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Measuring AI Capacity in Countries with National AI Strategies: An Integrated D-CRITIC & SPOTIS Approach

Yıl 2026, Cilt: 9 Sayı: 2, 1047 - 1077, 16.03.2026
https://doi.org/10.47495/okufbed.1782495
https://izlik.org/JA66YD33RD

Öz

Artificial Intelligence (AI) is a strategic element that supports countries' innovation capacities and their ability to achieve sustainable development goals on a global scale. With the development of AI, monitoring global policies has become crucial. In this regard, countries should be aware of the opportunities and threats associated with AI, change their existing policies, monitor their AI capacities, and take measures to improve their performance. There are indices based on subjective assessments for tracking AI capacity performance. However, in order to ensure the reliability and objectivity of the rankings, the assessment must be carried out using objective weighting methods. Motivated by this, the study aims to assess the AI capacities of upper-middle-income countries with national AI strategies objectively and comparatively. In this regard, the importance levels of the factors determining AI capacity were determined using Global AI Index data with the D-CRITIC method, and countries' performances were ranked using the SPOTIS approach. Furthermore, the sensitivity of the rankings was tested with sensitivity analyses conducted under different weighting scenarios, and the robustness of the results obtained was examined. The consistency of the rankings obtained with D-CRITIC and SPOTIS was also measured through comparative analysis. The results showed that the most critical dimension in determining AI capacity is the operating environment. In terms of performance between countries, China, Turkey, and Argentina were found to be the countries with the highest AI capacity in the upper-middle income group. The sensitivity analysis showed that the AI capacity rankings of countries at the top of the list were generally stable. In contrast, it was found that the performance of some countries in the middle and lower ranks was sensitive to criterion weights. The comparative analysis showed that the results of the study are consistent and compatible with other methods, and the rankings are not sensitive to methods. The findings confirm the robustness of countries' performance but also reveal that certain countries have a strategically more fragile structure.

Kaynakça

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  • Demaidi MN. Artificial intelligence national strategy in a developing country. AI and Society 2025; 40(2): 423-435. https://doi.org/10.1007/s00146-023-01779-x
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Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı

Yıl 2026, Cilt: 9 Sayı: 2, 1047 - 1077, 16.03.2026
https://doi.org/10.47495/okufbed.1782495
https://izlik.org/JA66YD33RD

Öz

Yapay zekâ küresel boyutta ülkelerin yenilikçilik kapasitelerini, sürdürülebilir kalkınma hedeflerine ulaşma yetkinliklerini destekleyen stratejik bir unsurdur. Yapay zekânın gelişmesiyle birlikte küresel politikaların izlenmesi daha önemli hale gelmektedir. Bu doğrultuda, ülkeler yapay zekâ ile ilişkili fırsatları ve tehditleri farkında olarak mevcut politikalarını değiştirmeli, yapay zekâ kapasitelerini izlemeli ve performanslarını iyileştirici önlemler almalıdır. Yapay zekâ kapasite performanslarının takip edilebilmesi için subjektif değerlendirmelere dayanan endeksler bulunmaktadır. Ancak sıralamaların güvenilirliği ve nesnelliğini sağlayabilmek adına objektif ağırlıklandırma yöntemleri kullanılarak değerlendirmenin yapılması gerekmektedir. Bu motivasyonla çalışmada üst-orta gelir grubunda yer alan ve ulusal yapay zekâ stratejisine sahip ülkelerin yapay zekâ kapasitelerini nesnel ve karşılaştırmalı bir biçimde değerlendirilmesi amaçlanmıştır. Bu doğrultuda, Global AI Index verileri kullanılarak yapay zekâ kapasitesini belirleyen faktörlerin önem dereceleri D-CRITIC yöntemiyle ortaya konmuş, ülkelerin performansları da SPOTIS yaklaşımıyla sıralanmıştır. Ayrıca farklı ağırlıklandırma senaryoları altında yapılan duyarlılık analizleriyle sıralamaların duyarlılığı test edilmiş, elde edilen sonuçların sağlamlığı incelenmiştir. D-CRITIC ve SPOTIS ile elde edilen sıralamaların diğer yöntemlerle tutarlılığı da karşılaştırmalı analiz ile ölçülmüştür. Sonuçlar, yapay zekâ kapasitesini belirlemede en kritik boyutun işletim ortamı olduğunu göstermiştir. Ülkeler arası performanslarda ise Çin, Türkiye ve Arjantin’in üst-orta gelir grubunda en yüksek yapay zekâ kapasitesine sahip ülkeler olduğu ortaya koyulmuştur. Duyarlılık analizi sonucunda ise özellikle üst sıralarda yer alan ülkelerin yapay zekâ kapasite sıralamalarının genel olarak istikrarlı olduğu görülmüştür. Buna karşılık orta ve alt sıralarda yer alan bazı ülkelerin performanslarının kriter ağırlıklarına duyarlı olduğu tespit edilmiştir. Karşılaştırmalı analiz sonucunda çalışmanın sonuçlarının tutarlı ve diğer yöntemlerle uyumlu olduğu, sıralamaların yöntem seçimine duyarlı olmadığı gösterilmiştir. Elde edilen bulgular, ülkelerin performanslarının sağlamlığını teyit etmekle birlikte, belirli ülkelerin stratejik olarak daha kırılgan bir yapıya sahip olduklarını da ortaya koymuştur.

