Türkiye'deki Ar-Ge Merkezli KOBİ ve Girişimlerde Uzman Odaklı Öğrenmeye Dayalı YZ Taksonomisinin Benimsenme Sıklığı

Öz

Yapay zekânın (YZ) kurumsal düzeyde benimsenmesi yaygın olarak incelenmiş olsa da, firmaların benimsediği spesifik algoritmalar ve bu seçimlerin nedenleri hakkında az bilgi bulunmaktadır. Bu çalışma, Türkiye'deki araştırma ve geliştirme (AR-GE) küçük ve orta ölçekli işletmeler (KOBİ'ler) ve yeni kurulan şirketler arasında YZ’nin benimsenmesini, yedi YZ ailesine gruplandırılmış 28 algoritmadan oluşan, uzman odaklı, öğrenme alanı tabanlı bir taksonomi önerisi aracılığıyla araştırmaktadır. Anket verileri İstanbul Ticaret Odası'na bağlı 36 kuruluştan toplanmıştır. Güvenilirlik ve geçerlilik sağlanmış ve benimseme, tanımlayıcı istatistikler ve parametrik olmayan Kruskal-Wallis ve Mann-Whitney testleri ile incelenmiştir. Algoritma düzeyinde, Karar Ağaçları, Sinir Ağları, Çok Katmanlı Algılayıcı ve Doğrusal Regresyon en çok benimsenen algoritmalar olup, yorumlanabilir ve yapısal olarak tanıdık yöntemlere olan tercihi yansıtmaktadır. Buna karşılık, Derin Q-Ağı, Politika Gradyanı ve Eşzamanlı Konumlandırma ve Haritalama en az benimsenen algoritmalar olmuştur. Aile düzeyinde, Denetimsiz Öğrenme ve Doğal Dil İşleme en yüksek sıralarda yer alırken, Takviyeli Öğrenme ve Otonom Davranış en düşük ve en farklı benimseme oranlarıyla sonuçlanmıştır. Benimseme sektörlere göre de farklılık göstermektedir. Finans/FinTech ve Mühendislik/ Savunma en geniş benimseme portföyünü içerirken, Sağlık ve Ticaret/e-Ticaret en dar portföyü sunmaktadır. Otonom Davranış ve Denetimsiz Öğrenme istatistiksel olarak sektörler arası anlamlı farklılıklar göstermektedir. Benimseme aralığı organizasyonel aşamalarda artarak kuluçka sonrası durumda zirveye ulaşmıştır. Bulgular, Teknoloji-Organizasyon-Çevre çerçevesini, yenilik yayılımı ve dinamik yetenek teorisiyle algoritma düzeyine getirmekte, hedef olarak KOBİ'lerin dijital dönüşümünde yapay zekâ algoritma bilgisinin önemini ortaya koymaktadır.

Anahtar Kelimeler

• Yapay zekanın benimsenmesi
• yapay zekanın taksonomisi
• yapay zeka ailesi
• yapay zeka algoritmaları
• teknoloji-organizasyon-çevre çerçevesi

Destekleyen Kurum

YOK

Etik Beyan

Bu makalenin özgün olduğunu, daha önce yayınlanmadığını ve şu anda başka bir dergide değerlendirme aşamasında olmadığını onaylıyorum. Çalışma için etik onay, İstanbul Medeniyet Üniversitesi Sosyal Bilimler Etik Kurulu'ndan alınmıştır. Tüm işlemler gerekli etik standartlara uygun olarak yürütülmüş ve tüm katılımcılardan bilgilendirilmiş onam alınmıştır.

Teşekkür

YOK

Adoption Frequencies of Expert- Driven Learning-Based AI Taxonomy among R&D SMEs and Start-ups in Turkiye

Öz

Artificial intelligence (AI) adoption is widely studied at the organizational level, yet much less is known about the specific algorithms organizations adopt and the reasons for these choices. This study investigates AI adoption among research-and-development (R&D) small and medium-sized enterprises (SMEs) and start-ups in Turkiye through a proposed expert-driven, learning-domain-based taxonomy comprising 28 algorithms grouped into seven AI families. Survey data were collected from 36 organizations affiliated with the Istanbul Chamber of Commerce. Reliability and validity were established, and adoption was examined through descriptive statistics and non-parametric Kruskal-Wallis and Mann-Whitney tests. At the algorithm level, Decision Trees, Neural Networks, Multilayer Perceptron, and Linear Regression were the most adopted, reflecting a preference for interpretable, structurally familiar methods, whereas Deep Q-Network, Policy Gradient, and Simultaneous Localization and Mapping were the least adopted. At the family level, Unsupervised Learning and Natural Language Processing ranked highest, while Reinforcement Learning and Autonomous Behavior recorded the lowest and most divergent adoption. Adoption also varied by sector—Financial/FinTech and Engineering/Defense showed the broadest portfolios and Healthcare and Commerce/e-Commerce the narrowest—with statistically significant cross-sector differences for Autonomous Behavior and Unsupervised Learning. Adoption range increased across organizational stages, peaking at post-incubation. The findings extend the Technology-Organization-Environment framework with diffusion of innovation and dynamic capability theory to the algorithm level and emphasize AI algorithms’ knowledge as a target for SMEs’ digital transformation.

Anahtar Kelimeler

• Artificial intelligence adoption
• artificial intelligence taxonomy
• artificial intelligence family
• artificial intelligence algorithms
• technology-organization-environment framework

Destekleyen Kurum

NA

Etik Beyan

I confirm that this manuscript is original, has not been previously published, and is not currently under review at any other journal. Ethical approval for the study was obtained from the Social Science Ethics Committee of Istanbul Medeniyet University. All procedures were conducted in accordance with the required ethical standards, and informed consent was obtained from all participants.

Teşekkür

NA

Kaynakça

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