COLLNET Journal of Scientometrics and Information Management最新文献

{"title":"Relationship between number of downloads and three journal-based metrics of 11 subject categories among 1575 Springer Nature journals","authors":"H. Okagbue, Boluwatife E. Akinsola, J. A. Teixeira da Silva","doi":"10.1080/09737766.2022.2117667","DOIUrl":"https://doi.org/10.1080/09737766.2022.2117667","url":null,"abstract":"The number of downloads (NOD) is a measure of the number of accesses to (or downloads of) published articles and a subset of altmetrics. In this study, we assessed the correlation between the journal impact factor (JIF) and NOD for 11 subject categories on Springer Nature’s Springerlink to determine if there were differences in NOD among Google Scholar, Scopus (CiteScore) and Clarivate’s JIF across these subject categories, and attempted to predict NOD using JIF. From a total of 1575 journals, 1155 (73.3%) were grouped under JIF, 275 (17.5%) under CiteScore, and 145 (9.2%) under Google Scholar. Among the 1155 JIF journals, 1007 (87.2%) were subscription or hybrid journals while 148 (12.8%) were open access journals. Except for “environment”, there was a significant positive correlation between NOD and JIF for all remaining subject categories. Correlations changed slightly even after open access was removed from all categories. The Kruskal Wallis test showed significant differences in median NOD for journals with a CiteScore, Google Scholar and JIF, and this was fortified by a posthoc test (Conover p-values without adjustment). After aggregating the data of all subject categories into two sub-categories (NOD and JIF) of the 1155 journals with a JIF, finally, Adaptive Boosting performed best among eight machine learning models to predict NOD using JIF (RMSE = 84139.1; R2 = 0.9669). This research extends researchers’ understanding of the relationship between altmetrics and citations with journal metrics that are typically obtained using citations. Knowledge of a JIF can predict NOD with some permissible error.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"371 - 388"},"PeriodicalIF":1.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41486932","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":"Gender differences, data carpentry and bibliometric studies in Mathematics","authors":"S. K. Jalal, Parthasarathi Mukhopadhyay","doi":"10.1080/09737766.2022.2090873","DOIUrl":"https://doi.org/10.1080/09737766.2022.2090873","url":null,"abstract":"Libraries deal with large amounts of data in the digital environment. Librarians manipulate, update and integrate data on e-journals & e-books every year to the new knowledge base or in their intended library software. Data need to be cleaned, transformed and refined before uploading. OpenRefine is a useful data wrangling tool to filter, clean and transform the data before migration. The paper exercises largescale data cleaning, extraction and analysis of publication data (81,729) downloaded from Scopus during 2016-2020 in the field of Mathematics where at least one author is affiliated with an Indian institute or University. The result shows that 76,712(93.86%) documents have DOIs; sharp increase in ORCID from 4.27% (2016) to 26.25% (2020). The paper also shed a light on gender analysis and the gravity of its disparity in the field of Mathematics. Based on first author analysis, the result reveals that 73% are male authors whereas 27% are female based on the study of over half-lakh papers on Mathematics, where at least one author is from India. There is extreme inequality in gender distribution in the scientific research publications in mathematics.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"465 - 476"},"PeriodicalIF":1.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45609116","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":"Hegemony in global rankings: A Gramscian analysis of bibliometric indices and ranking results","authors":"Cüneyt Belenkuyu, Engin Karadağ","doi":"10.1080/09737766.2022.2106165","DOIUrl":"https://doi.org/10.1080/09737766.2022.2106165","url":null,"abstract":"Research in academic university rankings mainly focuses on the methodological improvements in ranking or concern the practice, not the principle. There is a tendency in the core literature of rankings that they are ontologically accepted as reality-reflecting phenomena. However, this research tries a political analysis of ranking systems as hegemonic governing apparatus within the Gramscian Theory of Hegemony framework. For this purpose, we analyzed the top 100 lists of global university rankings and indices used in the rankings as research indicator sources. Even if this research is designed as political analysis, we integrated statistical findings to reveal the hegemonic oligarchs in rankings. The results show that there is a dominance of the USA and major Western European countries in ranking results and indices in terms of possession of journals. Moreover, correlation analysis gives evidence that different ranking system results reproduce a pre-given hierarchy. Drawing on Gramsci, the article resists the view of rankings as apolitical, subjective performance criteria of educational value, instead makes the rankings open to discussion in the realm of contestable politics as valuation and hierarchization tools of academic capitalist and neoliberalist forces to shape higher education globally within the frames of the best model, defined by global elites.