Journal of Scientometric Research最新文献

Scientific Productivity and Collaboration Networks in Lifelong Learning: A Longitudinal Bibliometric Analysis (1963-2022) 终身学习中的科学生产力与合作网络：纵向文献计量分析（1963-2022 年）

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.17

Kannan Thamizhiniyan, Kathirkamanathan Chellamani, Abdul Huq Jahitha Begum, Sheriff Naseema

{"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}

引用次数: 0

Bibliometric Cartography on Personality Traits and Stress: In Quest of Panaceas for Contemporary Workplace Challenges 关于人格特质和压力的文献计量制图：寻求应对当代职场挑战的良方

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.25

M. H. Raamkhumar, T. Swamy

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The Development of Research on Investor Sentiment in Emerging and Frontier Markets with the Bibliometric Method 利用文献计量法开展新兴市场和前沿市场投资者情绪研究

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.7

Anh Luong, Hiep Hung Pham, Hai Dinh Luong, Huong Thi Thu Phung, Thanh Trung Le

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Tourism and Hospitality Research Trends in South Asia: A Comprehensive Bibliometric Analysis from 1992-2021 南亚旅游业和酒店业研究趋势：1992-2021 年文献计量学综合分析

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.11

Anoop Kumar, Lawrence Kwaku Armah, Gunjan Malik

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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 社交媒体促进科学与科学、科学与社会的联系：通过分析研究论文在社交媒体上的可见度评估印度的准备情况

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.20

Vivek Kumar Singh, Mousumi Karmakar, Anurag Kanaujia, S. Bhattacharya

引用次数: 0

A Bibliometric Analysis of Translation Studies of Children’s Literature and Its Implications 儿童文学翻译研究的文献计量分析及其启示

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.22

Kun Zhu, Guoliang Guo

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Computational Mapping of Indian Organic Chemistry Research: An Analysis with Data Mining Tools 印度有机化学研究的计算映射：利用数据挖掘工具进行分析

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.15

Dhrubajyoti Borgohain, Raj Kumar Bhardwaj, Manoj Kumar Verma

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Usability Testing: A Bibliometric Analysis Based on WoS Data 可用性测试：基于 WoS 数据的文献计量分析

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.2

M.S. Baghini, Mehdi Mohammadi, Narges Norouzkhani

{"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}

引用次数: 0

A Worldwide Analysis of Top Scientists across Scientific Fields 对全球各科学领域顶尖科学家的分析

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.16

Giorgos Vasiliadis, Eleytheria Kiriakidi, C. Panagiotakis

引用次数: 0

Comparing Research Topics through Metatags Analysis: A Multi-module Machine Algorithm Approaches Using Real World Data on Digital Humanities 通过元标签分析比较研究课题：使用真实世界数字人文数据的多模块机器算法方法

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Journal of Scientometric Research Pub Date : 2024-04-15 DOI: 10.5530/jscires.13.1.5

Bhaskar Mukherjee, Debasis Majhi, Priya Tiwari, Saloni Chaudhary

{"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}

引用次数: 0