Evaluating research performance of Coronavirus and Covid-19 pandemic using scientometric indicators

Global Trend on Machine Learning in Helicobacter within One Decade: A Scientometric Study

Purpose

This study aims to create a science map, provide structural analysis, investigate evolution, and identify new trends in Helicobacter pylori (H. pylori) research articles.

Methods

All Helicobacter publications were gathered from the Web of Science (WoS) database from August 2010 to 2021. The data were required for bibliometric analysis. The bibliometric analysis was performed with Bibliometrix R Tool. Bibliometric data were analyzed using the Bibliometrix Biblioshiny R-package software.

Results

A total of 17,413 articles were reviewed and analyzed, with descriptive characteristics of the H. pylori literature included. In journals, 21,102 keywords plus and 20,490 author keywords were reported. These articles were also written by 56,106 different authors, with 262 being single-author articles. Most authors' abstracts, titles, and keywords included "Helicobacter-pylori." Since 2010, the total number of H. pylori-related publications has been decreasing. Gut, PLOS ONE, and Gastroenterology are the most influential H. pylori journals, according to source impact. China, the United States, and Japan are the countries with most affiliations and subjects. In addition, Seoul National University has published the most articles about H. pylori. According to the cloud word plot, the authors' most frequently used keywords are gastric cancer (GC), H. pylori, gastritis, eradication, and inflammation. "Helicobacter pylori" and "infection" have the steepest slopes in terms of the upward trend of words used in articles from 2010 to 2021. Subjects such as GC, intestinal metaplasia, epidemiology, peptic ulcer, eradication, and clarithromycin are included in the diagram's motor theme section, according to strategic diagrams. According to the thematic evolution map, topics such as Helicobacter pylori infection, B-cell lymphoma, CagA, Helicobacter pylori, and infection were largely discussed between 2010 and 2015. From 2016 to 2021, the top topics covered included Helicobacter pylori, H. pylori infection, and infection.

Citation advantage of COVID-19-related publications

With the global spread of the COVID-19 pandemic, scientists from various disciplines responded quickly to this historical public health emergency. The sudden boom of COVID-19-related papers in a short period of time may bring unexpected influence to some commonly used bibliometric indicators. By a large-scale investigation using Science Citation Index Expanded and Social Sciences Citation Index, this brief communication confirms the citation advantage of COVID-19-related papers empirically through the lens of Essential Science Indicators’ highly cited paper. More than 8% of COVID-19-related papers published during 2020 and 2021 were selected as Essential Science Indicators highly cited papers, which was much higher than the set global benchmark value of 1%. The citation advantage of COVID-19-related papers for different Web of Science categories/countries/journal impact factor quartiles was also demonstrated. The distortions of COVID-19-related papers’ citation advantage to some bibliometric indicators such as journal impact factor were discussed at the end of this brief communication.

Artificial intelligence centric scientific research on COVID-19: an analysis based on scientometrics data

With the spread of the deadly coronavirus disease throughout the geographies of the globe, expertise from every field has been sought to fight the impact of the virus. The use of Artificial Intelligence (AI), especially, has been the center of attention due to its capability to produce trustworthy results in a reasonable time. As a result, AI centric based research on coronavirus (or COVID-19) has been receiving growing attention from different domains ranging from medicine, virology, and psychiatry etc. We present this comprehensive study that closely monitors the impact of the pandemic on global research activities related exclusively to AI. In this article, we produce highly informative insights pertaining to publications, such as the best articles, research areas, most productive and influential journals, authors, and institutions. Studies are made on top 50 most cited articles to identify the most influential AI subcategories. We also study the outcome of research from different geographic areas while identifying the research collaborations that have had an impact. This study also compares the outcome of research from the different countries around the globe and produces insights on the same.

A bibliometric analysis of linguistic research on COVID-19

Research on COVID-19 has drawn the attention of scholars around the world since the outbreak of the pandemic. Several literature reviews of research topics and themes based on scientometric indicators or bibliometric analyses have already been conducted. However, topics and themes in linguistic-specific research on COVID-19 remain under-studied. With the help of the CiteSpace software, the present study reviewed linguistic research published in SSCI and A&HCI journals to address the identified gap in the literature. The overall performance of the documents was described and document co-citations, keyword co-occurrence, and keyword clusters were visualized via CiteSpace. The main topic areas identified in the reviewed studies ranged from the influences of COVID-19 on language education, and speech-language pathology to crisis communication. The results of the study indicate not only that COVID-19-related linguistic research is topically limited but also that insufficient attention has been accorded by linguistic researchers to Conceptual Metaphor Theory, Critical Discourse Analysis, Pragmatics, and Corpus-based discourse analysis in exploring pandemic discourses and texts.

