The impact of editorial policies and journal models on the number of articles on social humanities and communication studies related to Covid-19; Evidence from international and Indonesian scholarly work

This study examines scientific publications during a pandemic through bibliometric network analysis. We explored three different journal databases to map COVID-19 research in humanities and social sciences, then zoom in to communication studies. Government policy, e-learning, anxiety, economic impact are popular keywords in international and Indonesian articles on Social and Humanities, while disinformation, health communication, behavior change, and literacy are more prominent in communication articles. The researcher chose the keyword occurrence analysis as the basis for mapping the research theme. The bibliographic network was deployed in three strategies to obtain keyword data co-occurrence from research abstracts, keywords from researchers, and coder's approval keywords. Lastly, Vos viewer is used to creating macro and detailed perspective networks for interpretation. The results show that journal policies and models affect the number of COVID-19 publications in the journal. Finally, this study provides an overview that normative theory and behaviorism play a role in social and communication research.

Discovering the role model of authors by embedding research history

A role model that supports career planning is important for authors in the academic area to improve research abilities. In this study, we discovered a role model in bibliographic networks based on two perspectives: (1) high research performance to be exemplary and (2) a similar research history that can be easily followed by authors. We assume that the year-wise subgraphs in the dynamic bibliographic network signify the ‘research history’. We discovered role models of authors in three steps: (1) learning vector representations of research history in dynamic bibliographic networks, (2) measuring the similarity of authors according to the research history and (3) visualising role models. With this process, we can recommend a reasonable role model whose research path the authors can easily follow. In addition, we verified the effectiveness of the research history embeddings and the accuracy of the recommended role model in a real data set.

THIRTY YEARS OF TEFLIN Journal - A publication on the teaching and learning of English: A BIBLIOMETRIC PORTRAIT THROUGH THE LENS OF MICROSOFT ACADEMIC

To examine bodies of literature from the levels of topics, regions, nations, and journals, a lot of bibliometric studies have been conducted in many fields. However, such studies are a rare undertaking in the field of English language teaching, especially at a journal level. To celebrate the TEFLIN Journal - A publication on the teaching and learning of English’s 30th anniversary, this study exhibits a bibliometric portrait of its publication, indexation, and citation from 1990 to 2019. Two pieces of free software, Publish or Perish and VOSviewer were adopted to conduct the descriptive and network analyses of bibliographic data from Microsoft Academic. In terms of 19 out of 27 metrics in Publish or Perish software, the journal’s publication and citation metrics have risen during its lifetime. The bibliographic network identifies the most productive authors, institutions, and countries along with the co-authorship pattern, type of top-cited articles, and top-used keywords. The article relatedness is also sighted in terms of the citation frequency and number of shared references. Even though the analyses were complicated by some missing articles and improper indexation, this study could still take a full-length bibliometric portrait of the journal during its 30-year journey between the commitment to competence and the quest for higher impact.

A Literature Review on MOOCs Integrated With Learning Analytics

Although there have been numerous studies committed to MOOCs integrated with learning analytics, fewer of them have systematically reviewed the related literature. Using clustering techniques, bibliographic network visualization, content analysis, and STARLITE method, this study systematically reviews the literature in terms of learning analytics models, platforms of MOOCs, effect of learning analytics, effect of engagement, self-regulation, motivation, and online interactions, as well as other influencing factors of the effectiveness of MOOCs integrated with learning analytics. It provides constructive suggestions for designers, researchers, learners, and instructors of MOOCs so as to lower down learner dropout rates, increase completion rates, enhance learner engagement, and better learning outcomes. Future research into MOOCs could focus on the improvements on learning analytics because learning analytics could exert an essential influence on the effectiveness of MOOCs.

Interinstitutional Research Team Formation Based on Bibliographic Network Embedding

This study aims at forming research teams for interinstitutional collaborations. Research institutes have their own purposes and topics of interest. Thus, supporting joint research between multiple institutes, we have to consider not only synergies between scholars but also purposes of the institutes. To solve this problem, we propose a bibliographic network embedding method that can learn characteristics of institutes, not only of each scholar. First, we compose a bibliographic network that consists of scholars, publications, venues, research projects, and institutes. Collaboration styles and research topics of institutes and scholars are extracted by mining subgraphs from the bibliographic network. Then, vector representations of network nodes are learned based on occurrences of subgraphs on the nodes and neighborhoods of the nodes. Based on the vector representations, we train multilayer perceptrons (MLP) to assess collaboration probability between scholars affiliated in different institutes. For training the MLP, we suggest three strategies: (i) considering every collaboration, (ii) focusing on interinstitutional collaborations, and (iii) focusing on collaboration outcomes. To evaluate the proposed methods, we have analyzed research collaborations of POSTECH (Pohang University of Science and Technology) and RIST (Research Institute of Industrial Science and Technology) from 2011 to 2020. Then, we conducted the research team formation for joint research of the two institutes according to two purposes: pure research and commercialization research.

Research on trade credit – a systematic review and bibliometric analysis

Purpose Trade credit (TC) is a financing provision by non-financing firms. The multi-disciplinary research field has sustained scholarly attention for long. Pursuant to the gap for a comprehensive summary of the literature confined to the areas of Finance and Economics, this study aims to provide quantitative and qualitative insights not fully captured or analysed in previous reviews. Design/methodology/approach Contextualized systematic literature review (SLR) and bibliometric techniques are used to map the thematic, intellectual and conceptual structures latent in 138 articles published in top journals. Findings The top authors, top journals and major themes are recognized using bibliometric techniques followed by an in-depth bibliographic-network-based-content-analysis. Five major clusters indicating the five research dimensions within the specialized field are identified and extensively reviewed. Empirical validation of key theories is discussed in the contents and a conceptual model is developed. Finally, the study has identified key research gaps to set the direction for future research. Research limitations/implications The scope of the literature selection is confined to the areas of finance and economics. Future studies could elaborate on a broader perspective. Originality/value The study contributes by offering a conceptual model latent in the literature on TC. It derives major research gaps to set the direction of future research. Also, the combination of SLR and bibliometrics is a methodological contribution in this research domain.

Systematic literature network analysis in accounting: A first application on integrated reporting research

A Over the past decade, we have been witnessing an exponential growth in the number of publications on Integrated Reporting, with the aim of exploring challenges, opportunities and implications of its adoption. Given the abundance of studies, which are often characterized by conflicting evidences, it can be complex to pinpoint all the seminal works already published: it raises the need to develop methodologies which can help to screen the existing literature and to detect the articles which contribute the most to the scientific research. However, little is known about structured approaches in accounting studies: thus, in order to extract the backbones of the research tradition on Integrated Reporting, in this paper we apply the dynamic literature review method called "Systematic Literature Network Analysis", which combines systematic literature review and bibliographic network analysis. Furthermore, our findings confirm how this methodology may be exploited as a research tool to support dynamic analyses for drawing agendas for future research in the accounting fields of study.