A Study of the Intellectual Structure of Secretarial Studies Using a Knowledge Network Analysis

2021 ◽  
Vol 30 (2) ◽  
pp. 29-52
Author(s):  
Ju-Young Moon
Keyword(s):  
Network Analysis ◽  
Intellectual Structure ◽  
Knowledge Network

Mapping the Topics and Evolutions of Chinese Children’s Bestsellers

Libri ◽  
2020 ◽  
Vol 70 (4) ◽  
pp. 305-317
Author(s):  
Jiming Hu ◽  
Xiang Zheng ◽  
Peng Wen ◽  
Jie Xu
Keyword(s):  
Social Network ◽  
Quantitative Analysis ◽  
Network Analysis ◽  
Intellectual Structure ◽  
Structure Evolution ◽  
Children's Books ◽  
Children’S Books ◽  
Correlation Structures ◽  
The World ◽  
Data Source

AbstractChildren’s books involve a large number of topics. Research on them has been paid much attention to by both scholars and practitioners. However, the existing achievements do not focus on China, which is the fastest growing market for children’s books in the world. Studies using quantitative analysis are low in number, especially on the intellectual structure, evolution patterns, and development trends of topics of children’s bestsellers in China. Dangdang.com, the biggest Chinese online bookstore, was chosen as a data source to obtain children’s bestsellers, and topic words in them were extracted from brief introductions. With the aid of co-occurrence theory and tools of social network analysis and visualization, the distribution, correlation structures, and evolution patterns of topics were revealed and visualized. This study shows that topics of Chinese children’s bestsellers are broad and relatively concentrated, but their distribution is unbalanced. There are four distinguished topic communities (Living, Animal, World, and Child) in terms of centrality and maturity, and they all establish their individual systems and tend to be mature. The evolution of these communities tends to be stable with powerful continuity.

scholarly journals Visualization of clinical teaching citations using social network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hakimeh Hazrati ◽  
Shoaleh Bigdeli ◽  
Seyed Kamran Soltani Arabshahi ◽  
Vahideh Zarea Gavgani ◽  
Nafiseh Vahed
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Collaboration ◽  
Clinical Teaching ◽  
Clustering Coefficient ◽  
Knowledge Network ◽  
Research Network ◽  
Centrality Measures ◽  
The Usa

Abstract Background Analyzing the previous research literature in the field of clinical teaching has potential to show the trend and future direction of this field. This study aimed to visualize the co-authorship networks and scientific map of research outputs of clinical teaching and medical education by Social Network Analysis (SNA). Methods We Identified 1229 publications on clinical teaching through a systematic search strategy in the Scopus (Elsevier), Web of Science (Clarivate Analytics) and Medline (NCBI/NLM) through PubMed from the year 1980 to 2018.The Ravar PreMap, Netdraw, UCINet and VOSviewer software were used for data visualization and analysis. Results Based on the findings of study the network of clinical teaching was weak in term of cohesion and the density in the co-authorship networks of authors (clustering coefficient (CC): 0.749, density: 0.0238) and collaboration of countries (CC: 0.655, density: 0.176). In regard to centrality measures; the most influential authors in the co-authorship network was Rosenbaum ME, from the USA (0.048). More, the USA, the UK, Canada, Australia and the Netherlands have central role in collaboration countries network and has the vertex co-authorship with other that participated in publishing articles in clinical teaching. Analysis of background and affiliation of authors showed that co-authorship between clinical researchers in medicine filed is weak. Nineteen subject clusters were identified in the clinical teaching research network, seven of which were related to the expected competencies of clinical teaching and three related to clinical teaching skills. Conclusions In order to improve the cohesion of the authorship network of clinical teaching, it is essential to improve research collaboration and co-authorship between new researchers and those who have better closeness or geodisk path with others, especially those with the clinical background. To reach to a dense and powerful topology in the knowledge network of this field encouraging policies to be made for international and national collaboration between clinicians and clinical teaching specialists. In addition, humanitarian and clinical reasoning need to be considered in clinical teaching as of new direction in the field from thematic aspects.

