Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis

Vincent Charles, Tatiana Gherman, Joe Zhu

Producción científica: Capítulo del libro/informe/acta de congresoCapítulorevisión exhaustiva

11 Citas (Scopus)

Resumen

Data envelopment analysis (DEA) is a powerful data-enabled, big data science tool for performance measurement and management, which over time has been applied across a myriad of domains. Over the past years, various advancements in big data have captured the attention of DEA scholars, which in turn, has translated into the emergence of new research strands. In the present work, we perform a systematic literature review with bibliometric analysis of studies integrating DEA with big data, in an attempt to answer the question: what are the current avenues of research for such studies? The results obtained are further complemented with a thematic analysis. Among others, findings indicate that big data is still a new entrant within the DEA literature, that most of the studies have focused on developing faster and more accurate computational techniques to handle problems with a large number of decision-making units (DMUs), and that most of the studies have been carried out in the area of environmental efficiency evaluation. This work should contribute to the construction of an overview of the existing literature on DEA-big data studies, as well as stimulate the interest in the topic.

Idioma originalInglés
Título de la publicación alojadaInternational Series in Operations Research and Management Science
EditorialSpringer
Páginas1-29
Número de páginas29
DOI
EstadoPublicada - 2021
Publicado de forma externa

Serie de la publicación

NombreInternational Series in Operations Research and Management Science
Volumen312
ISSN (versión impresa)0884-8289
ISSN (versión digital)2214-7934

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