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This paper aims to systematically find the main research differences and similarities between social media and social networks in marketing research using the bibliometric perspective and provides suggestions for firms to improve their marketing strategies effectively.

The methods of co-word analysis and network analysis have been used to analyze the two research fields of social media and social networks. Specifically, this study selects 2,424 articles from 27 marketing academic journals present in the database Web of Science, ranging from January 1, 1996 to August 8, 2020.

The results show that social networks and social media are both research hotspots within the discipline of marketing research. The different intimacy nodes of social networks are more complex than social media. Additionally, the research scope of social networks is broader than social media in marketing research as shown by the keyword co-occurrence analysis. The overlap between social media and social networks in marketing research is reflected in the strong focus on their mixed mutual effects.

This paper explores the differences and similarities between social networks and social media in marketing research from the bibliometric perspective and provides a developing trend of their research hotspots in social media and social networks marketing research by keyword co-occurrence analysis and cluster analysis. Additionally, this paper provides some suggestions for firms looking to improve the efficiency of their marketing strategies from social and economic perspectives.

The purpose of this study is to reveal how research on brain-based learning (BBL) addressing adult learners’ education and training issues has contributed to the overall knowledge base linking neuroscience, adult education and human resource development (HRD) research and practices. Through this comprehensive review of the BBL studies, this paper aims to expand the landscape of understanding educational phenomenon in adult education and organizational settings using the lens of neuroscience.

Using the content analysis method, this study extracts key research themes and methodological choices from the body of BBL studies. In addition, this paper explores the relationships and proximity among key concepts of BBL research using keyword network analysis. For data analysis, this study reviews the current literature on BBL addressing both adult education and HRD topics from 1985 to 2019.

The findings of this study provide a clearer picture of the potential mechanisms of BBL approaches observed in the literature of adult education and HRD. What has been found from the thematic analysis is that addressing misconceptions about the neuroscience of learning is regarded as an important topic. In terms of the methodological approaches, the literature review was a dominantly used method, whereas experimental or quantitative research has yet to be fully performed. Influential keywords and topics obtained from the keyword network analysis reveal the primary foci and structural patterns of current BBL research.

This study makes a significant contribution to theories and research in adult education and HRD scholarship as it provides an integrative view of key research themes and major issues about BBL. Additionally, our findings offer practical insights for adult educators and HR professionals to successfully apply neuroscientific approaches.

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.

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.

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.

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.

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.

This study aims to provide researchers a holistic approach for comprehensive understanding of the Bitcoin-related research by discovering its trends, subjects, relations, keywords and concepts.

An integrated approach of bibliometric analysis, network analysis and concept linking analysis was proposed for exploring Bitcoin-related studies from 70 countries in the Scopus database.

The bibliometric analysis shows that electronic money and blockchain are the mainstream issues of Bitcoin, and the domain distribution of the literature is mainly in engineering-related fields. Through the network analysis of cocitations, co-occurrences and cowords, research clusters were discovered respectively from different perspectives. The authors also have mastered a multilevel concept linking diagram for six related major concepts.

The major contribution of this research is about providing an integrated and comprehensive approach to extract the mainstream issues that can help researchers conduct Bitcoin-relevant research. This study shows the development trend, context and clusters of Bitcoin-related studies from various perspectives networks and produces a visual concept linking diagram that will enable researchers to quickly understand the contextual relationship between Bitcoin keywords during literature analysis. In addition, the most crucial studies in the main topics are extracted to save the considerable time and labor that would be required to manually read all the literature and summarize the issues.

This paper systematically evaluates the existing literature of innovations in freight transport, including all modes, to uncover the key research themes and methodologies employed by researchers to study innovations and their implications in this industry. It analyses the role of transport and the impact of innovations during crises, such as COVID-19.

Qualitative and quantitative analysis of the innovations in freight transport unravels the pre-requisites of such endeavours in achieving a resilient and sustainable transport network that effectively and efficiently operates during a crisis. The authors performed keyword co-occurrence network (KCON) analysis and research focus parallelship network (RFPN) analysis using BibExcel and Gephi to determine the major resulting research streams in freight transport.

The RFPN identified five emerging themes: transport operations, technological innovation, transport economics, transport policy and resilience and disaster management. Optimisation and simulation techniques, and more recently, artificial intelligence and machine learning (ML) approaches, have been used to model and solve freight transport problems. Automation innovations have also penetrated freight and supply chains. Information and communication technology (ICT)-based innovations have also been found to be effective in building resilient supply chains.

Given the growth of e-commerce during COVID-19 and the resulting logistics demand, along with the need for transporting food and medical emergency products, the role of automation, optimisation, monitoring systems and risk management in the transport industry has become more salient. Transport companies need to improve their operational efficiency using innovative technologies and data science for informed decision-making.

This paper advises researchers and practitioners involved in freight transport and innovation about main directions and gaps in the field through an integrated approach for evaluating research undertaken in the area. This paper also highlights the role of crisis, e.g. COVID-19, and its impacts on freight transport. Major contributions of this paper are as follows: (1) a qualitative and quantitative, systematic and effective assessment of the literature on freight transport through a network analysis of keywords supplemented by a review of the text of 148 papers; (2) unravelling major research areas; (3) identifying innovations in freight transport and their classification as technological and non-technological and (4) investigating the impact of crises and disruptions in freight transport.

The paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more low-cost and easily applicable method that relies on a small dataset, and how we can obtain this small dataset based on the features of the publications.

The paper proposes a topic analysis method based on prolific and authoritative researchers (PARs). First, the authors identify PARs in a specific discipline by considering the number of publications and citations of authors. Based on the research publications of PARs (small dataset), the authors then construct a keyword co-occurrence network and perform a topic analysis. Finally, the authors compare the method with the traditional method.

The authors found that using a small dataset (only 6.47% of the complete dataset in our experiment) for topic analysis yields relatively high-quality and reliable results. The comparison analysis reveals that the proposed method is quite similar to the results of traditional large dataset analysis in terms of publication time distribution, research areas, core keywords and keyword network density.

Expert opinions are needed in determining the parameters of PARs identification algorithm. The proposed method may neglect the publications of junior researchers and its biases should be discussed.

This paper gives a practical way on how to implement disciplinary analysis based on a small dataset, and how to identify this dataset by proposing a PARs-based topic analysis method. The proposed method presents a useful view of the data based on PARs that can produce results comparable to traditional method, and thus will improve the effectiveness and cost of interdisciplinary topic analysis.

This paper proposes a PARs-based topic analysis method and verifies that topic analysis can be performed using a small dataset.

The purpose of this paper is to explore the research status and development trend of the field of event detection in social media (ED in SM) through a bibliometric analysis of academic publications.

First, publication distributions are analyzed including the trends of publications and citations, subject distribution, predominant journals, affiliations, authors, etc. Second, an indicator of collaboration degree is used to measure scientific connective relations from different perspectives. A network analysis method is then applied to reveal scientific collaboration relations. Furthermore, based on keyword co-occurrence analysis, major research themes and their evolutions throughout time span are discovered. Finally, a network analysis method is applied to visualize the analysis results.

The area of ED in SM has received increasing attention and interest in academia with Computer Science and Engineering as two major research subjects. The USA and China contribute the most to the area development. Affiliations and authors tend to collaborate more with those within the same country. Among the 14 identified research themes, newly emerged themes such as Pharmacovigilance event detection are discovered.

This study is the first to comprehensively illustrate the research status of ED in SM by conducting a bibliometric analysis. Up-to-date findings are reported, which can help relevant researchers understand the research trend, seek scientific collaborators and optimize research topic choices.

This paper aims to help scholars know about the focus and frontier in the field of corporate social responsibility (CSR). Although related research in CSR started 60 years ago, there is not much systematical literature review on CSR in recent years. This paper applies scientometric method, especially co-word analysis, to explore the frontier and focus of CSR in the twenty-first century, based on the articles from 2001 to 2014 in SSCI database.

In this paper, the authors first use the scientometric method and co-word analysis for keywords filtering and apply social network methodology to investigate the networks of high-frequency keywords and high-frequency authors.

The authors summarize the current state of CSR research in two aspects. By co-word analysis of high-frequency keywords, the findings show that the focus and frontier are highly related to CSR. Based on the findings of social network analysis, this paper concludes four important research directions and possible future research of CSR.

The findings in this paper will help scholars of CSR or other related fields to realize the focus and frontier of CSR and provide some guidance for their future research.

This study aims to present a scientometric analysis of publications related to “Augmented Reality.” In today’s Information Technology-driven era, augmented reality (A.R.) has evolved as a new immersive data source for developing knowledge combining authentic and digital images. Consequently, extensive research is going on “Augmented Reality” to discover its potential in knowledge development.

The paper analyses and emphasizes the bibliographic data of Scopus articles with a suitable search query. The study was done concerning the chronological growth of research publications, highly cited publications, productive countries, prominent journals, prolific authors and institutions, author and country co-authorship network analysis and keywords analysis. The analysis was conducted by using open-source tools such as VOSViewer, Biblioshiny and Gephi.

The study reveals that a maximum number of publications on research in “Augmented Reality” are in the form of conference proceedings and articles. A.R., Virtual reality and A.R. application are the keywords with maximum number of occurrences. A significant number of publications are done in the USA, followed by Germany in the year 2020.

This study provides a precise idea of work done in different countries, a network of co-authorship between authors and countries, publication and citation impact of authors, journals, institutions and countries, year-wise progression and trending “augmented reality” topics research. This investigation will be advantageous for researchers and stakeholders to obtain rigorous bibliographic knowledge on literature related to the topic and work accordingly for R&D activities.

Using bibliometrics, this study aims to explore the intellectual structure of social segregation research, key contributors, thematic areas and hotspot topics.

A bibliometric analysis was performed for more than 15,000 research papers listed in one of the famous, rich and widely used scientific databases: Web of Science (WoS). This review approach was used to identify social research hotspots on segregation, intellectual structure, borders and development trends. VOSviewer and Gephi software were employed for mapping and analysis.

The study indicates a marked increase in segregation research, particularly from a spatial/urban perspective. The study reveals the interrelationship between segregation and many other social concepts, such as social equality, cohesion, integration and inclusion. In conclusion, addressing the ramifications resulting from the multiple forms of segregation will help in implementing social policies and evaluating their impact on achieving inclusive social development in general and the 2030 agenda of Sustainable Development Goals (SDGs) in specific.

This study remains limited to the precision and thoroughness of the bibliographic data gained from WoS.

This study is valuable for readers to gain rich insights into the state of research on social segregation. It also provides ideas for future research that prospective authors and interested research and academic institutions can investigate.