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"253 - 277"},"PeriodicalIF":1.0,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48384175","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":"The development of Indonesian e-Government: A bibliometric analysis","authors":"Ali Roziqin, Kismartini, A. N. Fajrina, Salahudin, T. Sulistyaningsih","doi":"10.1080/09737766.2021.2007036","DOIUrl":"https://doi.org/10.1080/09737766.2021.2007036","url":null,"abstract":"Researches on e-Government in Indonesia continue to proliferate. Although the development and discussion are multidisciplinary, a comprehensive understanding of the research direction and the latest developments is still challenging to understand and limited. This study provides the scientific information related to the Indonesian E-Government in the Scopus database through a bibliometric analysis, using the VOSviewer Software, Nvivo12 Plus, and Wordstat8. The study deals with the evaluation of structure, conceptual evolution, and trends of Indonesian e-Government following related publication. The results are from year 2015-2020, eighty-four publications are exploring Indonesian e-Government. There are seven clusters of concept related to eGovernment in Indonesia. The University of Indonesia is affiliated to most carries e-Government publications. The authors of Indonesia have involved other countries e-Government publications such as Australia, the United Kingdom, and Malaysia. Furthermore, e-Government study practices and theorists are more developed at the local level with the dominant theme of data, information, and services.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"49 - 74"},"PeriodicalIF":1.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48445726","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":"Unified theory of acceptance and use of technology (UTAUT) in mobile learning adoption : Systematic literature review and bibliometric analysis","authors":"A. Aytekin, Hakan Özköse, Ahmet Ayaz","doi":"10.1080/09737766.2021.2007037","DOIUrl":"https://doi.org/10.1080/09737766.2021.2007037","url":null,"abstract":"Various literature studies have been conducted to provide valuable information regarding the current research trend of Unified Technology Acceptance and Use Theory (UTAUT). When the literature was examined, it was seen that the UTAUT research on the adoption of mobile learning (M-learning) was ignored. Therefore, it was deemed necessary to conduct a literature study on the adoption of mobile learning. In this context, 31 research articles on the adoption of M-learning with UTAUT, published from 2003 to 2020, have been discussed for systematic literature research. These 31 specific research publications were discussed under four categories Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions. 63 different factors were identified after systematic literature review, except for UTAUT factors. These factors were grouped under 10 main factors. In addition, the authors in this field were identified by bibliometric analysis and the relationships between each other were determined by citation analysis. In addition to these, prominent terms have been determined according to the keywords and abstracts in the relevant articles. The connections between these terms have been created by the method of co-occurrence. Finally, the links between prominent terms and terms were examined with bibliometric analysis. According to the findings obtained, it has been determined that most UTAUT studies involving M-learning focus on extending UTAUT with external variables. It has been observed that the analyzed studies generally took place in the Asian countries. These studies have been carried out as multidisciplinary. In addition, it has been reported that most of these studies on M-learning take place in higher education settings. It is thought that the findings obtained at the end of the systematic literature review and bibliometric analysis study on the adoption of M-learning with UTAUT will constitute an important reference for academicians in this field.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"75 - 116"},"PeriodicalIF":1.