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PurposeThis study continues a long history of author co-citation analysis of the intellectual structure of information science into the time period of 2011–2020. It also examines changes in this structure from 2006–2010 through 2011–2015 to 2016–2020. Results will contribute to a better understanding of the information science research field.Design/methodology/approachThe well-established procedures and techniques for author co-citation analysis were followed. Full records of research articles in core information science journals published during 2011–2020 were retrieved and downloaded from the Web of Science database. About 150 most highly cited authors in each of the two five-year time periods were selected from this dataset to represent this field, and their co-citation counts were calculated. Each co-citation matrix was input into SPSS for factor analysis, and results were visualized in Pajek. Factors were interpreted as specialties and labeled upon an examination of articles written by authors who load primarily on each factor.FindingsThe two-camp structure of information science continued to be present clearly. Bibliometric indicators for research evaluation dominated the Knowledge Domain Analysis camp during both fivr-year time periods, whereas interactive information retrieval (IR) dominated the IR camp during 2011–2015 but shared dominance with information behavior during 2016–2020. Bridging between the two camps became increasingly weaker and was only provided by the scholarly communication specialty during 2016–2020. The IR systems specialty drifted further away from the IR camp. The information behavior specialty experienced a deep slump during 2011–2020 in its evolution process. Altmetrics grew to dominate the Webometrics specialty and brought it to a sharp increase during 2016–2020.Originality/valueAuthor co-citation analysis (ACA) is effective in revealing intellectual structures of research fields. Most related studies used term-based methods to identify individual research topics but did not examine the interrelationships between these topics or the overall structure of the field. The few studies that did discuss the overall structure paid little attention to the effect of changes to the source journals on the results. The present study does not have these problems and continues the long history of benchmark contributions to a better understanding of the information science field using ACA.

Quality function deployment (QFD) is a customer-driven product development tool used to convert customer requirements into engineering characteristics to maximize customer satisfaction. In real applications, however, the traditional QFD method has been criticized as having lots of deficiencies. Over the past decades, many models and approaches have been suggested for improving QFD, but a paucity of contributions are devoted to review and summarize the related researches on the basis of bibliometric analysis. In this paper, we conduct a comprehensive bibliometric analysis of the journal articles on QFD improvement during the years of 1999–2020. First, the metadata analysis of identified articles was presented to clarify the research progress and development trend in this field. Then, bibliometric analyses of the selected articles are conducted to identify the most prolific and influential researchers, the most productive institutions and their collaborations, and the intellectual structure of studies in QFD development. Via keyword analysis, the research focuses and emerging research trends for QFD improvement are found out. Finally, blind spots and future research directions in the area are summarized to provide valuable reference for the future research of QFD advancement.

This study introduces MAK approach to investigate intellectual structure of fields which combines text-net analysis (TNA), latent dirichlet allocation (LDA), and co-citation analysis. Researchers have previously deployed co-citation analysis to reveal the intellectual structure of fields. However, in these applications, the research has two technical limitations—small representativeness in datasets analyzed and the primary consideration for dated documents—towards the co-citation analysis. These limitations impede the formation of a larger picture in the structure. The present study seeks to eliminate these limitations by utilizing TNA and LDA methods as topic modeling approaches for 38,368 journal articles as references with 125,154 appearances in 2680 articles published between 1980 and 2019 in the Strategic Management Journal (SMJ). We suggest researchers should embrace MAK approach as complementary approach to research, with its focus on the intellectual structures of the field. We provide a workflow to show potential research applications and address advantages and limitations associated with the two new methods.

Studies around public–private partnerships (PPPs) have shaped a complex and colorful research field. A series of review studies have been performed to explore the knowledge base of this field. Despite their significant contributions, bibliometric research on the PPPs literature is still needed to capture more comprehensive, diverse and detailed information in this area from a holistic perspective, for reducing subjectivity and one-sidedness. Under this situation, this paper continues the bibliometric journey by conducting a comprehensive metrological and content analysis of the PPPs research field. By applying a newly developed Bibliometrix R-package tool, the overview of PPPs research is presented via metrological analysis using a series of indexes. The intellectual structure of this domain is then explored via content analysis using the methods of keywords analysis and citation analysis from both static and dynamic perspectives. Consequently, the panoramic view including the overview, pivotal points of topics, thematic evolution and research focuses of this domain are depicted visually and intuitively via a set of science maps. This new attempt crystallizes out key findings and valuable information of PPPs research, which can consolidate and broaden the bibliometric findings of previous PPPs literature studies and act as a guidance for analyzing the knowledge base of other research fields.

