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

In: Data-Enabled Analytics

Author

Listed:
  • Vincent Charles

    (University of Wales Trinity Saint David)

  • Tatiana Gherman

    (University of Northampton)

  • Joe Zhu

    (Worcester Polytechnic Institute)

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.

Suggested Citation

  • Vincent Charles & Tatiana Gherman & Joe Zhu, 2021. "Data Envelopment Analysis and Big Data: A Systematic Literature Review with Bibliometric Analysis," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 1-29, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-75162-3_1
    DOI: 10.1007/978-3-030-75162-3_1
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    Citations

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    Cited by:

    1. Sinuany-Stern, Zilla, 2023. "Foundations of operations research: From linear programming to data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1069-1080.
    2. Ebrahimi, Bohlool & Dhamotharan, Lalitha & Ghasemi, Mohammad Reza & Charles, Vincent, 2022. "A cross-inefficiency approach based on the deviation variables framework," Omega, Elsevier, vol. 111(C).
    3. Gulati, Rachita & Charles, Vincent & Hassan, M. Kabir & Kumar, Sunil, 2023. "COVID-19 crisis and the efficiency of Indian banks: Have they weathered the storm?," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).

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