Cybersecurity Awareness Literature Review: A Bibliometric Analysis

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  • Published: 15 December 2023

Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements

  • Ali Montazeri   ORCID: 1 ,
  • Samira Mohammadi 1 ,
  • Parisa M.Hesari 2 ,
  • Marjan Ghaemi 3 ,
  • Hedyeh Riazi 4 &
  • Zahra Sheikhi-Mobarakeh 5  

Systematic Reviews volume  12 , Article number:  239 ( 2023 ) Cite this article

Metrics details

A bibliometric review of the biomedical literature could be essential in synthesizing evidence if thoroughly conducted and documented. Although very similar to review papers in nature, it slightly differs in synthesizing the data when it comes to providing a pile of evidence from different studies into a single document. This paper provides a preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO).

The BIBLIO was developed through two major processes: literature review and the consensus process. The BIBLIO started with a comprehensive review of publications on the conduct and reporting of bibliometric studies. The databases searched included PubMed, Scopus, Web of Sciences, and Cochrane Library. The process followed the general recommendations of the EQUATOR Network on how to develop a reporting guideline, of which one fundamental part is a consensus process. A panel of experts was invited to identify additional items and was asked to choose preferred options or suggest another item that should be included in the checklist. Finally, the checklist was completed based on the comments and responses of the panel members in four rounds.

The BIBLIO includes 20 items as follows: title (2 items), abstract (1 item), introduction/background (2 items), methods (7 items), results (4 items), discussion (4 items). These should be described as a minimum requirements in reporting a bibliometric review.


The BIBLIO for the first time provides a preliminary guideline of its own kind. It is hoped that it could contribute to the transparent reporting of bibliometric reviews. The quality and utility of BIBILO remain to be investigated further.

Peer Review reports

Several guidelines exist for reporting findings of different study designs. The detailed explanations and checklists for such guidelines can be found in Enhancing the Quality and Transparency of Health Research (EQUATOR) Network [ 1 ] and are available to research communities [ 2 ]. For instance, the quality of reporting of meta-analyses (QUOROM) statement for improving the quality of reporting meta-analyses of randomized controlled trials was first published in The Lancet in 1999 [ 3 ]. Consequently, the work was further improved, and it was replaced with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) [ 4 ]. This guideline was published simultaneously in 6 journals in 2009 [ 4 , 5 , 6 , 7 , 8 , 9 ], and since then, many biomedical journals and investigators have adhered to this instruction. The instruction also was extended, and complementary versions of the guideline either are developed (such as PRISMA for Abstracts) [ 10 ] or are under development (e.g., PRISMA for children) [ 11 ]. Even the preferred reporting items for overviews of reviews (PRIOR) are proposed [ 12 ], and a recent call by Systematic Reviews (the journal) indicates that attempts to enhance the knowledge of this type of reporting are in progress [ 12 , 13 ].

However, we believe there is also a need for a guideline for another type of reporting, namely, Guideline for Reporting Bibliometric Reviews of the Biomedical Literature (BIBLIO). A bibliometric or a bibliographic review of the literature is different from an overview. Recently, the literature witnessed a relatively considerable number of bibliometric analyses of the biomedical literature [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The number of publications related to various topics with bibliometric or bibliography/bibliographic in the title during the last 10 years is presented in Fig.  1 . Therefore, this paper attempts to propose a preliminary version of a guideline for reporting bibliometric reviews of the literature. The guideline was developed based on all existing guidelines presented in the EQUATOR Network [ 1 ]. In addition, experiences from writing a number of bibliometric reviews [ 24 , 25 , 26 , 27 , 28 ] helped the authors to formulate this first version of the work with the courage that it could be improved further by receiving feedbacks from other scholars in the field.

figure 1

Papers with bibliography/bibliographic and bibliometric in the title of publications during 2013–2022 (PubMed)

Although BIBLIO is in its preliminary stage of development and there is no evidence of its quality and utility, it is hoped that it could contribute to the transparent reporting of bibliometric reviews. The application of bibliometric reviews enables one to analyze vast amounts of publications and their production patterns on macroscopic and microscopic levels [ 29 ]. Therefore, this study aimed to provide a guideline for reporting bibliometric reviews. The BIBLIO checklist was registered in the EQUATOR Network on 19 October 2021 [ 30 ].

The term bibliometric and bibliography are used interchangeably in the literature. Earlier, the term bibliography was more popular, but it was gradually replaced with the bibliometric expression (Fig.  2 ). The history of the statistical bibliography as reviewed by Thackray [ 31 ] indicates that the root goes back to early 1900s as this was acknowledged in a paper by Garfield [ 32 ] and a number of scholar such as Cole and Eames (1917), Hulme (1923), Lotka (1926), and Gross and Gross (1927) were listed as those who contributed to the technic of statistical analysis of the literature. However, it was Otlet in 1934 who first used the term “bibliometrie” and defined it as “the measurement of all aspects related to the publication and reading of books and documents” [ 33 , 34 ]. Then in 1969, Pritchard coined the term “bibliometrics” and defined it as “all the studies which seek to quantify the processes of written communication” [ 35 ]. The detailed history since 1934 is presented in Table  1 .

figure 2

Trends of using bibliography/bibliographic or bibliometric in the title of publications during 2013–2022 (PubMed)

Bibliometric is a type of review that can be used to look at different and important areas of investigations and obtain a general synopsis of published literature [ 39 ]. This guideline defines a bibliometric review as follows “a review of all full published papers that appear in the biomedical journals and includes all types of evidence such as descriptive studies, observational studies, experimental studies, qualitative studies, and systematic reviews in order to account for every single evidence exist. The bibliometric of the literature does not include electronic publications a head of print since the ultimate date for such publications are not known”. This definition was formulated based on chronological account of the term bibliometric and its developments [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].

Similarities and differences between systematic reviews and bibliometrics

Bibliometric is similar to systematic review in retrieving the literature [ 40 ], but they have low agreement rate regarding relevant literature and the purpose. While systematic reviews are seeking to respond to a very clear question based on good quality evidences, bibliometrics is rather a numeration of evidence without quality assessment. Bibliometrics often rely on the interpretation of quantitative details of publications such as main topics, authors, sources, most impactful authors, most impactful articles, and countries in a particular area in the existing literature. In this type of study, mapping techniques including graphical representations, tabulated forms, network diagrams, and so on are used to present results usually performing these with the assistance of softwares [ 39 , 40 , 41 , 42 ].

Development of BIBLIO

The BIBLIO was developed through two major processes: literature review and the consensus process. These are briefly described as follows:

1. Literature review for item selection

The BIBLIO started with a comprehensive review to identify potential items for including in this guideline. The databases searched included PubMed, Scopus, Web of Sciences, and Cochrane Library. The aim was to examine and review all methodological papers on the conduct and reporting of bibliometric studies up to 2021. The search was updated in January 2022 and once during the process of revisions in September 2023. Papers were retrieved using different keywords and MeSH terms including “bibliometric,” “bibliography,” and “bibliographic” in the title of papers. All potentially relevant publications were extracted and reviewed independently by two authors (AM and SM). Overall, 13,720 papers were identified. After removing duplicates and irrelevant documents, only 19 papers [ 40 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ] were found that were dealing with methodological issues. Also, we visited all reporting guidelines for review studies that are indexed in the EQUATOR [ 1 ]. The items derived from the literature are shown in Table  2 .

2. Consensus process

The process followed the general recommendations of the EQUATOR Network on how to develop a reporting guideline, of which one fundamental part is a consensus process [ 1 ]. We used Delphi consensus to obtain advice on how to report a “bibliometrics.” Delphi was performed based on the conducting and reporting Delphi studies (CREDES) guideline [ 61 ]. A panel consisted of eleven experts, including bibliometrician, epidemiologist, clinician, librarian, statistician, journal editor, and a research fellow. They were invited to see the list of items derived from the previous stage and asked to identify additional items and to choose preferred options or suggest other items that should be included in the checklist. In each round of the Delphi, the feedback process allowed and encouraged the selected participants to review and assess their own initial judgments. Thus, the results of previous iterations regarding specific items were changed or modified by each member of the expert panel in later iterations based on the review and assessing the comments and feedback provided by the other Delphi panelists [ 62 ].

In the first round of the Delphi process, we used an open-ended questionnaire to solicit specific information and to add suggested items to the list of items and increase the rich of data collection. After receiving the experts’ responses, we converted the collected information into a well-structured questionnaire on a five-point scale with content analysis technique. This questionnaire was used as the survey questionnaire for the second round of data collection. Each Delphi participant received a second questionnaire and was asked to review the items summarized based on the information provided in the first round. Accordingly, we asked Delphi panelists to rate items and state the rationale concerning rating priorities. In the third round, each Delphi panelist received a questionnaire that included the items and ratings summarized in the previous round and was asked to revise their judgments. The remaining items, ratings, minority opinions, and items achieved consensus were distributed to the panelists in the final round. The fourth round provided a final opportunity for participants to revise their responses after formal feedback of the group. At last, the checklist was finalized based on the comments and answers of the panel members in four rounds. The cut-off for consensus was determined by percentage of agreement (mainly 75 to 80%). The duration of each round of Delphi was about 8 weeks, and the length of the overall study process was 8 months. Before beginning the Delphi survey, all experts were asked to disclose any conflicts of interest. The response rate was 100% for all four rounds of the Delphi process.

Scope of the guideline

BIBLIO is for use in reporting bibliometric reviews and has been designed primarily for bibliometric reviews that evaluate published papers irrespective of the design of the studies. The BIBLIO items are relevant for all types of quantitative and qualitative studies. BIBLIO can be used for reporting original bibliometric reviews and updated bibliometric reviews. BIBLIO is not to guide a bibliometric review conduct. However, familiarity with BIBLIO is helpful when planning and conducting bibliometric reviews to ensure that all recommended information is captured.

The BIBLIO checklist

The development team provided a list of items based on the literature review and presented them into the consensus process. Participants made revisions to the phrasing and format of the checklist by consolidating and eliminating items during the consensus process. Eventually, the BIBLIO checklist consisted of 20 items that should be described as a minimum requirements in reporting a bibliometric review as follows: title (2 items), abstract (1 item), introduction/background (2 items), methods (7 items), results (4 items), discussion (4 items). The full description of the items is in progress and will be available in due course. However, as an example here, we elaborate on item 15. As shown item 15 provides guidelines for reporting the results. As such four options are proposed. In the following section, we describe each option ensuring that examples given could help investigators to better summarize the findings. Since the opening part of each option is the same here the focus is on how organize the main findings:

Option 1: Organization based on study design and main study types

Research design is a blueprint of a scientific study. We could summarize studies based on different designs and main study types. For instance, one might summarize main study types based on randomized trials, observational studies, study protocols, diagnostic/prognostic studies, case reports, clinical practice guidelines, and qualitative studies on a given topic.

