Literature survey on DEA in the insurance industry with a focus on identification of research hotspots with text mining
Journal
The Journal of Corporate Governance, Insurance, and Risk Management (JCGIRM)
Date Issued
2021
Author(s)
Tasheva, Marija
DOI
https://doi.org/10.51410/jcgirm.8.1.13
Abstract
DEA is a frequently used non-parametric methodology for
measuring the relative efficiency of Decision-Making Units
(DMUs) that use the same inputs to produce the same outputs.
Emrouznejad and Yang (2018) provided a literature survey on
DEA with 10,300 peer-reviewed journal articles from 1978 to the
end of 2016. Our article focuses on DEA applications in the
insurance industry in convergence with the existing relevant
literature as Kaffash et al (2020), who have surveyed 132 DEA
articles in the insurance industry for the period from 1993 to
2018. We include particular keyword analyses necessary to
identify research hotspots in different periods. This article aims
to conduct a bibliometric analysis of DEA-published documents
(articles in journals and book chapters) in the insurance industry
from 1993 to 2021, focusing on identifying research hotspots
based on keyword co-occurrence analysis. We have analyzed
published documents from relevant databases, such as Scopus,
Web of Science, Ebsco and ProQuest. We use descriptive
analytics and text mining as the main methods in our analysis.
We provide descriptive statistics for articles per year and
category of the insurance industry, geographical distribution, top
five journals and authors by citations, and citation analysis. An
additional qualitative factor of our article is in-depth keyword co occurrence analysis by using text mining to identify research
hotspots in the insurance industry. Our analysis aims to
contribute to researchers and insurance practitioners as an
empirical and applicative point for initiating and developing
research.
measuring the relative efficiency of Decision-Making Units
(DMUs) that use the same inputs to produce the same outputs.
Emrouznejad and Yang (2018) provided a literature survey on
DEA with 10,300 peer-reviewed journal articles from 1978 to the
end of 2016. Our article focuses on DEA applications in the
insurance industry in convergence with the existing relevant
literature as Kaffash et al (2020), who have surveyed 132 DEA
articles in the insurance industry for the period from 1993 to
2018. We include particular keyword analyses necessary to
identify research hotspots in different periods. This article aims
to conduct a bibliometric analysis of DEA-published documents
(articles in journals and book chapters) in the insurance industry
from 1993 to 2021, focusing on identifying research hotspots
based on keyword co-occurrence analysis. We have analyzed
published documents from relevant databases, such as Scopus,
Web of Science, Ebsco and ProQuest. We use descriptive
analytics and text mining as the main methods in our analysis.
We provide descriptive statistics for articles per year and
category of the insurance industry, geographical distribution, top
five journals and authors by citations, and citation analysis. An
additional qualitative factor of our article is in-depth keyword co occurrence analysis by using text mining to identify research
hotspots in the insurance industry. Our analysis aims to
contribute to researchers and insurance practitioners as an
empirical and applicative point for initiating and developing
research.
Subjects
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