ANALYSIS OF ARTIFICIAL INTELLIGENCE READINESS PERFORMANCE OF EUROPEAN UNION COUNTRIES: AN APPLICATION USING THE LOPCOW-BASED GRA METHOD

Furkan Fahri Altıntaş

Makale detayları

ANALYSIS OF ARTIFICIAL INTELLIGENCE READINESS PERFORMANCE OF EUROPEAN UNION COUNTRIES: AN APPLICATION USING THE LOPCOW-BASED GRA METHOD

Yazarlar:

Furkan Fahri Altıntaş

Yayıncı:

Marmara University

Ana Başlıklar:

Avrupa Birliği

Dergi:

Marmara Üniversitesi Avrupa Araştırmaları Enstitüsü Avrupa Araştırmaları Dergisi Vol.33 Issue 1, pp.23-51

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359371

pISSN:

1301-1359

Anahtar kelimeler:

Özet:

 The activities and strategies of the European Union EU, one of the worlds most significant economic actors, in the field of ArtificiaI Intelligence AI have the potential to influence the global economy and the AI policies of other countries. Therefore, analyzing the AI readiness performance of EU countries is considered crucial. In this study, the AI readiness performance of EU countries for the most recent year, 2023, was measured using the LOPCOW-based Grey Relational Analysis GRA Multi-Criteria Decision Making MCDM method, based on the Government AI Readiness Index GAIRI criteria values. According to the findings, the most critical GAIRI criterion for countries was identified as the government. Secondly, the top three countries with the highest AI readiness performance were found to be Finland, France, and Germany, while the bottom three were Croatia, Romania, and Greece, respectively. Additionally, the average AI readiness performance value for countries was measured, and it was assessed that EU countries with below-average performance need to improve their AI readiness to contribute to the global economy. Finally, sensitivity, comparison, and simulation analyses indicated that the AI readiness performance of EU countries under the GAIRI framework can be measured using the LOPCOW-based GRA method.

 The activities and strategies of the European Union EU, one of the worlds most significant economic actors, in the field of ArtificiaI Intelligence AI have the potential to influence the global economy and the AI policies of other countries. Therefore, analyzing the AI readiness performance of EU countries is considered crucial. In this study, the AI readiness performance of EU countries for the most recent year, 2023, was measured using the LOPCOW-based Grey Relational Analysis GRA Multi-Criteria Decision Making MCDM method, based on the Government AI Readiness Index GAIRI criteria values. According to the findings, the most critical GAIRI criterion for countries was identified as the government. Secondly, the top three countries with the highest AI readiness performance were found to be Finland, France, and Germany, while the bottom three were Croatia, Romania, and Greece, respectively. Additionally, the average AI readiness performance value for countries was measured, and it was assessed that EU countries with below-average performance need to improve their AI readiness to contribute to the global economy. Finally, sensitivity, comparison, and simulation analyses indicated that the AI readiness performance of EU countries under the GAIRI framework can be measured using the LOPCOW-based GRA method.

 The activities and strategies of the European Union EU, one of the worlds most significant economic actors, in the field of ArtificiaI Intelligence AI have the potential to influence the global economy and the AI policies of other countries. Therefore, analyzing the AI readiness performance of EU countries is considered crucial. In this study, the AI readiness performance of EU countries for the most recent year, 2023, was measured using the LOPCOW-based Grey Relational Analysis GRA Multi-Criteria Decision Making MCDM method, based on the Government AI Readiness Index GAIRI criteria values. According to the findings, the most critical GAIRI criterion for countries was identified as the government. Secondly, the top three countries with the highest AI readiness performance were found to be Finland, France, and Germany, while the bottom three were Croatia, Romania, and Greece, respectively. Additionally, the average AI readiness performance value for countries was measured, and it was assessed that EU countries with below-average performance need to improve their AI readiness to contribute to the global economy. Finally, sensitivity, comparison, and simulation analyses indicated that the AI readiness performance of EU countries under the GAIRI framework can be measured using the LOPCOW-based GRA method.

 Dünyanın en önemli ekonomik aktölerinen olan Avrupa Birliği ABnin yapay zekâ YZ konusundaki faaliyetleri ve stratejileri küresel akonomiyi ve diğer ülkelerin YZ konusundaki politikalarını etkileyebilemktedir. Dolayısıyla AB ülkelerinin YZ hazırlık performanslarının analizinin önemli olduğu düşünülebilir. Bu kapsamda araştırmada, en son ve güncel olan 2023 yılı için AB ülkelerinin Hükümet Yapay Zeka Hazırlık Performansı HYZHP kriter değerleri üzerinden ülkelerin yapay zeka hazırlık performansları LOPCOW tabanlı Gri İlişkisel Analiz GİA Çok Kriterli Karar Verme ÇKKV yöntemi ile ölçülmüştür. Bulgular kapsaminda ilk olarak ülkelere göre en önemli HYZHP kriterinin Hükümet olduğu tespit edilmişit. İkinci olarak en fazla YZ hazırlık perforsına sahip ilk üç ülkenin Finlandiya, Fransa ve Almanya, buna karşın en az performansa sahip olan ilk üç ülkenin ise sırasıyla Hırvatistan, Romanya ve Yunanistan olduğu belirlenmiştir. Ayrıca ülkelere göre ortalama YZ hazırlık performans değeri ölçülmüş ve ortalama değerden az performansa sahip olan AB ülkelerinin küresel ekonomiye katkılarının olması için YZ hazırlık performanslarını artırmaları gerektiği değerlendirilmiştir. Son olarak duyarlılık, karşılaştırma ve simülasyon analizlerine göre AB ülkelerinin HYZHP kapsamında YZ hazırlık performanslarının LOPCOW tabanlı GİA yöntemi ile ölçülebileceği sonucuna ulaşılmıştır.

Makale detayları

ANALYSIS OF ARTIFICIAL INTELLIGENCE READINESS PERFORMANCE OF EUROPEAN UNION COUNTRIES: AN APPLICATION USING THE LOPCOW-BASED GRA METHOD

Yazarlar:

Furkan Fahri Altıntaş

Yayıncı:

Marmara University

Ana Başlıklar:

Avrupa Birliği

Dergi:

Marmara Üniversitesi Avrupa Araştırmaları Enstitüsü Avrupa Araştırmaları Dergisi Vol.33 Issue 1, pp.23-51

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359371

pISSN:

1301-1359

Artificial Intelligence (AI)

AI readiness performance

European Union (EU) countries

LOPCOW

LOPCOW-based GRAYapay zekâ (YZ)

yapay zekâ hazırlık performansı

Avrupa Birliği (AB) ülkeleri

LOPCOW

LOPCOW tabanlı GRA.

Sayı

Makale detayları

Yazarlar: Furkan Fahri Altıntaş

Yayıncı: Marmara University

Ana Başlıklar: Avrupa Birliği

Dergi: Marmara Üniversitesi Avrupa Araştırmaları Enstitüsü Avrupa Araştırmaları Dergisi Vol.33 Issue 1, pp.23-51

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001359371

pISSN: 1301-1359

Sayı

Yazarlar:

Furkan Fahri Altıntaş

Yayıncı:

Marmara University

Ana Başlıklar:

Avrupa Birliği

Dergi:

Marmara Üniversitesi Avrupa Araştırmaları Enstitüsü Avrupa Araştırmaları Dergisi Vol.33 Issue 1, pp.23-51

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359371

pISSN:

1301-1359