The evolution of the intellectual structure of strategic management between 1980 and 2019

2020 ◽  
Vol 13 (4) ◽  
pp. 503-534
Author(s):  
Mehmet Ali Köseoğlu ◽  
John Parnell
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Strategic Management ◽  
Citation Analysis ◽  
Intellectual Structure ◽  
Future Directions ◽  
Content Type ◽  
The Social ◽  
Economic Foundation

PurposeThe authors evaluate the evolution of the intellectual structure of strategic management (SM) by employing a document co-citation analysis through a network analysis for academic citations in articles published in the Strategic Management Journal (SMJ).Design/methodology/approachThe authors employed the co-citation analysis through the social network analysis.FindingsThe authors outlined the evolution of the academic foundations of the structure and emphasized several domains. The economic foundation of SM research with macro and micro perspectives has generated a solid knowledge stock in the literature. Industrial organization (IO) psychology has also been another dominant foundation. Its robust development and extension in the literature have focused on cognitive issues in actors' behaviors as a behavioral foundation of SM. Methodological issues in SM research have become dominant between 2004 and 2011, but their influence has been inconsistent. The authors concluded by recommending future directions to increase maturity in the SM research domain.Originality/valueThis is the first paper to elucidate the intellectual structure of SM by adopting the co-citation analysis through the social network analysis.

Science mapping: a bibliometric analysis of female entrepreneurship studies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wei Deng ◽  
Qiaozhuan Liang ◽  
Jie Li ◽  
Wei Wang
Keyword(s):  
Network Analysis ◽  
Intellectual Structure ◽  
Future Research ◽  
Female Entrepreneurship ◽  
Science Mapping ◽  
Content Type ◽  
Entrepreneurship Research ◽  
Bibliometric Review ◽  
The Impact ◽  
Practical Implications

Purpose This bibliometric review aims to display visually the intellectual communities (i.e. the cooperation networks among various countries, institutions, journals and individuals), the intellectual structure (i.e. the status quo and development trajectory of the intellectual base) and emerging hot topics of the female entrepreneurship research in 1975–2018. Based on the comprehensive review of the state-of-the-science, this paper aims to identify significant research gaps in extant studies and develop potential future research agendas that may catalyse new streams of female entrepreneurship research. Design/methodology/approach Bibliometric analysis via science mapping provides in-depth analyzes, highlights the intellectual structure and identifies hot topics. Using CiteSpace, co-citation networks of contributing countries, institutions, cited journals and authors are mapped first. Second, co-citation network analysis helps to identify the key “nodes” in the intellectual structure. The landscape view identifies main clusters from an overall perspective, while a timeline view delineates the characteristics and evolution of focal clusters. Major clusters are interpreted in detail with the help of foam tree graph processed by Carrot. Finally, the co-occurrence network analysis is conducted by using VOSviewer to examine hot topics and research frontiers Findings The findings show that the publications of female entrepreneurship increase exponentially. The major driving force of female entrepreneurship research is from the USA and England. In terms of intellectual structure, key concepts behind different clusters represent the major milestones in relation to individual determinants of female entrepreneurship, the impact of cultural and contextual factors on female entrepreneurship and female entrepreneurship in non-OECD countries, as well as the impact of family, social and institutional factors on the survival and exit of male and female enterprises. Hot topics include financing sources, the embeddedness nature, the impact and environmental factors of female entrepreneurship. Practical implications This study presents important practical implications. The findings suggest that intellectual communities of the female entrepreneurship field are relatively loose. Close contact and cooperation among different countries, institutions and researchers are lacking. To promote the evolution of the field, researchers who belong to different institutions in different countries may need to strengthen contact and cooperation. Additionally, papers in journals from the business and management discipline are most cited in this field, preventing new knowledge from other disciplines flowing into the female entrepreneurship field. Accordingly, female entrepreneurship research journals may need to expand their focus and combine knowledge from various domains. Originality/value This bibliometric review provides a more comprehensive, systematic and objective review of the female entrepreneurship field. Previous qualitative reviews are typically based on personal judgement, while a few quantitative reviews only describe statistical data. This study is based on thousands of citation data rather than a small number of papers pre-selected by the researcher, thus, is more data-grounded and less biased than prior reviews. It expands previous reviews by transparently visualizing the underlying structure and evolution of the field. Moreover, it highlights significant gaps in extant studies and develops future research agendas to catalyse new streams of research.

