Regional Disparities in Entrepreneurship in Turkey with Respect to Gender Using a Regression of Pooling Cross Sections: 2006–2015

Karadeniz, Esra; Özçam, Ahmet

doi:10.1007/978-3-319-75913-5_15

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…This research is novel in the sense that machine learning models are applied on the GEM data for the first time in Turkey. The findings support the results of the research conducted by Özdemir and Karadeniz (2011) and Karadeniz and Özçam (2018). The contribution of this research to the literature is that it has yields results similar to those of the aforementioned studies and extends them using machine learning.…”

Section: Discussionsupporting

confidence: 87%

“…The aim of this study is to analyze the factors affecting entrepreneurship activities in Turkey in terms of SD by applying machine learning, which is a relatively new method in social sciences, and using Global Entrepreneurship Monitor (GEM) data. This research analyzes and extends the previous results of Özdemir and Karadeniz (2011) and Karadeniz and Özçam (2018) from individual entrepreneur gains in the Turkish context for the first time through the application of machine learning algorithms.…”

Section: Aim Of the Studymentioning

confidence: 59%

“…According to the results of the Stochastic Gradient Boosting, it is observed that regional development and conditions in Turkey affect the probability of individuals to become entrepreneurs. This research analyzes and extends the previous results of Özdemir and Karadeniz (2011) and Karadeniz and Özçam (2018) from individual entrepreneur gains in the Turkish context for the first time through the application of machine learning algorithms. Entrepreneurial activities should be planned within the framework of policies that support sustainable development.…”

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confidence: 59%

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Türkiye’de Girişimcilik ve Sürdürülebilir Bölgesel Kalkınma: Makine Öğrenmesi Yaklaşımlarından Elde Edilen Bulgular

Aydoğan¹,

Karadeniz²,

Celbiş³

2021

Ekonomi, Politika &Amp; Finans Araştırmaları Dergisi

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Birleşmiş Milletler Kalkınma Programı (UNDP), Sürdürülebilir Kalkınma'nın (SK) sağlanmasında girişimciliği önemli bir faktör olarak tanımlamaktadır. Bu çalışmanın amacı, sosyal bilimlerde nispeten yeni bir yöntem olan makine öğrenmesini uygulayarak ve Küresel Girişimcilik Monitörü (GEM) verilerini kullanarak Türkiye'deki girişimcilik faaliyetlerini SK açısından etkileyen faktörleri analiz etmektir. Rassal Orman model bulgularına göre çalışma durumu, başka bir girişimci tanımak, kişinin kendi bilgisi ve becerisi hakkındaki görüşü, ihtiyaç/mecburiyet ve yaş, girişimci olmak için önemli faktörlerdir. Ayrıca, hanehalkı geliri ve nüfusu, kişinin çalışma durumu, cinsiyeti, eğitim düzeyi ve anket zamanı itibariyle şirket içi girişimcilik aktivitesinde bulunması da önemli faktörler olarak tespit edilmişlerdir. Stokastik Gradyan Artırma sonuçlarına göre Türkiye'de bölgesel kalkınmanın ve koşulların bireylerin girişimci olma olasılıklarını etkilediği gözlenmiştir. Bu araştırma, Özdemir ve Karadeniz (2011) ile Karadeniz ve Özçam'ın (2018) Türkiye bağlamında bireysel girişimci kazanımlarından buldukları önceki sonuçları ilk kez makine öğrenmesi algoritmaları uygulaması aracılığıyla analiz etmekte ve genişletmektedir. Girişimcilik faaliyetleri sürdürülebilir kalkınmayı destekleyen politikalar çerçevesinde planlanmalıdır.

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Section: Discussionsupporting

confidence: 87%

Section: Aim Of the Studymentioning

confidence: 59%

mentioning

confidence: 59%

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Türkiye’de Girişimcilik ve Sürdürülebilir Bölgesel Kalkınma: Makine Öğrenmesi Yaklaşımlarından Elde Edilen Bulgular

Aydoğan¹,

Karadeniz²,

Celbiş³

2021

Ekonomi, Politika &Amp; Finans Araştırmaları Dergisi

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

KAYGISIZ,

SAHİN,

KARA

2024

Ömer Halisdemir Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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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.

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Regional Disparities in Entrepreneurship in Turkey with Respect to Gender Using a Regression of Pooling Cross Sections: 2006–2015

Cited by 2 publications

References 13 publications

Türkiye’de Girişimcilik ve Sürdürülebilir Bölgesel Kalkınma: Makine Öğrenmesi Yaklaşımlarından Elde Edilen Bulgular

Türkiye’de Girişimcilik ve Sürdürülebilir Bölgesel Kalkınma: Makine Öğrenmesi Yaklaşımlarından Elde Edilen Bulgular

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

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