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İÇ DENETİMDE YAPAY ZEKÂ KULLANIMINI ETKİLEYEN FAKTÖRLER

Yıl 2024, Sayı: 31, 210 - 223, 01.12.2024
https://doi.org/10.58348/denetisim.1541957

Öz

Yapay Zekâ (YZ), gelişmiş denetim kalitesi, verimlilik ve risk yönetimi sunarak iç denetimi dönüştürmektedir. Potansiyeline rağmen, iç denetimde YZ kullanımı, 'kara kutu' algoritmalarına ilişkin endişeler de dâhil olmak üzere çeşitli zorluklarla karşı karşıyadır. Bu çalışma, iç denetimde YZ benimsenmesini teşvik eden veya engelleyen faktörleri araştırmayı amaçlamaktadır. Yapılan Sistematik Literatür Taraması (SLT), bu alanda sınırlı araştırma olduğunu ortaya koymuştur. Bu araştırma boşluğunu gidermek için, Teknoloji-Organizasyon-Çevre (TOÇ) çerçevesine dayalı kapsamlı bir model geliştirilmiştir. Bulgular ışığında, iç denetimde YZ benimsenmesini etkileyen faktörler tespit edilmiştir. Teknolojik bağlamda, göreceli avantaj, denetim kalitesi, güvenlik, gizlilik, karmaşıklık ve güven öne çıkmaktadır. Organizasyonel bağlam, üst yönetim desteği, teknolojik beceriler, hazır olma durumu ve BT altyapısını içermektedir. Çevresel bağlam ise ekosistem baskısı, devlet politikaları, düzenlemeler, standartlar, etik, ahlak ve şeffaflığı kapsamaktadır. Bu çalışma, iç denetimde YZ benimsenmesinin anlaşılmasına katkıda bulunarak, uygulayıcılara ve araştırmacılara dijital çağda denetim uygulamalarını optimize etmeleri için içgörüler sağlamaktadır.

