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ESTIMATION OF THE EFFICIENCY OF TECHNOLOGY DEVELOPMENT REGIONS BY ARTIFICIAL NEURAL NETWORKS AND LOGISTICS REGRESSION ANALYSIS ON THE BASIS OF DATA ENVELOPMENT ANALYSIS

Year 2019, Volume: 37 Issue: 2, 271 - 293, 28.06.2019
https://doi.org/10.17065/huniibf.414156

Abstract

Technology
development regions are the places where technological knowledge is produced
and commercialized by sharing the experiences of university and industry with
together. Technological development regions or technoparks those are at the
center of technology policies of the countries are a matter which is important
for our country as it is all over the world and continuous investments are made
to establish new technoparks. In this study, it is aimed to develop two
different models that predict the efficiency of the technology development
zones and to compare the predictive performances of these models using
Artificial Neural Networks-Data Envelopment Analysis and Logistic Regression
Analysis-Data Envelopment Analysis models. Based on the input variables, the
future performance of a new technology development zone is estimated. The results
of the analysis have showed that Artificial Neural Networks classify the
efficient and non-efficient technology development regions as 100% correctly
while the classification performance of the Logistic Regression Analysis is
89.7%.

References

  • Akgöbek, Ö., E. Yakut (2014), “Efficiency Measurement in Turkish Manufacturing Sector Using Data Envelopment Analysis (DEA) and Artificial Neural Networks (ANN)”, Journal of Economic & Financial Studies, 2(3), 35-45.
  • Albahari, A., G. Catalano, P Landoni (2013), “Evaluation of National Science Park Systems: A Theoretical Framework and Its Application to the Italian and Spanish Systems”, Technology Analysis & Strategic Management, 25(5), 599-614.
  • Almeida, A., C. Santos, M. Rui Silva (2009), “Science And Technologic Parks in Regional Innovation Systems: A Cluster Analysis”, 1. Cape Verde Congress of Regional Development, 6-11 Temmuz 2009, Cape Verde.
  • Andreevna, M.A. (2013), “The Balanced Scorecard for Estimation of Science and Technology Park”, World Applied Sciences Journal, 25(5), 720-727.
  • Aslani, A., H. Eftekhari, M. Didari (2015), “Comparative Analysis of the Science and Technology Parks of the US Universities and a Selected Developing Country”, Journal on Innovation and Sustainability, 6(2), 25-33.
  • Aslani, G., S.H. Momeni-Masuleh, A. Malek, F. Ghorbani (2009), “Bank Efficiency Evaluation Using A Neural Network-DEA Method”, Iranian Journal of Mathematical Sciences and Informatics, 4(2), 33-48.
  • Azadeh, A., M. Saberi, R.T. Moghaddam, L Javanmardi (2011), “An Integrated Data Envelopment Analysis-Artificial Neural Network-Rough Set Algorithm for Assessment of Personnel Efficiency”, Expert Systems with Applications, 38(3), 1364-1373.
  • Baykul, A., K.O. Oruç, M.A. Dulupçu (2016), “Teknoloji Geliştirme Bölgesi Yönetici Şirketlerinin Ar-Ge ve Yenilikçi Etkinliklerinin Veri Zarflama Analizi ile Değerlendirilmesi”, AİBÜ Sosyal Bilimler Enstitüsü Dergisi, 16(2), 51-72.
  • Bellini, N., J. Teräs, H. Ylinenpää (2012), “Science and Technology Parks in the Age of Open Innovation. The Finnish Case”, Emerging Issues in Management-Innovation Management in Global Markets, 1, 25-44.
  • Bilim, Sanayi ve Teknoloji Bakanlığı, Bilim ve Teknoloji Genel Müdürlüğü (2015), Teknoloji Geliştirme Bölgeleri Performans Endeksi-2015, btgm.sanayi.gov.tr.
  • Bolat, B., G.T. Temur, H. Gürler (2016), “Türkiye’deki Havalimanlarının Etkinlik Tahmini: Veri Zarflama Analizi ve Yapay Sınır Ağlarının Birlikte Kullanımı”, Ege Akademik Bakış, 16, 1-10.