Unveiling research productivity of premier IIMs of India (2010–2021)

Purpose

This study aims to investigate the research productivity in terms of publications count of the top four premiers Indian Institute of Management (IIM) institutions and to explore the current research trends.

Design/methodology/approach

Bibliometric techniques were employed to assess the performance in terms of research productivity of authors affiliated with IIMs. The Elsevier Scopus database was selected as a tool to extract the prospective publications data limiting the time frame for 2010–2021. The IIM-Ahmedabad, IIM-Bangalore, IIM-Calcutta and IIM-Lucknow have been selected for the study. The harvested data were analyzed by using the standard bibliometric indicators and scientometric parameters to measure the research landscape such as average growth rate, compound average growth rate, relative growth rate, doubling time, degree of collaboration, collaborative index, collaborative coefficient and modified collaborative coefficient. VOSviewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

Findings

The research productivity of selected four IIMs has shown an upward trend during the study period from 2010–2021 and accrued 4,397 publications with an average of 366 publications per year. The authorship patterns demonstrate the collaborative trends as most of the publications were produced by the multiple-authors (81.03%). IIM-Ahmedabad has produced the maximum number of publications (32.20%). The research productivity of IIMs has come out in collaboration with the 125 nations across the world and the USA, the UK, Canada, Germany and China are the front runners with IIMs in the collaborative network. The high magnitude and density of collaboration are evident from the calculated mean values of the degree of collaboration (0.82). The mean values of the collaborative index (2.64), collaborative coefficient (0.51) and modified collaborative coefficient (0.51) demonstrated a positive trend, but indicate the fluctuation in the collaborative pattern as time proceeds.

Research limitations/implications

The study is limited to the publications data indexed in the Scopus database, therefore the outcome may not be generalized across other databases available in the public domain like Web of Science (WoS), PubMed, Dimensions and Google Scholars.

Practical implications

The findings of the study may aid academics and library professionals in identifying research trends, collaboration networks and evaluating other academic and research institutions by using the current advancement in data analysis.

Originality/value

The present study is the first effort to evaluate the research productivity of IIMs. The expanding literature will make an important contribution to identifying patterns and evaluating current research trends on a worldwide scale.

Impact of COVID-19 pandemic on researcher collaboration in business and economics areas on national level: a scientometric analysis

Purpose

The coronavirus disease 2019 (COVID-19) pandemic has greatly impacted society and academic life and research practices. This study is an attempt to comprehend whether a global emergency of COVID-19 pandemic has an impact on researcher international collaboration. The authors analyze the research collaboration before and after the beginning of the COVID-19 pandemic to understand how scientists collaborated within their own nation's borders and beyond.

Design/methodology/approach

The authors analyze the research collaboration before and after the beginning of the COVID-19 pandemic to understand how scientists collaborated within their own nation's borders and beyond. The authors collected a dataset of research publications published in journals in the research area of business and economics and indexed in the WoS Core Collection database by researchers from 11 countries (Austria, Denmark, Greece, Indonesia, Iran, Ireland, Korea (South), Mexico, Pakistan, Romania and Vietnam). In total, 14,824 publication records were considered for the literature analysis. This study presented the scientometric analysis of these publications using bibliometric, statistical, factor analysis and network analysis methods. The results are evaluated and interpreted in the context of the Hofstede's model of cultural dimensions. The results of this study provide evidence to research management to properly allocate their efforts to improve the researcher cooperation during the ongoing COVID-19 pandemic and to overcome its negative outcomes in the years to come.

Findings

The results of our study show that uncertainty avoidance as the cultural factor defined by the Hofstede's model has significantly influenced the properties of research collaboration networks in the domain of business and economics. Uncertainty avoidance focuses on how cultures adapt to changes and cope with uncertainty, while the global COVID-19 pandemic introduced a lot of change and uncertainty all levels of society around the world.

Research limitations/implications

The study exclusively examines 14,824 research outputs which have been indexed in the WoS Core Collection database from 2019 till 15 November 2020 and only covered one research area (business economics). Thus, documents published in any other different channels and sources which are not covered in WoS are excluded from this study. The authors have analyzed the publications from just 11 countries, which represent a small part of the global research output. Also, the Hofstede’s cultural dimensions model is not a unique way to study cultural characteristics at the national level.

Practical implications

The results of this study will provide evidence to research management to properly allocate their efforts to improve the researcher cooperation during the still ongoing COVID-19 pandemic and to overcome its negative outcomes in the years to come.

