Kannan Thamizhiniyan, Kathirkamanathan Chellamani, Abdul Huq Jahitha Begum, Sheriff Naseema
In the post-pandemic era, lifelong learning (LLL) emerged as the key to professional development and the core competency of all disciplines. Even globally, there is a dearth of evidence based bibliometric analysis, notably on LLL. This study addresses this gap by examining the data retrieved from the Elsevier Scopus database. A systematic search method was adopted to retrieve 1806 publications from 790 journals from 1963 to 2022. The R package, Biblioshiny, was used for data analysis, including productivity/performance analysis, citation analysis, and collaboration network analysis of social structure. The findings showed that the number of publications has significantly increased over time. A large number of studies were published in 2022. Overall, 85 countries contributed to LLL. Among them, the United States was the most productive with 787 publications, and the United Kingdom was the country with 4731 citations. Learning was the trending topic, and skill development was an emerging theme in LLL. The results will aid the stakeholders in identifying largely unexplored areas of research that need more attention and funding. This study outlines not only the current scientific developments but also the potential future of LLL research. This study will also be used as a resource for researchers and teachers in LLL. Future research directions in this area of knowledge are also outlined.
{"title":"Scientific Productivity and Collaboration Networks in Lifelong Learning: A Longitudinal Bibliometric Analysis (1963-2022)","authors":"Kannan Thamizhiniyan, Kathirkamanathan Chellamani, Abdul Huq Jahitha Begum, Sheriff Naseema","doi":"10.5530/jscires.13.1.17","DOIUrl":"https://doi.org/10.5530/jscires.13.1.17","url":null,"abstract":"In the post-pandemic era, lifelong learning (LLL) emerged as the key to professional development and the core competency of all disciplines. Even globally, there is a dearth of evidence based bibliometric analysis, notably on LLL. This study addresses this gap by examining the data retrieved from the Elsevier Scopus database. A systematic search method was adopted to retrieve 1806 publications from 790 journals from 1963 to 2022. The R package, Biblioshiny, was used for data analysis, including productivity/performance analysis, citation analysis, and collaboration network analysis of social structure. The findings showed that the number of publications has significantly increased over time. A large number of studies were published in 2022. Overall, 85 countries contributed to LLL. Among them, the United States was the most productive with 787 publications, and the United Kingdom was the country with 4731 citations. Learning was the trending topic, and skill development was an emerging theme in LLL. The results will aid the stakeholders in identifying largely unexplored areas of research that need more attention and funding. This study outlines not only the current scientific developments but also the potential future of LLL research. This study will also be used as a resource for researchers and teachers in LLL. Future research directions in this area of knowledge are also outlined.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bibliometric Cartography on Personality Traits and Stress: In Quest of Panaceas for Contemporary Workplace Challenges","authors":"M. H. Raamkhumar, T. Swamy","doi":"10.5530/jscires.13.1.25","DOIUrl":"https://doi.org/10.5530/jscires.13.1.25","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140704009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anh Luong, Hiep Hung Pham, Hai Dinh Luong, Huong Thi Thu Phung, Thanh Trung Le
In line with the development of behavioural finance in developed markets, research on investor sentiment has increased in recent years. The primary purpose of this study is to investigate the development of research on investor sentiment in emerging and frontier markets. This study will help researchers understand the interest of authors and journals in finding appropriate coordinators and future research topics in this research field. Using bibliometric analysis, we assessed 508 documents between 1999 and 2020 located in the Scopus database. The results show that publications on investor sentiment in emerging and frontier markets grew steadily in the 21 st century. “Herding behaviour” is the most prominent research theme in this area. In the following years, return predictability, principal component analysis, investor attention, and economic policy uncertainty with asymmetric effects are the dominant topics that have reshaped research on investor sentiment in emerging and frontier markets.
