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

Enver Çakın , Aslı Özdemir

https://doi.org/10.17065/huniibf.414156

Cited By: 4

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%.

Keywords

Data Envelopment Analysis, artificial neural networks, logistic regression analysis, technology development regions

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

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.

Keywords

Veri Zarflama Analizi, Yapay Sinir Ağları, Lojistik Regresyon Analizi, Teknoloji Geliştirme Bölgeleri

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