A bibliometric analysis of research on multiple criteria decision making with emphasis on Energy Sector between (2019-2023)

Authors

  • Dianese David Department of Computing and Meta Technology, Universiti Pendidikan Sultan Idris (UPSI), Perak,
  • Abdullah Alamoodi Department of Computing and Meta Technology, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia

DOI:

https://doi.org/10.58496/ADSA/2023/013

Keywords:

Bibliometrics, multiple attribute decision making, social network analysis, VOS viewer, Energy, multiple criteria decision making

Abstract

In the present study, a bibliometric analysis of research works that have been conducted over the last five years in connection to Multiple Criteria Decision making (MCDM) and its application in the energy sector is presented. In the beginning, a statistical study of influential publications, journals, countries/territories, and authors was carried out. In the following step, an analysis was performed based on four distinct time periods to determine the evolving patterns of authors' cooperation structure and study themes. According to the findings, there has been a rise in the quality of collaboration between writers, as well as an increase in the number of publications and authors who have contributed to the study on MCDM during the last five years. Researchers should be able to successfully conduct investigations in linked domains with the assistance of the complete and scientific analysis of MCDM. It also concludes that there are more opportunities in the future in the field of energy applications with MCDM, and this can be encouraging for researchers from both fields, as well as those from the industrial and economic fields, to consider MCDM in their utilization of energy alternatives and to make decisions that are informed by such findings.

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References

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Published

2023-11-29

How to Cite

David, D., & Alamoodi, A. (2023). A bibliometric analysis of research on multiple criteria decision making with emphasis on Energy Sector between (2019-2023). Applied Data Science and Analysis, 2023, 143–149. https://doi.org/10.58496/ADSA/2023/013
CITATION
DOI: 10.58496/ADSA/2023/013
Published: 2023-11-29

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