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The COVID-19 crisis has adversely affected entrepreneurs, innovators and their ventures and, arguably, entrepreneurship research. This study aims to map the knowledge of entrepreneurship research during the COVID-19 pandemic to provide evidence of literature evolution in the field with the purpose of supporting future decision-making for policymakers, academics and practitioners in the post-COVID-19 era.

The study examines various bibliometric and scientometric indicators of entrepreneurship research in the Web of Science database using bibliometric techniques and visualization tools. Using the information gained, the scientometrics of entrepreneurship research during the COVID-19 time slice (2020–02-12 to 2022–10-15) are synthesized and comprehensively presented, and future research avenues for the post-COVID-19 era are suggested.

The results of rigorous quantitative analyses show that entrepreneurship research activities were not disrupted by COVID-19, although entrepreneurial activities themselves were impacted worldwide. In addition to providing key insights into the research field, including the most relevant keywords, keyword co-occurrences, publication sources, countries' contribution and collaboration, and source co-citations, the conceptual structural analysis separates the current trends (hotspots) into ten themes. Based on the evolution of author keywords and research themes, the study identified numerous future research directions, including 1) entrepreneurship in emerging countries, 2) firm performance in different categories of enterprises, 3) immigrants and transnational entrepreneurs, 4) technology in entrepreneurship education and 5) the impact of COVID-19 on the entrepreneurial ecosystem and entrepreneurship.

By building firm foundations for advancing the field in innovative and systematic ways, this timely study contributes to entrepreneurship literature and facilitates the understanding of the features and structures of entrepreneurship research towards the end of the pandemic. The research also has important implications for research management and entrepreneurship policymaking. The study's main limitation is that the results can only represent the time slice between 2020-02-12 and 2022-10-15.

Policymakers and managers of research and development can utilize this research to prepare a crisis-related minimization handbook in advance.

This first data mapping and thematic analysis research for entrepreneurship during the period of COVID-19 provides the latest knowledge in the field at the beginning of the end of the pandemic. It empowers scholars by 1) providing a one-stop literature overview for this global crisis time slice, 2) identifying research focuses and gaps, 3) developing new research avenues for investigation and 4) contributing conceptual structure for specific entrepreneurship research projects.

Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.

This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.

Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.

This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

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.

The purpose of this study is to apply a corpus-assisted analysis of keywords and their collocations in the US presidential discourse from Clinton to Trump to discover the meanings of these words and the collocates they have. Keywords are salient words in a corpus whose frequency is unusually high (positive keywords) or low (negative keywords) in comparison with a reference corpus. Collocation is the co-occurrence of words.

To achieve this purpose, the investigation of keywords and collocations is generated by AntConc, a corpus processing software.

This analysis leads to shed light on the similarities and/or differences amongst the past four American presidents concerning their key topics. Keyword analysis through keyness makes it evident that Clinton and Obama, being Democrats, demonstrate a clear tendency to improve Americans’ life inside their social sphere. Obama surpasses Clinton as regard foreign affairs. Clinton and Obama’s infrequent subjects have to do with terrorism and immigration. This complies with their condensed focus on social and economic improvements. Bush, a republican, concentrates only on external issues. This is proven by his keywords signifying war against terrorism. Bush’s negative use of words marking cooperative actions conforms to his positive use of words indicating external war. Trump’s positive keywords are about exaggerated descriptions without a defined target. He also shows an unusual frequency in referring to his name and position. His words used with negative keyness refer to reforming programs and external issues. Collocations around each top content keyword clarify the word and harmonize with the presidential orientation negotiated by the keywords.

Limitations have to do with the issue of the accurate representation of the samples.

This research is original in its methodology of applying corpus linguistics tools in the analysis of presidential discourses.

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.

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 paper aims to explore the development trend of OA articles and their advantages and disadvantages in the process of fighting the pandemic, and conduct a multi-level and multi-angle analysis of the relationship between publishing costs and the influence of OA articles.

This study first compares the total number of articles in Web of Science with the number of OA articles, and the total number of COVID-19 related articles with the total number of OA articles. Subsequently, using the methods of institutional cooperation co-occurrence network, keyword co-occurrence and multidimensional scale analysis, and using the literature on the topic of COVID-19 in CNKI (Chinese National Knowledge Infrastructure) as the data set, we generate visualized maps of research results distribution and keyword co-occurrence network with the help of the Statistical Analysis Toolkit for Infometrics (SATI)

The research results show that the citation frequency and use frequency of OA articles related to COVID-19 are significantly higher than that of non-OA articles. OA articles dominate in the anti-pandemic process, with a series of advantages such as short review cycle, timeliness, high social benefit, high participation and fast dissemination playing an important role. Under the model of author's non-payment for OA article, the degree of institutional cooperation and author cooperation is enhanced, which improves the fluidity of knowledge, strengthens close links between keywords and enhances significant academic influence; OA articles will continue to promote research in the field of COVID-19, but the lack of quality of some OA articles may hinder their development. Then OA articles will further focus on clinical medicine, and related results will continue to promote the development and communication of OA articles in this field.

Corresponding measures are also proposed for the existing problems of OA articles, to provide a reference for the publication and dissemination of OA articles in public health emergencies in the future.

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.

Tourism and hospitality are industries that have been dramatically transformed by information technology (IT). This study aims to use a keyword analysis to quantitatively review how IT reshaped these industries.

In total, 3,282 keywords were collected from 24 high-impact tourism and hospitality journals and a social network analysis was used for the analysis.

This study contributes to research and practice by providing a visual digital knowledge map for tourism and hospitality, and seven research hotspots were identified from the results of the keyword analysis.

A parsimonious eMarketing model for tourism and hospitality is proposed to direct future studies concerning these themes and guide practitioners in allocating the appropriate resources for IT investment.

This map not only identifies seven themes that explain, which and how IT-related factors influence tourism and hospitality but also demonstrates the patterns and intellectual structure of the related body of knowledge. The trend analysis indicates how IT transforms the tourism and hospitality industries in terms of mode and scope.