  • Budak, H., S. Erpolat (2012), “Kredi Riski Tahmininde Yapay Sinir Ağları ve Lojistik Regresyon Analizi Karşılaştırılması”, Online Academic Journal of Information Technology, 3(9), 23-30.
  • Campos-Garcia R.M., M.A. Garcia-Vidales, M.Y. Garcia-Vidales, O. Gonzalez-Gomez, A. Altamirano-Corro (2012), “Logistics Efficiency in Small and Medium Enterprises: A Logistics, Data Envelopment Analysis Combined with Artificial Neural Network (DEA-ANN) Approach”, African Journal of Business Management, 6(49), 11819-11827.
  • Charnes, A., W.W. Cooper, E. Rhodes (1978), “Measuring The Efficiency of Decision Making Units”, European Journal of Operational Research, 2, 429-444.
  • Cheba, K., J Hołub-Iwan (2014), “How to Measure the Effectiveness of Technology Parks? The Case of Poland”, Ekonometria, 1(43), 27-34.
  • Cooper, W.W., L.M. Seiford, J. Zhu (2011), “Data Envelopment Analysis: History, Models, and Interpretations”, in W.W. Cooper, L.M. Seiford and J. Zhu (ed.), Handbook on Data Envelopment Analysis, USA: Springer Science+Business Media, 1-39.
  • Çelebi, D., D. Bayraktar (2008), “An Integrated Neural Network and Data Envelopment Analysis for Supplier Evaluation Under Incomplete Information”, Expert Systems with Applications, 35(4), 1698-1710.
  • Demirci, A., E. Yakut, M. Gündüz (2013), “Measurement of the Economical and Social Efficiency of OECD Countries by Means of Data Envelopment Analysis and Artificial Neural Network”, International Journal of Business and Social Science, 4(16), 67-80.
  • Demirci, E., S. Şahin (2015), “Uluslararası Ortaklık Yapısının Hisse Senedi Getirisi Üzerindeki Etkisi: Borsa İstanbul Uygulaması”, Ekonomik ve Sosyal Araştırmalar Dergisi, 11(1), 93-105.
  • Farahmand, M., M.I. Desa, M Nilashi (2014), “Hybrid Data Envelopment Analysis and Neural Networks for Suppliers Efficiency Prediction and Ranking”, 1st International Conference of Recent Trends in Information and Communication Technologies, 12-14 September 2014, Johor, Malaysia, 392-401.
  • Festel, G., M. Würmseher (2014), “Benchmarking of Industrial Park Infrastructures in Germany”, Benchmarking: An International Journal, 21(6), 854-883.
  • Gök, A.C., A. Özdemir (2011), “Lojistik Regresyon Analizi ile Banka Sektör Paylarının Tahminlenmesi”, İşletme Fakültesi Dergisi, 12(1), 43-51.
  • Hematia, M., M. Mardani (2012), “Designing A Performance Appraisal System Based on Balanced Scorecard for Improving Productivity: Case Study in Semnan Technology and Science Park”, Management Science Letters, 2, 1619-1630.
  • Hu, J.L., T.F. Han, F.Y. Yeh, C.L. Lu (2010), “Efficiency of Science and Technology Industrial Parks in China”, Journal of Management Research, 10(3), 151-166.
  • Hung, N.Q., M.S. Babel, S. Weesakul, N.K. Tripathi (2009), “An Artificial Neural Network Model for Rainfall Forecasting in Bangkok, Thailand”, Hydrology and Earth System Sciences, 13, 1413-1425.
  • Jablonsky, J. (2016), “Ranking Models in Data Envelopment Analysis”, Business Trends, 6(4), 36-42.
  • Ji, Y.B., C. Lee (2010), “Data Envelopment Analysis in Stata”, The Stata Journal, 10(2), 1-13.
  • Leite da Silva, A.S., Forte, S.H.A.C (2016), “Technology Parks Strategic Capacity Evaluation Structure: A Framework Proposal for Implementation in Latin America”, RAI Revista de Administração e Inovação, 13(1), 67-75.
  • Li, E.Y. (1994), “Artificial Neural Networks and Their Business Applications”, Information & Management, 27, 303-313.
  • Marti, L., R. Puertas, J.C. Martin (2017), A DEA-Logistic Performance Index, Journal of Applied Economics, 20(1), 169-192.
  • Nosratabadi, H.E., S. Pourdarab, M. Abbasian (2011), “Evaluation of Science and Technology Parks by Using Fuzzy Expert System”, The Journal of Mathematics and Computer Science, 2(4), 594-606. Öztemel, E. (2012), Yapay Sinir Ağları, İstanbul: Papatya Yayıncılık.