{"title":"The Development of Research on Investor Sentiment in Emerging and Frontier Markets with the Bibliometric Method","authors":"Anh Luong, Hiep Hung Pham, Hai Dinh Luong, Huong Thi Thu Phung, Thanh Trung Le","doi":"10.5530/jscires.13.1.7","DOIUrl":"https://doi.org/10.5530/jscires.13.1.7","url":null,"abstract":"In line with the development of behavioural finance in developed markets, research on investor sentiment has increased in recent years. The primary purpose of this study is to investigate the development of research on investor sentiment in emerging and frontier markets. This study will help researchers understand the interest of authors and journals in finding appropriate coordinators and future research topics in this research field. Using bibliometric analysis, we assessed 508 documents between 1999 and 2020 located in the Scopus database. The results show that publications on investor sentiment in emerging and frontier markets grew steadily in the 21 st century. “Herding behaviour” is the most prominent research theme in this area. In the following years, return predictability, principal component analysis, investor attention, and economic policy uncertainty with asymmetric effects are the dominant topics that have reshaped research on investor sentiment in emerging and frontier markets.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tourism and Hospitality Research Trends in South Asia: A Comprehensive Bibliometric Analysis from 1992-2021","authors":"Anoop Kumar, Lawrence Kwaku Armah, Gunjan Malik","doi":"10.5530/jscires.13.1.11","DOIUrl":"https://doi.org/10.5530/jscires.13.1.11","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vivek Kumar Singh, Mousumi Karmakar, Anurag Kanaujia, S. Bhattacharya
{"title":"Social Media for Science-Science and Science-Society Connects: Assessing the Readiness in Indian Context through an Analysis of Social Media Visibility of Research Papers","authors":"Vivek Kumar Singh, Mousumi Karmakar, Anurag Kanaujia, S. Bhattacharya","doi":"10.5530/jscires.13.1.20","DOIUrl":"https://doi.org/10.5530/jscires.13.1.20","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Bibliometric Analysis of Translation Studies of Children’s Literature and Its Implications","authors":"Kun Zhu, Guoliang Guo","doi":"10.5530/jscires.13.1.22","DOIUrl":"https://doi.org/10.5530/jscires.13.1.22","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140700498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Computational Mapping of Indian Organic Chemistry Research: An Analysis with Data Mining Tools","authors":"Dhrubajyoti Borgohain, Raj Kumar Bhardwaj, Manoj Kumar Verma","doi":"10.5530/jscires.13.1.15","DOIUrl":"https://doi.org/10.5530/jscires.13.1.15","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Usability is a qualitative characteristic that evaluates the ease of use of user interfaces. This study aims to conduct a systematic bibliometric analysis of usability testing and to understand the research context and trends in this field. A total of 5273 scientific publications from the Web of Science core collection were included in the study. Performance analysis, scientific mapping, and visualization were done using the RStudio package and the VOSviewer software tool. The results show that the interest in the area of usability testing has significantly increased, especially from 1991 to 2022. The United States has the highest number of publications, citations, co-citations, and ratios. Toronto University was top in terms of institutional contributions. The JMIR mHealth and uHealth led in the number of publications and citations. Khajouei has the highest number of publications, but Jaspers has received the most citations on usability testing. With 10264 total link strength, Nielsen has the most potent co-citation papers. This study reveals the latest research trends and hotspots and the current state of international collaboration in usability testing research, to indicate the most influential research channels. These findings include; the prominent countries, institutions, journals, original articles, and authors. To the best of the author’s knowledge, this study is the first of its kind to conduct the bibliometric analysis on usability testing. These findings can be useful in shaping the direction of future studies on usability testing
{"title":"Usability Testing: A Bibliometric Analysis Based on WoS Data","authors":"M.S. Baghini, Mehdi Mohammadi, Narges Norouzkhani","doi":"10.5530/jscires.13.1.2","DOIUrl":"https://doi.org/10.5530/jscires.13.1.2","url":null,"abstract":"Usability is a qualitative characteristic that evaluates the ease of use of user interfaces. This study aims to conduct a systematic bibliometric analysis of usability testing and to understand the research context and trends in this field. A total of 5273 scientific publications from the Web of Science core collection were included in the study. Performance analysis, scientific mapping, and visualization were done using the RStudio package and the VOSviewer software tool. The results show that the interest in the area of usability testing has significantly increased, especially from 1991 to 2022. The United States has the highest number of publications, citations, co-citations, and ratios. Toronto University was top in terms of institutional contributions. The JMIR mHealth and