Kaynakça

  • Abdel Aziz NM., Mohamed D., Soliman H. A multi-criteria decision-making framework for evaluating emerging digital technologies in supply chain optimization. Neutrosophic Sets and Systems 2025; 87: 379-433.
  • Adali EA., Öztaş GZ., Öztaş T., Tuş A. Assessment of European cities from a smartness perspective: An integrated grey MCDM approach. Sustainable cities and society 2022; 84, 104021.
  • Ali W., Khan AZ. Factors influencing readiness for artificial intelligence: a systematic literature review. Data Science and Management 2025; 8(2): 224-236.
  • Bączkiewicz A., Kizielewicz B., Shekhovtsov A., Yelmikheiev M., Kozlov V., Sałabun W. Comparative analysis of solar panels with determination of local significance levels of criteria using the MCDM methods resistant to the rank reversal phenomenon. Energies 2021; 14: 5727. https://doi.org/10.3390/en14185727
  • Bui HA., Tran NT., Nguyen DL. Multi-criteria decision making in the powder-mixed electrical discharge machining process based on the CoCoSo, SPOTIS algorithms and the weighting methods. International Journal of Modern Manufacturing Technologies 2023; 15(1): 69-79. https://doi.org/10.54684/ijmmt.2023.15.1.69
  • Britannica Ansiklopedisi. Artificial intelligence. Encyclopaedia Britannica 2023. https://www.britannica.com/technology/artificial-intelligence. Erişim Tarihi:12.08.2025.
  • Campello BSC., Pelegrina GD., Pelissari R., Suyama R., Duarte LT. Mitigating subjectivity and bias in AI development indices: a robust approach to redefining country rankings. Expert Systems with Applications 2024; 255: 124803. https://doi.org/10.1016/j.eswa.2024.124803
  • Challoumis C., Eriotis N. The impact of artificial intelligence on the Greek economy. Journal of Open Innovation: Technology, Market, and Complexity 2025; 11(3): 100578. https://doi.org/10.1016/j.joitmc.2025.100578
  • Demaidi MN. Artificial intelligence national strategy in a developing country. AI and Society 2025; 40(2): 423-435. https://doi.org/10.1007/s00146-023-01779-x
  • Dezert J., Tchamova A., Han D., Tacnet JM. The SPOTIS rank reversal free method for multi-criteria decision-making support. Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020, July 1, 2020, 1-8, Rustenburg, South Africa. https://doi.org/10.23919/FUSION45008.2020.9190347
  • Diakoulaki D., Mavrotas G., Papayannakis L. Determining objective weights in multiple criteria problems: the CRITIC method. Computers & Operations Research 1995; 22(7): 763-770.
  • Diallo K., Smith J., Okolo CT., Nyamwaya D., Kgomo J., Ngamita R. Case studies of AI policy development in Africa. Data and Policy 2025; 7: e15. https://doi.org/10.1017/dap.2024.71
  • Dong Y., Jiang N., Zhou R., Zhu C., Cao L., Liu T., Xu Y., Li X. A novel multi-criteria conflict evidence combination method and its application to pattern recognition. Information Fusion 2024; 108: 102346. https://doi.org/10.1016/j.inffus.2024.102346
  • Duo L., Sánchez-Juny M., Bladé i Castellet E. Ecological environment assessment system in river–riparian areas based on a protocol for hydromorphological quality evaluation. Water (Switzerland) 2024; 16: 3025. https://doi.org/10.3390/w16213025
  • Düzdar İ., Beşbaş Z., Öztürk Peker U. Dijital dönüşüm endeksi parametrelerinin çok kriterli karar verme yöntemleri ile değerlendirilmesi. International Journal of 3D Printing Technologies and Digital Industry. 2024; 8(1): 143-153. doi:10.46519/ij3dptdi.1321818
  • Ecer F., Aycin E. Novel comprehensive MEREC Weighting-Based score aggregation model for measuring ınnovation performance: The Case of G7 Countries, Informatica 2023; 34(1): 53-83.
  • Eman K., Chung ES., Ayugi BO. Investigating the skills of HighResMIP in capturing historical and future mean precipitation shifts over Pakistan. International Journal of Climatology 2024; 44(11): 3888-3911. https://doi.org/10.1002/joc.8558
  • Fattorini L., Maslej N., Perrault R., Parli V., Etchemendy J., Shoham Y., Ligett K. The Global AI Vibrancy Tool November 2024. https://hai.stanford.edu/research/the-global-ai-vibrancy-tool-2024 . Erişim Tarihi:02.07.2025. Ghaemi-Zadeh N., Eghbali-Zarch M. Evaluation of business strategies based on the financial performance of the corporation and investors’ behavior using D-CRITIC and fuzzy MULTI-MOORA techniques: a real case study. Expert Systems with Applications 2024; 247: 123183. https://doi.org/10.1016/j.eswa.2024.123183
  • Gorgulu Y., Ozceylan E., Ozkan B. UI GreenMetric ranking of Turkish universities using entropy weight and COPRAS methods. In Proceedings of the International Conference on Industrial Engineering and Operations Management 2021; Bangalore, India, August (pp. 16-18).
  • Hwang CL., Yoon K. Multiple attribute decision making: Methods and applications – A state-of-the-art survey. 1981; New York: Springer-Verlag.
