{"title":"保险业的效率评估和趋势:DEA 应用的文献计量分析","authors":"K. S. Druva Kumar, Senthil Kumar J. P.","doi":"10.21511/ins.15(1).2024.07","DOIUrl":null,"url":null,"abstract":"Data Envelopment Analysis is a crucial tool for evaluating the performance of insurance companies, considering its ability to handle multiple inputs and outputs. This study provides a comprehensive bibliometric analysis of Data Envelopment Analysis (DEA) application in the insurance industry from 2010 to 2023, examining 405 documents from 432 sources. Materials from academic databases (Web of Science and Scopus) were used for the analysis. The methodological flow included three stages. For analysis, two sets of keywords were identified: one set oriented toward DEA and the other tailored to the Insurance Industry domain. To analyze and visualize the data, VOSviewer software, version 1.6.19, and RSTUDIO were used. This paper highlights the evolution of DEA methodologies, incorporating advanced techniques like Artificial Intelligence and Machine Learning, and addresses emerging trends such as digital transformation, customer-centric assessments, and sustainability. The analysis reveals significant geographical and sectoral differences in efficiency assessments, with higher efficiency levels typically found in developed markets such as North America and Europe compared to emerging markets in Asia and Africa. It also notes the distinctive efficiency patterns between life and non-life insurance firms, influenced by product complexity and market competition. The findings indicate that DEA remains versatile and essential for performance evaluation in the insurance industry, adapting to challenges through methodological advancements.","PeriodicalId":32827,"journal":{"name":"Insurance Markets and Companies","volume":"27 16","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficiency assessment and trends in the insurance industry: A bibliometric analysis of DEA application\",\"authors\":\"K. S. Druva Kumar, Senthil Kumar J. P.\",\"doi\":\"10.21511/ins.15(1).2024.07\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data Envelopment Analysis is a crucial tool for evaluating the performance of insurance companies, considering its ability to handle multiple inputs and outputs. This study provides a comprehensive bibliometric analysis of Data Envelopment Analysis (DEA) application in the insurance industry from 2010 to 2023, examining 405 documents from 432 sources. Materials from academic databases (Web of Science and Scopus) were used for the analysis. The methodological flow included three stages. For analysis, two sets of keywords were identified: one set oriented toward DEA and the other tailored to the Insurance Industry domain. To analyze and visualize the data, VOSviewer software, version 1.6.19, and RSTUDIO were used. This paper highlights the evolution of DEA methodologies, incorporating advanced techniques like Artificial Intelligence and Machine Learning, and addresses emerging trends such as digital transformation, customer-centric assessments, and sustainability. The analysis reveals significant geographical and sectoral differences in efficiency assessments, with higher efficiency levels typically found in developed markets such as North America and Europe compared to emerging markets in Asia and Africa. It also notes the distinctive efficiency patterns between life and non-life insurance firms, influenced by product complexity and market competition. The findings indicate that DEA remains versatile and essential for performance evaluation in the insurance industry, adapting to challenges through methodological advancements.\",\"PeriodicalId\":32827,\"journal\":{\"name\":\"Insurance Markets and Companies\",\"volume\":\"27 16\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Insurance Markets and Companies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21511/ins.15(1).2024.07\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Economics, Econometrics and Finance\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insurance Markets and Companies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21511/ins.15(1).2024.07","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
引用次数: 0
摘要
数据包络分析法能够处理多种输入和输出,是评估保险公司绩效的重要工具。本研究对 2010 年至 2023 年数据包络分析法(DEA)在保险行业的应用进行了全面的文献计量分析,研究了 432 个来源的 405 篇文献。分析使用了学术数据库(Web of Science 和 Scopus)中的资料。方法流程包括三个阶段。为进行分析,确定了两组关键词:一组以 DEA 为导向,另一组则针对保险业领域。为了对数据进行分析和可视化,使用了 VOSviewer 软件 1.6.19 版和 RSTUDIO。本文重点介绍了 DEA 方法的演变,将人工智能和机器学习等先进技术融入其中,并探讨了数字化转型、以客户为中心的评估和可持续发展等新兴趋势。分析显示,效率评估在地域和行业方面存在显著差异,与亚洲和非洲的新兴市场相比,北美和欧洲等发达市场的效率水平通常更高。分析还注意到,受产品复杂性和市场竞争的影响,寿险公司和非寿险公司的效率模式各不相同。研究结果表明,DEA 仍是保险业业绩评估的多面手和必备工具,可通过方法的进步来应对挑战。
Efficiency assessment and trends in the insurance industry: A bibliometric analysis of DEA application
Data Envelopment Analysis is a crucial tool for evaluating the performance of insurance companies, considering its ability to handle multiple inputs and outputs. This study provides a comprehensive bibliometric analysis of Data Envelopment Analysis (DEA) application in the insurance industry from 2010 to 2023, examining 405 documents from 432 sources. Materials from academic databases (Web of Science and Scopus) were used for the analysis. The methodological flow included three stages. For analysis, two sets of keywords were identified: one set oriented toward DEA and the other tailored to the Insurance Industry domain. To analyze and visualize the data, VOSviewer software, version 1.6.19, and RSTUDIO were used. This paper highlights the evolution of DEA methodologies, incorporating advanced techniques like Artificial Intelligence and Machine Learning, and addresses emerging trends such as digital transformation, customer-centric assessments, and sustainability. The analysis reveals significant geographical and sectoral differences in efficiency assessments, with higher efficiency levels typically found in developed markets such as North America and Europe compared to emerging markets in Asia and Africa. It also notes the distinctive efficiency patterns between life and non-life insurance firms, influenced by product complexity and market competition. The findings indicate that DEA remains versatile and essential for performance evaluation in the insurance industry, adapting to challenges through methodological advancements.