Ecuador has shown a growth in its scientific production since 2011, representing 85% of the total historical production. These investigations are reflected in scientific publications, which address world interest topics and serve as a link for the university, business, and society. This work aims to analyze the scientific production generated by Ecuador in the period of 1920–2020 using bibliometric methods to evaluate its intellectual structure and performance. The methodology applied in this study includes: (i) terms definition and search criteria; (ii) database selection, initial search, and document compilation; (iii) data extraction and software selection; and finally, (iv) analysis of results. The results show that scientific production has been consolidated in 30,205 documents, developed in 27 subject areas, in 13 languages under the contribution of 84 countries. This intellectual structure is in harmony with the global context when presenting research topics related to “Biology and regional climate change”, “Higher education and its various approaches”, “Technology and Computer Science”, “Medicine”, “Energy, food and water”, and ”Development and applications on the Web”. Topics framed in the Sustainable Development Goals (SDGs), sustainability, climate change, and others. This study contributes to the academic community, considering current re-search issues and global concerns, the collaboration between universities and countries that allow establishing future collaboration links.

MACBETH stands for measuring attractiveness by a categorical based evaluation technique. It is a multiple criteria decision analysis technique created by Carlos Bana e Costa and Jean-Claude Vansnick in the early 1990s. Due to both the increasing application of the technique and progress achieved to date, this study aims to conduct a bibliometric literature review on MACBETH. The database for the review contains 192 scientific articles published in scholarly publications indexed in the Scopus database from 1994 to 2016. Although other literature reviews of the technique exist, this study has a different emphasis and uses two distinct citation analysis methods-bibliographic coupling and co-citation-, allowing us to generate new insights regarding some of the most active and influential articles, journals and authors, as well as to reveal rankings, clusters of contributions and collaboration networks. The results obtained are important to enhance our understanding of the technique's anatomy (i.e., its intellectual structure), and can support the identification of new lines of application and development. info:eu-repo/semantics/publishedVersion

Intelligent systems in the framework of scientific and technological progress are now widely used in various fields of human activity, optimizing a fairly wide range of processes, ranging from complex production to simple household tasks. The focus of this study is shifted to such a part of these systems as the knowledge base, which mostly is identified with the database. In this case, we are talking about a knowledge base as an element of not a generalized intellectual structure as a whole, but it is the specific support and decision-making system (ISSDM) used to monitor the safety of infrastructure facilities.

Data mining has made rapid progress in the past decade and detecting intellectual structure of data mining research is of great help to researchers. The purpose of this study is to detect the evolutional intellectual structure of data mining from the aspect of keywords. This study takes the 5,380 papers, published between 2007 and 2016 retrieved from 11 leading data mining journals defined by Google Scholar Metrics as the dataset. After data pre-processing, keyword frequency analysis is caried out to detect the three different developing patterns of keywords, which indicates that the research focus of data mining has shifted from such topics as association rule mining to large-scale complex networks. Then this paper constructs co-word matrices of high-frequency keywords of different time periods, namely 2007 to 2016 for the whole picture during these years, 2007 to 2011 and 2012 to 2016 for two periods. Clustering results show that there are four main data mining topics, and the attention has been paid more to graph data mining and complex network analysis in the past five years.

The goal of this paper is to provide a bibliometric analysis of scientific publications that employ text mining in management. To accomplish this, the authors collected 1282 documents from the Web of Science and performed performance analysis and science mapping with the help of the Bibliometrix package in Rstudio. The performance analysis used a range of bibliometric indicators such as productivity, citations, h-index, and m-quotient, in order to identify research trends and the most influential journals, authors, countries, and literature in the study. Science mapping used author keywords co-occurrence, co-authorship, and co-citation analysis to reflect the conceptual, social, and intellectual structure of the research. Specifically, we have seen an exponential increase in the use of text mining in management in recent years. The United States is the dominant country for research, having the earliest studies and the highest number of literature and citations. Furthermore, the research themes showed that topic modeling is at the forefront of current text mining research about management. This study will help scholars and management practitioners interested in the intersection of text mining and management to quickly understand the latest advances in research.

