Artificial Intelligence (AI) in Economics and Business-Related Research with Data Envelopment Analysis (DEA) Application: A Systematic Literature Review

The subject of artificial intelligence is nowadays very topical and popular in business circles. However, the published scholarly literature reveals a literature gap and no relevant scientific findings regarding the application of artificial intelligence in economics and business-related literature. The overarching objective of this paper is to survey, identify and qualitatively analyse all the published studies in the area of artificial intelligence in economics and business-related research with the application of the leading nonparametric DEA (Data Envelopment Analysis) methodology thus far, as well as to reveal their findings, map some hotspots and trends as well as to conclude regarding the state of the art in the scholarly literature in this research area. Moreover, another goal is to provide a theoretical background to the concept of artificial intelligence (AI) as well as to the DEA methodology. Systematic literature reviews (SLR) have been accepted as an expedient for the presentation of the state of the art in a scientific area to the academic community. Therefore, a systematic literature review combining both electronic and manual searches to identify relevant studies using keywords the "DATA ENVELOPMENT ANALYSIS" and "ARTIFICIAL INTELLIGENCE" was suitable for this study. The methodology resulted in the identification of 10 relevant published papers in economics and business-related research employing DEA methodology in AI. The findings reveal specific areas of application of AI in economics and business, and suggestions and guidelines for future work are provided. The scientific and practical contributions of this study are twofold. First, the state of AI with the application of DEA in economics and business-related research is presented, and second, it provides crucial information regarding the application of the DEA methodology in AI in economics and business-related research, which is valuable to regulatory bodies, governments and potential inventors and software engineers.