A COMPARATIVE STUDY OF USING THE METHODS OF MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS IN ORGANIZATIONAL CORRELATIONS FOR THE FIELDS OF MANAGEMENT AND ORGANIZATION

Abstract

The purpose of this study is to compare the performances of the Multiple Linear Regression MLR and Artificial Neural Networks ANN used in correlations for the fields of management and organization, and to demonstrate that the ANN method can be used in organizational studies. Therefore, first, comprehensive information is provided about the ANN method and its use in literature and comparative studies. Work–family conflict, affective commitment, and turnover intention have been used as the variable group to compare the performance of the ANN with that of the MLR. Validity and reliability tests have been carried out, and the correlation coefficient R correlation coefficient and RMSE Root Mean Square Error have been considered as the criteria for performance. Within the scope of the findings, the ANN method is found to demonstrate higher R and lower RMSE values, when compared with the MLR method, although the effect coefficients obtained through both methods regarding correlations are noted to be similar

Keywords

• Management and Organization,Organization Behavior,Regression,Artificial Neural Network.

YÖNETİM VE ORGANİZASYON ALANINDA ÖRGÜTSEL DEĞİŞKENLER ARASI İLİŞKİLERDE REGRESYON VE YAPAY SİNİR AĞLARI YÖNTEMLERİNİN KARŞILAŞTIRMASI

Öz

Bu araştırmanın amacı, Yönetim ve Organizasyon alanında değişkenler arası ilişkilerde kullanılan Çoklu Doğrusal Regresyon analizi MLR ile Yapay Sinir Ağları ANN yönteminin performanslarını karşılaştırarak ANN yönteminin örgütsel araştırmalarda uygulanabilirliğini ortaya koyabilmektir. Araştırmanın örneklemi kamu ve özel sektör kuruluşlarından tesadüfi örnekleme yoluyla seçilen 392 çalışandan oluşmaktadır. Araştırmada geçerlilik ve güvenilirlik testleri yapılmış, performans ölçütü olarak R2 correlation coefficient ve RMSE root mean square error dikkate alınmıştır. Elde edilen bulgular çerçevesinde değişkenler arası ilişkilere yönelik etki katsayıları birbirine benzemekle birlikte, ANN yönteminin MLR yöntemine göre daha yüksek R2 ve daha düşük RMSE değerleri ortaya koyduğu tespit edilmiştir

Anahtar Kelimeler

• Yönetim ve Organizasyon,Örgütsel Davranış,Regresyon,Yapay Sinir Ağları

Kaynakça

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