  • Raut, R.D., S.S. Kamble, M.G. Kharat, H. Joshi, C. Singhal, S.J. Kamble (2017), “A Hybrid Approach Using Data Envelopment Analysis And Artificial Neural Network For Optimising 3PL Supplier Selection”, International Journal of Logistics Systems and Management (IJLSM), 26(2), 203-223.
  • Ribeiro, J., A. Higuchi, M. Bronzo, R. Veiga, A. Faria (2016), “Framework for the Strategic Management of Science & Technology Parks”, Journal of Technology Management & Innovation, 11(4), 80-90.
  • Saberi, M., M.R. Rostami, M. Hamidian, N. Aghami (2016), “Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network”, Advances in Mathematical Finance & Applications, 1(2), 95-104.
  • Sharifi, M., J. Rezaeian (2016), “Efficiency Evaluation of Mazandaran Industrial Parks by Using Neuro-DEA Approach”, International Journal Industrial and Systems Engineering, 23(1), 111-123.
  • Shokrollahpour, E., F.H. Lotfi, M. Zandieh (2016), “An Integrated Data Envelopment Analysis-Artificial Neural Network Approach for Benchmarking of Bank Branches”, Journal of Industrial Engineering International, 12, 137-143.
  • Sorayaei, A., M. Majidi (2016), “Evaluating and Predicting Performance of Saderat Bank Using Models Data Envelopment Analysis, Neural Networks Genetic Algorithms Case Study: Saderat Bank Mazandaran Province”, Journal of Administrative Management, Education and Training, 12(4), 804-811.
  • Teknoloji Geliştirme Bölgeleri Kanunu (2001), Kanun Sayısı: 4691, Kabul Tarihi: 26/06/2001, www.resmigazete.gov.tr. Teknoloji Geliştirme Bölgeleri, https://teknopark.sanayi.gov.tr/, E.T.: 04.11.2017.
  • Tepe, S., A.H. Zaim (2016), “Türkiye ve Dünyada Teknopark Uygulamaları: Teknopark İstanbul Örneği”, İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 15(29), 19-43.
  • Tsai, C.L., H.C. Chang (2016), “Evaluation of Critical Factors for The Regional Innovation System within The Hsinchu Science-Based Park”, Kybernetes, 45(4), 699-716.
  • Tütek, H.H., Ş., Gümüşoğlu, A. Özdemir (2016), Sayısal Yöntemler: Yönetsel Yaklaşım, İzmir: Beta Basım A.Ş.
  • Ukhanova, I.О. (2015), “Some Questions of the Evaluation of Technopark”, Economics, 2(18), 35-40.
  • Ural, K., Ş. Gürarda, M.B. Önemli (2015), “Lojistik Regresyon Modeli ile Finansal Başarısızlık Tahminlemesi: Borsa İstanbul’da Faaliyet Gösteren Gıda, İçki ve Tütün Şirketlerinde Uygulama”, Muhasebe ve Finansman Dergisi, Temmuz/2015, 85-100.
  • Veleva, V., P. Lowitt, N. Angus, D. Neely (2016), “Benchmarking Eco-Industrial Park Development: The Case of Devens”, Benchmarking: An International Journal, 23(5), 1147-1170.
  • Yan, M.R., K.M. Chien (2013), “Evaluating the Economic Performance of High-Technology Industry and Energy Efficiency: A Case Study of Science Parks in Taiwan”, Energies, 6, 973-987.
  • Yang, J., X. Li (2016), “Performance Evaluation of Innovation Ecosystem of Sci-Tech Park Based on Two Stage DEA - a Case Study of National High Tech Zone”, Journal of Residuals Science & Technology, 13(6), 1-8.
  • Zeng, S., X. Xie, C. Tam (2010), “Evaluating Innovation Capabilities for Science Parks: A System Model”, Technological and Economic Development of Economy Baltic Journal on Sustainability, 16(3), 397-413.
  • Zenilda da Silva, M., A. Steimback, A. Dutra, G. Martignago, V. Dezem (2016), “Performance Evaluation of Technology Park Implementation Phase through Multicriteria Methodology for Constructivist Decision Aid (MCDA-C)”, Modern Economy, 7, 1687-1705.
  • Zhang, G., B.E. Patuwo, M.Y. Hu (1998), “Forecasting with Artificial Neural Networks: The State of The Art”, International Journal of Forecasting, 14, 35-62

VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ

Year 2019, Volume: 37 Issue: 2, 271 - 293, 28.06.2019
https://doi.org/10.17065/huniibf.414156

Abstract

Teknoloji geliştirme bölgeleri,
üniversite ve sanayinin deneyimlerini paylaşarak teknolojik bilgilerin
üretildiği ve ticarileştirildiği ortamlardır. Ülkelerin teknoloji
politikalarının odağında olan teknoloji geliştirme bölgeleri ya da
teknoparklar, tüm dünyada olduğu gibi ülkemizin de önem verdiği bir konudur ve
sürekli yatırımlar yapılarak yeni teknoparkların açılması sağlanmaktadır. Bu
çalışmada, Yapay Sinir Ağları ve Lojistik Regresyon Analizi, Veri Zarflama
Analizi ile bütünleşik olarak kullanılarak teknoloji geliştirme bölgelerinin
etkinliklerini tahminleyen iki farklı model geliştirilmesi ve bu modellerin
tahmin performanslarının karşılaştırılması amaçlanmıştır. Girdi değişkenlerine
bağlı olarak yeni kurulacak bir teknoloji geliştirme bölgesinin ileride
gerçekleştirecek performansı tahminlenmiştir. Analiz sonuçları, Yapay Sinir
Ağlarının etkin olan ve olmayan teknoloji geliştirme bölgelerini % 100 oranında
doğru olarak sınıflandırdığını, Lojistik Regresyon Analizinin ise sınıflandırma
performansının % 89.7 olduğunu ortaya koymuştur.