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43307671","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":"Bibliographic coupling and types of centralities: A review of ASME journals - 2000 to 2020","authors":"S. B. Chaturbhuj, M. Sadik Batcha","doi":"10.1080/09737766.2022.2063090","DOIUrl":"https://doi.org/10.1080/09737766.2022.2063090","url":null,"abstract":"The present study deals with one of the three primary citation analysis methods, i.e., bibliographic coupling. It is believed that the two with the more common references are more related and have similar research interests. The study examined the author’s bibliographic coupling structure with the help of network analysis metrics and found a stronger association between the authors who contributed to the ASME journals. The analysis was conducted at the global level and the local level. In the global level analysis, the bibliographic coupling network analysed by the metrics like average degree, average weighted degree, the diameter of the network, average shortest path length, modularity, and average clustering coefficient. The local level analysis deals with cluster wise analysis to find dominant authors with the most bibliographical coupling strength. The top 50 bibliographically coupled authors represented in the study. Different types of centralities are used to retrieve different aspects and roles of authors in bibliographic coupling. Han, Je-Chin has found the highest bibliographic coupling strength with 53789 total link strengths. Zhu, Hui-Ren is the author who influences other authors in bibliographic coupling relation as his closeness centrality is 1.00. Li, Wei is the most prominent author who helps to expand the bibliographical coupling relation as his betweenness centrality is 13008.13. The study shows an individual network of the top ten bibliographically coupled authors and their coupling relation with others.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"161 - 186"},"PeriodicalIF":1.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44154567","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":"Scientometric analysis and visualisation of global information literacy from higher education perspective","authors":"Mallikarjun Kappi, B. S. Biradar","doi":"10.1080/09737766.2021.2017763","DOIUrl":"https://doi.org/10.1080/09737766.2021.2017763","url":null,"abstract":"Information literacy in higher education and academic research has proliferated. From 1991 to 2020, a total of 9,400 research publications on information literacy and higher education were produced steadily, as indexed in Web of Science (WoS) on 10 June 2021. This study shows the scientometric visualisation of information literacy and research in higher education using quantifiable characteristics from the publication’s dataset. The results disclose that the publication growth rate (16.84%) is highly significant for a synergistic response. Due to the productivity of authors, total of 470 papers were produced on an average per year from 1991 to 2020. Several academic publishers have allowed immediate access to their preprints and also allowed open access. The research output on Information Literacy has been published in more than 1256 journals. The results shows that most of the publications were in the domain of educational research and Library and Information Science. However, closely associated terms are health literacy, education, information literacy, higher education, and so on. Academic pivots are mainly located in Germany, USA, Australia, India, and Canada. The University of California, USA; The State University System of Florida, USA; and The University of London, UK are outstanding productive institutions. The G20 countries together produced 90% of the world’s research output on information literacy and higher education and also identified encouraging trends in collaborative research in several countries. Thus, the CI (3.757), DC (0.862), and CC (0.584) values are very substantial. Lastly, the geographic range of collaborating authors thereby visualized their linkages through co-occurrences. It analysed the influence of publications to show the most dominant contributions of global research on information literacy and higher education.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"125 - 143"},"PeriodicalIF":1.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45182791","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":"Scientometric mapping of global publication trends in health informatics domain","authors":"Garima Gujral, J. Shivarama","doi":"10.1080/09737766.2022.2030201","DOIUrl":"https://doi.org/10.1080/09737766.2022.2030201","url":null,"abstract":"Purpose: Over the last two decades, health informatics has garnered much attention with a rapid increase in the research output. This study aims to review and evaluate the global progress in the Health Informatics domain and assess the scholarly publication productivity. Design/Methodology: Based on data from the Web of Science databases, scientometric methods and knowledge visualization techniques were applied to evaluate the global trends, perform thematic analysis, identify gaps in knowledge and predict future trends of the health informatics domain from 2009 to 2021. Findings: The findings revealed that the field of Health Informatics has increased rapidly over the last decade. 