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Analysis of Artificial Intelligence Readiness Performances of G7 Countries: An Application with LOPCOW-based MARCOS Method

Yıl 2024, Cilt: 9 Sayı: Issue: 2, 99 - 121
https://doi.org/10.53070/bbd.1537792

Öz

The artificial intelligence (AI) readiness performance of major economies can significantly impact the global economy. Therefore, analyzing the AI readiness performance of these economies is of great importance. In this study, the AI readiness performances of G7 countries were assessed using the most recent Government Artificial Intelligence Readiness Index (GAIRI) data for 2023. The analysis revealed that the importance of GAIRI components varies by country, with Data and Infrastructure generally being the most significant components. The countries were ranked according to their AI readiness performances using the LOPCOW-based MARCOS method as follows: USA, United Kingdom, Canada, France, Japan, Germany, and Italy. Notably, Italy's AI readiness performance was below the average, indicating the need for improvement to enhance its contribution to the global economy. The method applied proved to be sensitive in sensitivity analysis, credible and reliable in comparative analysis, and robust and stable in simulation analysis.

Kaynakça

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G7 Ülkelerinin Yapay Zeka Performanslarının Analizi: LOPCOW tabanlı MARCOS Yöntemi ile Bir Uygulama

Yıl 2024, Cilt: 9 Sayı: Issue: 2, 99 - 121
https://doi.org/10.53070/bbd.1537792

Öz

Büyük ekonomilerin yapay zeka hazırlık performansları, küresel ekonomi üzerinde önemli bir etkiye sahip olabilir. Bu nedenle, bu ekonomilerin yapay zeka hazırlık performanslarının analizi büyük önem taşımaktadır. Bu çalışmada, G7 ülkelerinin yapay zeka hazırlık performansları, 2023 yılına ait en güncel Hükümet Yapay Zeka Hazırlık Endeksi (YZHE) verileri kullanılarak değerlendirilmiştir. Analiz, GAIRI bileşenlerinin öneminin ülkelere göre farklılık gösterdiğini, Veri ve Altyapı bileşenlerinin genellikle en önemli bileşenler olduğunu ortaya koymuştur. Ülkeler, yapay zeka hazırlık performanslarına göre LOPCOW tabanlı MARCOS yöntemi kullanılarak şu şekilde sıralanmıştır: ABD, Birleşik Krallık, Kanada, Fransa, Japonya, Almanya ve İtalya. Özellikle İtalya'nın yapay zeka hazırlık performansının ortalamanın altında olduğu belirlenmiş olup, küresel ekonomiye katkısını artırmak için iyileştirilmesi gerektiğini göstermektedir. Uygulanan yöntem, duyarlılık analizinde hassas, karşılaştırmalı analizde güvenilir ve simülasyon analizinde sağlam ve istikrarlı olduğunu kanıtlamıştır.

Kaynakça

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  • Mastilo, Z., Štilić, A., Gligović, D., & Puška, A. (2024). Assessing the banking sector of Bosnia and Herzegovina: An analysis of financial indicators through the MEREC and MARCOS methods. Journal of Central Banking Theory and Practice, 1, p. 167-197. doi: 10.2478/jcbtp-2024-0008.
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  • Putra, A. D., Arshad, M. W., Setiawansyah, & Sintaro, S. (2024). Decision Support System for Best Honorary Teacher Performance Assessment Using a Combination of LOPCOW and MARCOS. Journal of Computer System and Informatics (JoSYC), 5(3), 578-590. doi: 10.47065/josyc.v5i3.5127
  • Raj, M., & Seamans, R. (2019). Primer on artificial intelligence and robotics. Journal of Organization Design, 8(11), 1-14.
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  • Rogerson, A., Hankins, E., Nettel, P. F., & Rahim, S. (2022). Goverment Artifical Intellingence Readiness Index (2022). Frederiksberg: Oxford Insights.
  • Rong, Y., Yu, L., Liu, Y., Simic , V., & Garg, H. (2023). The FMEA model based on LOPCOW ARAS methods with interval valued Fermatean fuzzy information for risk assessment of R&D projects in industrial robot offline programming systems. Computational and Applied Analysis, 43, 1-43. doi: 10.1007/s40314-023-02532-2
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  • Ulutaş, A., Topal, A., Görçün, Ö. F., & Ecer, F. (2024). Evaluation of third party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI MACONT model. Expert Systems with Applications, 241(1), 1-35. doi: 10.1016/j.eswa.2023.122680
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  • Wang, Y., Wang, W., Wang, Z., Deveci, M., Roy, S. K., & Kadry, S. (2024). Selection of sustainable food suppliers using the Pythagorean fuzzy CRITIC-MARCOS method. Information Sciences(664), 1-22. doi: 10.1016/j.ins.2024.120326
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  • Yoganandham, G., & Elanchezhian, G. (2023). Artificial intelligence and economic growth with reference to decision-making, social governance, accelerate industry 4.0, and foster innovation -A theoretical assessment. Science, Technology and Development, 13(8), 224-236.
  • Zhao, H., & Guo, S. (2024). Urban integrated energy system construction plan selection: A hybrid multi-criteria decision-making framework. Environ. Dev. Sustain., 1-13. doi: 10.1007/s10668-024-04491-y
  • Zia, M. T., Nadim, M., Khan, M. A., Akram, N., & Atta, F. (2024). The Role and Impact of Artificial Intelligence on Project Management. The Asian Bulletin of Big Data Management, 4(2), 178-185. doi: 10.62019/abbdm.v4i02.160
Toplam 94 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Planlama ve Karar Verme
Bölüm PAPERS
Yazarlar

Furkan Fahri Altıntaş 0000-0002-0161-5862

Erken Görünüm Tarihi 24 Aralık 2024
Yayımlanma Tarihi
Gönderilme Tarihi 23 Ağustos 2024
Kabul Tarihi 9 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 9 Sayı: Issue: 2

Kaynak Göster

APA Altıntaş, F. F. (2024). Analysis of Artificial Intelligence Readiness Performances of G7 Countries: An Application with LOPCOW-based MARCOS Method. Computer Science, 9(Issue: 2), 99-121. https://doi.org/10.53070/bbd.1537792

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