Rockfall is one of the main phenomena in mountainous environments due to its fast and high speed of movement, its unpredictability, and, therefore, the difficulty of identifying signs of instability and detachment of the blocks. Compared to other types of sliding, the proportion of rockfall research is smaller and sometimes little known, but, in the last five years, rapid growth in this area has been shown. Therefore, this research aimed to review the intellectual structure of rockfall, through analysis of scientific production using bibliometric techniques that allow its analysis, knowledge, global evolution, and future trends in rockfall. The research methodology consists of three steps: (1) data compilation, (2) software and data cleaning, and (3) analysis, interpretation, and visualization. This analysis focuses on the period from 1975 to 2019. For the data, a total of 811 academic publications were retrieved from the Scopus database. The results indicate an increasing trend of annual publications on rockfall. This analysis reveals the main topics, countries, and most influential institutions in the world that have carried out relevant research in scientific publications, it also shows the journals that have the most publications. VOSviewer software was adopted to evaluate the co-occurrence of author keywords. Currently, the hotspots rockfall issues mainly include: hazard-risk assessment, remote sensing, and rockfall monitoring. Finally, this article analyzes the limitations of current research and proposes a future direction for the development of new research.

International Journal of Computers Communications & Control (IJCCC) is an open access peer-reviewed journal publishing original research papers and it is considered by professionals, academics and researches as one of the main sources of knowledge in the integrated solutions in computer-based control and communications, computational intelligence methods and soft computing, and advanced decision support systems fields. With this in mind, this research conducts a bibliometric performance and intellectual structure analysis of the IJCCC from 2015 to 2019. It provides a framework to support computer, communication and control researchers and professionals in the development and direction of future researches identifying core, transversal, emerging and declining themes. For this purpose, the IJCCC’s intellectual structure and thematic networks are analyzed according to the data retrieved from Web of Science Core Collection, putting the focus on the main research themes and its performance. Finally, this analysis has been developed using SciMAT, an open source (GPLv3) bibliometric software tool developed to perform a science mapping analysis under a longitudinal framework.

Background and Aim: The existence of an intellectual structure for every field is essential for managers and scholars. Intellectual structures provide a comprehensive map of knowledge that can guide researchers and managers to have a better view of their fields. Besides, with high-speed and massive amounts of data and information generation, reading and surveying of all resources are severely tricky. Intellectual maps solve this problem and make a situation for control and monitoring this voluminous and high-speed generated data. Epidemiology is regarded as one of the exciting fields which many researchers focused on it. A study of the structure and criteria of different epidemiological fields has not been done yet. Indeed, there is no serious effort for knowledge discovery of hidden information on epidemiological texts. Methods: In this paper, in order to survey this field, an intellectual structure is provided using co-word analysis. Utilizing co-word analysis discloses relationships and structure among research subjects and topics in a field. Results: Finally, four main clusters were determined, namely: genetic (with 30.53% of surveyed papers), illness (29.47%), modeling (23.16%), and prevention (16.84%). Conclusion: According to epidemiology co-word network, epidemiology area has not been studied from enough different areas, especially from novel technologies.

Vinegar is one of the most widely used acidic condiments. In recent decades, rapid advances have been made in the area of vinegar research, and the intellectual structure pertaining to this domain has significantly evolved. Thus, it is important that scientists keep abreast of associated developments to ensure an appropriate understanding of this field. To facilitate this current study, a bibliometric analysis method was adopted to visualize the knowledge map of vinegar research based on literature data retrieved from the Web of Science Core Collection (WoSCC) database. In total, 883 original research and review articles from between 1998 and 2019 with 19,663 references were analyzed by CiteSpace. Both a macroscopical sketch and microscopical characterization of the whole knowledge domain were realized. According to the research contents, the main themes that underlie vinegar research can be divided into six categories, that is, microorganisms, substances, health functions, production technologies, adjuvant medicines, and vinegar residues. In addition to the latter analysis, emerging trends and future research foci were predicted. Finally, the evolutionary stage of vinegar research was discerned according to Shneider’s four-stage theory. This review will help scientists to discern the dynamic evolution of vinegar research, as well as highlight areas for future research.

Background Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics. Objective This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods. Methods The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities. Results The results showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped. Conclusions The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians.

In this study, we sought to apply recent advances in informetrics to the analysis of literature related to big data in the field of medicine. Our aim was to elucidate research trends, identify knowledge clusters and decipher the links between them. We also sought to ascertain the theories most commonly applied in the processing of medical data and identify potential research gaps. The most important keywords over the last 10 years have been ‘big data’, ‘data mining’, ‘healthcare’, ‘cloud computing’, ‘machine learning’ and ‘electronic health record system’. These could be viewed as the core issues of research associated with big data in the field of medicine. We also identified a number of keywords that are expected to play a pivotal role in this field in the near future. These terms include the ‘internet of things’, ‘e-health’, ‘sensors’, ‘predictive modeling’, ‘quantified self’, ‘smart city’, ‘wearable device’ and ‘m-health’. Finally, we compiled co-word networks indicating the degree of connectivity between keywords, for use in locating knowledge gaps by revealing the overall context of issues commonly encountered when investigating big data. Our findings form a solid academic foundation on which to develop medical technologies, managerial strategies and theory related to big data.

