Ülkelerin girişimcilik etkinliklerinin belirlenmesi ve sınıflandırılması: Veri zarflama analizi ve hiyerarşik kümeleme analizi

Year 2024, Volume: 17 Issue: 1, 85 - 112, 31.01.2024

Esra Kaygısız , Begum Sahin , Karahan Kara

https://doi.org/10.25287/ohuiibf.1316415

Abstract

Girişimcilik teşebbüslerinin ülke ekonomileri üzerindeki yadsınamaz etkileri bulunmaktadır. Devletler ve hükümetler girişimciliğin ülke ekonomisine katkılarını artırmak adına çeşitli stratejiler ve politikalar üretmektedirler. Çeşitli kuruluşlar tarafından da ülkelerin girişimcilik düzeyleri tespit edilmektedir. “Global Entrepreneurship Monitör (GEM)” tarafından düzenli olarak ülke girişimcilik skorları ve sıralamaları yayınlanmaktadır. Ülkeler ise bu raporlara göre mevcut girişimcilik düzeylerini tespit etmektedirler. Bu araştırmada GEM 2021 raporlarından elde edilen verilerle ülkelerin girişimcilik aktivite etkinlik düzeylerinin tespit edilmesi ve etkinlik düzeylerine göre kümelenmesi amaçlanmıştır. Bu kapsamda 2021 GEM raporunda kırk iki ülkeye ait sunulan beş indikatör kullanılmıştır. Araştırma iki aşamada gerçekleştirilmiştir. Birinci aşamada dört adet çıktı odaklı veri zarflama (DEA) modeli oluşturularak ülkelerin girişimcilik aktiviteleri etkinlik düzeyleri tespit edilmiştir. İkinci aşamada ülkelerin etkinlik düzeylerine göre kümelemesi hiyerarşik kümeleme analiziyle gerçekleştirilmiştir. Araştırma bulgularına göre DEA-1 modelinde 21 ülke tam etkinlik düzeyinde, DEA-2 modelinde 22 ülke tam etkinlik düzeyinde, DEA-3 ve DEA-4 modellerinde 18 ülke tam etkinlik düzeyinde olarak tespit edilmiştir. Hiyerarşik kümeleme analizi bulgularına göre ülkeler üç grupta kümelenmiştir. Cluster-1’de yirmi iki ülke, Cluster-2’de yedi ülke, Cluster-3’te on üç ülke yer almıştır. Cluster-1, Cluster-2 ve Cluster-3 sırasıyla etkinlik düzeyi yüksek, orta ve düşük olarak nitelendirilmiştir. Araştırma sonucunda ülkelere girişimcilik etkinliklerini geliştirmelerine yönelik öneriler sunulmuştur.

Keywords

Ulusal Girişimcilik, Ülke Girişimcilik Etkinliği, Veri Zarflama Analizi, Hiyerarşik Kümeleme Analizi

Determination and classification of entrepreneurial efficiency of countries: Data envelopment analysis and hierarchical clustering analysis

Abstract

Entrepreneurship initiatives have undeniable effects on national economies. States and governments produce various strategies and policies to increase the contribution of entrepreneurship to the country's economy. Entrepreneurship levels of countries are determined by various organizations. Country entrepreneurship scores and rankings are regularly published by the “Global entrepreneurship monitor (GEM)”. Countries, on the other hand, understand their current level of entrepreneurship according to these reports. In this research, it is aimed to determine the entrepreneurial activity efficiency levels (EAE) of the countries with the data obtained from the GEM 2021 reports and to cluster them according to their activity levels. In this context, forty-two country data of five indicators presented in the 2021 GEM report were used. The research was carried out in two stages. In the first stage, four output-oriented data envelopment (DEA) models were created and the EAE were determined. In the second stage, the clustering of countries according to their EAEs was carried out by hierarchical clustering analysis. According to the research findings, 21 countries were at full efficiency in the DEA-1 model, 22 countries were at full efficiency in the DEA-2 model, and 18 countries were at full efficiency in the DEA-3 and DEA-4 models. In the hierarchical clustering analysis, the countries are clustered in three groups. Twenty-two countries were included in Cluster-1, seven countries in Cluster-2, and thirteen countries in Cluster-3. Cluster-1, Cluster-2 and Cluster-3 were characterized as high, middle, and low efficiency levels, respectively. As a result of the research, suggestions were made to countries to improve their entrepreneurial activities.

Keywords

National Entrepreneurship, Country Entrepreneurship Efficiency, Data Envelopment Analysis, Hierarchical Cluster Analysis

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