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

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.

Keywords

• Academic Spin-Offs
• Firm Retention
• Machine Learning
• SHAP
• SMOTE

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

Öz

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.

Anahtar Kelimeler

• Akademik Spin-Off
• Firma Kalıcılığı
• Makine Öğrenmesi
• SHAP
• SMOTE

Kaynakça

1. Batista, G.E.A.P.A., Prati, R.C. and Monard, M.C. (2004). "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data", ACM SIGKDD Explorations Newsletter, 6(1), 20-29. https://doi.org/10.1145/1007730.1007735
2. Bercovitz, J. and Feldman, M. (2008). "Academic Entrepreneurs: Organizational Change at the Individual Level", Organization Science, 19(1), 69-89. https://doi.org/10.1287/orsc.1070.0295
3. Biau, G. and Scornet, E. (2016). "A Random Forest Guided Tour", Test, 25(2), 197-227. https://doi.org/10.1007/s11749-016-0481-7
4. Branco, P., Torgo, L. and Ribeiro, R.P. (2016). "A Survey of Predictive Modelling Under Imbalanced Distributions", ACM Computing Surveys, 49(2), 1-50. https://doi.org/10.1145/2907070
5. Breiman, L. (2001). "Random Forests", Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
6. Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P. (2002). "SMOTE: Synthetic Minority Over-Sampling Technique", Journal of Artificial Intelligence Research, 16, 321-357. https://doi.org/10.1613/jair.953
7. Chuang, L.Y., Tsai, S.-W. and Yang, C.-H. (2011). "Chaotic Binary Genetic Algorithm for Feature Selection", Expert Systems with Applications, 38(10), 13367-13377. https://doi.org/10.1016/j.eswa.2011.04.147
8. Civera, A., Meoli, M. and Vismara, S. (2019). "Do Academic Spinoffs Internationalize?", Journal of Technology Transfer, 44(2), 381-403. https://doi.org/10.1007/s10961-018-9683-3