  • IBM Research. AI Adoption Index 2023. IBM 2024. https://www.ibm.com/think/reports/ai-in-action. Erişim Tarihi:08.07.2025.
  • Ismail JIMS., Muhammad MN., Mosali NA. Ranking of innovation related factors influencing artificial intelligence performance. International Journal of Sustainable Construction Engineering and Technology 2022; 13(4): 154-164. https://doi.org/10.30880/ijscet.2022.13.04.013
  • Iuga IC., Socol A. Government artificial intelligence readiness and brain drain: influencing factors and spatial effects in the European Union member states. Journal of Business Economics and Management 2024; 25(2): 268-296. https://doi.org/10.3846/jbem.2024.21136
  • Jeon G. Leveraging AI for ESG performance: a global analysis of national AI capabilities and sustainability in international business. Business Strategy and Development 2025; 8(3): e70162. https://doi.org/10.1002/bsd2.70162
  • Kanat G., Yang Z., Wang C., Akbar I., Mominov S. Evaluation of competitiveness of e-commerce websites in Kazakhstan. Sustainability (Switzerland) 2024; 16: 10972. https://doi.org/10.3390/su162410972
  • Karasan A., Kutlu Gündoǧdu F., Aydın S. Decision-making methodology by using multi-expert knowledge for uncertain environments: green metric assessment of universities. Environment, Development and Sustainability 2023; 25(8): 7393-7422.
  • Keleş N., Ersoy N. Analyzing climate change performance over the last five years of G20 countries using a multi-criteria decision-making framework. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi 2023; 24(2): 13-34.
  • Kizielewicz B., Więckowski J., Shekhovtsov A., Wątróbski J., Depczyński R., Sałabun W. Study towards the time-based MCDA ranking analysis – a supplier selection case study. Facta Universitatis, Series: Mechanical Engineering 2021; 19(3, Special Issue): 381-399. https://doi.org/10.22190/FUME210130048K
  • Krishnan AR., Kasim M.M., Hamid R., Ghazali M.F. A modified CRITIC method to estimate the objective weights of decision criteria. Symmetry 2021; 13: 973. https://doi.org/10.3390/sym13060973.
  • Kourtit K., Macharis C., Nijkamp P. A multi-actor multi-criteria analysis of the performance of global cities. Applied Geography 2014; 49: 24-36.
  • Li Q., Luo T., Cheng T., Yang S., She H., Li J., Wang B., Kuai J., Wang J., Xu Z., Zhou G. Evaluation and screening of rapeseed varieties (Brassica napus L.) suitable for mechanized harvesting with high yield and quality. Agronomy 2023; 13: 795. https://doi.org/10.3390/agronomy13030795
  • Li K., Du T., Zhou R., Fan Q. Multi-objective optimization of material properties for enhanced battery performance using artificial intelligence. Expert Systems with Applications 2025; 288(2025): 128179. https://doi.org/10.1016/j.eswa.2025.128179
  • Liu Y., Zhu X., Wang Y. Revisiting and evaluation of the index of sustainable economic welfare based on artificial intelligence: data from 30 Chinese provinces from 2003 to 2019. Environment, Development and Sustainability 2023; 25(4): 3123-3152.
  • Lukić R. Analysıs of climate change performance of G7 countries based on AHP-CODAS methods. The holistic approach to environment 2025; 15(2): 47-56.
  • Luo T., Sheng Z., Zhang C., Li Q., Liu X., Qu Z., Xu Z. Seed characteristics affect low-temperature stress tolerance performance of rapeseed (Brassica napus L.) during seed germination and seedling emergence stages. Agronomy 2022; 12: 1969. https://doi.org/10.3390/agronomy12081969
  • Qiu L., Yang X., Tang J., Fan L. Machine learning-driven multi-objective optimization for sustainable, cost-effective, and low-emission gold mining. Journal of Cleaner Production 2025; 511(2025): 145621. https://doi.org/10.1016/j.jclepro.2025.145621
  • Mandon P. Beyond the AI divide: a straightforward approach to identifying global and local overperformers in AI preparedness. Digital Business 2025; 5(2025): 100136. https://doi.org/10.1016/j.digbus.2025.100136
  • Maneengam A. Multi-objective optimization of the multimodal routing problem using the adaptive ε-constraint method and modified TOPSIS with the D-CRITIC method. Sustainability (Switzerland) 2023; 15: 12066. https://doi.org/10.3390/su151512066
  • Marinaș LE., Păun CV., Diaconescu M., Smirna TG. Artificial intelligence readiness and employment: a global panel analysis. Economic Computation and Economic Cybernetics Studies and Research 2024; 58(4): 57-74. https://doi.org/10.24818/18423264/58.4.24.04
  • Mohamad D., Ahmad SAS., Azhar H. Fuzzy ARAS method with objective weight for solving logistic provider problem. AIP Conference Proceedings 2024; 2905(1): 060003. https://doi.org/10.1063/5.0201666
  • Nasser AA., Alghawli ASA., Saleh S., Elsayed AAK. Exploring health security trends in low-income countries using a hybrid D-CRITIC-CoCoSo and K-means approach. Global Security - Health, Science and Policy 2025; 10(1): 2540095. https://doi.org/10.1080/23779497.2025.2540095