Option 2: Organization based on outcome measures

The other suggestive way to summarize the main findings is based on outcomes. For instance, a bibliometric analysis that evaluated the impact of race on postoperative outcomes and complications following elective spine surgery was classified based on outcomes providing four categories including general complications, medical complications, surgical complications, and postoperative outcomes [ 63 ].

Option 3: Organization based on concept

To simplify and clarify this presentation approach, we explain this option with an example. A study on bibliometric analysis of health literacy instruments summarized the findings in four tables according to the concept behind instruments including general instruments, condition-specific health literacy instruments (disease and content), population-specific instruments, and electronic health [ 28 ]. Authors could invent such concepts or use the literature for categorizing and summarizing the findings.

Option 4: Organization based on different subtitles relevant to the main topic

This presentation approach is well known and was used in many studies. One example for this option is a bibliometric study on health-related quality of life in breast cancer patients. In this study, the findings were summarized and presented according to treatments modalities and a number of classifications including surgical treatment, systemic therapies, psychological distress, supportive care, and common symptoms [ 26 ]. One should note there are many ways that we could summarize and tabulate the findings to provide a quick and at the same time a comprehensive perspectives of the studies under review. The checklist is presented in Table  3 .

A bibliometric review is a helpful means for accurately and reliably summarizing the evidence, specifically when a large number of papers exist on a given topic [ 69 ]. The bibliometric studies that are well done usually could help to grasp the current literature, identify knowledge gaps, derive novel ideas for investigation, and position their intended contributions to the field [ 43 ].

The bibliometric methods are quantitative and descriptive by nature but also used to make pronouncements about qualitative aspects. The principal purpose of bibliometric studies is to change intangible knowledge (scientific quality) into manageable entities [ 70 ]. Bibliometrics are not in-depth and evaluative reviews. However, they could briefly report on effectiveness and evaluations. Overall, a good bibliometric review should provide a take-home message for its readers.

A number of recommendations are proposed to improve readability of bibliometric reviews. For instance, it was proposed using easy-to-interpret metrics, as non-experts have a difficulty understanding of complex indicators. Also, it was recommended to avoid inventing the indicators, especially composite metrics that mix several indicators in a single measure. Likewise, it was suggested to avoid conscious efforts to manipulate the findings, for instance, choosing metrics that may favor your institution, certain areas, or researchers within it [ 44 ].

A bibliometric review could reveal how much effort has been made into a specific topic. In addition, presenting and summarizing the studies allows scholars to use bibliometric analysis to uncover emerging trends in article publishing, journals’ performances, collaboration patterns, and exploring the intellectual structure of a specific domain in the extant literature [ 71 , 72 ]. Describing the evidence could help policymakers, managers, and other decision-makers to formulate appropriate recommendations for practice or policy [ 73 ] and help editors judge the merits of publishing reports of new studies [ 74 ]. The bibliometric also helps translate and map the cumulative scientific knowledge and evolutionary nuances of well-established fields by making sense of large volumes of unstructured data in rigorous ways [ 43 ].

The use of BIBLIO similar to other guidelines [ 3 , 4 , 75 ] has the potential to benefit many stakeholders. The BIBLIO provides readers with a complete understanding of evidence about the necessity of each item. We have attempted to ensure that the guideline is helpful to authors seeking guidance on what to include in a bibliometric review. We hope the BIBLIO will help increase the quality of reported and published bibliometric reviews. Peer reviewers, editors, and other interested readers might also find the BIBLIO helpful in assessing such reviews. We hope journal editors will encourage authors to include the BIBLIO checklist when submitting a bibliometric review for publication.

Finally, although we followed the general recommendations of the EQUATOR Network and used a literature review and a Delphi consensus process to develop the BIBLIO checklist, it seems that its main limitation is the fact that there is no evidence to suggest it will improve the quality of bibliometric reviews. In this regard, feedback from editors and researchers about details and overall structure can be helpful. Additionally, one should note that bibliometric reviews is not an in-depth review of the literature and rather the most important contribution of this type of reviews is to collect and summarize evidence when we witness a pile of evidence on a topic. As such it reveals that how much effort has been conducted on a topic. In addition, this approach might help investigators to create new questions to conduct more focused studies on the topic in the future [ 26 ].

The BIBLIO provides a reporting guideline for bibliometric reviews of the biomedical literature. We hope that the guideline could result in more transparent and accurate reporting of bibliometric reviews.

Availability of data and materials

Not applicable.


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Author information, authors and affiliations.

Population Health Research Group, Health Metrics Research Center, Iranian Institute for Health Sciences Research, ACECR, Tehran, Iran

Ali Montazeri & Samira Mohammadi

Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Canada

Parisa M.Hesari

Vali-E-Asr Reproductive Health Research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Marjan Ghaemi

Department of Midwifery and Reproductive Health, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Hedyeh Riazi

Quality of Life Research Groups, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran

Zahra Sheikhi-Mobarakeh

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Montazeri, A., Mohammadi, S., M.Hesari, P. et al. Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements. Syst Rev 12 , 239 (2023).

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Please note you do not have access to teaching notes, a systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018.

Journal of Enterprise Information Management

ISSN : 1741-0398

Article publication date: 31 March 2020

Issue publication date: 28 January 2021

The volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.


This study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.

The benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.

Practical implications

This research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.


The study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.

  • Big-data analytics (BDA)
  • Supply chain management
  • Systematic literature review (SLR)
  • Bibliometric analysis (BA)
  • Sustainable supply chain management

Inamdar, Z. , Raut, R. , Narwane, V.S. , Gardas, B. , Narkhede, B. and Sagnak, M. (2021), "A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018", Journal of Enterprise Information Management , Vol. 34 No. 1, pp. 101-139.

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Original research article, quality of higher education: a bibliometric review study.

a literature review with bibliometric analysis

  • 1 Department of Administrative Sciences, University of Bisha, Bisha, Saudi Arabia
  • 2 Department of Management Sciences, University of Oum El Bouaghi, Oum El Bouaghi, Algeria
  • 3 Department of Computer and Information Systems, University of Bisha, Bisha, Saudi Arabia
  • 4 Department of Comparative Literature, Binghamton University, Binghamton, NY, United States
  • 5 Department of Education, Nazareth College of Rochester, Rochester, NY, United States

For more than three decades, higher education has attracted growing interest from scholars, students, and academic institutions worldwide. This paper aims to analyze the literature review of quality of higher education, using the bibliometric analysis adapted from VOSviewer software to examine the data of 500 studies published in the Web of Science from 2000 to 2018 related to this topic. The results were presented and discussed with the following approaches: keywords, authors, references (research papers), research work, countries, and research institutions. The study found that bibliometric analysis is fundamental in detailing the theoretical literature and developing an integrated theoretical framework on quality of higher education. This review provides reference points for entry into this interdisciplinary field.


Perhaps the diversity of knowledge fields in administrative sciences has contributed to the diversity and multiplicity of research work. Preparing any study in these sciences is linked to different and complex frameworks.

Previous studies have been very repetitive; their abundance makes it very difficult for researchers to define concepts and chart the right course of the research, and could result in losing the right direction due to a lack of knowledge of prestigious studies or influential researchers. Who can rely on, control, and deal with this large number of research? Some databases organize them (like Science Web, ISI, Scopus, and Google Scholar). Management sciences and researchers have led to distinguished studies’ preparation, which creates the need to explore how to deal with this spread.

Computer programs help manage a large amount of data and organize, store, publish, distribute, and deal with many studies. Software such as Citespace and VOSviewer and programs help gather the most influential researchers in the world. Therefore, the field should focus on references, keywords, research cases, and organizations.

The study examines bibliometric analysis and its importance compared to previous studies’ methods (meta-analysis and systematic review), especially concerning quality of higher education. Therefore, this paper analyses higher education’s scientific production as indexed in Web of Science (WOS) and Scopus (2000–2018). The motivation of the study is directly related to the purpose. By doing so, we will detect its scope and identify research trends for this area; this could help in increasing the number of readers familiar with the topic and enable the scientific community to become more knowledgeable about the development of higher. The justification and significance of this study’s analysis is based on seven research questions that guided the study. The primary motivation is to understand the higher education trends in the scientific literature and detect the source titles, organizations, authors, and countries with the highest scientific output on higher education. According to Mulet-Forteza et al. (2021) , the research questions of this study are as follows:

RQ. What is bibliometric analysis’s contribution to the review and development of the theoretical literature on quality of higher education?

The sub-questions are:

RQ 1. What is the importance of bibliometric analysis in defining the theoretical frameworks for the quality of higher education?

RQ2. What structure is formed by the publications and citations in the quality of higher education?

RQ3. Which keywords do authors on the quality of higher education use the most frequently?

RQ4. Who are the most cited authors in the field of quality of higher education?

RQ5. Which research documents are cited the most frequently by authors in the field of quality of higher education?

RQ6. What are the most important research institutions concerning the production of research papers in quality of higher education?

RQ7. What are the most important countries concerning the production of research papers in quality of higher education?

The study aims to determine the bibliometric analysis results and the results of the process, which will benefit the researchers in administrative sciences in drawing the correct direction. This study, then, includes identifying keywords, the most influential researchers in the field, the research work, reference sources, countries, and reference research institutions. Therefore, the study compares bibliometric analysis with traditional literature reviews in administrative sciences and the methods of bibliometric analysis and methodology for studying bibliometric studies in administrative sciences, as well as offering a bibliographic analysis of the issue of quality of higher education.

Literature Review

Bibliographic studies have developed a new style of reviewing the theoretical literature in various fields of knowledge, including management science, theories associated with these studies, or bibliometric analysis. Quality and education are an essential part of society. Getting an excellent education is a fundamental pillar in looking at the future of nations, as it reveals the educational development they are going through ( de Matos Pedro et al., 2020 ). Therefore, ensuring quality of higher education is also crucial for social development ( Salas-Zapata et al., 2018 ). The concept refers to service quality, particularly from a higher education sector research perspective ( Rieckmann, 2012 ).

The Initial studies from the educational sector indicate that the idea of quality in higher education has become unclear and agrees that quality is the result of comparing service expectations with the perception of actual service received ( Seymour, 1992 ; Green, 1994 ; Quinn et al., 2009 ). The study by Cameron et al. concluded that it focuses on integrating effective participatory methods into the teaching process, motivating members to obtain knowledge, the educational community, social future, knowledge, skills, attitudes, and core values.

Carvalho and de Oliveira Mota (2010) studied the educational model dynamics’ position the student, as the recipient of education, has, turning them into service recipients. Then, in their study, DiDomenico and Bonnici (1996) analyze the quality of service they require to thrive in a competitive environment and discussof the quality of educational services that provide a degree of quality assurance. Investing in education will help us in the long run, as it will provide for future generations.

