Bibliometric analysis of the published literature on machine learning in economics and econometrics - ÇAĞLAYAN AKAY, EBRU; YILMAZ SOYDAN, NACİYE TUBA (My University, 2022)
An extensive literature providing information on published materials in machine learning exists. However, machine learning is still a rather new concept in the fields of economics and econometrics. This study aims to identify different properties of published documents about machine learning in economics and econometrics and therefore to draw a detailed picture of recent publications from bibliometric analysis perspectives. For the aim of the study, the data are collected from the publications indexed by Web of Science and Scopus databases from the period 1991 to 2020. Inthe study, the data have been illustrated by VOSviewer for science mapping. The analysis of variance has also been used to identify the links between the number of citations of articles and years. The findings obtained provides information about the studies on machine learning in the relevant field conducted in the past, as well as providing an opportunity to gain knowledge about the researched area by shedding light on what the future research areas would be. There is no doubt that it attracts attention has increased significantly on machine learning in the field of economics and econometrics and academic publications on machine learning in the relevant field have increased over the last decade.
Keywords: Bilgisayar Bilimleri; Bilgi Güvenliği ve Güvenilirliği; Mühendislik ve Teknoloji; Computer Sciences; Information Security and Reliability; Engineering and Technology; BİLGİSAYAR BİLİMİ, BİLGİ SİSTEMLERİ; Bilgisayar Bilimi; Mühendislik, Bilişim ve Teknoloji (ENG); COMPUTER SCIENCE, INFORMATION SYSTEMS; COMPUTER SCIENCE; Engineering, Computing & Technology (ENG); Bilgi sistemi; Bilgisayar Bilimi Uygulamaları; Bilgisayar Bilimi (çeşitli); Genel Bilgisayar Bilimi; Fizik Bilimleri; Information Systems; Computer Science Applications; Computer Science (miscellaneous); General Computer Science; Physical Sciences; Bibliometric analysis; Economics; Econometrics; Machine learning; Science mapping; Scopus; Web of science; BIG-DATA; MANAGEMENT; CITATION; BUSINESS; TRENDS