9. Civera, A., Meoli, M. and Vismara, S. (2020). "Engagement of Academics in University Technology Transfer: Opportunity and Necessity Academic Entrepreneurship", European Economic Review, 123, 103376. https://doi.org/10.1016/j.euroecorev.2020.103376
10. Cover, T.M. and Thomas, J.A. (2006). “Elements of Information Theory”, (2nd ed.), Wiley.
11. Criaco, G., Minola, T., Migliorini, P. and Serarols-Tarrés, C. (2014). "To Have and Have Not: Founders’ Human Capital and University Start-Up Survival", Journal of Technology Transfer, 39(4), 567-593. https://doi.org/10.1007/s10961-013-9312-0
12. Czarnitzki, D., Rammer, C. and Toole, A.A. (2014). "University Spin-Offs and the Performance Premium", Small Business Economics, 43(2), 309-326. https://doi.org/10.1007/s11187-013-9538-0
13. Darst, R.P., Malecki, K.M.C. and Engelman, C.D. (2018). "Using Recursive Feature Elimination in Random Forest to Account for Correlated Variables in High-Dimensional Data", BMC Research Notes, 11(1), 364. https://doi.org/10.1186/s13104-018-3593-1
14. Djokovic, D. and Souitaris, V. (2008). "Spinouts from Academic Institutions: A Literature Review with Suggestions for Further Research", Journal of Technology Transfer, 33(3), 225-247. https://doi.org/10.1007/s10961-006-9000-4
15. Etzkowitz, H. (2003). "Research Groups as Quasi-Firms: The Invention of the Entrepreneurial University", Research Policy, 32(1), 109-121. https://doi.org/10.1016/S0048-7333(02)00009-4
16. Fackler, D., Schnabel, C. and Schmucker, A. (2016). "Spinoffs in Germany: Characteristics, Survival, and the Role of Their Parents", Small Business Economics, 46(1), 93-114. https://doi.org/10.1007/s11187-015-9673-x
17. Fernández, A., García, S., Galar, M., Prati, R.C., Krawczyk, B. and Herrera, F. (2018). “Learning from Imbalanced Data Sets”, Springer.
18. Géron, A. (2022). “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow”, O’Reilly.
19. Guyon, I. and Elisseeff, A. (2003). "An Introduction to Variable and Feature Selection", Journal of Machine Learning Research, 3, 1157-1182.
20. Guyon, I., Weston, J., Barnhill, S. and Vapnik, V. (2002). "Gene Selection for Cancer Classification Using Support Vector Machines", Machine Learning, 46(1-3), 389-422. https://doi.org/10.1023/A:1012487302797
21. Han, J., Kamber, M. and Pei, J. (2012). “Data Mining: Concepts and Techniques”, (3rd ed.), Morgan Kaufmann. Hastie, T., Tibshirani, R. and Friedman, J. (2009). “The Elements of Statistical Learning”, Springer.
22. He, H. and Garcia, E.A. (2009). "Learning from Imbalanced Data", IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284. https://doi.org/10.1109/TKDE.2008.239
23. Holland, J.H. (1975). “Adaptation in Natural and Artificial Systems”, University of Michigan Press.
24. Hossinger, S.M., Chen, X. and Werner, A. (2020). "Drivers, Barriers and Success Factors of Academic Spin-Offs: A Systematic Literature Review", Management Review Quarterly, 70(1), 97-134. https://doi.org/10.1007/s11301-019-00161-w
25. Jain, S., George, G. and Maltarich, M. (2009). "Academics or Entrepreneurs? Investigating Role Identity Modification of University Scientists Involved in Commercialization Activity", Research Policy, 38(6), 922-935. https://doi.org/10.1016/j.respol.2009.02.007
26. Kuhn, M. and Johnson, K. (2019). “Applied Predictive Modeling”, Springer.
27. Muscio, A., Quaglione, D. and Ramaciotti, L. (2016). "The Effects of University Rules on Spinoff Creation: The Case of Academia in Italy", Research Policy, 45(7), 1386-1396. https://doi.org/10.1016/j.respol.2016.04.011
28. Official Gazette. (2001). “Law No. 4691 on Technology Development Zones, Number: 24454”, https://www.resmigazete.gov.tr/eskiler/2001/07/20010706.htm, (Accessed: 11.09.2025).
29. Official Gazette. (2008). “Law No. 5746 on Supporting R&D and Design Activities, Number: 26814”, https://www.resmigazete.gov.tr/eskiler/2008/03/20080312.htm, (Accessed: 11.09.2025).
30. O’Shea, R.P., Allen, T.J., Chevalier, A. and Roche, F. (2005). "Entrepreneurial Orientation, Technology Transfer and Spinoff Performance of U.S. Universities", Research Policy, 34(7), 994-1009. https://doi.org/10.1016/j.respol.2005.05.011
31. Powers, J.B. and McDougall, P.P. (2005). "University Start-Up Formation and Technology Licensing with Firms That Go Public", Journal of Business Venturing, 20(3), 291-311. https://doi.org/10.1016/j.jbusvent.2003.12.008
32. Prokop, D. (2023). "The Academic Spinoff Theory of the Firm", International Journal of Entrepreneurship and Innovation, 24(4), 233-243. https://doi.org/10.1177/146575032110660 Republic of Türkiye Ministry of Industry and Technology. (2025). "Technology Development Zones Statistics”, https://sanayi.gov.tr, (Accessed: 06.09.2025).
33. Rodríguez-Gulías, M.J., Fernández-López, S. and Rodeiro-Pazos, D. (2016). "Growth Determinants in Entrepreneurship: A Longitudinal Study of Spanish Technology-Based University Spin-Offs", Journal of International Entrepreneurship, 14(3), 323-344. https://doi.org/10.1007/s10843-016-0185-9
34. Rodríguez-Gulías, M.J., Rodeiro-Pazos, D. and Fernández-López, S. (2017). "The Effect of University and Regional Knowledge Spillovers on Firms’ Performance: An Analysis of the Spanish USOs", International Entrepreneurship and Management Journal, 13(1), 191-209. https://doi.org/10.1007/s11365-016-0399-2
35. Rodeiro-Pazos, D. (2021). "Size and Survival: An Analysis of University Spin-Offs", Technological Forecasting and Social Change, 171, 120953. https://doi.org/10.1016/j.techfore.2021.120953
36. Siedlecki, W. and Sklansky, J. (1989). "A Note on Genetic Algorithms for Large-Scale Feature Selection", Pattern Recognition Letters, 10(5), 335-347.
37. Soetanto, D. and Jack, S. (2016). "The Impact of University-Based Incubation Support on the Innovation Strategy of Academic Spin-Offs", Technovation, 25-40. https://doi.org/10.1016/j.technovation.2015.11.001
38. Soetanto, D. and van Geenhuizen, M. (2019). "Life After Incubation: The Impact of Entrepreneurial Universities on the Long-Term Performance of Their Spin-Offs", Technological Forecasting and Social Change, 141, 263-276. https://doi.org/10.1016/j.techfore.2018.10.021
39. TÜBİTAK. (2025). "1513 Technology Transfer Offices (TTO) Support Programme", https://tubitak.gov.tr/en/funds/sanayi/ulusal-destek-programlari/1513-technology-transfer-office-support-program, (Accessed: 10.09.2025).
40. Vergara, J.R. and Estévez, P.A. (2014). "A Review of Feature Selection Methods Based on Mutual Information", Neural Computing and Applications, 24(1), 175-186. https://doi.org/10.1007/s00521-013-1368-0
41. Visintin, F. and Pittino, D. (2014). "Founding Team Composition and Early Performance of University-Based Spin-Off Companies", Technovation, 34(1), 31-43. https://doi.org/10.1016/j.technovation.2013.09.004
42. Wang, P., Li, Y. and Reddy, C.K. (2019). "Machine Learning for Survival Analysis: A Survey", ACM Computing Surveys, 51(6), 1-36. https://doi.org/10.1145/3214306
43. Wennberg, K., Wiklund, J. and Wright, M. (2011). "The Effectiveness of University Knowledge Spillovers: Performance Differences Between University Spinoffs and Corporate Spinoffs", Research Policy, 40(8), 1128-1143. https://doi.org/10.1016/j.respol.2011.05.014
44. Xue, B., Zhang, M., Browne, W.N. and Yao, X. (2016). "A Survey on Evolutionary Computation for Feature Selection", IEEE Transactions on Cybernetics, 46(3), 239-261. https://doi.org/10.1109/TCYB.2015.2501371
45. Zhang, J. (2009). "The Performance of University Spin-Offs: An Exploratory Analysis Using Venture Capital Data", Journal of Technology Transfer, 34(3), 255-285. https://doi.org/10.1007/s10961-008-9088-9