Bibliometric analysis, according to Lotka (1926) , is the “Method for measuring researchers’ productivity.” Bradford (1934) defines it as “Laws for Dissecting Scientific Knowledge.” Zipf (1949) states it to be “A template for the distribution of words and the frequency in the text.” Pritchard (1969) describes it as “A collection of studies intended to qualify research communications operations.” Fonseca (1973) defines it as a quantitative and statistical method for measuring scientific production rates and disseminating scientific knowledge. For Abdi et al. (2018) , a number of definitions of bibliometric analysis were cited, the first of which was referred to by Hung as a set of methods used to examine or measure texts and information. Also, Hussain, Fatima, and Kumar believe it to be a system that uses a quantitative approach based on various aspects of written articles and publishers.

According to Merigó and Yang (2017) , bibliometric analysis is defined as “a quantitative study of bibliographic material (data) and provides a general picture of a research field that research papers, authors, and fields can include categories.”

Tsay believes that bibliometric analysis techniques rely on references used in research work to develop statistical models for the flow of scientific relationships between them ( Tsay and Shu, 2011 ). For example, citations can be used to map relationships between files, journals, or others. On this basis, it can be noted that the analysis of bibliometric analysis or reference citations is a quantitative analysis of written research works (scientific production), such as articles, books, and research papers. The search network of relationships’ linking and privacy of work (titles, authors, research institutions, countries, keywords) is also included, where this network is based on items or indicators such as reference citations, bibliographical links, and co-authors. This allows readers to find out more about the most influential research, researchers, research institutions in the field.

The studies of Zupic and Čater (2015) mentioned the importance of this type of analysis compared to the classical method of reviewing theoretical literature. They mentioned the importance of this type of analysis compared to the classical method of reviewing. Theoretical literature was among studies in bibliometric analysis methods in administrative sciences. “The volume of research work has increased dramatically in recent years, making it difficult for researchers to track the literature relevant to their field of work, which has led them to use quantitative bibliometric analysis methods that can deal with this wealth of data. Also, to filter research work through estimating their impact and discovering the foundation.”

Traditional methods of reviewing and evaluating the theoretical literature are primarily meta-analysis and systematic literature review. A meta-analysis seeks to gather empirical evidence from quantitative studies ( Aguinis et al., 2011 ). Through this, the researcher selects lessons based on the exact relationship he wishes to explore ( Raghuram et al., 2010 ) and combines multiple findings in these relationships to discover one comprehensive finding. That is a compelling method, but it is limited in the studies’ nature and breadth that can be analyzed. A systematic review can address the diversity of tasks and methodological approaches. This method can provide an in-depth analysis of the literature and understand the conceptual context ( Raghuram et al., 2010 ). However, this process is time-consuming, and the number of works analyzed is limited and subject to research bias, so there is a real possibility of excluding essential studies.

Compared to traditional methods, scientific mapping using bibliometric methods provides a different perspective in this field; any study can analyze the link between the current studies and the studies’ analyses. Therefore, bibliometric research offers an opportunity to engage in various tasks to avoid bias and studies’ choices ( Mulet-Forteza et al., 2019 ).

Further, bibliometric analysis methods cannot be considered an alternative to traditional theoretical literature reviews in administrative sciences. However, they are complementary because they help the researcher choose the most important research studies in the field, the most influential researchers in the area, the basic ones in the field, and even research institutions and countries in the field.

There are three basic laws of bibliometric analysis: the law of Lotka on the scientific productivity of researchers, the law of Bradford on the dispersion of scientific production, and the law of Zipf on the appearance of words in the text. More details on Bizotto et al. (2015) the basic laws of bibliometric analysis can be found in Table 1 .

Table 1. Fundamental laws of bibliometric analysis.

It is evident from the Table 1 that bibliometric analysis has axes regarded as empirical predictors ( Waltman and Noyons, 2018 ). Moreover, Corrall et al. (2013) imply a quantitative calculated scientific finding, scientific factors, and scientific collaboration as objectives of the bibliometric analysis. These are based on indicators such as quotations, bibliographical conjugations, reference quotes, researchers participating in the authorship, and more.

This review studied 500 studies published in the WOS from 2000 to 2018 related to quality of higher education. The bibliometric review was adapted using VOSviewer software packages and discusses the following approaches: keywords, authors, references (research papers), research work, countries, and research institutions.

Here, a distinction must be made between the indicators corresponding to the analysis method and the unit of analysis, where the indicators are authorship researchers, quotation, bibliographic conjugation, reference quotation, and level of appearance ( Gingras, 2016 ). The analysis units are authors, terms or keywords, research papers, journals or resources, research institutions, and countries. For reference, the indicators provide quantitative measurements for research units, and it is understood that there are different bibliometric analysis methods.

Materials and Methods

This science mapping study of the literature used bibliometric methods to review research on higher education. Research reviews grounded in bibliometric practices do not examine the substantive findings of studies. Instead, their value extends from the capability to document and synthesize broad trends that describe a knowledge base’s landscape, composition, and intellectual structure. Thus, science mapping offers insights into knowledge accumulation patterns that would be difficult to “see” using traditional research reviews ( Zupic and Čater, 2015 ).

Zupic and Atherater ( Zupic and Čater, 2015 ) provided a summary of the methods of bibliometric analysis, as shown in Table 2 .

Table 2. Bibliometric analysis approaches.

As mentioned above, the approach pointed to several indicators used to link research units as a map or an information network in the bibliometric analysis. As shown, the data quality and the package or software used in the study is affected.

Bibliographic analysis requires reliable data sources since the WOS developed by Clarivate Analytics and Scopus developed by Elsevier is the most widely used (requires subscription) ( Aria and Cuccurullo, 2017 ). Google Scholar is characterized as free database with quality problems of data. Google Scholar also uses Google Scholar (free of charge, but with data quality problems). Databases may also operate in a specific cognitive field such as INSPIRE (High Energy Physics), MathSciNet (Mathematics), PsycINFO (Psychology), and PubMed (Biomedical Research). For a bibliometric analysis that focuses on a specific region, they can use data sources particular to that region, such as the Russian citation base or the Chinese citation base ( Waltman and Noyons, 2018 ).

In this study, we chose the WOS databases; all resources published from 2000 to 2018 related to quality of higher education were selected. This data was analyzed with VOSviewer software using the following approaches: keywords, authors, references (research papers), research work, countries, and research institutions.

The software used in the bibliometric analysis has evolved and diversified with the diversity of approaches to this type of research; basic software is widely used internationally in this field, as shown in Table 3 ( Van Eck and Waltman, 2014 ).

Table 3. Software used in bibliometric analysis.

According to Zupic and Čater (2015) , the practical steps for conducting bibliometric analysis are study design, collection of bibliometric data, analysis, results presentation, and interpretation. The VOSviewer used in the study is widely use in the international publication of scientific articles in the bibliometric research.

Methodology and Data for the Bibliometric Study

The data used in the bibliometric analysis are the basis for the achievement of accurate results based on sound methodology and selected approaches. As mentioned earlier, the approach pointed to some indicators used to link research units as a map or an information network in the bibliometric analysis. Each of these methods has its advantages and disadvantages. It is also affected by the quality of the data and the package or software used in the analysis. The study selected 500 studies published from 2000 to 2018 related to quality of higher education (Article, Article; Proceedings Paper, Book Review, Correction, Editorial Material, Letter, Meeting Abstract, News Item, Review) (Q3; R2).

Then, a co-citation analysis was performed to obtain an initial picture of the documents that contributed to this literature’s development. Based on the methods and approaches of bibliometric analysis, emphasis may be placed on analytical methods that relate to indicators such as quotation, reference quotes, bibliographic association, co-authoring, and terminology sharing, and may focus on analysis units such as keywords (Co-occurrence of all keywords), authors (Co-citation authors), sources (Co-citation sources), organizations (Citation organizations), and countries (Citation countries) (Q3; R2). According to the objectives of the study, the focus was on analysis units to determine what is essential in the quality of higher education for these units: the definition of keywords; the most influential authors; and the most important sources, countries, and reference research institutions. To define the density and networks of units the data is filtered as follows: 65 keywords selected based on Co-occurrence of all key-words, 75 authors according to Co-citation, 80 sources according to Co-citation, 37 organizations selected based on citation, and 43 countries selected based on citation (Q5; R2).

The experimental stages and preparation of the bibliometric study (study design, data collection, analysis, presentation, interpretation) was done using the VOSviewer [see guides ( Van and Waltman, 2018 ) and ( VOSViewer Manual, 2020 )]. A leader in the field and the preparation of international articles with several pieces of software can be used, allowing the researcher to summarize the bibliometric study results in summarized maps and networks. They also, shown in the following section, related to the bibliometric analysis results of quality of higher education.

Discuss the Results

Based on the WOS data for quality of higher education and using VOSviewer, the bibliometric analysis units were presented and discussed a set of results.

The Most Frequently Occurring Keywords

The Figure 1 shows the network and intensity of words or keywords according to their level of visibility in the database or the WOS quality education sources.

Figure 1. Network and density of the appearance of keywords in the quality of higher education.

Previous concepts and essential words that can be considered keywords appear in Figure 1 (see Appendix 1 ); the researcher must focus on the subject of quality of higher education, the first of which is higher education, which appears 140 times in the data. The second is quality assurance (61 times) and the third is quality (51 times); those three words must be researched in-depth because they form the basis of the subject of quality education, and they deepened to a lesser degree in terms of quality of service, management, performance, students, and university (universities). In-depth research is conducted to a lesser extent on contentment, model (models) Expectations, accountability, quality of the educational process, and atmospheric management comprehensive results, results, and policy.

It is also clear that there are research clusters that the researcher directs when he focuses on a specific part of the quality of higher education, and this did not appear clearly in the network ( Raghuram et al., 2019 ). For example, in density, when we speak in a research paper on quality assurance as part of research on the quality of higher education, we initially talk about the research cluster. The second demonstrates the VOSviewer, which includes seven terms, quality assurance, institutional quality, quality of higher education, quality improvement, quality management, quality indicators, and quality culture to these clusters, which are parts of the research areas the researcher should determine in the case of his focus.

The lines linking the keywords express the sharing of their appearance in the same research work. For example, the term “higher education in blue” is a part of the words universities, expectations, quality management, overall quality management, and quality of higher education; the thickness of the line connecting these words to a basket is linked to each other through research. They also constitute another criterion for choosing the fields of research in which the researcher moves.

The Most Cited Authors in the Quality of Higher Education

It is remarkable to understand the knowledge of the most influential researchers in the field through the analysis of previous studies, and this is what VOSviewer provides, where the researcher can know this through joint citation, as shown in Figure 2 .

