Assessing The Level of Manufacturing Value Added of G-20 and Its Relation to Innovation Inputs and Outputs

Murat Unanoglu; Çiğdem Özarı

doi:10.35674/kent.1417436

TR EN

G-20 Ülkelerinin İmalat Sanayi Katma Değerinin Değerlendirilmesi ve Yenilik Girdi ve Çıktı Göstergeleriyle İlişkisi

Öz

Bu çalışmanın temel amacı, Küresel İnovasyon Endeksi'nden elde edilen temel boyutları kullanarak G-20 ülkelerinin imalat sanayi katma değer düzeylerini tahmin etmektir ve aynı zamanda inovasyon göstergelerinin imalat sanayi katma değerindeki farklılıklara ne ölçüde katkı sağladığını belirlemektir. Karmaşık veri kümeleriyle başa çıkma yeteneği ve hassasiyetiyle bilinen makine öğrenme yöntemlerinden rassal orman algoritması, 2013-2022 döneminde G-20 ülkelerinin katma değer düzeylerini tahmin etmek için kullanılmıştır. Ortalama ve standart sapma kullanılarak elde edilen G-20 ülkelerinin MVA seviyeleri, inovasyon girdi ve çıkış göstergelerinin yardımıyla %54,14 hata oranıyla tahmin edilmiştir. En iyi tahmin edilen seviye ise ortalamaya yakın olan gruptur. Bu çalışmanın benzersizliği, rassal orman algoritmasının kullanılarak Küresel İnovasyon Endeksi'nin temel boyutlarını oluşturan inovasyon girdi ve çıktı göstergeleriyle ülkelerin katma değer düzeylerini tahmin etmektir.

Anahtar Kelimeler

Küresel İnovasyon Endeksi , G-20 Ülkeleri , Rassal Orman , Sanayi Katma Değeri , İnovasyon

Assessing The Level of Manufacturing Value Added of G-20 and Its Relation to Innovation Inputs and Outputs

Abstract

The primary objective of this study is to forecast the manufacturing value added levels of G-20 countries by leveraging the fundamental dimensions extracted from the Global Innovation Index and ascertain the extent to which innovation indicators contribute to variations in manufacturing value added. The Random Forest algorithm, known for its versatility and precision in dealing with complex datasets, has been employed as a prominent machine learning technique to predict the manufacturing value added levels of G-20 countries during the period 2013-2022. The MVA levels of G-20 countries, obtained using average and standard deviation, were predicted with a 54.14% error rate through the assistance of innovation input and output indicators. The level predicted with the highest accuracy is the one closely aligned with the average. This study's uniqueness lies in its utilization of the Random Forest algorithm to predict value added levels based on innovation inputs and outputs, which constitute the fundamental dimensions of the Global Innovation Index.

Keywords

Global Innovation Index , G-20 Countries , Random Forest , Innovation , Manufacturing Value Added

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Kalkınma Coğrafyası

Bölüm

Araştırma Makalesi

Yazarlar

Murat Unanoglu
0000-0002-5186-885X
Türkiye

Çiğdem Özarı ^*
0000-0002-2948-8957
Türkiye

Yayımlanma Tarihi

17 Mart 2024

Gönderilme Tarihi

10 Ocak 2024

Kabul Tarihi

16 Mart 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 17 Sayı: 2

DOI

https://doi.org/10.35674/kent.1417436

IZ

https://izlik.org/JA76JK24HK

APA

Unanoglu, M., & Özarı, Ç. (2024). Assessing The Level of Manufacturing Value Added of G-20 and Its Relation to Innovation Inputs and Outputs. Kent Akademisi, 17(2), 592-605. https://doi.org/10.35674/kent.1417436

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G-20 Ülkelerinin İmalat Sanayi Katma Değerinin Değerlendirilmesi ve Yenilik Girdi ve Çıktı Göstergeleriyle İlişkisi

Öz

Anahtar Kelimeler

Assessing The Level of Manufacturing Value Added of G-20 and Its Relation to Innovation Inputs and Outputs

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

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