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

Murat Unanoglu; Çiğdem Özarı

Makale detayları

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

Yazarlar:

Murat Unanoglu | Çiğdem Özarı

Yayıncı:

Ahmet FİDAN

Ana Başlıklar:

Kalkınma Coğrafyası

Dergi:

Kent Akademisi Vol.17 Issue 2, pp.592-605

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313306

pISSN:

2146-9229

Anahtar kelimeler:

Özet:

 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 studys 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.

 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 studys 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.

 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 studys 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.

 Bu çalışmanın temel amacı, Küresel İnovasyon Endeksinden 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 Endeksinin temel boyutlarını oluşturan inovasyon girdi ve çıktı göstergeleriyle ülkelerin katma değer düzeylerini tahmin etmektir.

Makale detayları

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

Yazarlar:

Murat Unanoglu | Çiğdem Özarı

Yayıncı:

Ahmet FİDAN

Ana Başlıklar:

Kalkınma Coğrafyası

Dergi:

Kent Akademisi Vol.17 Issue 2, pp.592-605

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313306

pISSN:

2146-9229

Global Innovation Index

G-20 Countries

Random Forest

Innovation

Manufacturing Value AddedKüresel İnovasyon Endeksi

G-20 Ülkeleri

Rassal Orman

Sanayi Katma Değeri

İnovasyon

Sayı

Makale detayları

Yazarlar: Murat Unanoglu | Çiğdem Özarı

Yayıncı: Ahmet FİDAN

Ana Başlıklar: Kalkınma Coğrafyası

Dergi: Kent Akademisi Vol.17 Issue 2, pp.592-605

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001313306

pISSN: 2146-9229

Sayı

Yazarlar:

Murat Unanoglu | Çiğdem Özarı

Yayıncı:

Ahmet FİDAN

Ana Başlıklar:

Kalkınma Coğrafyası

Dergi:

Kent Akademisi Vol.17 Issue 2, pp.592-605

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313306

pISSN:

2146-9229