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Journals / Verimlilik Dergisi / 2026 / Cilt: 60 Sayı: 1

Machine Learning-Based Feature Selection Analysis of Academic Spin-Off Survival in Technoparks Located in Türkiye

Türkiye’deki Teknoparklarda Yer Alan Akademik Spin-Off’ların Hayatta Kalma Durumunun Makine Öğrenmesi Tabanlı Özellik Seçimi Analizi

Başak Apaydın Avşar(BİLİM, SANAYİ VE TEKNOLOJİ BAKANLIĞI)0000-0001-8000-8472
Mehmet Yılmaz(ANKARA ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ)0000-0002-9762-6688
Pages
277–294
Publication Date
2026-01-28
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings offer evidence-based insights for policymakers seeking to strengthen the sustainability of academic entrepreneurship.

📌 3 Key Takeaways

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Objective & Hypothesis:Purpose: This study aims to identify the key determinants influencing the survival of academic spin-off (ASO) firms operating in Technology Development Zones (TDZs) in Türkiye.
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Key Findings & Data:Methodology: An original dataset covering all ASOs active between 2021 and 2024 was analysed using Mutual Information, Random Forest importance, Recursive Feature Elimination (RFE), and a Genetic Algorithm (GA).
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Conclusion & Impact:The findings offer evidence-based insights for policymakers seeking to strengthen the sustainability of academic entrepreneurship.
🔬Design:Araştırma Makalesi
📍Scope:2021 Verileri
⚙️Variables:Akademik Spin-Off · Firma Kalıcılığı · Makine Öğrenmesi

Abstract

Purpose: This study aims to identify the key determinants influencing the survival of academic spin-off (ASO) firms operating in Technology Development Zones (TDZs) in Türkiye. It contributes to the limited empirical evidence on the long-term sustainability of university-originated ventures in emerging innovation ecosystems. Methodology: An original dataset covering all ASOs active between 2021 and 2024 was analysed using Mutual Information, Random Forest importance, Recursive Feature Elimination (RFE), and a Genetic Algorithm (GA). Class imbalance was addressed through SMOTE applied only to the training set, and predictor contributions were interpreted using SHAP. Findings: RFE achieved the highest predictive performance (Accuracy = 0.9837; ROC-AUC = 0.9958). The number of ongoing projects emerged as the strongest predictor of ASO survival, reflecting the regulatory requirement for maintaining at least one active project. Additionally, R&D expenditures, public R&D support, and incubation participation enhance firms’ financial resilience and increase the likelihood of continued operation. Originality: This study is the first data-driven research to examine ASO survival in Türkiye using multiple feature selection techniques combined with explainable artificial intelligence. The findings offer evidence-based insights for policymakers seeking to strengthen the sustainability of academic entrepreneurship.

Özet

Amaç: Bu çalışma, Türkiye’deki Teknoloji Geliştirme Bölgeleri’nde (TGB) faaliyet gösteren akademik spin-off (ASO) firmalarının hayatta kalmasını etkileyen temel belirleyicileri ortaya koymayı amaçlamaktadır. Araştırma, üniversite kökenli girişimlerin sürdürülebilirliğine ilişkin sınırlı ampirik literatüre katkı sunmaktadır. Yöntem: 2021–2024 döneminde TGB’lerde aktif olan tüm ASO’ları kapsayan veri seti; Karşılıklı Bilgi (Mutual Information), Rastgele Orman önem düzeyi, Özyinelemeli Özellik Eleme (RFE) ve Genetik Algoritma (GA) yöntemleri kullanılarak analiz edilmiştir. Sınıf dengesizliği yalnızca eğitim verisine uygulanan SMOTE yöntemiyle giderilmiş; değişkenlerin etkileri SHAP ile yorumlanmıştır. Bulgular: En yüksek tahmin performansı RFE yöntemiyle elde edilmiştir (Doğruluk = 0,9837; ROC-AUC = 0,9958). Devam eden proje sayısı, mevzuat gereği proje sürekliliğinin zorunlu olması nedeniyle ASO hayatta kalmasının en güçlü belirleyicisi olarak öne çıkmaktadır. Ar-Ge harcamaları, kamu Ar-Ge destekleri ve kuluçka programlarına katılım ise firmaların finansal dayanıklılığını artırarak hayatta kalma olasılığını yükseltmektedir. Özgünlük: Bu çalışma, Türkiye’de akademik spin-off hayatta kalmasını çoklu özellik seçimi yöntemleri ve açıklanabilir yapay zekâ teknikleriyle bütüncül biçimde inceleyen ilk veri odaklı araştırmadır. Sonuçlar, akademik girişimciliğin sürdürülebilirliğini artırmaya yönelik politika yapıcılar için önemli kanıta dayalı çıkarımlar sunmaktadır.

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Cite This Article

Avşar, B. A., Yılmaz, M. (2026). Machine Learning-Based Feature Selection Analysis of Academic Spin-Off Survival in Technoparks Located in Türkiye. Verimlilik Dergisi, Cilt: 60 Sayı: 1, 277–294. https://doi.org/10.51551/verimlilik.1840976

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