{"title":"通过标准间关联(CRITIC)法了解标准重要性的研究趋势:使用 Tableau 软件对书目数据进行可视化分析","authors":"Anath Rau Krishnan","doi":"10.1108/idd-02-2024-0030","DOIUrl":null,"url":null,"abstract":"Purpose\nCriteria importance through intercriteria correlation (CRITIC) is a multicriteria decision-making method that helps compute the weights of decision criteria by considering the contrast intensity and conflicting relationships inherent in each criterion. This study aims to conduct a bibliometric analysis to provide quantitative insights into the research trends concerning the CRITIC method.\n\nDesign/methodology/approach\nThe study assembled bibliographic data from 220 CRITIC-based publications retrieved from the Scopus database. Subsequently, the gathered data were processed using Tableau software, using specific functions within the software to format them to suit the analysis requirements. Following data preparation, a visual analysis was then conducted based on five parameters that can characterize the research evolutions in CRITIC. These parameters include research productivity across years, dominant countries, dominant researchers, dominant publication outlets and popular research topics. Various visualization techniques, such as combined charts, geographical maps and word clouds, were used to draw conclusions for each parameter.\n\nFindings\nThe study discovered a burgeoning trend in CRITIC research in recent years, particularly from 2019 onwards. The COVID-19 pandemic unexpectedly contributed to this upward trend, prompting remarkable collaboration among researchers who used diverse decision-making methods, such as CRITIC, to provide data-driven solutions for addressing COVID-19 challenges. Additionally, the study identified China and Iran as the leading countries in CRITIC research, with notable researchers such as Xindong Peng and Mehdi Keshavarz-Ghorabaee predominantly affiliated with institutions in these countries. Keyword analysis indicated the application of CRITIC across various trending topics, including Industry 4.0 and environmental sustainability.\n\nOriginality/value\nNo bibliometric analyses have been conducted on the CRITIC method in the literature since its inception in 1995, leaving the scientific community clueless about its research trends. To the best of the author’s knowledge, this study serves as the first bibliometric analysis, providing quantitative evidence on the research trends associated with the CRITIC method. By shedding light on these trends, this study enables the scientific community, including researchers and funding agencies, to make informed decisions regarding future research endeavors involving the CRITIC method.\n","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research trends in criteria importance through intercriteria correlation (CRITIC) method: a visual analysis of bibliographic data using the Tableau software\",\"authors\":\"Anath Rau Krishnan\",\"doi\":\"10.1108/idd-02-2024-0030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose\\nCriteria importance through intercriteria correlation (CRITIC) is a multicriteria decision-making method that helps compute the weights of decision criteria by considering the contrast intensity and conflicting relationships inherent in each criterion. This study aims to conduct a bibliometric analysis to provide quantitative insights into the research trends concerning the CRITIC method.\\n\\nDesign/methodology/approach\\nThe study assembled bibliographic data from 220 CRITIC-based publications retrieved from the Scopus database. Subsequently, the gathered data were processed using Tableau software, using specific functions within the software to format them to suit the analysis requirements. Following data preparation, a visual analysis was then conducted based on five parameters that can characterize the research evolutions in CRITIC. These parameters include research productivity across years, dominant countries, dominant researchers, dominant publication outlets and popular research topics. Various visualization techniques, such as combined charts, geographical maps and word clouds, were used to draw conclusions for each parameter.\\n\\nFindings\\nThe study discovered a burgeoning trend in CRITIC research in recent years, particularly from 2019 onwards. The COVID-19 pandemic unexpectedly contributed to this upward trend, prompting remarkable collaboration among researchers who used diverse decision-making methods, such as CRITIC, to provide data-driven solutions for addressing COVID-19 challenges. Additionally, the study identified China and Iran as the leading countries in CRITIC research, with notable researchers such as Xindong Peng and Mehdi Keshavarz-Ghorabaee predominantly affiliated with institutions in these countries. Keyword analysis indicated the application of CRITIC across various trending topics, including Industry 4.0 and environmental sustainability.\\n\\nOriginality/value\\nNo bibliometric analyses have been conducted on the CRITIC method in the literature since its inception in 1995, leaving the scientific community clueless about its research trends. To the best of the author’s knowledge, this study serves as the first bibliometric analysis, providing quantitative evidence on the research trends associated with the CRITIC method. By shedding light on these trends, this study enables the scientific community, including researchers and funding agencies, to make informed decisions regarding future research endeavors involving the CRITIC method.\\n\",\"PeriodicalId\":43488,\"journal\":{\"name\":\"Information Discovery and Delivery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Discovery and Delivery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/idd-02-2024-0030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Discovery and Delivery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/idd-02-2024-0030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
Research trends in criteria importance through intercriteria correlation (CRITIC) method: a visual analysis of bibliographic data using the Tableau software
Purpose
Criteria importance through intercriteria correlation (CRITIC) is a multicriteria decision-making method that helps compute the weights of decision criteria by considering the contrast intensity and conflicting relationships inherent in each criterion. This study aims to conduct a bibliometric analysis to provide quantitative insights into the research trends concerning the CRITIC method.
Design/methodology/approach
The study assembled bibliographic data from 220 CRITIC-based publications retrieved from the Scopus database. Subsequently, the gathered data were processed using Tableau software, using specific functions within the software to format them to suit the analysis requirements. Following data preparation, a visual analysis was then conducted based on five parameters that can characterize the research evolutions in CRITIC. These parameters include research productivity across years, dominant countries, dominant researchers, dominant publication outlets and popular research topics. Various visualization techniques, such as combined charts, geographical maps and word clouds, were used to draw conclusions for each parameter.
Findings
The study discovered a burgeoning trend in CRITIC research in recent years, particularly from 2019 onwards. The COVID-19 pandemic unexpectedly contributed to this upward trend, prompting remarkable collaboration among researchers who used diverse decision-making methods, such as CRITIC, to provide data-driven solutions for addressing COVID-19 challenges. Additionally, the study identified China and Iran as the leading countries in CRITIC research, with notable researchers such as Xindong Peng and Mehdi Keshavarz-Ghorabaee predominantly affiliated with institutions in these countries. Keyword analysis indicated the application of CRITIC across various trending topics, including Industry 4.0 and environmental sustainability.
Originality/value
No bibliometric analyses have been conducted on the CRITIC method in the literature since its inception in 1995, leaving the scientific community clueless about its research trends. To the best of the author’s knowledge, this study serves as the first bibliometric analysis, providing quantitative evidence on the research trends associated with the CRITIC method. By shedding light on these trends, this study enables the scientific community, including researchers and funding agencies, to make informed decisions regarding future research endeavors involving the CRITIC method.
期刊介绍:
Information Discovery and Delivery covers information discovery and access for digital information researchers. This includes educators, knowledge professionals in education and cultural organisations, knowledge managers in media, health care and government, as well as librarians. The journal publishes research and practice which explores the digital information supply chain ie transport, flows, tracking, exchange and sharing, including within and between libraries. It is also interested in digital information capture, packaging and storage by ‘collectors’ of all kinds. Information is widely defined, including but not limited to: Records, Documents, Learning objects, Visual and sound files, Data and metadata and , User-generated content.