Kaynakça

  • Ajzen, I. (1991). The theory of planned behavior. Human Behavior and Emerging Technologies, 2, 314–324. https://doi.org/10.1002/hbe2.195
  • Aldemir, C., & Uçma Uysal, T. (2024). AI Competencies for Internal Auditors in the Public Sector. EDPACS, 69(1), 3–21. https://doi.org/10.1080/07366981.2024.2312001
  • Alina, C. M., & Cerasela, S. E. (2018). Internal Audit Role in Artificial Intelligence. “Ovidius” University Annals, Economic Sciences Series, XVIII(1).
  • Aysan, H., & Fırat, Z. (2024). Yapay Zekâ Uygulamaları İç Denetim Mesleğine Neler Kazandırabilir? Mesleki Değişim ve Teknoloji Yönetimi. Ombudsman Akademik, 20, Article 20.
  • Castka, P., & Searcy, C. (2023). Audits and COVID-19: A paradigm shift in the making. Business Horizons, 66(1), 5–11. https://doi.org/10.1016/j.bushor.2021.11.003
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  • Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x
  • Goto, M. (2023). Anticipatory innovation of professional services: The case of auditing and artificial intelligence. Research Policy, 52(8), 104828. https://doi.org/10.1016/j.respol.2023.104828
  • Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48, 100598. https://doi.org/10.1016/j.accinf.2022.100598
  • Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2020). Identifying Key Factors For Adopting Artificial Intelligence-Enabled Auditing Techniques By Joint Utilization Of Fuzzy-Rough Set Theory And MRDM Technique. Technological and Economic Development of Economy, 27(2), 459–492. https://doi.org/10.3846/tede.2020.13181
  • IIA. (2017). The IIA’s AI Audit Framework: Artificial Intelligence: Considerations for the Profession of Internal Auditing. IIA. https://iaia.org.ar/wp-content/uploads/2017/07/Global-Perspectives-and-Insights-2017-10-Artificial-Intelligence-Report.pdf
  • IIA. (2024a). Complete Global Internal Audit Standards. The Institute of Internal Auditors. https://www.theiia.org/en/standards/2024-standards/global-internal-audit-standards/free-documents/complete-global-internal-audit-standards/
  • IIA. (2024b). Global Perspectives and Insights Artificial Intelligence: Considerations for the Profession of Internal Auditing. The Institute of Internal Auditors.
  • Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1–26.
  • Koç, B., Şener, U., & Eren, P. E. (2022, December). Determinative factors of cloud computing adoption in government organizations. In 2022 3rd International Informatics and Software Engineering Conference (IISEC), 1-6. IEEE. https://doi.org/10.1109/IISEC56263.2022.9998286
  • Kotb, A., Elbardan, H., & Halabi, H. (2020). Mapping of internal audit research: A post-Enron structured literature review. Accounting, Auditing & Accountability Journal, 33(8), 1969–1996. https://doi.org/10.1108/AAAJ-07-2018-3581
  • Menon, S. S., Trenker, J., Owens, T., & Tas, O. (2023). The double-edged sword of AI: Will we lose our jobs or become extremely productive? Statista. https://www.statista.com/site/insights-compass-ai-future-ai-work
  • Musa, A. M. H., & Lefkir, H. (2024). The role of artificial intelligence in achieving auditing quality for small and medium enterprises in the Kingdom of Saudi Arabia. International Journal of Data and Network Science, 8(2), 835–844. https://doi.org/10.5267/j.ijdns.2023.12.021
  • Özyiğit, H. (2023). Yapay Zekânın İç Denetçilerin Algısına Etkisi: BIST 100 Şirketlerine Yönelik Bir Araştırma. Muhasebe ve Finansman Dergisi, 0(98), 21–42. https://doi.org/10.25095/mufad.1259939
  • PCAOB. (2019). Changes in Use of Data and Technology in the Conduct of Audits. The United States Public Company Accounting Oversight Board.
  • Puthukulam, G., Ravikumar, A., Sharma, R. V. K., & Meesaala, K. M. (2021). Auditors’ Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman. Universal Journal of Accounting and Finance, 9(5), 1184–1190. https://doi.org/10.13189/ujaf.2021.090527