Social commerce has rapidly developed as a new area in electronic commerce field and has risen the attention of both researchers and practitioners. This paper investigated the intellectual structure, development, and evolution of social commerce quantitatively to provide a systematic review of the current state of the social commerce literature. We used a visualization tool called CiteSpace and mapped our result into the I-model for classification. According to 503 articles focusing on social commerce with associated 12,089 references, we have identified the most influential authors, institutions, publications, keywords, and references in this field, as well as the hot topics and future trends. In addition, we have developed the visual network of these research elements through CiteSpace for a deep level analysis, especially for future research trends prediction. Finally, our combination of bibliometric methods and a systematic review makes for a better understanding in the development of social commerce for both researchers and practitioners.

Reviewing the intellectual structure of discipline is an important issue in scientometrics. Traditionally, the intellectual structure is described based on the journal article, which is falling behind the research activity. This paper uses the funded project data to generate the intellectual structure of a discipline. The results can reflect the research activity timely. The geology discipline in China is selected as the empirical object. The 7619 funded projects during 2006 to 2017about geology from the National Natural Science Foundation of China (NSFC) are collected. The empirical results indicate that the number and fund amount of projects has grown rapidly. Engineering geology is the largest sub-discipline. The China University of Geosciences (Wuhan), the Institute of Geology and Geophysics Chinese Academy of Sciences, and the China University of Geosciences (Beijing) are the top 3 institutes. The numerical simulation, Tibet plateau, and South China are the top 3 frequent keywords.

Big data (BD) is one of the emerging topics in the field of information systems. This article utilized citation and co-citation analysis to explore research articles in the field of BD to examine the scientific development in the area. The research data was retrieved from the WOS database from the period between 2005 and June 2016, which consists of 366 articles. In the citation analysis, this article relies on the degree centrality and betweenness centrality for identifying 38 important papers in BD. In the co-citation analysis, a principal component factor analysis of the co-citation matrix is employed for identifying six major research themes: foundations, BD applications, techniques and technologies, challenges, adoption and impacts and literature review. This literature review is one of the first studies to examine the knowledge structure of BD research in the information systems discipline by using evidence-based analysis methods. Recommendations for future research directions in BD are provided based on the analysis and results of this study.

Digital technologies are ubiquitous in our everyday lives. They have been widely embedded into the core processes of services and products, as well as into the operations of many organizations. These digital technologies bring many changes to our work and lifestyles, to the organization of companies, and even to the structures of whole industries. Thus, digital innovation is a research hotspot. This study investigates the development of digital innovation research using a bibliometric method with visual mapping. The status of digital innovation research is analyzed through citation analysis. Influential references, authors, and top-tier journals are identified. The literature’s intellectual structure is mapped through co-word analysis. In addition, co-citation network analysis is used to describe the evolution of digital innovation research, and cluster analysis of the main research stream is conducted. Several research topics addressed by recent papers are also discussed. Finally, cross-country comparisons reveal that the US is currently taking the leading role and that China is a newcomer with potential contributions to make in this research area. Keywords: Digital innovation, Citation analysis, Cross-country comparison, Evolutionary footprint, Research trend

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database. © 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

In recent years, with the widespread of the concept of big data, data mining has gradually been applied in different disciplines as well as bibliometrics. Bibliographic data analysis provides a way for researchers to know the intellectual structure of a specific area. However, most existing methods for bibliographic data analysis firstly compute the similarity matrix between authors or papers then conduct some kind of clustering method on the matrix, and they tend to ignore the external user-provided information. Enlightened by a newly proposed heterogeneous data clustering method, this paper proposed an algorithm called ISDHBD (Intellectual Structures Detection in Heterogeneous Bibliographic Data) which discovers intellectual structures from the perspective of heterogeneous networks. ISDHBD involves three main steps and takes papers from bibliographic data as the central objects while the other kinds of objects as attribute objects. Lastly, a running example extracted from CNKI (China National Knowledge Infrastructure) illustrated the principles and procedures of ISDHBD.