Figure 2. (A) The network of the most cited authors in the quality of higher education. (B) The density of the most cited authors in the quality of higher education.

The author’s co-citation analysis has been used to reveal the knowledge base’s intellectual structure in quality of higher education. This was accomplished in VOSviewer, which created an author’s co-citation map depicting similarities between scholars strongly cited in this literature.

Figure 2 (see Appendix 2 ) presented the most influential authors in the field of quality of higher education are, who are Lee Harvey, A Parsu Parasuraman, and Bjørn Stensaker. The researcher should rely on their theories and ideas in this field. Harvey, who is a Professor of Higher Education at Copenhagen Business School in Denmark, specializes in research and further research on defining the quality of higher education in five aspects: quality in the sense of excellence, quality in the mind of error, quality in the sense of relevance to objectives, quality as cash value, and quality in the sense of transformation ( Harvey and Askling, 2003 and ( Gingras, 2016 ). Parasuraman is Professor of Higher Education and Research Fellow at the University of Interest at the University of Miami in the United States of America and his colleagues Valarie A. Zeithaml and Leonard L Berry are famous for the SERVQUAL quality service model; these dimensions are represented by responsiveness, reliability, response, warranty, and sympathy ( Parasuraman et al., 2002 ). Stensaker is a professor at the University of Oslo, Norway, best known for his quality assurance and higher education management work.

Other researchers located in the orange or yellow ocean (see density), who include Abdullah, Marginson, and Owlia, should also be relied on, particularly in quality of higher education, as they are prominent in this field.

Hence, in the author co-citation map, the “clusters” of co-authors are treated. A common color map indicates these combinations in the citation map. The author’s importance in the literature is indicated by the size of the node and the density of “links” to other authors. Links between authors represent citations shared between these particular authors. A cross-citation map between groups and schools that included the QHE knowledge base was revealed.

The Most Cited References in the Quality of Higher Education

The presentation and analysis of the researchers’ results provided knowledge on the most influential researchers in the field of quality of higher education. However, these have many and varied contributions. Which of them and which of their research contributions and works were the most influential and most reliable in the field? This is known as the analysis of research works, as shown in Figure 3 (see Appendix 3 ).

Figure 3. Network and density of the most referenced research papers in the field of quality of higher education.

Reference is made to the research work most frequently cited and referred to in quality of higher education, which has made researchers more influential in this field, such as Harvey, Parasuraman, Stensaker, and others. Harvey’s work defines quality in higher education through: higher education appreciation and evaluation ( Harvey and Green, 1993 ), changing higher education ( Harvey and Knight, 1996 ), fifteen years of higher education quality ( Harvey and Williams, 2010 ). Parasuraman works on the SERVQUAL model which is a multidimensional measure of assessing the quality of services through customer perception ( Parasuraman et al., 1994 ), SERVQUAL Scale Refining and Reassessing ( Parasuraman et al., 1991 ) and others.

These works, which represent the original studies in quality of higher education, must be relied upon by the researcher as a previous reference study. Then, the necessary research work is based throughout the stages of his research.

The Most Cited Sources in the Quality of Higher Education

The Figure 4 shows that the researcher’s resources should include quality of higher education, and in-depth readings of their content help in building his research vision. He uses necessary references, mainly referred to or based on researchers and specialists in this field.

Figure 4. The network and density of the most relevant sources in the field of quality of higher education.

Figure 4 (see Appendix 4 ) shows that the primary references or resources of quality of higher education Springer with 281 citations, discussing quality assurance in education, Emerald with 253 citations, discussing quality in higher education (quality in higher education) Routledge with 181 citations, discussing measurement and evaluation in higher education, by 173 citations, followed by other journals such as higher education studies, overall quality management, marketing journal, overall quality management, and business excellence.

These sources or references, which are located in the orange desert areas in density, and with large circles in the network, must be carefully considered by the researcher in the field of quality of higher education, in particular in terms of depth of reading or in terms of frequent reference to and dependence on research. On the other hand, it should be noted that the most influential researchers in the field, the research work of these researchers, and the most relevant works in quality of higher education are undisputedly identified in these essential references, or preferably sources, which call for the researcher to obtain them.

The Most Cited Countries in the Quality of Higher Education

The Figure 5 shows the presentation and analysis of the results of most cited countries, research work, and citations in the field of quality of higher education, which the researcher should refer to in this search for knowledge.

Figure 5. The network and density of the countries with the highest reference in the field of quality in higher education.

Figure 5 (see Appendix 5 ) shows that the most influencing countries on quality of higher education are England, known internationally as the Quality Assurance Agency for Higher Education (QAA), the United States, known internationally by quality award models, such as the Malcolm Baldrige National Quality Award (MBNQA), Australia, known internationally as the Tertiary Education Quality and Standards Agency (TEQSA), which is well-known for its ranking in the Shanghai International University Rankings, and Spain, which is hypothetically known as the University Rankings (Webometrics Ranking of World Universities.) These countries appear in large circles in the grid of orange and yellow spaces in density, as shown in Figure 5 .

Those are countries that the researcher should refer to in the field of quality of higher education. He knows or can work on the research and consider them successful examples and experiences to build his research model on. Perhaps he could suggest a model for his country based on these countries. Let us talk about the classification of these countries in terms of the quality of higher education. We find them ranked, which confirms the accuracy of these results and the VOSviewer software’s effectiveness.

The Most Cited Institutions in the Quality of Higher Education

The most referenced research institutions on the quality of higher education at the international level shown is in the following form:

Let us talk about universities that are considered research institutions that produce knowledge (research work). According to Figure 6 (see Appendix 6 ), we find that the leaders in quality of higher education are Western Australia University, the University of London, the University of Arizona, United States, Rochester Institute of Technology, United States, University of East Anglia, England, and DePaul University, United States.

Figure 6. The network and density of the most renowned research institutions in the field of quality of higher education.

These are the first in terms of citation and citation intensity and the most relevant research work in quality of higher education. These results are consistent with the presentation and analysis of country results in Figure 6 . Therefore, research institutions impact the quality of quality of higher education research authors to broaden their conceptual development paths and research paths. Collecting the bibliometric analysis results of all of the elements mentioned above (words, researchers, research works, sources, countries, and research institutions) concludes by providing a general summary.

This study discusses bibliometric analysis and analysis concepts compared to classical studying theoretical literature in management sciences–rules, foundations, methods, methods, data, and software. The bibliometric study’s process and stages discuss quality of higher education related to the emergence of words, the most influential researchers in this field, critical research work, reference sources, reference countries, and references. Research institutions rely on the VOSviewer network, density software outputs, research results, and suggestions.

A bibliometric analysis using the VOSviewer software on quality of higher education is an example of study knowledge and research work ( Hallinger and Kovačević, 2019 ). The analysis also discusses the most influential researchers in the field, as well as reference sources. Others also made available a database of all research work on this topic during (2000–2018), accordingly, from the research bias that was avoided.

The bibliometric analysis stated that it is necessary to refer to some important research works, references, the most influential researchers, and essential terms on quality of higher education, countries, and reference research institutions on this subject; these results intersect with the findings of the study by Baporikar (2021) . This contributes to the definition of theoretical frameworks for the quality of higher education. Bibliographic analysis contributes to quality of higher education theoretical frameworks by identifying terms, most influential researchers, studies, sources, countries, and reference research institutions, and this is extremely important for future research directions.

This paper has investigated the theoretical analysis of the various concepts related to bibliometric analysis and the presentation and discussion on quality of higher education. We achieved results in the bibliometric analysis compared to traditional methods, which allowed for a wide range of studies (databases) to avoid bias and search selection problems. The researcher choice complements the bibliometric analysis methods but cannot substitute the traditional methods of reviewing the theoretical literature. The bibliographic analysis is also valuable for defining key terms, the most influential researchers in the field, research work and reference sources, and countries and reference research institutions (analytical units).

This study indicates a set of indicators [keywords, authors, references (research papers), research work, countries, and research institutions] that confirm this united existence. We have obtained an accurate bibliometric analysis of the appearance of terms. The basic terms of quality of higher education are higher education, quality assurance, quality, and exploration.

The bibliometric analysis of the researchers shows that the most influential researchers in the field of quality of higher education are Lee Harvey, A Parsu Parasuraman, and Bjørn Stensaker, and they should be relied upon by the researcher to theorize this field and to surround their theories with exceptional research depth. The research work in quality of higher education has returned to the most influential researchers in this field. Reference sources in quality of higher education should be based on the bibliometric analysis of references in the following international journals: Journal of Higher Education, Quality Assurance of Education, Quality in Higher Education Measurement, and Evaluation in Higher Education. These are the references that the researcher must have in this field.

In conclusion, the countries most researched in quality of higher education are England, the United States of America, Australia, China, and Spain. The Benchmark higher education research institutions are represented internationally at the following universities: University of Western Australia, University of London, University of Arizona America, Rochester Institute of Technology America, East Anglia University, and DePaul America.

This study will help researchers and educational policymakers in higher learning to understand the status of quality requirements and identify trends in higher education. This study also reinforces the growing recognition that education plays a significant role in society and will allow for quality of higher education trends, especially digital education and its requirements, to be achieved.

This is also evident by the growth path of the quality of higher education literature, its interdisciplinary composition, the breadth of areas displaying quality of higher education content, and the quality of journals and scholars who have participated in this topic.

This study’s results can determine the quality assessment of higher education institutions and take measures and policies that support the future quality of higher education trends. More specifically, the results can be used directly by higher education institutions to assess quality as strategic dimensions and to influence policymakers’ visions.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

KC and SB conceived of the presented idea. AA and RZ contributed to the design and implementation of the research and performed the revision. SB, AM, and KC verified the analytical methods and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.

The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (UB-56-1442).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The authors would like to thank the reviewers and the editor for their insightful comments and suggestions.

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Appendix Table 1. The most frequently occurring keywords.

Appendix Table 2. The most cited authors in the quality of higher education.

Appendix Table 3. The most cited references in the quality of higher education.

Appendix Table 4. The most cited sources in the quality of higher education.

Appendix Table 5. The most cited countries in the quality of higher education.

Appendix Table 6. The most cited research institutions in the quality of higher education.

Keywords : bibliometric analysis, quality of higher education, VOSviewer, network, density

Citation: Brika SKM, Algamdi A, Chergui K, Musa AA and Zouaghi R (2021) Quality of Higher Education: A Bibliometric Review Study. Front. Educ. 6:666087. doi: 10.3389/feduc.2021.666087

Received: 09 February 2021; Accepted: 06 April 2021; Published: 19 May 2021.

