Mapping the Knowledge Domain of Machine Learning Methods in Performance Evaluation

Abstract

Today, businesses are interested in innovation by way of technological exploration to increase reach and efficiency. This study aims to visualize the research done in business management and performance, with an implication of machine learning techniques. The Scopus database was used to collect the source documents relating to this study area. A bibliometric analysis, including a network link analysis study, has been conducted to understand the relatedness of the various descriptive characteristics of the literature. The analysis is done using a threefold approach; co-citation map, bibliographic coupling map and co-word analysis. The results show increased application of machine learning tools in analyzing the operational and financial performance, predicting and knowledge management systems. Machine learning techniques like deep learning, neural network and long short-term memory are spotted in very few recent studies which enhances the opportunity for future researchers to use the same in various performance analysis and forecasting models for organizations.