References

  • Akgöbek, Ö., E. Yakut (2014), “Efficiency Measurement in Turkish Manufacturing Sector Using Data Envelopment Analysis (DEA) and Artificial Neural Networks (ANN)”, Journal of Economic & Financial Studies, 2(3), 35-45.
  • Albahari, A., G. Catalano, P Landoni (2013), “Evaluation of National Science Park Systems: A Theoretical Framework and Its Application to the Italian and Spanish Systems”, Technology Analysis & Strategic Management, 25(5), 599-614.
  • Almeida, A., C. Santos, M. Rui Silva (2009), “Science And Technologic Parks in Regional Innovation Systems: A Cluster Analysis”, 1. Cape Verde Congress of Regional Development, 6-11 Temmuz 2009, Cape Verde.
  • Andreevna, M.A. (2013), “The Balanced Scorecard for Estimation of Science and Technology Park”, World Applied Sciences Journal, 25(5), 720-727.
  • Aslani, A., H. Eftekhari, M. Didari (2015), “Comparative Analysis of the Science and Technology Parks of the US Universities and a Selected Developing Country”, Journal on Innovation and Sustainability, 6(2), 25-33.
  • Aslani, G., S.H. Momeni-Masuleh, A. Malek, F. Ghorbani (2009), “Bank Efficiency Evaluation Using A Neural Network-DEA Method”, Iranian Journal of Mathematical Sciences and Informatics, 4(2), 33-48.
  • Azadeh, A., M. Saberi, R.T. Moghaddam, L Javanmardi (2011), “An Integrated Data Envelopment Analysis-Artificial Neural Network-Rough Set Algorithm for Assessment of Personnel Efficiency”, Expert Systems with Applications, 38(3), 1364-1373.
  • Baykul, A., K.O. Oruç, M.A. Dulupçu (2016), “Teknoloji Geliştirme Bölgesi Yönetici Şirketlerinin Ar-Ge ve Yenilikçi Etkinliklerinin Veri Zarflama Analizi ile Değerlendirilmesi”, AİBÜ Sosyal Bilimler Enstitüsü Dergisi, 16(2), 51-72.
  • Bellini, N., J. Teräs, H. Ylinenpää (2012), “Science and Technology Parks in the Age of Open Innovation. The Finnish Case”, Emerging Issues in Management-Innovation Management in Global Markets, 1, 25-44.
  • Bilim, Sanayi ve Teknoloji Bakanlığı, Bilim ve Teknoloji Genel Müdürlüğü (2015), Teknoloji Geliştirme Bölgeleri Performans Endeksi-2015, btgm.sanayi.gov.tr.
  • Bolat, B., G.T. Temur, H. Gürler (2016), “Türkiye’deki Havalimanlarının Etkinlik Tahmini: Veri Zarflama Analizi ve Yapay Sınır Ağlarının Birlikte Kullanımı”, Ege Akademik Bakış, 16, 1-10.
  • Budak, H., S. Erpolat (2012), “Kredi Riski Tahmininde Yapay Sinir Ağları ve Lojistik Regresyon Analizi Karşılaştırılması”, Online Academic Journal of Information Technology, 3(9), 23-30.
  • Campos-Garcia R.M., M.A. Garcia-Vidales, M.Y. Garcia-Vidales, O. Gonzalez-Gomez, A. Altamirano-Corro (2012), “Logistics Efficiency in Small and Medium Enterprises: A Logistics, Data Envelopment Analysis Combined with Artificial Neural Network (DEA-ANN) Approach”, African Journal of Business Management, 6(49), 11819-11827.
  • Charnes, A., W.W. Cooper, E. Rhodes (1978), “Measuring The Efficiency of Decision Making Units”, European Journal of Operational Research, 2, 429-444.
  • Cheba, K., J Hołub-Iwan (2014), “How to Measure the Effectiveness of Technology Parks? The Case of Poland”, Ekonometria, 1(43), 27-34.
  • Cooper, W.W., L.M. Seiford, J. Zhu (2011), “Data Envelopment Analysis: History, Models, and Interpretations”, in W.W. Cooper, L.M. Seiford and J. Zhu (ed.), Handbook on Data Envelopment Analysis, USA: Springer Science+Business Media, 1-39.
  • Çelebi, D., D. Bayraktar (2008), “An Integrated Neural Network and Data Envelopment Analysis for Supplier Evaluation Under Incomplete Information”, Expert Systems with Applications, 35(4), 1698-1710.
  • Demirci, A., E. Yakut, M. Gündüz (2013), “Measurement of the Economical and Social Efficiency of OECD Countries by Means of Data Envelopment Analysis and Artificial Neural Network”, International Journal of Business and Social Science, 4(16), 67-80.
  • Demirci, E., S. Şahin (2015), “Uluslararası Ortaklık Yapısının Hisse Senedi Getirisi Üzerindeki Etkisi: Borsa İstanbul Uygulaması”, Ekonomik ve Sosyal Araştırmalar Dergisi, 11(1), 93-105.
  • Farahmand, M., M.I. Desa, M Nilashi (2014), “Hybrid Data Envelopment Analysis and Neural Networks for Suppliers Efficiency Prediction and Ranking”, 1st International Conference of Recent Trends in Information and Communication Technologies, 12-14 September 2014, Johor, Malaysia, 392-401.
  • Festel, G., M. Würmseher (2014), “Benchmarking of Industrial Park Infrastructures in Germany”, Benchmarking: An International Journal, 21(6), 854-883.
  • Gök, A.C., A. Özdemir (2011), “Lojistik Regresyon Analizi ile Banka Sektör Paylarının Tahminlenmesi”, İşletme Fakültesi Dergisi, 12(1), 43-51.
  • Hematia, M., M. Mardani (2012), “Designing A Performance Appraisal System Based on Balanced Scorecard for Improving Productivity: Case Study in Semnan Technology and Science Park”, Management Science Letters, 2, 1619-1630.
