Key nodes mining in the inventor–author knowledge diffusion network

Do academic inventors have diverse interests?

Academic inventors bridge science and technology, and have attracted increasing attention. However, little is known about whether they have more diverse research interests than researchers with a single role, and whether their important position for science–technology interactions correlates with their diverse interests. For this purpose, we describe a rule-based approach for matching and identifying academic inventors, and an author interest discovery model with credit allocation schemes is utilized to measure the diversity of each researcher’s interests. Finally, extensive empirical results on the DrugBank dataset provide several valuable insights. Contrary to our intuitive expectation, the research interests of academic inventors are the least diverse, while those of authors are the most. In addition, the important position of the researchers has a certain relation with the diversity of research interests. More specifically, the degree of centrality has a significant positive correlation with the diversity of interests, and the constraint presents a significant negative correlation. A significant weaker negative correlation can also be observed between the diversity of research interests of academic inventors and their closeness centrality. The normalized betweenness centrality seems be independent from interest diversity. These conclusions help understand the mechanisms of the important position of academic inventors for science–technology interactions, from the perspective of research interests.

Mapping the literature on the application of artificial intelligence in libraries (AAIL): a scientometric analysis

Purpose

Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is applied in all spheres of life as reflected in the review of the literature section here. As applicable in the field of libraries too, this study scientifically mapped the papers on AAIL and analyze its growth, collaboration network, trending topics, or research hot spots to highlight the challenges and opportunities in adopting AI-based advancements in library systems and processes.

Design/methodology/approach

The study was developed with a bibliometric approach, considering a decade, 2012 to 2021 for data extraction from a premier database, Scopus. The steps followed are (1) identification, selection of keywords, and forming the search strategy with the approval of a panel of computer scientists and librarians and (2) design and development of a perfect algorithm to verify these selected keywords in title-abstract-keywords of Scopus (3) Performing data processing in some state-of-the-art bibliometric visualization tools, Biblioshiny R and VOSviewer (4) discussing the findings for practical implications of the study and limitations.

Findings

As evident from several papers, not much research has been conducted on AI applications in libraries in comparison to topics like AI applications in cancer, health, medicine, education, and agriculture. As per the Price law, the growth pattern is exponential. The total number of papers relevant to the subject is 1462 (single and multi-authored) contributed by 5400 authors with 0.271 documents per author and around 4 authors per document. Papers occurred mostly in open-access journals. The productive journal is the Journal of Chemical Information and Modelling (NP = 63) while the highly consistent and impactful is the Journal of Machine Learning Research (z-index=63.58 and CPP = 56.17). In the case of authors, J Chen (z-index=28.86 and CPP = 43.75) is the most consistent and impactful author. At the country level, the USA has recorded the highest number of papers positioned at the center of the co-authorship network but at the institutional level, China takes the 1st position. The trending topics of research are machine learning, large dataset, deep learning, high-level languages, etc. The present information system has a high potential to improve if integrated with AI technologies.

Practical implications

The number of scientific papers has increased over time. The evolution of themes like machine learning implicates AI as a broad field of knowledge that converges with other disciplines. The themes like large datasets imply that AI may be applied to analyze and interpret these data and support decision-making in public sector enterprises. Theme named high-level language emerged as a research hotspot which indicated that extensive research has been going on in this area to improve computer systems for facilitating the processing of data with high momentum. These implications are of high strategic worth for policymakers, library stakeholders, researchers and the government as a whole for decision-making.

Originality/value

The analysis of collaboration, prolific authors/journals using consistency factor and CPP, testing the relationship between consistency (z-index) and impact (h-index), using state-of-the-art network visualization and cluster analysis techniques make this study novel and differentiates it from the traditional bibliometric analysis. To the best of the author's knowledge, this work is the first attempt to comprehend the research streams and provide a holistic view of research on the application of AI in libraries. The insights obtained from this analysis are instrumental for both academics and practitioners.

Analysing scientific publications in the field of mobile information systems using bibliometric analysis

Purpose

This paper aims to present a longitudinal and visualising study using bibliometric approaches to depict the emerging trends and research hotspots within the mobile information system domain.

Design/methodology/approach

Publications included in the Web of Science (WoS) database for 2001–2021 are reviewed and analysed on various aspects through coauthorship, cocitation and co-occurrence analysis. The analyses are conducted using VOSViewer, a scientific visualisation software program.

Findings

Academic publications related to mobile information systems fluctuated at a low level during the initial part of the 21st century and have grown rapidly in number in the past decade. The USA and China are the leading contributors to these publications and hold dominant positions in the obtained collaboration network. Computer science, engineering and telecommunications are the top three research areas in which mainstream mobile information system research occurs. Moreover, medical informatics and health-care science services have gradually become new research hotspots.

Originality/value

This study provides a systematic and holistic account of the developmental landscape of the mobile information system domain. This study provides a good basis for analysing the evolution of research in mobile information systems and may serve as a potential foundation for future research.

Exploring the intellectual structure and evolution of 24 top business journals: a scientometric analysis

Purpose

This paper aims to present a longitudinal and visualizing study using scientometric approaches to depict the historical changes in the academic community, intellectual base and research hotspots within the business domain.

Design/methodology/approach

Two mapping methods are used, namely, co-citation analysis and co-occurrence analysis. Both the co-citation analysis and co-occurrence analysis in this study are conducted using CiteSpace, a Java-based scientific visualization software.

Findings

This paper detects changes in academic communities in 24 business journals chosen by the University of Texas at Dallas as leading journals (UTD24) and identifies the research hotspots such as corporate governance, organizational research and capital research. Many authors and academic communities appear in two or even three periods, which indicates the lasting academic vitality of scholars in this field. This paper determines the evolution of scholars' research interests by identifying high-frequency keywords during the entire period.

Originality/value

This paper reveals a systematic and holistic picture of the developmental landscape of the business domain, which can provide a potential guide for future research. Furthermore, based on empirical data and knowledge visualization, the intellectual structure and evolution of the business domain can be identified more objectively.