Many studies have proved that author bibliographic coupling analysis (ABCA) and author co-citation analysis (ACA) can provide important information for the detection of intellectual structure. However, most of these studies are focused on the first author. This implies a lack of consideration of all authors in the ABCA and ACA studies. This paper studies the all-author bibliographic coupling analysis (AABCA) and all-author co-citation analysis (AACA) to test whether and in what aspects these two all-author network methods produce different results when detecting the intellectual structure of a discipline. We find that the intellectual structures identified by AABCA and AACA are generally similar, but when it comes to detailed structures, the results are different. AABCA detects a richer variety of research topics that seem to better reflect current status and trends in the discipline, and more clearly identifies the active authors corresponding to a research topic. In contrast, AACA is more sensitive to traditional and stable research topics than to emerging niche research topics. In addition, it is more comprehensive in the discovery of authors' research interests, and it can identify multiple research interests of one author. Therefore, AABCA and AACA play different roles in the study of the intellectual structure of a discipline, indicating that the combination of the two approaches helps to obtain a more comprehensive and specific intellectual structure.

다양한 매체와 유형으로 생산되는 정보자원에 대한 이용이 높아짐에 따라, 정보자원을 기술하기 위한 정보조직의 도구로서 메타데이터에 대한 중요성이 높아지고 있다. 본 연구에서는 메타데이터 분야의 연구 영역을 파악할 수 있도록 동시출현단어분석을 사용하여 메타데이터 분야의 지적 구조를 규명하고자 하였다. 이를 위하여 1998년 1월 1일부터 2016년 7월 8일까지 Web of Science 핵심컬렉션에 등재된 저널에 게재된 문헌을 대상으로 ‘metadata’라는 질의어로 Topic 검색을 수행하여, 총 727건의 논문에 대한 서지정보를 수집하였다. 이 중 저자 키워드를 가진 410건의 논문의 저자 키워드로 수집하고, 전처리 과정을 거쳐 저자 키워드 총 1,137개를 추출하여 최종적으로 빈도수 6회 이상의 키워드 37개를 분석대상으로 선정하였다. 이후 메타데이터 분야의 지적구조 규명을 위해 첫째, 네트워크 분석을 통하여 2개 영역 9개 군집을 도출하였으며, 메타데이터 분야 키워드들의 지적 관계를 시각화하고, 중심성 분석을 통한 전역 중심 키워드와 지역 중심이 높은 키워드를 제시하였다. 둘째, 군집분석을 실시하여 형성된 6개의 군집을 다차원축적지도상에 표시하였으며, 각 키워드들 간의 상관관계에 따른 지적구조를 제시하였다. 이러한 연구의 결과는 메타데이터 분야의 지적구조를 시각적으로 파악할 수 있게 하며, 향후 메타데이터 관련 교육과 연구의 방향성 모색에 유용하게 사용될 수 있을 것이다.

Scholars within fields frequently introspect about the evolution of their research disciplines, asking if the discipline is developing a cumulative research tradition, influencing scholarly work in other fields, and expanding its boundaries. In this spirit, I examine the intellectual structure and evolution of one disciplinary journal, ISR, over the most recent four-year period (2012–2015). I use a census of citation data and classify journals citing ISR and cited by ISR into the disciplines they represent. Analyzing 12,833 citations to ISR from 100 journals, and 7,731 citations from ISR to papers published in 67 journals I find that, consistent with prior studies, ISR reflects the multidisciplinarity of the information systems (IS) discipline, its boundaries are expanding to include new disciplines, and the journal draws extensively on other IS journals. I raise some questions for further introspection about the impact and visibility of IS research.

Purpose – The purpose of this paper is to analyse the intellectual structure and research fronts of discipline of supply chain risk management (SCRM), in order to identify the knowledge groups in the research area to date, as well as to reveal any relationships between these subfields and the central influential trends. Design/methodology/approach – By means of a bibliometric study, the 32 most co-cited articles on SCRM published in 16 top business-related academic journals are analysed using multivariate statistical techniques, i.e. multi-dimensional scaling, cluster analysis and correspondence factor analysis. Findings – The results demonstrate a clearly identifiable structure as a result of the performed co-citation analysis. The conducted cluster analysis and factor bring forward that the research field is arranged in five different areas of interest: explaining supply chain (SC) risk phenomena, concepts, frameworks and insights of SCRM; modelling risks for SCs; inventory risks affecting supply efficiency; SC and product design methods; and SC risk mitigating strategies. Originality/value – Overall, the intellectual structure of SCRM is first examined through a bibliometric approach using quantitative techniques – for improved understanding of its origins, and to identify the state of the science – and to offer suggestions for future studies that could cover current gaps. This study represents the potential to advance the SCRM literature landscape.