  • OECD.AI. Global AI Initiatives Navigator (GAIIN). 2025. https://oecd.ai/dashboards. Erişim Tarihi:17.08.2025. Oxford Insights. Oxford AI Readiness Index 2024. 2024. https://www.oxfordinsights.com/. Erişim Tarihi:17.08.2025.
  • Ozkaya G., Demirhan A. Analysis of countries in terms of artificial intelligence technologies: PROMETHEE and GAIA method approach. Sustainability (Switzerland) 2023; 15: 4604. https://doi.org/10.3390/su15054604
  • Özkaya G, Erdin C. Evaluation of smart and sustainable cities through a hybrid MCDM approach based on ANP and TOPSIS technique. Heliyon 2020; 6(10). e05052.
  • Pamučar D., Ćirović G. The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC) and MultAttributive Ideal-Real Comparative Analysis (MAIRCA). Expert Systems with Applications 2015; 42(6): 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057
  • Puška A., Hodžić I., Štilić A., Murtič S. Evaluating European Union countries on climate change management: A fuzzy MABAC approach to the climate change performance index. Journal of Green Economy and Low-Carbon Development 2024; 3(1): 15-25.
  • Saqlain M., Kumam P., Kumam W. Optimizing agricultural decision-making with integrated MCDM-MCDA methods: a case study on crop economics. Yugoslav Journal of Operations Research 2024; 0: 8-8. https://doi.org/10.2298/YJOR240915008S
  • Sharma R., Kumar Mahanti G., Panda G., Singh A. Thyroid nodules classification using weighted average ensemble and D-CRITIC based TOPSIS methods for ultrasound images. Current Medical Imaging 2023; 20(1): E050423215446. https://doi.org/10.2174/1573405620666230405085358
  • Sotoudeh-Anvari A. Root Assessment Method (RAM): A novel multi-criteria decision making method and its applications in sustainability challenges. Journal of Cleaner Production 2023; 423: 138695.
  • Stanford HAI. AI Index Report 2024. Stanford University 2024. https://hai.stanford.edu/. Erişim Tarihi:17.08.2025.
  • Stehlíková L. Artificial intelligence and tourism in the EU: A data-driven analysis of adoption and economic contribution. Folia Geographica 2025; 67(1): 70-99. https://orcid.org/0000-0003-1064-6254
  • Stević Ž., Pamučar D., Puška A., Chatterjee P. Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering 2020; 140: 106231. https://doi.org/10.1016/j.cie.2019.106231
  • Thakur MS. AI readiness across nations: a panel analysis of policy, governance, and innovation factors. International Journal for Multidisciplinary Research 2025; 7(3): 1-11.
  • The World Bank. World Bank country and lending groups. 2025. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519. Erişim Tarihi:23.08.2025.
  • Torres PS., Gomes CFS., Santos M. dos. Selection of unmanned aerial vehicle systems for border monitoring using the MPSI-SPOTIS method. Journal of Defense Analytics and Logistics 2024; 8(1): 80-104. https://doi.org/10.1108/JDAL-12-2023-0016
  • Tortoise. The Global Artificial Intelligence Index 2024. 2024. https://www.tortoisemedia.com/_app/immutable/assets/AI-Methodology-2409.BGTLUPC-.pdf. Erişim Tarihi:17.08.2025.
  • Tran NT. Application of the multi-criteria analysis method MAIRCA, SPOTIS, COMET for the optimisation of sustainable electricity technology development. EUREKA, Physics and Engineering 2024; 2024(1): 180-188. https://doi.org/10.21303/2461-4262.2024.003133
  • Tripathy SR., Rath A., Sharma R., Panda G., Sharma M. Advancing cardiac disease detection using feature extraction, feature selection, and ensemble learning approaches. Journal of Scientific and Industrial Research 2025; 84(2): 207-218. https://doi.org/10.56042/jsir.v84i02.13919
  • Tuş A., Öztaş GZ., Öztaş T., Özçil A., Aytaç Adalı E. Türkiye’nin dijital dönüşüm endeksinin hesaplanması için alternatif bir yaklaşım: Bayesian BWM. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 2023; 29(8): 842-854.
  • Yazdani M., Zarate P., Zavadskas EK., Turskis Z. A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Economic Computation and Economic Cybernetics Studies and Research 2019; 53(2): 25–40. https://doi.org/10.24818/18423264/53.2.19.03
  • Zarali F., Kılıçarslan Z., Dumrul Y. AB ülkelerinin dijital dönüşüm performanslarının entropi tabanlı TOPSIS yöntemiyle değerlendirilmesi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2024; (69): 195-203.
  • Zarova E., Abdurakhmanova G., Tursunov B. The relationship of the global index of artificial intelligence and the level of employment: a cluster approach in assessing cross-country differences. ACM International Conference Proceeding Series 2023: 682-688. https://doi.org/10.1145/3644713.3644827
  • Zhang H., Wei G. Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method. Computational and Applied Mathematics 2023; 42(60): 1-35. https://doi.org/10.1007/s40314-022-02183-9
Toplam 63 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Çok Ölçütlü Karar Verme
Bölüm Araştırma Makalesi
Yazarlar