Reviewed by:

Copyright © 2021 Brika, Algamdi, Chergui, Musa and Zouaghi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Said Khalfa M. Brika, [email protected]

This article is part of the Research Topic

Psychosocial Factors and Teaching Trends in Higher Education: Active Methodologies and Sustainable Development

Systematic literature review and bibliometric analysis on virtual reality and education


  • 1 Costa Rica Institute of Technology, Cartago, Costa Rica.
  • 2 Department of Financial Economics and Operations, University of Sevilla, Seville, Spain.
  • 3 NECE-UBI Research Unit in Business Sciences, University of Beira Interior (UBI), Covilhã, Portugal.
  • 4 Departamento de Economía Financiera y Contabilidad, Universidad de Extremadura, Badajoz, España.
  • PMID: 35789766
  • PMCID: PMC9244075
  • DOI: 10.1007/s10639-022-11167-5

The objective of this study is to identify and analyze the scientific literature with a bibliometric analysis to find the main topics, authors, sources, most cited articles, and countries in the literature on virtual reality in education. Another aim is to understand the conceptual, intellectual, and social structure of the literature on the subject and identify the knowledge base of the use of VR in education and whether it is commonly used and integrated into teaching-learning processes. To do this, articles indexed in the Main Collections of the Web of Science, Scopus and Lens were analyzed for the period 2010 to 2021. The research results are presented in two parts: the first is a quantitative analysis that provides an overview of virtual reality (VR) technology used in the educational field, with tables, graphs, and maps, highlighting the main performance indicators for the production of articles and their citation. The results obtained found a total of 718 articles of which the following were analyzed 273 published articles. The second stage consisted of an inductive type of analysis that found six major groups in the cited articles, which are instruction and learning using VR, VR learning environments, use of VR in different fields of knowledge, learning processes using VR applications or games, learning processes employing simulation, and topics published during the Covid-19 pandemic. Another important aspect to mention is that VR is used in many different areas of education, but until the beginning of the pandemic the use of this so-called "disruptive process" came mainly from students, Institutions were reluctant and slow to accept and include VR in the teaching-learning processes.

Keywords: Bibliometrics; Educational innovation; Educational technology; Learning management; Learning processes; Learning transfer; Virtual classroom; Virtual reality.

© The Author(s) 2022.

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A Bibliometric Analysis and Review of Nudge Research Using VOSviewer

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With growing demands of decision making in the current era, the impact of the drivers behind individuals’ preferences and institutional strategies becomes prominent. Coined in 2008, nudge is used to describe incentives for individuals’ choices with foreseeable outcomes but without exclusion of alternative choices or reliance on financial stimuli. Consequently, nudge and its application in real-world situations led to a prosperous surge of studies in multiple disciplines. However, we are still facing a dearth of in-depth understanding of the status quo and future directions of research on nudge in a comprehensive fashion. To address the gap in knowledge, the present study adopted a bibliometric analysis of the existing literature related to the investigation and application of nudge by analyzing 1706 publications retrieved from Web of Science. The results indicated that (a) being a relatively newly developed theory, interest in nudge in academia has expanded both in volume and disciplines, with Western scholars and behavioral economists as the backbones; (b) future studies in nudge-related fields are expected to consolidate its current frontiers in individual behaviors while shedding light on new territories such as the digitalized environment. By incorporating state-of-the-art technologies to investigate extant research, the present study would be pivotal for the holistic understanding of the studies on nudge in recent years. Nevertheless, the inclusiveness and comprehensiveness of the review were limited by the size of the selected literature.

1. Introduction

We are faced with decision making every day, from the formulation of state policies and development strategies of companies to individuals’ preferences in provision, apparel, and accommodation. However, many times, individuals have limited rationality in the decision-making process. Available information, social influences, and intuition prevail and guide individuals’ decisions [ 1 ]. Therefore, behavioral economics research has shown application in helping people make better decisions, effectively helping governments and organizations of all kinds develop and implement public policies that better serve individuals. “Nudge” is a result of the rise of behavioral economics. Behavioral economics does not ban any options, limit freedom of choice, use economic leverage, or give orders. Instead, it changes people’s choices or economic behavior in a predictable way by changing the way they make decisions.

The concept of nudge comes from the book Nudge co-authored by behavioral economist Richard Thaler and law professor Cass R. Sunstein. They define the concept of nudge as “any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives” [ 2 ]. They argued that to be considered a mere nudge, the intervention had to be accessible and cheap, an example of which is the design of the school canteen mentioned in the book. To make students’ diets healthier, canteen managers place healthier foods, such as vegetables and fruits, in prominent, easy-to-choose locations, while moving unhealthy junk foods to unobtrusive, hard-to-choose corners. Unlike rationally convincing students of the importance of healthy eating, this approach uses students’ inertia to make their diets healthier [ 2 ]. From a practical point of view, this approach is more direct and effective than rational persuasion.

Why is nudge necessary for human behavioral decisions? Thaler and Sunstein pointed out that the daily decision-making behavior of the public is not always absolutely rational, as economists believe, but a “social human” paradigm with shortcuts and cognitive biases [ 3 ]. The irrational behavior of individuals can be perceived in four dimensions: cognition, emotion, willingness, and action. At the cognitive level, human judgment and decision making usually involve two main cognitive systems: the intuition-based heuristic system (System 1) and the rationality-based analytical system (System 2) [ 4 ]. The interaction of System 1 and System 2 constitutes the human mindset. This division of labor works well in most cases because System 1 is usually very good at perceiving the world around it, its familiarity models are accurate, and its short-term predictions are usually accurate. Precisely because System 1 is unconscious, the resulting cognitive biases are difficult to self-perceive, and if System 2 incorrectly accepts these cognitive illusions, it is difficult to avoid making the wrong decisions. At the emotional level, people tend to be unrealistically optimistic that they are the “lucky ones” and consider avoiding loss more than seeking profit. At the level of willingness and action, humans tend to have status quo bias and lack self-control to resist temptation; at the same time, they are easily influenced by the environment and make unwise decisions under the effect of social norms, herd mentality, and peer pressure.

As a tool of intervention in human choice, nudge is similar to the design of Persuasive Technology (PT), an interactive technique that can change people’s attitudes and behaviors proposed by Professor Fogg [ 5 ]. Fogg believes that the prerequisite for performing desired behavior is the person’s motivation, abilities, and triggers. These three factors must be present simultaneously for the changed behavior to occur. When motivation is insufficient, incentive to enhance motivation should be set up; when ability is insufficient, guides to enhance ability should be designed; when both motivation and ability are satisfied, we need to create some kind of reminder to trigger the person’s behavior [ 6 ]. The goal of both PT design and nudge is to influence individuals’ behavioral decisions. However, Persuasive Design aims at linking attitude and behavior change [ 7 ] and can be referred to as an attitude-oriented design strategy, whereas nudge is directly related to decision making [ 8 ], a decision-oriented design strategy. Additionally, PT uses a rational persuasion approach, while nudge is based on the irrational, unconscious elements of human behavior.

Nudge has led to a revolution in behavioral science research and has received widespread attention from scholars in management, economics, psychology, medicine, education, etc. It has been applied to various fields as a method to influence people’s behavioral decisions. Examples include influencing consumers’ dietary decisions [ 9 , 10 ], encouraging investors to implement sustainable and responsible investment decisions [ 11 , 12 ], promoting students’ self-directed learning educational decisions [ 13 , 14 ], reducing social media users’ misinformation sharing decisions [ 15 , 16 , 17 ], and changing individuals’ pro-social behavior decisions [ 18 , 19 , 20 ].

However, nudge is not always effective. Because nudge is applied by different mechanisms and to different populations, results from some studies have found limited effects of nudge [ 18 , 20 , 21 , 22 ]. A quantitative analysis by Hummel [ 23 ] showed that only 62% of nudges were statistically significant. Furthermore, a recent preprint tested the effectiveness of nudge in improving attitudes toward shared electric scooters. The results indicated that nudge was not only ineffective, but also worsened attitudes toward shared electric scooters and reduced the expected reverse effect [ 24 ].

It is worth noting that some scholars have also discussed the ethical issues of choice architecture in nudge [ 25 , 26 , 27 ]. The core of nudge has always been to influence (or manipulate) human behavior, especially digital nudging, and since there is no neutral way to present choices, all decisions related to user interface design can influence user behavior [ 28 ]. So, choice architects need to consider moral and ethical issues when designing nudge. For example, Lembcke et al. [ 26 ] discussed how much effort is reasonable for individuals to put into protecting their freedom of choice, how much concealment can be tolerated and still be considered transparent, or how the goals of the choice architect need to be aligned with the individual’s goals for the nudge to be considered reasonable. It is clear that nudge is intended to pursue the public interest and social welfare, yet there are still many uncertain conditions and methods to be explored, especially how to guarantee that the architects of choice have good design intentions so that nudge will not be abused and achieve better behavioral decision making.

Overall, we understand that nudge takes liberal paternalism as its spiritual core and uses different choice architecture mechanisms to advance the goal of boosting, both by providing options for human freedom of choice and by increasing the chances of making better choices. In recent years, behavioral science researchers have been exploring the potential applications and research of nudge in various fields while using different methods to obtain more reliable and generalizable evidence. The existing reviews on nudge are basically systematic literature reviews, with scholars focusing on topics such as healthy diet [ 29 , 30 ], medical care [ 31 ], and online user behavior [ 32 , 33 ].

Existing reviews, systematic reviews, and meta-analyses on nudge are becoming increasingly common. The systematic review is based on synthesizing primary research evidence to provide up-to-date knowledge for nudge’s research by addressing specific research questions. Meta-analysis is a quantitative-based systematic evaluation that combines the results of many empirical studies to assess the effectiveness of different nudge mechanisms for behavioral interventions through appropriate statistical methods. However, the available reviews tend to focus on a single aspect of the current status [ 32 , 34 ], and rarely provide a macroscopic view of the development process and trends in present nudge research. Bibliometrics can assist researchers in better understanding the large number of publications, provide visualization to help researchers identify which fields have achieved significant outcomes, and map future research directions based on this information [ 35 ]. Our work in this paper could contribute to fill the gap in this field.

Though the investigation of nudge has become a popular and emerging research topic, we are still facing a paucity of analysis on the relationship between the structure, evolution, collaboration of existing literature, and the clarification of potential research directions. So, this paper adopts a bibliometric analysis of nudge to understand and explore the current state of research on the application of nudge to individual behavior and organizational and governmental decision making. The findings of this study will help behavioral scientists, researchers, decision makers, and institutions of higher education identify research hotspots and emerging trends in nudge and will inform their future research efforts. Specifically, the following research questions would be answered in the present study:

  • RQ1: What trend in publication quantities and national and organizational involvement could be identified from nudge research during the period of 2012–2022?
  • RQ2: What are the most dominant nudge research disciplines and thematic clusters?
  • RQ3: What are the potential areas and future directions of nudge research?

