thesis on data envelopment analysis

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

• First Online: 17 December 2021

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• Vincent Charles 6 ,
• Tatiana Gherman 7 &
• Joe Zhu 8  

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 312))

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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.

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

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The authors are thankful to the reviewers for their valuable feedback on the previous version of this research.

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University of Wales Trinity Saint David, Birmingham, UK

Vincent Charles

Faculty of Business and Law, University of Northampton, Northampton, UK

Tatiana Gherman

Business School, Worcester Polytechnic Institute, Worcester, MA, USA

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Charles, V., Gherman, T., Zhu, J. (2021). Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis. In: Zhu, J., Charles, V. (eds) Data-Enabled Analytics. International Series in Operations Research & Management Science, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-75162-3_1

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DOI : https://doi.org/10.1007/978-3-030-75162-3_1

Published : 17 December 2021

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