  • PwC. (2023). AI Jobs Barometer. PwC. https://www.pwc.com.tr/ai-jobs-barometer
  • Rikhardsson, P., Thórisson, K. R., & Bergthorsson. (2021). Artificial intelligence and auditing in small‐ and medium‐sized firms: Expectations and applications. AI Magazine - Wiley Online Library. https://onlinelibrary.wiley.com/doi/10.1002/aaai.12066
  • Rodrigues, L., Pereira, J., Da Silva, A. F., & Ribeiro, H. (2023). The impact of artificial intelligence on audit profession. Journal of Information Systems Engineering and Management, 8(1), 19002. https://doi.org/10.55267/iadt.07.12743
  • Rogers, E. (1995). Diffusion of Innovations. New York.
  • Rogers, E. M. (1995). Diffusion of Innovations: Modifications of a Model for Telecommunications. In M.-W. Stoetzer & A. Mahler (Eds.), Die Diffusion von Innovationen in der Telekommunikation (pp. 25–38). Springer. https://doi.org/10.1007/978-3-642-79868-9_2
  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson. https://thuvienso.hoasen.edu.vn/handle/123456789/8967
  • Seethamraju, R., & Hecimovic, A. (2023). Adoption of artificial intelligence in auditing: An exploratory study. Australian Journal of Management, 48(4), 780–800. https://doi.org/10.1177/03128962221108440
  • Semenova, G. N., Mustafin, T. A., Telegina, Z. A., & Bodiako, A. V. (2023). Audit of Quality Management at a Smart Company: Independent Expertise vs. Artificial Intelligence. International Journal for Quality Research, 17(1), 1–12. https://doi.org/10.24874/IJQR17.01-01
  • Şener, U., Gökalp, E., & Eren, P. E. (2016). Cloud-Based Enterprise Information Systems: Determinants of Adoption in the Context of Organizations. In G. Dregvaite & R. Damasevicius (Eds.), Information and Software Technologies (pp. 53–66). Springer International Publishing. https://doi.org/10.1007/978-3-319-46254-7_5
  • Şener, U., Gökalp, E., & Eren, P. E. (2022). Dijital Olgunluk İndeksi: Organizasyonların Dijital Dönüşüm Yolculuğunda Verimliliği Artırmak İçin Bir Kantitatif Yöntem. Journal of Productivity, 17–29. https://doi.org/10.51551/verimlilik.1002353
  • Şener, U., Gökalp, E., & Eren, P. E. (2023). Intelligent Digital Transformation Strategy Management: Development of a Measurement Framework. In: Kahraman, C., Haktanır, E. (eds) Intelligent Systems in Digital Transformation. Lecture Notes in Networks and Systems (Vol. 549). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-031-16598-6_4
  • Şener, U., Gökalp, E., & Eren, P. E. (2024). CLOUD-QM: A quality model for benchmarking cloud-based enterprise information systems. Software Quality Journal, 32(3), 881–920. https://doi.org/10.1007/s11219-024-09669-1
  • Thottoli, M. M. (2024). Leveraging information communication technology (ICT) and artificial intelligence (AI) to enhance auditing practices. Accounting Research Journal, 37(2), 134–150. https://doi.org/10.1108/ARJ-09-2023-0269
  • Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation. Lexington, MA. Turing, A. M. (1980). Computing Machinery and Intelligence. Creative Computing, 6(1), 44–53.
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  • Wassie, F. A., & Lakatos, L. P. (2024). Artificial intelligence and the future of the internal audit function. Humanities and Social Sciences Communications, 11(1), 386. https://doi.org/10.1057/s41599-024-02905-w
  • WEF. (2023). The Future of Jobs Report 2023. Word Economic Forum. https://www.weforum.org/publications/the-future-of-jobs-report-2023/
  • Yamakawa, H. (2019). Peacekeeping Conditions for an Artificial Intelligence Society. Big Data and Cognitive Computing, 3(2), Article 2. https://doi.org/10.3390/bdcc3020034
  • Zhang, C. (Abigail), Cho, S., & Vasarhelyi, M. (2022). Explainable Artificial Intelligence (XAI) in auditing. International Journal of Accounting Information Systems, 46, 100572. https://doi.org/10.1016/j.accinf.2022.100572