2. Materials and Methods

With the development of technology and continuous investment in scientific research, bibliometric analysis has been applied to remedy the limitation of conventional narrative literature review in the evaluation of academic contribution, assessment of merits of studies, and determination of research trends for literature of growing quantity. Bibliometric analysis is an extensive and accurate method for examining and analyzing large amounts of scientific data. The technique aims to understand the interconnections between journal citations and summarize updates on current or rising research topics [ 36 ]. As the availability and operability of bibliometric software and scientific databases have increased, this quantitative analysis of the literature, independent of personal subjectivity and other non-scientific factors, is widely used in multiple disciplines.

We first chose the Web of Science (WoS) for bibliographic research on nudge, which is a web-based product developed by Clarivate Analytics and includes the three major citation indexing databases (i.e., SCI, SSCI, A&HCI). WoS includes authoritative and influential journals in various subject areas, and its strict selection criteria and citation indexing mechanism make it one of the most important basic evaluation tools in bibliometrics and scientometrics, while serving as a literature search tool [ 37 ]. Previous researchers have argued that WoS has a significant advantage over other databases because the journals it includes demonstrate a high level of editorial rigor and the best practices [ 38 , 39 ]. To quantify the bibliographic material in nudge-related studies, we selected the WoS Core Collection database and set up the following search profile: TS = (topic: “nudge”) AND (title: “nudge” OR “choice architecture”). The search was conducted in late August 2022 and was limited to documents published between 2012 and 2022. The initial search yielded a list of 1752 publications. Afterwards, we manually excluded publications not related to nudge by browsing titles and abstracts, such as “nudged elastic band” used in the discussion of chemical materials. According to this criterion, 1706 references were obtained. We extracted these publication data text files containing useful information about each publication, such as category, journal name, country, organization, and keywords, anchored as the basis for the bibliometric analysis in this work, and enabling us to answer the research questions more explicitly.

There are many tools available for bibliometric analysis, such as CiteSpace, VOSviewer, and HistCite, which provide visual views based on user interfaces, the Bibliometrix package in R, which is based on code commands, and Pajek and Gephi, which focus on constructing complicated network analysis. Among them, Visualization of Similarities viewer (VOS) [ 40 ] is becoming increasingly popular in bibliometric studies, with its outstanding visualization capabilities and usability to load and export information from many sources for creating maps based on network data, and to visualize and explore these maps [ 37 ]. Citation links, bibliographic coupling, and co-occurrence analysis allow researchers to obtain the themes or clusters used in the titles and abstracts of countries, institutions, and published papers [ 41 ]. The software is widely used in bibliometric studies for analysis in different fields such as geography [ 42 ], agriculture [ 43 ], knowledge management [ 39 ], and education [ 44 ]. Therefore, in this study, VOSviewer (version 1.6.18) software is used as the main metric tool to visualize and analyze the key hotspots and development evolution of nudge research in the form of knowledge graphs with Network Visualization, Overlay Visualization, and Density Visualization presenting its keyword co-occurrence and literature co-citations.

To generate visualization results for the bibliographic analysis, we imported the downloaded data files into VOSviewer. It allows us to select and set parameters according to different analysis purposes and data sources, as it is usually necessary to perform data cleaning when creating maps based on web data. Therefore, the parameters of this study are set as follows. (a) When creating mappings based on text data, it is possible to use the thesaurus files provided by VOSviewer to merge or ignore certain terms. Therefore, “nudge”, “nudging”, and “nudges” were merged using the thesaurus file, and keywords not relevant to this study that were not screened out manually, such as “nudged elastic band”, were ignored for more accurate clustering analysis. (b) The association strength method was chosen as the strength of association between normative items [ 45 ], which was considered to be the most consistent with the normalized method for this study. (c) After testing, the layout with the parameter of attraction set to 2 and the parameter of repulsion set to −1 (creating a map of the author’s co-authorship network) and 0 (creating a map of the co-occurrence network of keywords or a map of the citation network of documents) yielded the optimal visual outcomes [ 46 ]. In addition, the other options are default parameters.

Based on the above criteria for searching, filtering, and data processing, Figure 1 presents the bibliometric flow implemented in this study.

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The bibliometric flowchart of this study.

3. Results and Discussion

3.1. yearly publication, document type, and research categories.

The number of publications on nudge research is presented in Figure 2 , which depicts the development from year 2012 to 2021. The overall growth trend is supported by an increasing number of published articles. In terms of the average annual number of publications, the observed trend in research can be divided into three phases. (1) Before 2015, it was a slow growth period for nudge research, with the number of publications remaining below 100 per year. The average number of publications was about 76 per year. (2) In 2016–2017, the number of publications experienced a slight decline. To explore the reasons for the decrease in publication, we compared the publication categories between the two years. The comparison found that research in economics, law, and ethics were the categories that attracted the most attention, with 20 more publications in these three categories in 2016 than in 2017, while publications in 2017 were more dispersed across disciplines. (3) Research in nudge has grown rapidly since 2018, with a slight negative growth in publications in 2020 likely due to COVID-19, with the number of publications peaking in 2021 (n = 315, 18.46%), and a further increase in research trends likely to be seen in 2022.

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Published yearly output of nudge.

We examined the types of documents that included nudge studies, as shown in Table 1 . Publications with titles of nudge and nudge topics are indicated by document type. The statistics shown indicate that 71.34% (n = 1217) of the documents were concentrated in one category and were published as articles. Editorial material accounts for 8.50% of the document types, probably because nudge, while being considered an emerging research topic that has received a lot of attention from scholars, has also been discussed and disputed in informal settings. Conference papers accounted for 7.5% of the documents. Because nudge has also been proposed from 2008 to the present in no more than two thousand papers and the findings are not entirely clear, reviews account for only 3.81% of publications. Other document types include book chapters, conference abstracts, news, notes, book reviews, and letters.

Publications by document type.

Data for the research categories were generated from the search results of the WoS database. Table 2 shows the top 20 research categories of nudge publications. Nudge originated from behavioral economics, and Thaler, the founder of behavioral economics, pioneered the introduction of psychology into economic research, focusing on human behavior, especially human economic behavior. It was later applied to various categories as a tool to intervene in human decision making. As such, the statistics show that nudge research consists of a wide variety of disciplines, with “Economics” (n = 226, 13.25%) remaining the most studied discipline in nudge. “Public Environmental Occupational Health” (n = 116, 6.8%) and “Ethics” (n = 102, 5.98%) were also important research categories for nudge. The categories “Psychology Multidisciplinary”, “Political Science”, “Public Administration”, and “Law”, as the first areas of applied research in nudge, continue to receive attention. In addition, nudge-related research covers such categories as “Social Science Biomedicine”, “Business”, “Environmental Science”, “Nutritional Dietetics”, and other categories, aiming to focus on better serving people in their daily lives and guiding them to choose healthier and more sustainable decisions. Notably, an emerging trend in applying nudge in computer-related fields such as “Computer Science Information Systems” and communication is witnessed, especially after the introduction of digital nudging by Weinmann et al. [ 47 ].

Top 20 research categories of nudge publications.

3.1.1. Journal Distribution

In general, Nudge Theory can be applied in research pertaining to decision making. We found that articles on nudge-related research are published in a wide range of journals, indicating a significant development in the field. The 1706 articles screened and selected by the researchers were published in a total of 1006 different journals. The top 20 most productive journals are summarized in Table 3 and their number of citations and the impact factors of the journals are reported. It should be noted that we found two journals (i.e., Journal of Chemical Physics and Journal of Chemical Theory and Computation ) from the data obtained from the WoS database, whose keywords are “nudged elastic band”, which is a technique for finding transition paths between a given initial state and final state in chemical research. It does not match the original meaning of “nudge” and is hence excluded. Two books and conference proceedings were also excluded, and the final top 20 journals were ranked according to their number of publications. In the case of a tie, the impact factor of the journal was taken into account.

Top 20 most productive journals.

AC = average citations; SCIE/SSCI = Science Citation Index Expanded/Social Science Citation Index; IF = impact factor.

When journals are ranked based on the number of published articles, their citation counts do not correspond to their rankings. Despite publishing a small number of papers, several journals have relatively high citation counts. To explore the reasons for this, we created a treemap of the average citations (AC) of the top 20 journals (as shown in Figure 3 ), which is an efficient way to visualize the data in a space-saving manner [ 48 ]. The data in Table 3 show that six of the most productive journals are related to medicine and health, while three of the top five journals with the highest AC in Figure 3 are also related to health behavior and health consumption. Although nudge originated from behavioral economics, economics journals are not the most productive. This is presumably because in medicine and health, which have more to do with individual behavior than economics and management, the design and interventions of nudge are more operational. Researchers hope to improve personal health or promote public health by slightly intervening in people’s behavior, for example, by encouraging people to eat more healthily or to have regular health checkups.

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Treemap of the top 20 journals with average citations.

Specifically, the most published journal, American Journal of Bioethics , with a total of 33 nudge-related studies and 483 citations, is with a modest citation count per paper (n = 13.7). The main reason is that the paper by Blumenthal-Barby and Burroughs [ 49 ] has been cited 194 times in WoS. The journal with the highest total and average number of citations was BMC Public Health , with studies such as Hollands et al. [ 50 ] and Arno and Thomas [ 51 ] contributing the major citations. Nature , one of the most prestigious scientific journals in the world, published nine papers with only 122 citations, five of which were related to vaccination against COVID-19. While Review of Philosophy and Psychology published only 11 papers that focused on nudge in political and legal applications, the number of citations was 295, ranking third in average citations (n = 26.8). In addition, Food Quality and Preference , a journal related to sensory science and food research that primarily focuses on the use of nudge to promote healthier food choices or healthier eating habits among consumers, also contributed the third highest number of citations (n = 324) with an average of 24.9 citations.

We used VOSviewer to analyze 1706 articles for co-citation and formed a journal co-citation network with four clusters containing 332 journals, each with a minimum number of citations of 30. As can be seen in Figure 4 , each node represents a journal, with its size indicating the number of papers published, and the lines between the nodes indicate the intensity of co-citation, with thicker lines representing higher intensity.

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The co-citation network of journals.