uHealth led in the number of publications and citations. Khajouei has the highest number of publications, but Jaspers has received the most citations on usability testing. With 10264 total link strength, Nielsen has the most potent co-citation papers. This study reveals the latest research trends and hotspots and the current state of international collaboration in usability testing research, to indicate the most influential research channels. These findings include; the prominent countries, institutions, journals, original articles, and authors. To the best of the author’s knowledge, this study is the first of its kind to conduct the bibliometric analysis on usability testing. These findings can be useful in shaping the direction of future studies on usability testing","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giorgos Vasiliadis, Eleytheria Kiriakidi, C. Panagiotakis
{"title":"A Worldwide Analysis of Top Scientists across Scientific Fields","authors":"Giorgos Vasiliadis, Eleytheria Kiriakidi, C. Panagiotakis","doi":"10.5530/jscires.13.1.16","DOIUrl":"https://doi.org/10.5530/jscires.13.1.16","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140702082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present study extract, map and compare the lexical and semantic similarity of terms from author-provided keywords with machine extracted terms and topics from titles and abstracts of an inter-disciplinary field like ‘digital humanities’. Author-provided terms (keywords) were first extracted and mapped through visualization software like Gephi and then these extracted terms were compared with terms extracted from title and abstract of the research articles through NLP based statistical modules. Also, the interdisciplinary of significant topics were measured through the Brillouin index. A set of 7483 articles downloaded from Scopus database on the domain of digital humanities and its associated fields were used for the purpose. We observed the researches on digital humanities are spread over a considerable number of concepts like ‘Industry 4.0’, ‘topic modelling, ‘open science’. Further, the machine algorithm-based extraction compared and identified a larger lexical similarity between these author-provided keywords and title-extracted keywords, rather than abstract-extracted keywords. Jaccard similarity of all author-keywords with machine extracted title keywords came 0.83 and SBERT BiEncoder_score was 0.7374. The top research areas extracted from titles, through unsupervised approach of term extraction resulted in topics like digital humanities approach, digital humanities visualization, indicating a strong connection to the discipline of digital humanities. The average interdisciplinarity index of top significant topics came between 1.217 and 1.284, with the highest index value for ‘computational digital humanities’. As this study is based on real-world data, it is highly useful to understand how far machine algorithm-based text extraction can be helpful for information retrieval process.
{"title":"Comparing Research Topics through Metatags Analysis: A Multi-module Machine Algorithm Approaches Using Real World Data on Digital Humanities","authors":"Bhaskar Mukherjee, Debasis Majhi, Priya Tiwari, Saloni Chaudhary","doi":"10.5530/jscires.13.1.5","DOIUrl":"https://doi.org/10.5530/jscires.13.1.5","url":null,"abstract":"The present study extract, map and compare the lexical and semantic similarity of terms from author-provided keywords with machine extracted terms and topics from titles and abstracts of an inter-disciplinary field like ‘digital humanities’. Author-provided terms (keywords) were first extracted and mapped through visualization software like Gephi and then these extracted terms were compared with terms extracted from title and abstract of the research articles through NLP based statistical modules. Also, the interdisciplinary of significant topics were measured through the Brillouin index. A set of 7483 articles downloaded from Scopus database on the domain of digital humanities and its associated fields were used for the purpose. We observed the researches on digital humanities are spread over a considerable number of concepts like ‘Industry 4.0’, ‘topic modelling, ‘open science’. Further, the machine algorithm-based extraction compared and identified a larger lexical similarity between these author-provided keywords and title-extracted keywords, rather than abstract-extracted keywords. Jaccard similarity of all author-keywords with machine extracted title keywords came 0.83 and SBERT BiEncoder_score was 0.7374. The top research areas extracted from titles, through unsupervised approach of term extraction resulted in topics like digital humanities approach, digital humanities visualization, indicating a strong connection to the discipline of digital humanities. The average interdisciplinarity index of top significant topics came between 1.217 and 1.284, with the highest index value for ‘computational digital humanities’. As this study is based on real-world data, it is highly useful to understand how far machine algorithm-based text extraction can be helpful for information retrieval process.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140704144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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