This chapter reports on a second bibliometric analysis in which two hybrid techniques are combined, which allow clusters of related documents obtained from a co-citation analysis to be labeled using textual data. The aim of this bibliometric study, which continues the analysis of the first two decades of research on smart city development, is to (1) visualize the network of publications shaping the overall intellectual structure of the smart city research field by considering the period between 1992 and 2012, (2) map the clusters of thematically related publications, and (3) reveal the emerging development paths that each thematic cluster represents and strategic principles they embody. The findings demonstrate that the emerging development paths of smart cities are not only two but five. The development paths that this second analysis uncovers and the strategic principles each of them stands on are then compared by reviewing the most recent literature on smart city development. Overall, this chapter offers a systematic review of the smart city research produced between 1992 and 2018, and it serves to further expose the division within its intellectual structure. A division which is caused by the dichotomous nature of the development paths of smart cities that each thematic cluster relates to.

Waste printed circuit boards (WPCBs) containing various metals and hazardous materials are considered as a secondary resource and an environmental pollution source. A systematic overview of WPCB study was conducted by using CiteSpace. The relevant knowledge of 242 documents was collected from SCI-Expanded database (SCIE) and saved as txt files. A 556-node and 636-link network and 17 clusters were obtained. Based on co-citation network, nonmetallic material treatment and recycling, metal recovery, pyrolysis treatment, and new technology development were successively the most attractive fields in the study period. Timeline pattern showed that mechanical processing attracted great attention in the initial period and profitability assessment was the latest hot spot in WPCB study. The results could provide a reference for future work in WPCB field.

With the rapid development of mobile Internet, the growth amount of data has exploded, and data mining has played an increasingly important role in data analyzing. More and more researches have been done on data mining. Detecting intellectual structure of data mining research is of significance in understanding its research topics and research fronts. Focusing on the method of bibliographic coupling analysis, this paper explores the intellectual structure of data mining during 2007-2016, based on 12625 cleaned bibliographic data of data mining-related articles retrieved from Web of Science. Experiments results show that there are mainly 10 research topics in the field of data mining, such as classification, frequent pattern mining and clustering, among which the first three topics are the research on domain of data mining itself, and the last seven topics are the research on data mining applications.

The purpose of this study is to analyze the existing literature on human resource management (HRM) from all the research papers published in The International Journal of Human Resource Management between 2000 and 2012. The authors apply bibliometric methods to identify the main research lines within this scientific field; in other words, its ‘intellectual structure’. Social network analysis is also used to perform a visualization of this structure. The results of the analysis allow us to define the different research lines or fronts which shape the intellectual structure of research on HRM.

Purpose This study intended to introduce new approaches to identify the intellectual structure of supply chain management(SCM) researches, which combines author co-citation analysis(ACA) and social network analysis(SNA). Design/methodology/approach We searched RISS(www.riss.kr) and NDSL(www.ndsl.or.kr) database and collected 292 academic papers on supply chain management between 2001 and 2011. Among 9,637 references of these papers, we analyzed 1,848 references that were published by domestic authors. We produced a correlation matrix of 32 author co-citation matrix and conducted multi-variate statistical analysis such as factor analysis. We also performed social network analysis to identify the main researchers in SCM. Findings We found four main sub-areas of supply chain management research: SCM adoption factors, logistics, SCM performance, and SCM structure. We could present the authors who played important roles within the network by using SNA indicators. The finding of this research also suggests more collaborations among domestic researchers are required to overcome the low co-citation rates among domestic authors.

By document co-citation analysis with Korean Citation Index (KCI) data, this study accurately identified the research fronts and hot topics in Korean library and information science (LIS) from 2004 to 2013. 159 core papers in LIS domain and their citations are scraped manually from Korean Citation Index web site. In the cluster analysis and network analysis, 159 core papers were grouped into 27 clusters with multiple papers and 8 singlton clusters. Among the 27 clusters which have multple papers, 'LIS education' cluster was the largest with 16 core papers, and 'citation analysis & intellectual structure analysis' cluster had the strongest citation impact according to the ehs-index. Closer observation of the citations to the core papers in each research front showed that 67.5% of the citations were made by LIS research papers and 32.5% of the citations were made by non-LIS research papers. Considering the share of citations and the citation impact growth index, 'local documentation', 'citation analysis & intellectual structure analysis', and 'research trends analysis' were identified as the most emerging research front in Korean library and information science. The analytical methods used in this study have great potential in discovering the characteristics of research fronts in Korean interdisciplinary research domains.

