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

Vincent Charles (), Tatiana Gherman () and Joe Zhu ()
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Vincent Charles: University of Wales Trinity Saint David
Tatiana Gherman: University of Northampton
Joe Zhu: Worcester Polytechnic Institute

A chapter in Data-Enabled Analytics, 2021, pp 1-29 from Springer

Abstract: Abstract 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.

Keywords: Data envelopment analysis; Data-enabled analytics; Big data; Systematic literature review; Bibliometric analysis (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-75162-3_1

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