Originality/value

Considering the global impact and social distress due to the outbreak of COVID-19 pandemic, this study is significant in the present scenario for identifying the changes in the characteristics of research collaboration networks of 11 diverse (in terms of geographical distribution and cultural differences in terms of the Hofstede’s cultural dimensions model) countries between 2019 (the year before COVID-19) and 2020 (the year of COVID-19), which has not been done before.

Between panic and motivation: did the first wave of COVID-19 affect scientific publishing in Mediterranean countries?

Due to the ongoing COVID-19 pandemic that began in the winter of 2020, all communities and activities globally have been positively or negatively affected. This scientometric study raises an interesting question concerning whether the volume and characteristics of scientific publishing in all disciplines in 23 Mediterranean countries have been impacted by the pandemic and whether variations in the cumulative totals of COVID-19 cases have resulted in significant changes in this context. The Scopus database and SciVal tool supplied the necessary data for the years targeted for comparison (2019 and 2020), and the annual growth rates and differences were computed. The study used the Mann–Whitney test to examine the significance of the differences between the two years and the Spearman and Kendall correlation tests to evaluate the effect of the number of infections on these differences for all aspects of scientific performance. The findings demonstrated that the COVID-19 pandemic served as a powerful incentive, and the Mediterranean region experienced considerable differences in the volume and features of publications during this crisis. The most substantial implications were the significant growth from 3.1 to 9.4% in productivity and the increases in the annual growth rates of international collaboration, by 12% for the collaboration among Mediterranean countries and 10% for collaboration with the top ten epidemic countries. It was also proven that some characteristics of the publications were positively correlated with the total number of infections. This investigation can help university leaders and decision-makers in higher education and research institutions in these countries make decisions and implement measures to bridge the gaps and motivate researchers in all fields to conduct more research during this ongoing pandemic.

Thematic evolution of coronavirus disease: a longitudinal co-word analysis

Purpose

Using science mapping analysis approach and co-word analysis, the present study explores and visualizes research fields and thematic evolution of the coronavirus. Based on this method, one can get a picture of the real content of the themes in the mentioned thematic area and identify the main minor and emerging themes.

Design/methodology/approach

This study was conducted based on co-word science mapping analysis under a longitudinal study (from 1988 to 2020). The collection of documents in this study was further divided into three subperiods: 1988–1998, 1999–2009 and 2010–2020. In order to perform science mapping analysis based on co-word bibliographic networks, SciMAT was utilized as a bibliometric tool. Moreover, WoS, PubMed and Scopus bibliographic databases were used to download all records.

Findings

In this study, strategic diagrams were demonstrated for the coronavirus research for a chronological period to assess the most relevant themes. Each diagram depended on the sum of documents linked to each research topic. In the first period (1988–1998), the most centralizations were on virology and evaluation of coronavirus structure and its structural and nonstructural proteins. In the second period (1999–2009), with due attention to high population density in eastern Asia and the increasing number of people affected with the new generation of coronavirus (named severe acute respiratory syndrome virus or SARS virus), publications have been concentrated on “antiviral activity.” In the third period (2010–2020), there was a tendency to investigate clinical syndromes, and most of the publications and citations were about hot topics like “severe acute respiratory syndrome,” “coronavirus” and “respiratory tract disease.” Scientometric analysis of the field of coronavirus can be regarded as a roadmap for future research and policymaking in this important area.

Originality/value

The originality of this research can be considered in two ways. First, the strategic diagrams of coronavirus are drawn in four thematic areas including motor cluster, basic and transversal cluster, highly developed cluster and emerging and declining cluster. Second, COVID-19 is mentioned as a hot topic of research.

The state of social science research on COVID-19

Research on COVID-19 has proliferated rapidly since the outbreak of the pandemic at the end of 2019. Many articles have aimed to provide insight into this fast-growing theme. The social sciences have also put effort into research on problems related to COVID-19, with numerous documents having been published. Some studies have evaluated the growth of scientific literature on COVID-19 based on scientometric analysis, but most of these analyses focused on medical research while ignoring social science research on COVID-19. This is the first scientometric study of the performance of social science research on COVID-19. It provides insight into the landscape, the research fields, and international collaboration in this domain. Data obtained from SSCI on the Web of Science platform was analyzed using VOSviewer. The overall performance of the documents was described, and then keyword co-occurrence and co-authorship networks were visualized. The six main research fields with highly active topics were confirmed by analysis and visualization. Mental health and psychology were clearly shown to be the focus of most social science research related to COVID-19. The USA made the most contributions, with the most extensive collaborations globally, with Harvard University as the leading institution. Collaborations throughout the world were strongly related to geographical location. Considering the social impact of the COVID-19 pandemic, this scientometric study is significant for identifying the growth of literature in the social sciences and can help researchers within this field gain quantitative insights into the development of research on COVID-19. The results are useful for finding potential collaborators and for identifying the frontier and gaps in social science research on COVID-19 to shape future studies.