3856 publications were produced from 2009 to 2021 and have gradually increased from 4.85% in 2009 to 69.63% in 2021 North American continent had the highest productivity with 63.38% global publication share out of 3856. USA (58.35%), Canada (7.62%) Australia (5.44%), and China (4.12%) were the leading countries with the highest publication productivity. Journal of The American Medical Informatics Association published by the American Medical Informatics Association is the leading journal with impact factor 3.428 (2018) and 236 publications. Harvard University was the leading position with 6.40% of publications. The United States Department of Health and Human Services is the leading funding body it has funded 782 publications. Conclusions: These findings will provide evidence of the current status and trends in Health Informatics all over the world, thus, helping scientific researchers and policymakers understand the panorama of Health Informatics and predict the dynamic directions of research.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"145 - 159"},"PeriodicalIF":1.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47018597","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":"Predicting access mode of multidisciplinary and library and information sciences journals using machine learning","authors":"H. Okagbue, C. A. Nzeadibe, J. A. Teixeira da Silva","doi":"10.1080/09737766.2021.2009745","DOIUrl":"https://doi.org/10.1080/09737766.2021.2009745","url":null,"abstract":"Academics and librarians might want to identify whether a journal is open access (OA) or subscription-based. While indexes and digital libraries might provide such information for known collections, it is possible that the access mode of a journal or body of journals might be unknown a priori. In this short analysis, a machine learning-based method is used to classify a journal’s access mode, OA or subscription, using its CiteScore and Journal Impact Factor (JIF). Using an initial pool of 91 multidisciplinary journals with a CiteScore, 38 journals with both a JIF and a CiteScore were selected (24 = OA; 14 = subscription). Using a data mining tool (Orange), ten machine learning models were applied (k nearest neighbor (kNN), Tree, support vector machine (SVM), Random forest, Neural network, Naïve Bayes, Logistic regression, Adaptive boosting (Adaboost)), Gradient Boosting (Scikit-learn) (GBS) and Gradient Boosting (catboost) (GBC). Adaboost, GBS and GBC showed the highest (100%) precision, sensitivity, and specificity. The 3 models correctly classify the access mode with zero error. The 3 optimum models were validated using then to predict the access mode of 54 (7 = OA; 47 = subscription) library and information science (LIS) journals and Adaboost and GBS gave perfect results with no misclassification. With these model, the access mode of multidisciplinary and LIS journals can be accurately and correctly predicted using only JIF-CiteScore data. Libraries in low-resource settings will benefit from the implementation of this research by designing a decision support system for the selection of journals.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"117 - 124"},"PeriodicalIF":1.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45899452","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":"Measuring the impact of co-author count on citation count of research publications","authors":"Ali Daud, Malik Khizar Hayat, Abdulrahman A. Alshdadi, Ameena T Banjar, W. Alharbi","doi":"10.1080/09737766.2021.2016356","DOIUrl":"https://doi.org/10.1080/09737766.2021.2016356","url":null,"abstract":"Practically, co-authored research work reaches higher visibility and impact as compared to the individual published work. The objective of this study is to analyze the correlation between the number of coauthors in a published paper and the number of times that paper is cited in the literature. The analysis is divided into three categories: (i) research field-based analysis; (ii) influential co-author-based analysis and (iii) influential first author-based analysis. The ArnetMiner dataset version 6 is used for analysis. The research methodology is composed of research-field-based, influential co-authors-based, and influential co-author as a first author-based correlational analysis of citations for research articles. The research area is defined for each research article using the abstract from the dataset. The results show that most of the research fields have increasing citability with a greater number of co-authors. Research fields like programming languages carry more citations and knowledge representation and reasoning carry fewer citations with a higher number of co-authors in a paper. With an increased H-index of co-author and first co-author in a paper, the association between co-authors and citations is more negative than positive. However, in the field of bioinformatics, the association is positive both with influential an co-author and first co-author of a paper. This paper fulfils the need to identify role of collaboration in gaining research citability. It enhances the credibility of research both in academia and industry.","PeriodicalId":10501,"journal":{"name":"COLLNET Journal of Scientometrics and Information Management","volume":"16 1","pages":"35 - 48"},"PeriodicalIF":1.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42567868","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}