A Bibliometric Study on Learning Analytics

Learning analytics tools and techniques are continually developed and published in scholarly discourse. This study aims at examining the intellectual structure of the Learning Analytics domain by collecting and analyzing empirical articles on Learning Analytics for the period of 2004-2018. First, bibliometric analysis and citation analyses of 2730 documents from Scopus identified the top authors, key research affiliations, leading publication sources (journals and conferences), and research themes of the learning analytics domain. Second, Domain Analysis (DA) techniques were used to investigate the intellectual structures of learning analytics research, publication, organization, and communication (Hjørland & Bourdieu 2014). The software of VOSviewer is used to analyze the relationship by publication: historical and institutional; author and institutional relationships and the dissemination of Learning Analytics knowledge. The results of this study showed that Learning Analytics had captured the attention of the global community. The United States, Spain, and the United Kingdom are among the leading countries contributing to the dissemination of learning analytics knowledge. The leading publication sources are ACM International Conference Proceeding Series, and Lecture Notes in Computer Science. The intellectual structures of the learning analytics domain are presented in this study the LA research taxonomy can be re-used by teachers, administrators, and other stakeholders to support the teaching and learning environments in a higher education institution

Community, Natural Resources, and Sustainability: Overview of an Interdisciplinary and International Literature

The Special Issue Community, Natural Resources, and Sustainability seeks to engage in an interdisciplinary and international dialogue on the interrelationships of society, natural resources, and sustainability at the community level. In addition to introducing the twelve research articles published in this collection, we provide an overview of the existing literature on community and natural resource management, mainly through a review of previous reviews and a bibliometric analysis. While this literature is dominated by studies on various aspects of community-based natural resource management, the present Special Issue showcases multiple thematic areas of research that collectively contribute to a more complete understanding of the community-resources-sustainability linkages. Our review also pinpoints important gaps in existing meta-analyses and bibliometric analyses. Promising directions for future research are highlighted

Research collaboration analysis among the faculty members of Vidyasagar University during 1998-2022: a bibliometric study

Vidyasagar University, named after Pandit Iswar Chandra Vidyasagar was established as a state university in 1981. This university is known for research excellence in fields like science and technology, besides other branches of social sciences. This paper attempts to analyse the research collaboration among the faculty members of Vidyasagar University during 1998 to 2022 through bibliometric analysis. To this end, the paper extracted the data from Scopus database for the publications of the faculty members of Vidyasagar University during the said period. To analyse the data and visualise the research aspects Bibliometrix R package, Bibexcel, MS Excel and VOSviewer were used. The study showed that, the total number of publications during the period was 4824 with an average of 1.36 authors per publication. The faculty members of Vidyasagar University exhibited a high degree of collaboration with a collaboration index of 0.94. The study concludes that the faculty members of Vidyasagar University have shown remarkable research collaboration and enabled the university to achieve a considerable degree of research excellence

Science Models as Value-Added Services for Scholarly Information Systems

The paper introduces scholarly Information Retrieval (IR) as a further dimension that should be considered in the science modeling debate. The IR use case is seen as a validation model of the adequacy of science models in representing and predicting structure and dynamics in science. Particular conceptualizations of scholarly activity and structures in science are used as value-added search services to improve retrieval quality: a co-word model depicting the cognitive structure of a field (used for query expansion), the Bradford law of information concentration, and a model of co-authorship networks (both used for re-ranking search results). An evaluation of the retrieval quality when science model driven services are used turned out that the models proposed actually provide beneficial effects to retrieval quality. From an IR perspective, the models studied are therefore verified as expressive conceptualizations of central phenomena in science. Thus, it could be shown that the IR perspective can significantly contribute to a better understanding of scholarly structures and activities.Comment: 26 pages, to appear in Scientometric

Identifying trends and flows in Communication and Information Processing by means of keyword network analysis

The purpose of this paper is to identify influential themes and knowledge flows in the area of communications and information processing and suggest trends that are likely to make (or continue making) an impact. We applied keyword network analysis on articles whose keywords match the themes of the International Conference on Communication and Information Processing, collected through the Thompson Reuters’ Web of Science and studied the articles’ thematic interconnections and their dynamics. The keyword network was found to be clustered around the themes cloud, data, mobile, security, semantic and social. Security and embeddedness are found to be the most dominant topics, common to all groups. Design and performance are key influencers of thematic flows and data mining/analysis are close to all nodes/keywords and therefore most popular. Big data, data fusion/integration and e-government are themes identified as potentially strong future influencers

Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions

Artificial Intelligence (AI) is increasingly adopted by organizations to innovate, and this is ever more reflected in scholarly work. To illustrate, assess and map research at the intersection of AI and innovation, we performed a Systematic Literature Review (SLR) of published work indexed in the Clarivate Web of Science (WOS) and Elsevier Scopus databases (the final sample includes 1448 articles). A bibliometric analysis was deployed to map the focal field in terms of dominant topics and their evolution over time. By deploying keyword co-occurrences, and bibliographic coupling techniques, we generate insights on the literature at the intersection of AI and innovation research. We leverage the SLR findings to provide an updated synopsis of extant scientific work on the focal research area and to develop an interpretive framework which sheds light on the drivers and outcomes of AI adoption for innovation. We identify economic, technological, and social factors of AI adoption in firms willing to innovate. We also uncover firms' economic, competitive and organizational, and innovation factors as key outcomes of AI deployment. We conclude this paper by developing an agenda for future research

A Bibliometric Analysis in Industry 4.0 and Advanced Manufacturing: What about the Sustainable Supply Chain?

During the last decade, different concepts, methodologies, and technologies have appeared, evolving industry toward what we know today as the fourth industrial evolution or Industry 4.0 (I4.0) and Advanced Manufacturing (AM). Based on both, Supply Chain (SC) is presented as the relevant process that sets the sustainability of manufacturing and, therefore, is defined as a key term in a sustainable approach to I4.0. However, there are no studies that analyze the evolution of science in the fields of I4.0 and AM together. In order to fill this gap, the aim of this research work is to analyze the tendencies of science research related to I4.0 and AM by conducting a bibliometric and network analysis and also to generate a new contribution through the analysis of scientific trends related to SC and Sustainable Supply Chain (SSC) within this scientific context, for the time span 2010–2019. The results show that the number of publications is growing exponentially and the most active countries are Germany and the U.S., with Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University being the most productive organization and Tecnologico de Monterrey the most collaborative. The analysis of the scientific terms allows us to conclude that the research field is in a growth phase, generating up to almost 4500 new terms in 2019

A BIBLIOMETRIC STUDY ON BLOCKCHAIN CONCEPT: A THEME ANALYSIS AND FUTURE DIRECTIONS FOR COMPUTER SCIENCE TRAINING

This paper aims to study the blockchain concept domain in the computer science field due to bibliometric study. Authors employed bibliometric and network analysis techniques to analyze existing literature. In total, 719 articles in the period of 2019 to August 2023 from the Web of Science (WOS) database were analyzed after applying search string, and criteria for inclusion and exclusion. Initial data screening involved the extraction of fundamental information, followed by data analysis based on co-occurrence, bibliographic coupling, and citation using special program software VOSviewer and R program. research areas "compute science" and "engineering". In addition to that, VOSviewer and R-based tools illustrate the application of text mining involves utilizing computational techniques to extract, analyze, and represent the key concepts and relationships within the field of blockchain technology. Data analysis primarily involved co-occurrence analysis, bibliographic coupling, co-authorship examination, citation analysis, and co-citation analysis. In the context of a blockchain concept thematic analysis, was applied clustering by coupling. Furthermore, it was conducted the thematic analysis to scrutinize the content of prior studies in the computer science field using clustering by coupling. Ranking of the authors, organizations, and countries was applied according to total link strength metric which was used to quantify the overall strength of connections between nodes within a network. Besides, citation analysis has also been conducted to assess the articles' ranking, considering both worldwide and localized citations. Bibliometric results indicate blockchain concepts within such thematic frameworks as access control scheme, identity management system, supply chain management, artificial intelligence integration, blockchain technology applications, and blockchain smart contract

Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis

The aim of the present research is to provide a new systematic methodology to explore potential R&D collaboration partners using patent information. The potential R&D collaboration partners are visualized as a patent assignee level-map based on technological similarity between patents by using network analysis. The proposed framework utilises two analytic methods to measure technological similarity. The first method, bibliographic coupling analysis, measures technological similarity based on the citation relationship using patent bibliographic information. Second, latent semantic analysis is utilized based on semantic similarity using patent textual information. The fuel cell membrane electrode assembly (MEA) technology field is selected and applied to illustrate the proposed methodology. The proposed approach allows firms, universities, research institutes, governments to identify potential R&D collaborators as a systematic decision-making support tool.This is an Accepted Manuscript of an article published by Taylor & Francis in Technology Analysis and Strategic Management on the 22nd of October 2014, available online: http://wwww.tandfonline.com/10.1080/09537325.2014.971004. This version will be under embargo until the 22nd of April 2016