The most visible cluster in the co-citation network is red, with 94 nodes. Among them, American Economic Review , with leading impact factors in its field, stands as the central position of the red cluster with the highest intensity of co-citations. The blue cluster has 77 nodes, and the most prominent one is Science , which has co-citation links to the other three clusters, although it has published only five nudge-related papers. Psychological Science and Journal of Personality and Social Psychology , also in the blue cluster, are also important, as they both belong to the Association for Psychological Science (APS) and focus on the frontiers and applications of psychology, and their co-citation relationships are relatively strong. The yellow cluster contains 76 nodes and the journals in this cluster are mostly related to health and diet. The highest co-citation is Appetile, which focuses on the behavior of humans and nonhuman animals toward food and has made important contributions to the study of using nudge to guide consumers to make healthy dietary choices. The most distinguished one out of the 85 nodes within the green clusters is the book Nudge, nudge, think, think , by John et al. [ 52 ] based on Nudge [ 2 ]. While acknowledging the power of nudge, they argue that a particular democratic institutional framework is needed to provide an environment that evokes public thinking that promotes listening and reasoned argument among citizens, as well as the type of reflection that can lead to shifts in preferences. Other journals in the green cluster focus on ethics, law, and behavioral studies, all of which have played important supporting roles in nudge research.

3.1.2. Country and Institution Distribution

In bibliometric analysis, country and institution, as important analytical variables, can reflect the research intensity and contribution of different regions or institutions in the research field. By analyzing the citation and co-citation of publications from different countries or institutions, we can gauge their academic level and collaborative networks [ 53 ].

From the data obtained from the WoS database, we found that the 1706 publications were distributed among 79 countries, and Table 4 shows the top 10 countries with the highest number of publications, which accounted for 90.7% of the total number of nudge publications (n = 1548). The United States ranked first with 631 publications, accounting for 37.0% of all publications, far ahead of other countries. England stands as the runner-up (260/1706, 15.2%), followed by Germany (159/1706, 9.3%).

Top 10 publication countries (n = 1548).

Next, we analyzed the most influential countries for nudge research through bibliographic coupling links. The logic behind bibliographic coupling is that two texts with a high number of shared literature references will be similar in content [ 54 ]. This means that the coupling analysis shows the number of identical references cited by the documents as a measure of the collaboration of the country to which the publication belongs. We selected the analysis type “bibliographic coupling” in VOSviewer, and the unit of analysis was “countries”. Additionally, we selected the minimum number of documents for countries to be 1 in VOSviewer to obtain the maximum number of links generated between countries. The studies of Ukraine and Iraq among the 79 countries in the network were not interlinked with other countries, so the maximum number of linked items in the final generated coupling network was 77 countries.

The country analysis by bibliographic coupling is presented in a network visualization of the five main clusters, as shown in Figure 5 . The most striking one of the clusters is undoubtedly the blue cluster, represented by the United States. It is the first one to apply nudge in practice, not only because the authors of Nudge , Thaler and Sunstein, were an American economist and an American legist, but also for the size of the country, the number of research scholars, and the investment in scientific research. During his term in office, former U.S. President Barack Obama signed an executive order establishing the Social and Behavioral Sciences Team, who translated Nudge Theory into improvements in federal policies and programs with success [ 55 ]. Canada, South Africa, Thailand, and the Philippines are also in this cluster, indicating that these countries cite similar research articles in nudge research.

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The bibliographic coupling network visualization of countries.

The green cluster contains 23 countries, most notably England, which was also the earliest country to start research and application of nudge. The rest of the cluster is dominated by European countries (e.g., France, Switzerland, Belgium, Portugal, etc.), Asian countries (e.g., Vietnam, Korea), and Oceanic countries (e.g., New Zealand). These countries have cited similar articles in their nudge studies. Twenty-eight countries in the red cluster are spread out, ranging from European countries such as Germany, Italy, Sweden, and Norway, to Asian countries such as China, India, and Japan, and American countries such as Brazil and the Dominican Republic. In addition to being geographically distant, these countries are cited in similar articles. Regarding the relatively sparser clusters, Australia and Denmark are most visible in the purple and yellow clusters, respectively.

To gain insight into which countries have recently embarked on the nudge study actively, we created an overlay visualization of the country analysis. The score values are color mapped by taking the average year of country studies by default, as shown in Figure 6 . We found that the average year of active research on nudge across countries began in 2017, which indicates that within the decade when nudge was first presented, its effectiveness and academic potential for behavioral interventions was far underestimated. Since 2017, the United States, U.K., France, Denmark, and Canada have taken the lead in starting or expanding participation in nudge study, followed by countries such as Italy, Germany, The Netherlands, China, and Japan. In the past two years, more and more developing countries have also invested in nudge research (e.g., Indonesia, Thailand, Philippines, Nigeria, etc.). We believe that this phenomenon is inseparable from the level of economic development and academic research in a country. Nudge was proposed to make better decisions for people’s health, wealth, and happiness, and countries with high levels of development focused on people’s well-being earlier and started research on nudge earlier. Therefore, based on the analysis and understanding of the status, we can expect and predict a vigorous development of research and applications in related fields.

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The overlay visualization of the country analysis.

3.1.3. Author Distribution

According to Thaler [ 56 ], the emerging and interdisciplinary field of behavioral economics allows scholars to understand human behavior from a more humanitarian perspective. By the same token, nudge-oriented research has become attractive to a growing number of researchers. Consequently, to obtain a synopsis of nudge-related studies, one of the most pivotal tasks is to identify the most productive and influential authors in the field. We conducted an author citation analysis to identify the top 10 most productive authors and rank them by document and citation. Table 5 shows the results of this analysis. Noticeably, that the number of publications is a metric that should be analyzed with discretion, taking into account factors including the length of each paper, the quality of the journal, and the number of authors per work [ 57 ]. The table is sorted by the number of articles by author, and in case of ties, the citations per author were considered. In addition, the h-index, a composite indicator combining productivity and impact, was appended to the table.

Prominent authors by documents and citations.

Among the top 10 authors, Cass R. Sunstein is the most productive, with 20 publications, and he also ranks first in citations (n = 1057). Given his groundbreaking contribution in relevant fields (e.g., co-authored the book Nudge , founded the Behavioral Economics and Public Policy Program at Harvard Law School), his leading position in both publication and citation numbers is understandable and well-expected. He has worked closely with the U.S. Behavioral Insights Team since its inception. In 2020, the World Health Organization appointed him as chair of its technical advisory group on Behavioral Insights and Sciences for Health. His work and research laid the foundations of behavioral economics and provided the shoulders of giants for subsequent scholars. The runner-up in the author list is Peter John, a professor in the Department of Political Economy at King’s College London, with a total of 15 nudge research publications. He is adept at using randomized controlled experiments to explore how nudge can be applied in public policy, and how best to engage citizens interested in public policy and management, and in turn deploy behavioral interventions.

Notably, four of the top ten productive scholars have medical backgrounds. Mitesh S. Patel, Anne Thorndike, Douglas Levy, and Joline Beulens all have a medical and health perspective on interventions that use behavioral economics strategies to improve individuals’ dietary intake and health behaviors. Of those, Mitesh S. Patel has 14 publications, and his research focuses on integrating nudge with scalable technology platforms to improve health and healthcare. He has collaborated with health systems, insurers, employers, and community organizations to conduct clinical trials using nudge, such as digital health interventions using wearables and smartphones, and health system interventions using electronic health records, advancing the research and application of nudge. The most cited of this cohort of researchers is Anne Thorndike, with 10 publications and 465 citations. Her research concentrates on the use of nudge and choice architecture, such as traffic light labels [ 58 ], to guide people to healthier food choices and maintain healthy lifestyles.

To further explore the authors’ collaborative research, we conducted a co-authorship analysis, which is a tool used to identify key organizations and scientists and examine their associations [ 59 ]. VOSviewer identified 4557 authors based on the WoS data, and the calculation generated 600 items when the minimum co-authorship of articles was set to two. The largest set of connected items contains 32 items, and Figure 7 shows the full co-authorship visualization. Interestingly, while Cass R. Sunstein was the most influential author, the highest scoring co-authorship was Denise de Ridder. She has 21 links, which implies that she has collaborated with 21 authors. Denise de Ridder is a professor of psychology at the Department of Social, Health and Organizational Psychology at Utrecht University and project leader of various research projects in the field of self-regulation and facilitation. She shows that she is proficient in collaborating with colleagues whose nudge research focuses on exploring self-awareness in nudge implementation and how nudge can help people to make healthier food choices. Besides her, the previously discussed authors are also relatively conspicuous in the co-authorship analysis visualization, with an average number of links of 10, which is inseparable from their overall number of publications. In conclusion, no scholars are in a dominant position in nudge research, as the field is still in its infancy and researchers can enhance their collaboration to explore more the potential of nudge in various fields of research. This might be an advantage for potential researchers and ongoing studies, as journal editors prefer a small group of highly productive researchers when deciding which articles to publish [ 60 ].

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Network visualization for co-authorship analysis.

3.1.4. Keywords Co-Occurrence Analysis

The last research questions for this study to explore concerned existing or future relationships between themes in the nudge studies by focusing on the content of the publication itself. For this purpose, keyword co-occurrence analysis was employed, which is a technique for examining the content of the publication by extracting keywords from the full text of the publications. Applied longitudinally, keyword co-occurrence analysis can be used to predict future research in the field with a view to enriching the study’s interpretation of co-citation analysis (in the past) or bibliographic coupling (in the present) and predicting the development of the field (in the future) [ 35 ]. In addition, to obtain more accurate results, less relevant keywords were manually removed and a minimum number of occurrences of keywords of six was set as a threshold level. The network visualization was constructed based on the co-occurrence frequency of 294 keywords out of a total of 5444 retrieved keywords, as shown in Figure 8 .

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Network visualization of keyword co-occurrence analysis.

In network visualization, each keyword is represented by a node, and the size of each node represents the number of publications in which that keyword appears. The clusters of the nodes are reflected by corresponding colors, with the distance between various clusters indicating the relatedness between them. Specifically, a close relatedness between two clusters could be identified if the distances between them is shorter, and vice versa. There are four main clusters in the network visualization shown in Figure 8 , which are red, green, blue, and yellow.

First, red clustering includes topics related to behavioral economic theory, public policy making, and ethical discussions. As an innovative approach to address policy issues, nudge is becoming increasingly popular in the field of public administration. In a recent study, John et al. [ 61 ] designed randomized trials of support for nudge and deliberate nudge in response to top-down regulation and freedom of choice. The results of the experiment showed that public support for both nudge policy options is higher compared to top-down regulation. They also found that support for nudge and deliberate nudge is more correlated with perceived fairness than with perceived efficacy. Similarly, a study showed that nudge interventions positively moderated the impact of two-way risk communication on public value consensus [ 62 ], which suggested that nudge can play a better role in public management than injunctive interventions. From another perspective, some studies have focused on whether there are ethical issues with nudge, such as doubting whether nudge may have the undesirable consequences of manipulating choice, reducing autonomy, and unintended behavior [ 63 , 64 ]. Conversely, others have assessed and argued that nudge does not usually interact with people’s rationality in a problematic way [ 65 ], and that ethicists should remain open to its application [ 66 ].