Synergistic networks of COVID-19’s top papers

Purpose

Synergy indicators and social network analysis (SNA), as practical tools, provide the possibility of explaining the pattern of scientific collaboration and visualization of network relations. Recognition of scientific capacities is the basis of synergy. The present study aims to measure and discover the synergistic networks of COVID-19’s top papers at the level of co-authorship, countries, journals, bibliographic couples and titles.

Design/methodology/approach

The synergy indicator, co-authorship co-citation network analysis methods were applied. The research population comprises COVID-19’s top papers indexed in Essential Science Indicator and Web of Science Core Collection 2020 and 2021. Excel 2016, UCINET 6.528.0.0 2017, NetDraw, Ravar Matrix, VOSviewer version 1.6.14 and Python 3.9.5 were applied to analyze the data and visualize the networks.

Findings

The findings indicate that considering the three possible possibilities for authors, countries and journals, more redundancy and information are created and potential for further cooperation is observed. The synergy of scientific collaboration has revealed that “Wang, Y,” “USA” and “Science of the Total Environment” have the most effective capabilities and results. “Guan (2020b)” and “Zhou (2020)” are bibliographic couplings that have received the most citations. The keywords “CORONAVIRUS DISEASE 2019 (COVID-19)” were the most frequent in article titles.

Originality/value

In a circumstance that the world is suffering from a COVID-19 pandemic and all scientists are conducting various researches to discover vaccines, medicines and new treatment methods, scientometric studies, and analysis of social networks of COVID-19 publications to be able to specify the synergy rate and the scientific collaboration networks, are not only innovative and original but also of great importance and priority; SNA tools along with the synergy indicator is capable of visualizing the complicated and multifaceted pattern of scientific collaboration in COVID-19. As a result, analyses can help identify existing capacities and define a new space for using COVID-19 researchers’ capabilities.

Mapping coronavirus research: quantitative and visualization approaches

Purpose

The present study aims to measure the global research landscape on coronavirus indexed in the Web of Science from 1989 to 2020. The study examines growth rates, authorship trends, institutional productivity, collaborative networks and prominent authors, institutions and countries.

Design/methodology/approach

The research literature on coronavirus published globally and indexed in the Web of Science core collection was retrieved using the term “Coronavirus” and its related and synonymous terms (e.g. COVID-19, SARS-COV, SARS-COV-2 and severe acute respiratory syndrome coronavirus) as per the Medical List of Subject Headings. A total of 5,625 publications were retrieved; however, the study was restricted to articles only (i.e. 4,471), and other document types were excluded. Quantitative and visualization techniques were used for data analysis and interpretation. VOSViewer software was employed to map collaborative networks of authors, institutions and countries.

Findings

A total of 4,471 articles have been published on coronavirus by 99 countries of the world with the maximum contribution from the USA, followed by the People's Republic of China. The United States, China, Canada, Netherlands and Germany are the front runners in the collaborative network and form strong sub-networks with other countries as well. More than 1,000 institutions collaborate in the field of coronavirus research among 99 contributing countries. The authorship pattern shows that 97.5% of publications are contributed by authors in collaboration in which 77.5% of publications are contributed by four or more than four authors. The range between degree of collaboration (DC) varies from 0.89 in 1993 to 1 in 2000 with an average of 0.96 from 1989 to 2020. The results confirm that the coronavirus research is carried out in teamwork at the individual, institutional and global levels with high magnitude and density of collaboration. The relative growth of the literature has shown inconsistency as a decreasing trend has been observed from 2007 onwards, thereby increasing the doubling time from 4.2 in the first ten years to 17.3 in the last ten years.

Research limitations

The study is limited to the publications indexed in the Web of Science; the findings cannot be generalized across other databases.

Practical implications

The results of the study may help medical scientists to identify the progress in COVID-19 research. Besdies, it will help to identify the prolific authors, institutions and countries in the development of research.

Social implications

The current COVID-19 pandemic poses urgent and prolonged threats to the health and well-being of the population worldwide. It has not only attacked the health of the people but the economy of nations as well. Therefore, it is feasible to know the research landscape of the disease to conquer the problem.

Originality/value

The current COVID-19 pandemic poses urgent and prolonged threats to the health and well-being of the population worldwide. It has not only attacked the health of the people but also the economy of nations as well. Therefore, it is feasible to know the research landscape of the disease to conquer the problem.