FACTORS INFLUENCING THE ADOPTION OF ARTIFICIAL INTELLIGENCE IN INTERNAL AUDITING

Yıl 2024, Sayı: 31, 210 - 223, 01.12.2024
https://doi.org/10.58348/denetisim.1541957

Öz

Artificial Intelligence (AI) is transforming internal auditing, offering enhanced audit quality, efficiency, and risk management. Despite its potential, AI usage in internal auditing faces challenges, including concerns over 'black-box' algorithms. This study aims to investigate factors that encourage or hinder AI adoption in internal auditing. A Systematic Literature Review (SLR) revealed limited research in this area. To address this research gap, a comprehensive model was developed based on the Technology-Organization-Environment (TOE) framework. Based on the findings, the factors influencing AI adoption in internal auditing are obtained. In the technological context, relative advantage, audit quality, security, privacy, complexity, and trust are key. The organizational context includes top management support, technological skills, readiness, and IT infrastructure. The environmental context encompasses ecosystem pressure, government policies, regulations, standards, ethics, morality, and transparency. This study contributes to the understanding of AI adoption in internal auditing, providing insights for practitioners and researchers to optimize auditing practices in the digital era.

Kaynakça

  • Ajzen, I. (1991). The theory of planned behavior. Human Behavior and Emerging Technologies, 2, 314–324. https://doi.org/10.1002/hbe2.195
  • Aldemir, C., & Uçma Uysal, T. (2024). AI Competencies for Internal Auditors in the Public Sector. EDPACS, 69(1), 3–21. https://doi.org/10.1080/07366981.2024.2312001
  • Alina, C. M., & Cerasela, S. E. (2018). Internal Audit Role in Artificial Intelligence. “Ovidius” University Annals, Economic Sciences Series, XVIII(1).
  • Aysan, H., & Fırat, Z. (2024). Yapay Zekâ Uygulamaları İç Denetim Mesleğine Neler Kazandırabilir? Mesleki Değişim ve Teknoloji Yönetimi. Ombudsman Akademik, 20, Article 20.
  • Castka, P., & Searcy, C. (2023). Audits and COVID-19: A paradigm shift in the making. Business Horizons, 66(1), 5–11. https://doi.org/10.1016/j.bushor.2021.11.003
  • Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  • Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27(3), 938–985. https://doi.org/10.1007/s11142-022-09697-x
  • Goto, M. (2023). Anticipatory innovation of professional services: The case of auditing and artificial intelligence. Research Policy, 52(8), 104828. https://doi.org/10.1016/j.respol.2023.104828
  • Han, H., Shiwakoti, R. K., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48, 100598. https://doi.org/10.1016/j.accinf.2022.100598
  • Hu, K.-H., Chen, F.-H., Hsu, M.-F., & Tzeng, G.-H. (2020). Identifying Key Factors For Adopting Artificial Intelligence-Enabled Auditing Techniques By Joint Utilization Of Fuzzy-Rough Set Theory And MRDM Technique. Technological and Economic Development of Economy, 27(2), 459–492. https://doi.org/10.3846/tede.2020.13181
  • IIA. (2017). The IIA’s AI Audit Framework: Artificial Intelligence: Considerations for the Profession of Internal Auditing. IIA. https://iaia.org.ar/wp-content/uploads/2017/07/Global-Perspectives-and-Insights-2017-10-Artificial-Intelligence-Report.pdf
  • IIA. (2024a). Complete Global Internal Audit Standards. The Institute of Internal Auditors. https://www.theiia.org/en/standards/2024-standards/global-internal-audit-standards/free-documents/complete-global-internal-audit-standards/
  • IIA. (2024b). Global Perspectives and Insights Artificial Intelligence: Considerations for the Profession of Internal Auditing. The Institute of Internal Auditors.
  • Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1–26.
  • Koç, B., Şener, U., & Eren, P. E. (2022, December). Determinative factors of cloud computing adoption in government organizations. In 2022 3rd International Informatics and Software Engineering Conference (IISEC), 1-6. IEEE. https://doi.org/10.1109/IISEC56263.2022.9998286
  • Kotb, A., Elbardan, H., & Halabi, H. (2020). Mapping of internal audit research: A post-Enron structured literature review. Accounting, Auditing & Accountability Journal, 33(8), 1969–1996. https://doi.org/10.1108/AAAJ-07-2018-3581
  • Menon, S. S., Trenker, J., Owens, T., & Tas, O. (2023). The double-edged sword of AI: Will we lose our jobs or become extremely productive? Statista. https://www.statista.com/site/insights-compass-ai-future-ai-work
  • Musa, A. M. H., & Lefkir, H. (2024). The role of artificial intelligence in achieving auditing quality for small and medium enterprises in the Kingdom of Saudi Arabia. International Journal of Data and Network Science, 8(2), 835–844. https://doi.org/10.5267/j.ijdns.2023.12.021
  • Özyiğit, H. (2023). Yapay Zekânın İç Denetçilerin Algısına Etkisi: BIST 100 Şirketlerine Yönelik Bir Araştırma. Muhasebe ve Finansman Dergisi, 0(98), 21–42. https://doi.org/10.25095/mufad.1259939