The green cluster focuses on online information, social media, digital nudging, and their impacts. Compared with traditional offline contexts, decision making in digital contexts is more dependent on human–computer interaction interfaces, so the interface design of human–computer interaction can have a significant impact on the decision-making process. This influence includes two main aspects: the interface provides the necessary elements for decision makers to access relevant information, and the way the interface provides this information affects the cognitive process, producing different decision outcomes than in a no-choice architecture context [ 47 ]. Thus, social media and other online applications can exploit digital nudging to play a leading role, such as against the sharing of fake news [ 67 ] and the protection of user privacy [ 68 ]. Particularly in crisis events, local organizations can use digital nudging to disseminate topic-specific tweets (e.g., emergency notifications, evacuation information, etc.) to support emergency management objectives and to manage the crisis properly [ 69 ]. In the long run, social media has become the most widespread channel for users to generate, access, and share all kinds of information. It is worthwhile to further explore how to use interface design and nudge to better assist users to search and share useful information more efficiently while privacy is effectively protected, and to guide other benign usage behaviors.

The blue cluster focuses on individual behavior, preferences, and the specific nudge mechanisms used in the implementation of behavioral interventions. At the early stage in applying Nudge Theory, Thaler and Sunstein [ 70 ] acronymized six mechanisms for optimizing choice systems to improve usage satisfaction into nudge , i.e., iNcentives, Understand mappings, Default, Give feedback, Expect error, and Structure complex choices. Since then, researchers have extended their design insights into additional fields, focused on the measurement and examination of influences that optimize nudge effects. As the network visualization shows, the main common nudge mechanisms are default options [ 71 , 72 ], social norms [ 73 ], incentives [ 74 ], and feedback [ 75 ]. Especially during the COVID-19 pandemic, nudge was used to promote vaccination, reduce social contact, disseminate trustworthy pandemic information, etc. [ 76 ]. In summary, the designer of any choice environment must be aware of its effect on people’s choices. Choice architects should be aware of the goals of the intervention to design and test nudge to maximize the desired effect [ 77 ]. Another study indicated that the vividness of image presentation increased gamification and improved the subjective usability of face-to-face counseling effects, which promoted counseling in real life for young people [ 78 ].

Finally, the yellow cluster, which concentrates on the extension of choice architecture to consumer behaviors that intervene in people’s consumption, such as healthy food choices, sustainable consumption behaviors, chronic disease or cancer prevention and treatment, and pro-environmental behaviors. The ever-accelerating pace of life, irrational eating patterns, and obsession with digital media affect physical and mental health, especially among students and young people in the workplace. Because of health and pro-social factors, most people are more receptive to nudge [ 79 ], although some studies have found that nudge to promote healthy eating is not effective [ 80 ], or even that employees can accept nudge while students do not accept nudge, leading to more unhealthy food choices and making nudge ineffective [ 81 ]. This may be because the nudge mechanism used is different and some nudges may be perceived as manipulative or uncomfortable, so the intervention is not as ideal [ 82 ]. In the context of disease prevention and treatment, it was demonstrated that moderate interventions in individual rights and relatively unproblematic moral imperatives, nudge proved valid in various situations [ 83 ], and in particular, that personally tailored and positively constructed messages were more persuasive than generic and/or negative messages [ 84 ].

On top of the network visualization, VOSviewer offered different mapping visualizations; the other two constructed are the overlay visualization (as in Figure 9 ) and the density visualization (as in Figure 10 ). Specifically, there are two groups of keywords in Figure 9 with the earliest average years of nudge research. The first group is Nudge Theory-related terms and research, such as “neoliberalism”, “behavioral economic”, “libertarian paternalism”, and “autonomy”. The second group is related to public health, such as “healthy food”, “disease”, and “obesity”, which is in line with the number of journal articles and citations discussed earlier and is a topic that has been studied early and consistently in nudge research. The above keywords are directly linked to both “nudge” and “choice architecture”, while several keywords on the right of the figure, such as “stimulation”, “sensitivity”, and “precipitation”, are linked to each other, but only “sensitivity” is linked to “nudge”. As there is no other connection that indicates relevance to the nudge discussed in this study, none are further reviewed. After 2019, increasing attention is paid to nudge in the digital environment, such as the keywords “digital nudging”, “fake news”, and “social media”, especially since the outbreak of COIVD-19 in early 2020. The spread of the pandemic has led to the implementation of stay-away orders and social distancing in many countries, in which personal use of social media multiplies. Researchers have also turned to the effects of nudge in this special period, with the keywords “COVID-19”, “vaccination”, and “hand hygiene” surging after 2020. Remarkably, there are also emerging keywords such as “sustainable consumption”, “artificial intelligence”, and “gamification”, indicating a growing enthusiasm for nudge research that extends to a growing number of fields.

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Overlay visualization of keyword co-occurrence analysis.

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Density visualization of keyword co-occurrence analysis.

The depth of research in the field related to nudge can be observed in Figure 10 . In the present analysis, colors range from blue to green to yellow to red. The higher the number of items in the proximity of a point and the higher the weights of the related items, the closer the color of the point is to red. Contrarily, the sparser and less impactful the point, the closer its color to blue. Through density visualization, we can quickly observe that features around nudge research consumption, health, management, ethics, donation, sustainability, specific nudge mechanisms, and information systems are currently widely discussed topics.

4. Conclusions

It has merely been 14 years since the proposed Theory of Nudge, and there have been an increasing number of scholars around the world trying to find solutions to social issues by following the principles of nudge. This study provides a comprehensive view of the current trend in nudge studies. Methodologically, this study used a bibliometric analysis of nudge to perform analysis of journals, publication types, countries, authors, and keyword co-occurrence in a repository of 1706 publications retrieved from Web of Science. The results show that nudge studies have been extended to several disciplines beyond the concept of behavioral economics, and that nudge intervenes as a mediator in the relationship between humans and the world. The process of nudge implementation influences human perceptions and actions, experiences, and practices, which in turn influence the decision making of human behaviors. In the study, the results of the analysis laid the groundwork for answering the research questions presented in the beginning sections.

Specifically, (1) the number of nudge publications has increased significantly since 2012, especially after 2018, with the number of articles accounting for more than half of the total, and this is expected to keep growing. At present, nudge studies were first and most frequently conducted by American scholars, followed by European countries such as England, Germany, and The Netherlands, while contributions are lacking in other countries. (2) Economics is the most studied discipline of nudge, but with the adoption of multidisciplinary perspectives, political science, ethics, medical science, business, and communications have also become involved in exploring the application of nudge. This is particularly significant in the areas of public administration, healthcare, and sustainable consumption, in line with nudge’s goal of enabling the public to make the best choices about their health, wealth, and well-being. (3) Nudge’s future research potential and development direction are likely to continue to focus on people’s everyday behaviors, such as healthy lifestyles, sustainable consumption, and travel. Especially in the digital environment, there is a stronger need to help increase the well-being of the people being nudged through the design of the interface and the choice of decisions. Certainly, it is also important to be aware of the transparency and ethics of nudge in the design and application process to prevent the abuse of technology.

Finally, several limitations were faced by this study, which set out to expand our understanding of nudge from a macroscopic view. First, this analysis is limited to the data of publications retrieved from WoS. Though WoS is considered to primarily comprise accredited data sources, we cannot claim that the data are error-free and cover all studies. Therefore, we recommend that future researchers consider additional databases (e.g., Scopus, SpringerLink, EI, etc.) to ensure that important journal indexes and other time periods are covered. Second, the monolingual nature of the publications analyzed limited the inclusiveness and comprehensiveness of the present study. Only English-language publications were selected for the analysis, but in practice, it must be taken into account that most non-English publications are not included in the WoS. This is the case for 12 papers published in the journal Acta Psychologica Sinica in 2018, which provided exploratory research on the application of nudge in China from different perspectives in pro-environmental and pro-social areas—their absence in the collected data caused lacunae for the present study.


We would like to extend our heart-felt thanks to Da Yan for his kind help. We were inspired by our discussions with him, and we appreciate the advice he provided during the proofreading. Thanks to our friend Xiang Luo for his suggestions in the visualization. In addition, all the thanks and appreciation from us to the editor and reviewers.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, C.J. and H.M.; methodology, C.J.; software, C.J.; formal analysis, C.J; writing—original draft preparation, C.J.; writing—review and editing, C.J.; visualization, C.J.; supervision, H.M.; project administration, H.M. All authors have read and agreed to the published version of the manuscript.

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The authors declare no conflict of interest.

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A bibliometric review of climate change cascading effects: past focus and future prospects

  • Published: 05 December 2023
  • Tian Zhou 1 ,
  • Dewei Yang   ORCID: 1 ,
  • Haishan Meng 1 ,
  • Min Wan 1 ,
  • Shuai Zhang 1 &
  • Ruifang Guo 1  

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The cascading effects of climate change have inflicted severe damage on natural ecosystems and socio-economic systems, posing formidable challenges to economic, social, and environmental development. However, a notable gap exists in systematic literature reviews that comprehensively detail current research in this domain. This paper seeks to bridge this gap by providing a systematic overview of the existing literature. Using the Citespace tool, we conducted a scientometric analysis of relevant literature spanning the years 1994 to 2022. This analysis allowed us to identify research priorities and trends by examining publishing countries, institutions, journals, and keywords. Our findings reveal a continuous increase in the number of relevant publications, with discernible stages of growth: slow, stable, and rapid. The United States is a prominent contributor in this area, with the largest number of publications, but Australia has the strongest international collaboration. However, geographical characteristics distinguish collaboration among different institutions, with cross-regional cooperation in research remaining relatively weak. Notably, research in developing countries is underrepresented, highlighting a priority for future investigations. Research focus areas encompass climate cascading in both natural systems and socio-economic systems, climate change mitigation, adaptation, and their co-benefits. The research hotspots have shifted from ecosystems and biodiversity towards human well-being and sustainable development. Future endeavors will emphasize curbing the negative cascading effects of climate change and promoting the integrated evolution of natural-human systems. The outcomes of this research hold the potential to provide a robust foundation for climate cascading studies, inform policymaking, and contribute significantly to sustainable development.

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a literature review with bibliometric analysis

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This work was supported by the National Natural Science Foundation of China (NO. 42171280) and the Special Fund for the Youth Team of Southwest University (SWU-XJPY202307).

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Tian Zhou, Dewei Yang, Haishan Meng, Min Wan, Shuai Zhang & Ruifang Guo

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DY contributed to the study conception and design. Material preparation, data collection and analysis were performed by TZ, HM, MW, SZ and RG. The first draft of the manuscript was written by TZ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Zhou, T., Yang, D., Meng, H. et al. A bibliometric review of climate change cascading effects: past focus and future prospects. Environ Dev Sustain (2023).

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