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The purpose of this paper is to generalize the teaching characteristics of some typical colleges in China which offer Informetrics courses, and to propose useful suggestions for the course construction of Informetrics through empirical investigation.

The statuses of Informetrics curriculum education of the top ten universities in Library and Information Science and Archives Management in Chinese mainland as well as Fu Jen Catholic University in Taiwan have been investigated and analyzed through a network survey and a telephone survey. Wuhan University and Fu Jen Catholic University are considered as the typical examples.

The educational development of Informetrics in China has gone through three stages: the initial stage, the developing stage and the improving stage. Based on the empirical investigation and analysis, some meaningful conclusions are drawn concerning the education system, teaching objectives, teaching contents and teaching methods of Informetrics courses.

This paper provides curricular information about Informetrics courses in China.

This article presents a novel user‐oriented interface for generalised informetric analysis and demonstrates how informetric calculations can easily and declaratively be specified through advanced data modelling techniques. The interface is declarative and at a high level. Therefore it is easy to use, flexible and extensible. It enables end users to perform basic informetric ad hoc calculations easily and often with much less effort than in contemporary online retrieval systems. It also provides several fruitful generalisations of typical informetric measurements like impact factors. These are based on substituting traditional foci of analysis, for instance journals, by other object types, such as authors, organisations or countries. In the interface, bibliographic data are modelled as complex objects (non‐first normal form relations) and terminological and citation networks involving transitive relationships are modelled as binary relations for deductive processing. The interface is flexible, because it makes it easy to switch focus between various object types for informetric calculations, e.g. from authors to institutions. Moreover, it is demonstrated that all informetric data can easily be broken down by criteria that foster advanced analysis, e.g. by years or content‐bearing attributes. Such modelling allows flexible data aggregation along many dimensions. These salient features emerge from the query interface‘s general data restructuring and aggregation capabilities combined with transitive processing capabilities. The features are illustrated by means of sample queries and results in the article.

The purpose of this article is to investigate the use of word co-occurrence analysis method in mapping of the scientific fields with emphasis on the field of Informetrics.

This is an applied study using scientometrics, co-word analysis and network analysis and its steps are summarised as follows: collecting the data related to the Informetrics field indexed in Web of Science (WOS) database, refining and standardising the keywords of the extracted articles from WOS and preparing a selected list of these keywords, drawing the word co-occurrence map in the Informetrics field and analysing of results.

Based on the resulted maps the concepts such as information science, library, bibliometric analysis, innovation and text mining are the most widely used topics in the field of Informetrics. The co-word occurrence maps drawn at different periods show the changes and stabilities in the concepts related to the field of Informetrics. A number of topics such as “bibliometric analysis” are present in all years, whereas others such as “innovation” have disappeared. New topics emerge as a recombination of existing topics and in interaction with new (technological) developments.

The results of these analytical studies can be used as a guide for determining research priorities in the scientific fields, and also for planning and management in academic institutions.

The aim of this paper is to propose a conceptual model of the field of informetrics. Specifically, the paper argues that informetrics comprises the study of entities in three dimensions: the social, documentary and epistemic dimensions containing respectively agents, documents, and concepts or cognitions.

The paper outlines a conceptual model, drawing on earlier work by Kochen, Leydesdorff, Borgman and others. Subsequently, each dimension and interdimensional relation is analyzed and discussed.

It is shown that not every study necessarily involves each of the three dimensions, but that the field as a whole cannot be reduced to one or two of them. Moreover, the dimensions should be kept separate but they are not completely independent. The paper discusses what kinds of relations exist between the dimensions. Special attention is given to the nature of the citation relation within this framework. The paper also considers the place of concepts like mapping, proximity and influence in the model.

This conceptual paper is a first step. Multi‐relational networks may be a key instrument to further the study of the interplay between the three dimensions.

The paper provides a framework to characterise informetric studies and makes the characteristics of the field explicit.

This paper focuses on the differences in domain intellectual structure discovery between author bibliographic coupling analysis (ABCA) and author co-citation analysis (ACA) considering all authors. The purpose of this study is to examine whether and in what ways these two all-author network approaches yield different results.

The sample was collected from the database of Web of Science, including all articles published in Scientometrics and Journal of Informetrics from 2011 to 2020. First, 100 representative authors were selected from each set, and ABCA matrices and ACA matrices were constructed. Second, factor analysis was carried out on the matrices, to detect the intellectual structure of scientometrics and informetrics.

The intellectual structures identified by ABCA and ACA are similar overall, but the results differ somewhat when it comes to specific structures. The ABCA is more sensitive to some highly collaborative research teams and presents a clearer picture of current intellectual structures and trends while ACA seems to have some advantages in representing the more traditional and proven research topics in the field. The combined use of ABCA and ACA allows for a more comprehensive and specific intellectual structure of research fields.

This paper compares the performance of ABCA and ACA detecting the intellectual structure of the domain from the perspective of all authors, revealing the intellectual structure of scientometrics and informetrics comprehensively.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2020-0540.