Gülin Zeynep Öztaş 0000-0002-6901-6559

Gönderilme Tarihi 11 Eylül 2025
Kabul Tarihi 5 Aralık 2025
Yayımlanma Tarihi 16 Mart 2026
DOI https://doi.org/10.47495/okufbed.1782495
IZ https://izlik.org/JA66YD33RD
Yayımlandığı Sayı Yıl 2026 Cilt: 9 Sayı: 2

Kaynak Göster

APA Öztaş, G. Z. (2026). Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 9(2), 1047-1077. https://doi.org/10.47495/okufbed.1782495
AMA 1.Öztaş GZ. Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2026;9(2):1047-1077. doi:10.47495/okufbed.1782495
Chicago Öztaş, Gülin Zeynep. 2026. “Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 9 (2): 1047-77. https://doi.org/10.47495/okufbed.1782495.
EndNote Öztaş GZ (01 Mart 2026) Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 9 2 1047–1077.
IEEE [1]G. Z. Öztaş, “Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı”, Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 9, sy 2, ss. 1047–1077, Mar. 2026, doi: 10.47495/okufbed.1782495.
ISNAD Öztaş, Gülin Zeynep. “Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 9/2 (01 Mart 2026): 1047-1077. https://doi.org/10.47495/okufbed.1782495.
JAMA 1.Öztaş GZ. Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2026;9:1047–1077.
MLA Öztaş, Gülin Zeynep. “Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 9, sy 2, Mart 2026, ss. 1047-7, doi:10.47495/okufbed.1782495.
Vancouver 1.Gülin Zeynep Öztaş. Ulusal Yapay Zekâ Stratejisine Sahip Ülkelerin Yapay Zekâ Kapasitelerinin Ölçümü: Bütünleşik D-CRITIC&SPOTIS Yaklaşımı. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 01 Mart 2026;9(2):1047-7. doi:10.47495/okufbed.1782495

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