The study utilized co-word analysis to explore papers in the field of Internet of Things to examine the scientific development in the area. The research data were retrieved from the WOS database from the period between 2000 and 2014, which consists of 758 papers. By using co-word analysis, this study found 7 clusters that represent the intellectual structure of IoT, including `IoT and Security', `Middleware', `RFID', `Internet', `Cloud computing', `Wireless sensor networks' and `6LoWPAN'. To understand these intellectual structures, this study used a co-occurrence matrix based on Pearson's correlation coefficient to create a clustering of the words using the hierarchical clustering technique. To visualize these intellectual structures, this study carried out a multidimensional scaling analysis, to which a PROXCAL algorithm was applied.

The aim of this study is to detect the evolutionary track of the electronic health from its birth to the present, and then analyze its foundational knowledge and research hotspots. We conducted a bibliometric analysis on the 3085 literatures data collected from the Web of Science core data collection in 1992–2017. We used several bibliometric tools such as CiteSpace and Netdraw to complete time-and-space analysis and keywords co-occurrence network analysis. This research can provide important academic reference for international electronic health and medical informatics researchers.

In the present study, we aim to quantitatively investigate and compare the intellectual landscapes of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) research between 2000 and 2014. The overall intellectual structure of these fields is illustrated by emerging trends of bursting keywords and thematic concentrations of co-cited references. This study is based on two sets of bibliographic records retrieved from the Web of Science database. The SWCNTs dataset contains 18,700 original research and review articles. The MWCNTs dataset, consisting of 23,584 records, is also collected from the database. We find that both domains have scrutinized chemical concepts which underlie the properties of the materials. Recent thematic trends show that MWCNTs research focuses on the improvement of the material while SWCNTs research lays more emphasis on their applications. In conclusion, it is argued that SWCNTs and MWCNTs have co-evolved. At the same time, both fields are distinctively diverging with their own scientific concerns.

A method of scientometrics analysis was adopted in this study to objectively visualize the research status quo, track the emerging trends and understand the intellectual structure of affordance research in the field of interaction design with 726 documents published in the ISI Web of Science Core Collection (WoSCC) database from 1995-2020. The results identified that annual trends of the papers will continue to grow. USA plays a significant role and is a pioneer in the field. There is no leading institution or author with high output or high influence. The study has formed 4 hot research topic: Theoretical study, Perception-level study, Behaviour-level study and Experience-level study. According to the references co-citation network, we can know that affordance research in the field of interaction design has produced a number of classic literature.

In this article, we introduce the use of Latent Semantic Analysis (LSA) as a technique for uncovering the intellectual structure of a discipline. LSA is an emerging quantitative method for content analysis that combines rigorous statistical techniques and scholarly judgment as it proceeds to extract and decipher key latent factors. We provide a stepwise explanation and illustration for implementing LSA. To demonstrate LSA's ability to uncover the intellectual structure of a discipline, we present a study of the field of Operations Management. We also discuss a number of potential applications of LSA to show how it can be used in empirical Operations Management research, specifically in areas that can benefit from analyzing large volumes of unstructured textual data.

The purpose of this study is to analyze the intellectual structure of reading studies by using Co-Word Analysis based on the mixed weight in which the level of academic journals and the position of keywords are calculated. To achieve it, 838 academic articles relating to reading studies from KCI during the period from 2003 to 2012 were retrieved and 56 keywords were extracted. The results of clustering analysis, MDS, network analysis are that the network based on the mixed weight has a better performance in above three methods and reading studies can be divided into 4 bigger divisions and 11 subdivisions. Finally, the result of document analysis shows reading studies changes its research tendency from theoretical studies to empirical studies. 키워드: 단어동시출현분석, 독서분야, 군집분석, 다차원척도분석, 네트워크분석 Co-Word Analysis, Reading Studies, Clustering Analysis, MDS, Network Analysis * ** 전남대학교 문헌정보학과 박사과정(s06303018@gmail.com) (제1저자) 전남대학교 문헌정보학과 교수(hjhong@jnu.ac.kr) (교신저자) 논문접수일자 : 2014년 2월 15일 논문심사일자 : 2014년 2월 24일 게재확정일자 : 2014년 3월 14일 한국비블리아학회지, 25(1): 295-318, 2014. [http://dx.doi.org/10.14699/kbiblia.2014.25.1.295] 296 한국비블리아학회지 제25권 제1호 2014

