Digital Transformation

Lin, Shi; Mai, Yingping; Wu, Yenchun Jim

doi:10.4018/joeuc.302637

Cited by 26 publications

(12 citation statements)

References 48 publications

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“…In addition, the present study follows past studies (e.g. Shi et al, 2022;Akbari et al, 2020;Alfawareh et al, 2023) in using Harzing's Publish or Perish for citation metrics and analysis, which can help future research identify avenues for further exploration as well as areas for improvement. Therefore, in Figure 1, we present the steps in bibliometric-based valuations.…”

Section: Methodology and Datamentioning

confidence: 73%

Analysis of global research trends on FinTech: a bibliometric study

Alfawareh,

Al-Kofahi

2023

JIDE

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PurposeThe key aim of this study is to highlight current financial technology (FinTech) trends by conducting a bibliometric review of literature derived from the Scopus database.Design/methodology/approach A bibliometric analysis was conducted on articles gathered from the Scopus database. Microsoft Excel was used to perform the frequency analysis, VOSviewer for visualising the data, and Harzing’s Publish or Perish for the metrics citation.Findings According to this investigation, research into FinTech has been consistently increasing since 2008. The results indicate that the most active publisher of FinTech literature is Bina Nusantara University in Indonesia. In terms of country of publication, China is identified as the most active. The most cited author is Buckley, R.P., with Rabbani, M.R., having the most publications. It was also identified that FinTech researches come under three primary domains namely business management, computer science and economics.Research limitations/implications The primary limitation of this current study is that it only relied on one data source, i.e. Scopus. Implications wise, researchers and practitioners can gain a deeper understanding of FinTech from this study, which also describes the trend in related publications on the concept. Future studies could significantly benefit from the findings of the present paper.Practical implications The outcomes of this study can assist researchers in better comprehending and summarising the key drivers of FinTech. In addition, the findings can help new researchers identify the starting point for their research on FinTech.Originality/value As far as the authors are aware, this is the first study that reviews FinTech publications derived from Scopus from 2008 to 2022. Hence, it is a pioneering study into FinTech bibliometric analysis, providing an understanding of the structural knowledge by reviewing the timeline of academic progression in FinTech.

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Section: Methodology and Datamentioning

confidence: 73%

Analysis of global research trends on FinTech: a bibliometric study

Alfawareh,

Al-Kofahi

2023

JIDE

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“…In comparing highly cited articles to all articles, we found a big difference. The impact rankings for each country and each journal provided are based on citation rates ( 23 ).…”

Section: Resultsmentioning

confidence: 99%

Bibliometric analysis of 100 top cited articles of heart failure–associated diseases in combination with machine learning

Kuang¹,

Zhong

Liang

et al. 2023

Front. Cardiovasc. Med.

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ObjectiveThe aim of this paper is to analyze the application of machine learning in heart failure-associated diseases using bibliometric methods and to provide a dynamic and longitudinal bibliometric analysis of heart failure–related machine learning publications.Materials and methodsWeb of Science was screened to gather the articles for the study. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility. Intuitive data analysis was employed to analyze the top-100 cited articles and VOSViewer was used to analyze the relevance and impact of all articles. The two analysis methods were then compared to get conclusions.ResultsThe search identified 3,312 articles. In the end, 2,392 papers were included in the study, which were published between 1985 and 2023. All articles were analyzed using VOSViewer. Key points of the analysis included the co-authorship map of authors, countries and organizations, the citation map of journal and documents and a visualization of keyword co-occurrence analysis. Among these 100 top-cited papers, with a mean of 122.9 citations, the most-cited article had 1,189, and the least cited article had 47. Harvard University and the University of California topped the list among all institutes with 10 papers each. More than one-ninth of the authors of these 100 top-cited papers wrote three or more articles. The 100 articles came from 49 journals. The articles were divided into seven areas according to the type of machine learning approach employed: Support Vector Machines, Convolutional Neural Networks, Logistic Regression, Recurrent Neural Networks, Random Forest, Naive Bayes, and Decision Tree. Support Vector Machines were the most popular method.ConclusionsThis analysis provides a comprehensive overview of the artificial intelligence (AI)-related research conducted in the field of heart failure, which helps healthcare institutions and researchers better understand the prospects of AI in heart failure and formulate more scientific and effective research plans. In addition, our bibliometric evaluation can assist healthcare institutions and researchers in determining the advantages, sustainability, risks, and potential impacts of AI technology in heart failure.

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“…Bibliometrics can be used to classify the main trends in a field based on published studies (Shi et al 2022). Ellegaard (2018) noted that bibliometric analyses are widely used by the scientific community, and he also pointed out that this method is not only used in the field of library and information science but is also rapidly being used by scholars in other fields (Ellegaard 2018).…”

Section: Methodsmentioning

confidence: 99%

Bibliometric analysis of traffic-related air pollution: using CiteSpace to explore the knowledge structure and trends

Lin

2024

Environ. Res. Commun.

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Although traffic-related air pollution (TRAP) has been a long-standing problem, few bibliometric- and visual analysis-based literature reviews have been performed. In light of this issue, future research plans and directions in the field of TRAP must be determined. Therefore, this study performed a bibliometric analysis of the TRAP publishing trends, including the countries, institutional collaborations, author collaborations, keywords, and hotspots. The information visualization software CiteSpace was used to analyze the relevant literature collected from the Web of Science (WoS) from 2003 to 2022. The main findings of this study included the following: (1) the main keywords in TRAP research are particulate matter, exposure, health, nitrogen dioxide, and mortality; (2) current research is focused on the impacts of TRAP on humans; and (3) potential hotspots for future TRAP research are source apportionment, asthma, heart rate variability, and mobile monitoring. This article aims to develop a better understanding of current research trends in TRAP and provide directions for future research.

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Digital Transformation

Cited by 26 publications

References 48 publications

Analysis of global research trends on FinTech: a bibliometric study

Analysis of global research trends on FinTech: a bibliometric study

Bibliometric analysis of 100 top cited articles of heart failure–associated diseases in combination with machine learning

Bibliometric analysis of traffic-related air pollution: using CiteSpace to explore the knowledge structure and trends

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