
Latan, H., & Ghozali, I. (2012). Partial Least Squares Konsep, Teknik, dan Aplikasi Menggunakan Program SmartPLS 2.0 M3 (P. P. Harto (ed.)). Badan Penerbit Universitas Dipenogoro.
Madhuri, S. T., Babu, R. E., Uma, B., & Lakshmi, M. B. (2021). Big-Data Driven Approaches in Materials Science for Real-Time Detection and Prevention of Fraud. Materials Today: Proceedings, 2–9.
Maulida, W. Y., & Bayunitri, B. I. (2021). The Influence of Whistleblowing System Toward Fraud Prevention. International Journal of Financial, Accounting, and Management, 2(4), 275–294.
Maulida, Wi. Y., & Bayunitri, B. I. (2021). The Influence of Whistleblowing System Toward Fraud Prevention. International Journal of Financial, Accounting and Management, 2(4), 275–294.
Noor, N. R. A. M., & Mansor, N. (2019). Exploring the Adaptation of Artificial Intelligence in Whistleblowing Practice of the Internal Auditors in Malaysia. Procedia Computer Science, 163, 434–439.
Ohlhorst, F. (2015). Big Data Analytics: Turning Big Data Into Big Money. John Wiley & Sons Inc.
Othman, R., Aris, N. A., Mardziyah, A., Zainan, N., & Amin, N. M. (2015). Fraud Detection and Prevention Methods in The Malaysian Public Sector: Accountants’ and Internal Auditors’ Perceptions. Procedia Economics and Finance, 28, 59–67.
Parker, D. W., Dressel, U., Chevers, D., & Zeppetella, L. (2018). Agency Theory Perpective on Public-Private-Patnerships: International Development Project. International Journal of Productivity and Performance Management, 67(2), 239–259.
Puryati, D., & Febriani, S. (2020). The Consequence of Whistleblowing System and Internal Control Toward Fraud Prevention: A Study on Indonesian State Owned Enterprise. International Journal of Business and Technology Management, 2(3), 35–48.
Rezaee, Z., & Wang, J. (2019). Relevance of Big Data to Forensic Accounting Practice and Education. Managerial Auditing Journal, 34(3), 268–288.
Shonhadji, N., & Maulidi, A. (2021). The Roles of Whistleblowing System and Fraud Awareness as Financial Statement Fraud Deterrent. International Journal of Ethics and Systems, 37(3), 370–389.
Sihotang, A. R. (2018). Pengaruh Persepsi Karyawan Mengenai Whistleblowing System dan Moralitas Individu terhadap Pencegahan Fraud pada Universitas Sriwijaya. Politeknik Negeri Sriwijaya.
Sow, A. N., Basiruddin, R., Mohammad, J., & Rasid, S. Z. A. (2018). Fraud Prevention in Malaysian Small and Medium Enterprises (SMEs). Journal of Financial Crime, 25(2), 499–517.
Suh, J. B., & Shim, H. S. (2020a). The Effect of Ethical Corporate Culture on Anti-fraud Strategies in South Korean Financial Companies: Mediation of Whistleblowing and A Sectoral Comparison Approach in Depository institutions. International Journal of Law, Crime and Justice, 60, 1–12.
Suh, J. B., & Shim, H. S. (2020b). The Effect of Ethical Corporate Culture on Anti-Fraud Strategies in South Korean Financial Companies: Mediation of Whistleblowing and A Sectoral Comparison Approach in Depository Intitutions. International Journal of Law, Crime and Justice, 60, 1–12.
Tang, J., & Karim, K. E. (2019). Financial Fraud Detection and Big Data Analytics – Implications on Auditors’ Use of Fraud Brainstorming Session. Managerial Auditing Journal, 34(3), 324–337.
Vousinas, G. L. (2019). Advancing Theory of Fraud: The S.C.O.R.E. Model. Journal of Financial Crime, 26(1), 372–381.
Wahyudi, S., Achmad, T., & Pamungkas, I. D. (2021). Village Apparatus Competence, Individual Morality, Internal Control System and Whistleblowing System on Village Fund Fraud. WSEAS Transactions on Environment and Development, 17(6), 672–684.
Wijayanti, P., & Hanafi, R. (2018). Pencegahan Fraud pada Pemerintahan Desa. Jurnal Akuntansi Multiparadigma, 9(2), 331–345.
Wolfe, D., & Hermanson, D. R. (2004). The Fraud Diamond: Considering Four Elements of Fraud. The CPA Journal, 74(12), 38–42.
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