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Purpose This research aims to examine the intellectual structure of iMetrics through author co-citation analysis. Design/methodology/approach This research uses common techniques in bibliometrics and social network analysis. It analyses 5,944 records from the Web of Science in the field of iMetrics that are published between 1978 and 2014. Findings Findings indicated that researchers including “Garfield”, “Egghe”, “Glanzel”, “Leydesdorff” and “Price” have received many co-citations. The author co-citation analysis in iMetrics resulted in eight thematic clusters, including “theoretical foundations and citation analysis”, “sociology of science”, “science mapping and visualization”, “network analysis”, “classic laws of bibliometrics”, “webometrics”, “technometrics” and “miscellaneous”. “Theoretical foundations and citation analysis” is the biggest cluster which comprises 59 authors. The results suggest the crucial role of price medallists in shaping the intellectual structure of knowledge in iMetrics. Originality/value Extracting the patterns embedded in the knowledge structure of iMetrics studies provides beneficial information for both researchers and policymakers. This research study is valuable that used an appropriate set of records regarding both recall and precision. Furthermore, this study helps us better understand the characteristics of iMetrics, its subject areas, and the prominent authors in those areas.

Purpose The purpose of this paper is to identify the top researchers in information behaviour (IB) based on ideational and social influence indicators. Design/methodology/approach The population included papers on IB indexed in the Web of Science from 1980 to 2015. UCINET and Bibexcel were the tools used for measuring the ideational and social influence indicators. The correlations among the study variables were measured by applying SPSS and LISREL. Findings There was a significant relationship between IB researchers’ productivity and performance, and between ideational influence and social influence. The structural equation modelling showed that a researcher with top placement in his/her co-authorship network can gain higher ideational influence. In total, it seems that the single and traditional criteria are increasingly replacing new and integrative ones in measuring researchers’ scientific influence in fields including IB studies. Results have shown that based on total scores of the studied indicators, Spink, A., Nicholas, D., Ford, N., Huntington, P., Wilson, T.D., and Jamali, H.R. gained the high scores. Originality/value The current study used an integrative method based on influence indicators to identify the influential researchers in IB studies. None of the few studies done using bibliometric methods in the realm of IB has investigated the ideational and social influence indicators altogether.

Background: Nowadays, due to the increased publication of articles in various scientific fields, identifying the publishing trends and emerging keywords in the texts of these articles is essential. Objectives: Thus, the present study identified and analyzed the keywords used in the published articles on medical librarianship and information. Methods: In the present investigation, an exploratory and descriptive approach was used to analyze librarianship and information articles published in specialized journals in this field from 1964 to 2019 by applying text mining techniques. The TF-IDF weighting algorithm was applied to identify the most important keywords used in the articles. The Python programming language was used to implement text mining algorithms. Results: The results obtained from the TF-IDF algorithm indicated that the words “Library”, “Patient”, and “Inform” with the weights of 95.087, 65.796, and 63.386, respectively, were the most important keywords in the published articles on medical librarianship and information. Also, the words “Catalog”, “Book”, and “Journal” were the most important keywords used in the articles published between the years 1960 and 1970, and the words “Patient”, “Bookstore”, and “Intervent” were the most important keywords used in articles on medical librarianship and information published from 2015 to 2020. The words “Blockchain”, “Telerehabilit”, “Instagram”, “WeChat”, and “Comic” were new keywords observed in articles on medical librarianship and information between 2015 and 2020. Conclusions: The results of the present study revealed that the keywords used in articles on medical librarianship and information were not consistent over time and have undergone a change at different periods so that nowadays, this field of science has also changed following the needs of society with the advent and growth of information technologies.

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