Purpose – The purpose of this paper is to show that by exploring the intellectual structure of activity-based costing (ABC) based on the citation database of Google Scholar, one can understand the evolution and core ideas of the ABC discipline. Design/methodology/approach – This study employs the document co-citation method to model the intellectual structure of ABC between 1988 and 2008. After an initial co-citation analysis of the condition-limited literature set to find the relationships between core articles, this study further implements multivariate statistical techniques to construct representations for the ABC intellectual structure. Findings – A total of four important subjects chronologically provide a panoramic view of the evolution of ABC. This study finds some dimensions of the results to be in accordance with prior research, but further achieves insights into the core ideas underpinning the ABC discipline. It demonstrates the validity of this study conducting a co-citation analysis based on the citation data of Google Scholar. Research limitations/implications – The classification, core articles, and evolution of the ABC literature published in the past two decades benefit academic researchers conducting future studies that build systematically on prior research. Practical implications – The intellectual structure of the ABC discipline explains and predicts the stages of ABC implementation's life cycle. For the accounting profession, this helps a consultant/practitioner in sub-fields of the ABC domain to quickly and easily enlarge the coverage and viewpoints or perspectives within his/her cluster of interest and to jointly consider the successful factors of implementing ABC in an organization. Originality/value – The empirical feasibility of using digitally archival information to model an intellectual structure of ABC is attained; and the intellectual structure and its various representations provide researchers, consultants, and practitioners with a macroscopic and dynamic view of the ABC discipline.

The study investigated the intellectual structure of the librar y and information science field by journal co-citation analysis on thirty LIS journals with hig h journal impact factors. Patterns of journal-to-journal citation show the structure of journals i n the field, visualized by a networked map of the journals. The result shows journals that can be cons idered as the core group and as the peripheral group, which corresponds to other studies for investigating the field’s intellectual structure using other techniques, such as cluster analysis and multidimensional scaling. 초 록 본 연구는 학술지를 이용한 동시인용분석을 통해 문헌정보학 분야의 지적구조를 분석하고자 하였다 . Journal Citation Reports 의 Journal Impact Factor 를 바탕으로 선택한 문헌정보학 분야의 30 개 학술지를 대상으로 조사된 동시인용 수치를 이용하여 네트워크 분석을 시행하였으며 , 이 연구의 결과를 통해 어떤 학술지가 이 분야의 주요한 (core) 학술지이며 그렇지 않은 학술지는 어떤 것들이 있는지 파악할 수 있다 . 네트워크 분석을 통해 비교적 간단한 방법으로 얻어진 결과임에도 불구하고 , 군집분석이나 다차원축적법 같은 통계적 접근법을 통한 대부분의 지적구조분석과 매우 유사한 결과를 보여주었다 . 키워드: Intellectual Structure, Domain Analysis, Knowledge Map, Journal Co-citation Analysis, Network Analysis지적구조분석, 도메인분석, 지식지도, 저널 동시인용분석, 네트워크 분석

본 연구는 저자서지결합분석을 사용하여 한국 문헌정보학의 1990년대와 2000년대 지적구조와 그 변화를 분석하는데 목적을 두고 있다. 이를 위해 첫째, 군집분석, 다차원척도법을 통하여 시기별 세부 주제 영역을 밝혔다. 둘째, 네트워크 분석을 통해 세부주제 영역 간 관계를 시각화하고 전역 중심성이 높은 주제 영역을 확인하였다. 셋째, 1990년대와 2000년대 지적구조 비교를 통해 시간의 경과에 따른 주제 영역의 흐름을 규명하였다.

Due to the development of science and technology, the convergen ce of various disciplines has been fostered. Accordingly, interdisciplinary studies have increasingly been expanded by integrating knowledge and methodology from different discipl ines. The primary focus of biblimetric methods is on investigating the intellectual str ucture a field, and analysis of the characterization of interdisciplinary studies is overlooked. In this study, we aim to identify the intellectual structure of the field of medical informatics through author co-citation analysis and co-word analysis by the representative journal “IEEE ENG MED BIOL.” In addition, we examine authors and MeSH Terms of top three representative journals for further analysis of the field. We examine the intellectual structure of the medical informatics field by author and word clusters to identify the n etwork structure of medical informatics disciplines.키워드: 의료정보학, 저자동시인용 분석, 동시출현단어 분석, 네트워크 분석 medical informatics, author co-citation analysis, co-word analy sis, network analysis