Knowledge Network Analysis on Hallyu Research

2017 ◽  
Vol 61 (6) ◽  
pp. 318-353
Author(s):  
Seok-Kyeong Hong ◽  
Daemin Park ◽  
Sojeong Park
Keyword(s):  
Network Analysis ◽  
Knowledge Network

scholarly journals A tudásérték meghatározása minőségügyi szempontból, hálózatelemzési módszerekkel (The knowledge value in terms of quality with network analysis methods)

2012 ◽  
pp. 55-62
Author(s):  
Tamás Csiszér ◽  
József Cziráki
Keyword(s):  
Network Analysis ◽  
Quality Management ◽  
Knowledge Network ◽  
Negative Effects ◽  
Knowledge Loss ◽  
Network Nodes ◽  
Core Elements ◽  
Value Production ◽  
Knowledge Value ◽  
Management Issues

A minőségügy egyik kulcsfeladata, hogy azonosítsa az értékteremtés szempontjából kritikus tényezőket, meghatározza ezek értékét, valamint intézkedjen negatív hatásuk megelőzése és csökkentése érdekében. Az értékteremtés sok esetben folyamatokon keresztül történik, amelyek tevékenységekből, elvégzendő feladatokból állnak. Ezekhez megfelelő munkatársak kellenek, akiknek az egyik legfontosabb jellemzője az általuk birtokolt tudás. Mindezek alapján a feladat-tudás-erőforrás kapcsolatrendszer ismerete és kezelése minőségügyi feladat is. A komplex rendszerek elemzésével foglalkozó hálózatkutatás eszközt biztosíthat ehhez, ezért indokolt a minőségügyi területen történő alkalmazhatóságának vizsgálata. Az alkalmazási lehetőségek rendszerezése érdekében a szerzők kategorizálták a minőségügyi hálózatokat az élek (kapcsolatok) és a csúcsok (hálózati pontok) típusai alapján. Ezt követően definiálták a multimodális (több különböző csúcstípusból álló) tudáshálózatot, amely a feladatokból, az erőforrásokból, a tudáselemekből és a közöttük lévő kapcsolatokból épül fel. A hálózat segítségével kategóriákba sorolták a tudáselemeket, valamint a fokszámok alapján meghatározták értéküket. A multimodális hálózatból képzett tudáselem-hálózatban megadták az összefüggő csoportok jelentését, majd megfogalmaztak egy összefüggést a tudáselem-elvesztés kockázatának meghatározására. _______ The aims of quality management are to identify those factors that have significant influence on value production, qualify or quantify them, and make preventive and corrective actions in order to reduce their negative effects. The core elements of value production are processes and tasks, along with workforce having the necessary knowledge to work. For that reason the task-resource-knowledge structure is pertinent to quality management. Network science provides methods to analyze complex systems; therefore it seems reasonable to study the use of tools of network analysis in association with quality management issues. First of all the authors categorized quality networks according to the types of nodes (vertices) and links (edges or arcs). Focusing on knowledge management, they defined the multimodal knowledge network, consisting of tasks, resources, knowledge items and their interconnections. Based on their degree, network nodes can be categorized and their value can be quantified. Derived from the multimodal network knowledge-item network is to be created, where the meaning of cohesive subgroups is defined. Eventually they proposed a formula for determining the risk of knowledge loss.

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