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

Vincent Charles, Tatiana Gherman, Joe Zhu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

11 Scopus citations

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.

Original languageEnglish
Title of host publicationInternational Series in Operations Research and Management Science
PublisherSpringer
Pages1-29
Number of pages29
DOIs
StatePublished - 2021
Externally publishedYes

Publication series

NameInternational Series in Operations Research and Management Science
Volume312
ISSN (Print)0884-8289
ISSN (Electronic)2214-7934

Keywords

  • Bibliometric analysis
  • Big data
  • Data envelopment analysis
  • Data-enabled analytics
  • Systematic literature review

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