  • Hu, J.L., T.F. Han, F.Y. Yeh, C.L. Lu (2010), “Efficiency of Science and Technology Industrial Parks in China”, Journal of Management Research, 10(3), 151-166.
  • Hung, N.Q., M.S. Babel, S. Weesakul, N.K. Tripathi (2009), “An Artificial Neural Network Model for Rainfall Forecasting in Bangkok, Thailand”, Hydrology and Earth System Sciences, 13, 1413-1425.
  • Jablonsky, J. (2016), “Ranking Models in Data Envelopment Analysis”, Business Trends, 6(4), 36-42.
  • Ji, Y.B., C. Lee (2010), “Data Envelopment Analysis in Stata”, The Stata Journal, 10(2), 1-13.
  • Leite da Silva, A.S., Forte, S.H.A.C (2016), “Technology Parks Strategic Capacity Evaluation Structure: A Framework Proposal for Implementation in Latin America”, RAI Revista de Administração e Inovação, 13(1), 67-75.
  • Li, E.Y. (1994), “Artificial Neural Networks and Their Business Applications”, Information & Management, 27, 303-313.
  • Marti, L., R. Puertas, J.C. Martin (2017), A DEA-Logistic Performance Index, Journal of Applied Economics, 20(1), 169-192.
  • Nosratabadi, H.E., S. Pourdarab, M. Abbasian (2011), “Evaluation of Science and Technology Parks by Using Fuzzy Expert System”, The Journal of Mathematics and Computer Science, 2(4), 594-606. Öztemel, E. (2012), Yapay Sinir Ağları, İstanbul: Papatya Yayıncılık.
  • Raut, R.D., S.S. Kamble, M.G. Kharat, H. Joshi, C. Singhal, S.J. Kamble (2017), “A Hybrid Approach Using Data Envelopment Analysis And Artificial Neural Network For Optimising 3PL Supplier Selection”, International Journal of Logistics Systems and Management (IJLSM), 26(2), 203-223.
  • Ribeiro, J., A. Higuchi, M. Bronzo, R. Veiga, A. Faria (2016), “Framework for the Strategic Management of Science & Technology Parks”, Journal of Technology Management & Innovation, 11(4), 80-90.
  • Saberi, M., M.R. Rostami, M. Hamidian, N. Aghami (2016), “Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network”, Advances in Mathematical Finance & Applications, 1(2), 95-104.
  • Sharifi, M., J. Rezaeian (2016), “Efficiency Evaluation of Mazandaran Industrial Parks by Using Neuro-DEA Approach”, International Journal Industrial and Systems Engineering, 23(1), 111-123.
  • Shokrollahpour, E., F.H. Lotfi, M. Zandieh (2016), “An Integrated Data Envelopment Analysis-Artificial Neural Network Approach for Benchmarking of Bank Branches”, Journal of Industrial Engineering International, 12, 137-143.
  • Sorayaei, A., M. Majidi (2016), “Evaluating and Predicting Performance of Saderat Bank Using Models Data Envelopment Analysis, Neural Networks Genetic Algorithms Case Study: Saderat Bank Mazandaran Province”, Journal of Administrative Management, Education and Training, 12(4), 804-811.
  • Teknoloji Geliştirme Bölgeleri Kanunu (2001), Kanun Sayısı: 4691, Kabul Tarihi: 26/06/2001, www.resmigazete.gov.tr. Teknoloji Geliştirme Bölgeleri, https://teknopark.sanayi.gov.tr/, E.T.: 04.11.2017.
  • Tepe, S., A.H. Zaim (2016), “Türkiye ve Dünyada Teknopark Uygulamaları: Teknopark İstanbul Örneği”, İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 15(29), 19-43.
  • Tsai, C.L., H.C. Chang (2016), “Evaluation of Critical Factors for The Regional Innovation System within The Hsinchu Science-Based Park”, Kybernetes, 45(4), 699-716.
  • Tütek, H.H., Ş., Gümüşoğlu, A. Özdemir (2016), Sayısal Yöntemler: Yönetsel Yaklaşım, İzmir: Beta Basım A.Ş.
  • Ukhanova, I.О. (2015), “Some Questions of the Evaluation of Technopark”, Economics, 2(18), 35-40.
  • Ural, K., Ş. Gürarda, M.B. Önemli (2015), “Lojistik Regresyon Modeli ile Finansal Başarısızlık Tahminlemesi: Borsa İstanbul’da Faaliyet Gösteren Gıda, İçki ve Tütün Şirketlerinde Uygulama”, Muhasebe ve Finansman Dergisi, Temmuz/2015, 85-100.
  • Veleva, V., P. Lowitt, N. Angus, D. Neely (2016), “Benchmarking Eco-Industrial Park Development: The Case of Devens”, Benchmarking: An International Journal, 23(5), 1147-1170.
  • Yan, M.R., K.M. Chien (2013), “Evaluating the Economic Performance of High-Technology Industry and Energy Efficiency: A Case Study of Science Parks in Taiwan”, Energies, 6, 973-987.
  • Yang, J., X. Li (2016), “Performance Evaluation of Innovation Ecosystem of Sci-Tech Park Based on Two Stage DEA - a Case Study of National High Tech Zone”, Journal of Residuals Science & Technology, 13(6), 1-8.
  • Zeng, S., X. Xie, C. Tam (2010), “Evaluating Innovation Capabilities for Science Parks: A System Model”, Technological and Economic Development of Economy Baltic Journal on Sustainability, 16(3), 397-413.
  • Zenilda da Silva, M., A. Steimback, A. Dutra, G. Martignago, V. Dezem (2016), “Performance Evaluation of Technology Park Implementation Phase through Multicriteria Methodology for Constructivist Decision Aid (MCDA-C)”, Modern Economy, 7, 1687-1705.
  • Zhang, G., B.E. Patuwo, M.Y. Hu (1998), “Forecasting with Artificial Neural Networks: The State of The Art”, International Journal of Forecasting, 14, 35-62
There are 49 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Enver Çakın