  • PCAOB. (2019). Changes in Use of Data and Technology in the Conduct of Audits. The United States Public Company Accounting Oversight Board.
  • Puthukulam, G., Ravikumar, A., Sharma, R. V. K., & Meesaala, K. M. (2021). Auditors’ Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman. Universal Journal of Accounting and Finance, 9(5), 1184–1190. https://doi.org/10.13189/ujaf.2021.090527
  • PwC. (2023). AI Jobs Barometer. PwC. https://www.pwc.com.tr/ai-jobs-barometer
  • Rikhardsson, P., Thórisson, K. R., & Bergthorsson. (2021). Artificial intelligence and auditing in small‐ and medium‐sized firms: Expectations and applications. AI Magazine - Wiley Online Library. https://onlinelibrary.wiley.com/doi/10.1002/aaai.12066
  • Rodrigues, L., Pereira, J., Da Silva, A. F., & Ribeiro, H. (2023). The impact of artificial intelligence on audit profession. Journal of Information Systems Engineering and Management, 8(1), 19002. https://doi.org/10.55267/iadt.07.12743
  • Rogers, E. (1995). Diffusion of Innovations. New York.
  • Rogers, E. M. (1995). Diffusion of Innovations: Modifications of a Model for Telecommunications. In M.-W. Stoetzer & A. Mahler (Eds.), Die Diffusion von Innovationen in der Telekommunikation (pp. 25–38). Springer. https://doi.org/10.1007/978-3-642-79868-9_2
  • Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson. https://thuvienso.hoasen.edu.vn/handle/123456789/8967
  • Seethamraju, R., & Hecimovic, A. (2023). Adoption of artificial intelligence in auditing: An exploratory study. Australian Journal of Management, 48(4), 780–800. https://doi.org/10.1177/03128962221108440
  • Semenova, G. N., Mustafin, T. A., Telegina, Z. A., & Bodiako, A. V. (2023). Audit of Quality Management at a Smart Company: Independent Expertise vs. Artificial Intelligence. International Journal for Quality Research, 17(1), 1–12. https://doi.org/10.24874/IJQR17.01-01
  • Şener, U., Gökalp, E., & Eren, P. E. (2016). Cloud-Based Enterprise Information Systems: Determinants of Adoption in the Context of Organizations. In G. Dregvaite & R. Damasevicius (Eds.), Information and Software Technologies (pp. 53–66). Springer International Publishing. https://doi.org/10.1007/978-3-319-46254-7_5
  • Şener, U., Gökalp, E., & Eren, P. E. (2022). Dijital Olgunluk İndeksi: Organizasyonların Dijital Dönüşüm Yolculuğunda Verimliliği Artırmak İçin Bir Kantitatif Yöntem. Journal of Productivity, 17–29. https://doi.org/10.51551/verimlilik.1002353
  • Şener, U., Gökalp, E., & Eren, P. E. (2023). Intelligent Digital Transformation Strategy Management: Development of a Measurement Framework. In: Kahraman, C., Haktanır, E. (eds) Intelligent Systems in Digital Transformation. Lecture Notes in Networks and Systems (Vol. 549). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-031-16598-6_4
  • Şener, U., Gökalp, E., & Eren, P. E. (2024). CLOUD-QM: A quality model for benchmarking cloud-based enterprise information systems. Software Quality Journal, 32(3), 881–920. https://doi.org/10.1007/s11219-024-09669-1
  • Thottoli, M. M. (2024). Leveraging information communication technology (ICT) and artificial intelligence (AI) to enhance auditing practices. Accounting Research Journal, 37(2), 134–150. https://doi.org/10.1108/ARJ-09-2023-0269
  • Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation. Lexington, MA. Turing, A. M. (1980). Computing Machinery and Intelligence. Creative Computing, 6(1), 44–53.
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  • Wassie, F. A., & Lakatos, L. P. (2024). Artificial intelligence and the future of the internal audit function. Humanities and Social Sciences Communications, 11(1), 386. https://doi.org/10.1057/s41599-024-02905-w
  • WEF. (2023). The Future of Jobs Report 2023. Word Economic Forum. https://www.weforum.org/publications/the-future-of-jobs-report-2023/
  • Yamakawa, H. (2019). Peacekeeping Conditions for an Artificial Intelligence Society. Big Data and Cognitive Computing, 3(2), Article 2. https://doi.org/10.3390/bdcc3020034
  • Zhang, C. (Abigail), Cho, S., & Vasarhelyi, M. (2022). Explainable Artificial Intelligence (XAI) in auditing. International Journal of Accounting Information Systems, 46, 100572. https://doi.org/10.1016/j.accinf.2022.100572
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi Sistemleri Organizasyonu ve Yönetimi, Bilgi Sistemleri (Diğer)
Bölüm Makale
Yazarlar

Umut Şener 0000-0002-1881-1886

Yayımlanma Tarihi 1 Aralık 2024
Gönderilme Tarihi 1 Eylül 2024
Kabul Tarihi 25 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 31

Kaynak Göster

APA Şener, U. (2024). İÇ DENETİMDE YAPAY ZEKÂ KULLANIMINI ETKİLEYEN FAKTÖRLER. Denetişim(31), 210-223. https://doi.org/10.58348/denetisim.1541957

TR Dizin'de yer alan Denetişim dergisi yayımladığı çalışmalarla; alanındaki profesyoneller, akademisyenler ve düzenleyiciler arasında etkili bir iletişim ağı kurarak, etkin bir denetim ve yönetim sistemine ulaşma yolculuğunda önemli mesafelerin kat edilmesine katkı sağlamaktadır.