Aslı Özdemir

Publication Date June 28, 2019
Submission Date April 10, 2018
Published in Issue Year 2019 Volume: 37 Issue: 2

Cite

APA Çakın, E., & Özdemir, A. (2019). VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ. Hacettepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 37(2), 271-293. https://doi.org/10.17065/huniibf.414156
AMA Çakın E, Özdemir A. VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. June 2019;37(2):271-293. doi:10.17065/huniibf.414156
Chicago Çakın, Enver, and Aslı Özdemir. “VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ”. Hacettepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi 37, no. 2 (June 2019): 271-93. https://doi.org/10.17065/huniibf.414156.
EndNote Çakın E, Özdemir A (June 1, 2019) VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 37 2 271–293.
IEEE E. Çakın and A. Özdemir, “VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ”, Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 37, no. 2, pp. 271–293, 2019, doi: 10.17065/huniibf.414156.
ISNAD Çakın, Enver - Özdemir, Aslı. “VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ”. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 37/2 (June 2019), 271-293. https://doi.org/10.17065/huniibf.414156.
JAMA Çakın E, Özdemir A. VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2019;37:271–293.
MLA Çakın, Enver and Aslı Özdemir. “VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ”. Hacettepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, vol. 37, no. 2, 2019, pp. 271-93, doi:10.17065/huniibf.414156.
Vancouver Çakın E, Özdemir A. VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2019;37(2):271-93.

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