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A Bibliometric Analysis of Objective and Subjective Risk

College of Business, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates
Oxford Centre for Islamic Studies, University of Oxford, Oxford OX1 2JD, UK
School of Histories, Languages and Cultures, The University of Liverpool, Liverpool L69 3BX, UK
Author to whom correspondence should be addressed.
Risks 2021, 9(7), 128;
Submission received: 20 May 2021 / Revised: 19 June 2021 / Accepted: 23 June 2021 / Published: 4 July 2021


In relation to “objective risk” or “subjective risk”, a bibliometric analysis was performed using documents found in the Scopus database. A search for related documents was narrowed down to 192 documents and these were considered in this study. The results of this study suggest that the use of the ranking method and descriptive statistics is not sufficient in presenting a concise bibliometric analysis. To create a more in-depth bibliometric analysis, the results of this study have to be analyzed together with a visualization map using VOSviewer software. This way, researchers can easily locate a specific gap in the literature, understand the relation between the papers on the same subject, and cite the literature studies based on their effectiveness.

1. Introduction

Risk identification, assessment, and management play a critical role in many fields. Risk can be also categorized into several categories such as pure and speculative risk, fundamental and particular risk, priority and liability risk, operational risk, technical environment risk, information security risk, technical and architectural risk, and objective and subjective risk. Objective risk is the relative variation of actual loss from expected loss, while subjective risk is the uncertainty based on a person’s mental condition or state of mind (Rejda and McNamara 2021). In the risk and insurance field, Andersen et al. (2014) define subjective risk probabilities as those probabilities that lead an agent to choose some prospects over others when the outcomes hang on upon random events that are not currently realized. Knight (1921) refers to objective risk as unalterable and subjective risk are alterable and malleable. On the other hand, Pfeffer (1956) argues that objective risk is a combination of hazards measured by probability while subjective risks are uncertainty that measured by a degree of belief, he also argued that risk is a state of the world, but uncertainty is a state of the mind, (An 2020; Houston 1964).
Objective and subjective risks are two opposite types of risks however they are connected by multiple variables. According to Suerdem et al. (2013), risks are possible outcomes of hazardous events. Based on the definition, Health and O’Hair (2009, p. 22) said that “objective risks” are risks that exist in reality whereas “subjective risks” are risks that are purely based on the judgment of other people. In general, the level of risk can be assessed or analyzed objectively or subjectively (BPP Learning Media 2014; Suerdem et al. 2013; Rowe 1981).
In practice, the focus of objective risk appraisal is to measure likelihood or probability including the impact of risk on people (BPP Learning Media 2014). For instance, objective risk measurement can be computed based on “frequency” or “magnitude” (Harris 2006, p. 129). Using subjective risk scales such as “high”, “medium”, and “low”, subjective risk appraisal is more focused on risk assessors’ knowledge and skills on factors that could influence the level of risk (Ettouney and Alampalli 2017, p. 31; BPP Learning Media 2014).
When applied in business or other real-life social events, risk assessor(s) would compute for estimates or make a forecast on the outcome of objective risk but not subjective risk (Suerdem et al. 2013; Rowe 1981). Therefore, a discrepancy may occur between the “actual risk” and the “expected risk” (Rowe 1981, p. 53). According to Ettouney and Alampalli (2017), risk assessment is useful in guiding risk assessors on how to make optimal decisions. Depending on the risk evaluation outcome, the risk assessor could either accept or create strategies that will prevent or avoid the consequences of risk (Rowe 1981).
The bibliometric analysis is a useful method when it comes to quantifying research outputs and when using the Scopus database. Using the bibliometric analysis method, this study aims to identify documents from the Scopus database that are most cited when doing a research study related to “objective risk” or “subjective risk”. Likewise, this study also adopted the use of the bibliometric analysis method in identifying the top 20 main sources of most cited documents, authors of most cited documents, and countries of most cited documents. Before the end of this study, the most used keywords including the clustering of commonly used keywords were analyzed in this study (Nobanee et al. 2021). Research output related to “objective risk” and “subjective risk” over the past 40 years was evaluated to provide a better understanding of the current situation of global research, current streams, and the direction of future research for this field (Wang et al. 2014). The analyzed characteristics covered not only the quantitative description of publications, such as most influential authors, most cited documents, leading countries, and organizations but also the authors’ and index keywords and their clusters to identify the sub-topics and current streams of “objective risk” and “subjective risk” research output. Even though review articles do not provide or suggest new theories, models, or methodologies, they contribute to the existing knowledge and literature significantly by providing a critical up-to-date overview of the developments in the study field (Amin et al. 2019). Given all of the above, this study presents a review of the literature regarding “objective risk” and “subjective risk” from 1980 to 2020. The main objective of this study is to provide broad insights into the literature on “objective risk” and “subjective risk” using a bibliometric analysis approach. Thus, insights in narrative clusters, research developments, trends, and leading authors, documents, organizations, countries, and journals in the research domain are obtained (Fu et al. 2021)
The scope includes gathering documents straight from the Scopus database, narrowing down the search for related documents using the advance search option found in the Scopus database, listing down documents that are directly related to “objective risk” or “subjective risk”, listing down the top 20 most cited documents on “objective risk” or “subjective risk”, top 20 authors of documents on “objective risk” or “subjective risk”, top 20 countries where researchers could locate the most cited documents on “objective risk” or “subjective risk”, and top 20 commonly used keywords in documents that talks about “objective risk” or “subjective risk”. Likewise, the scope of this study also includes a discussion on how the top 20 commonly used keywords were clustered in different groups. In general, the focus of this study is on “objective risk” or “subjective risk” only. Therefore, the bibliometric result of this study does not apply to other types of risk.
Aside from learning how to maximize the use of the Scopus database, this study will provide the readers with the opportunity to use VOSViewer in completing the bibliometric analysis. Overall, the result of this study will list down documents that are related to “objective risk” or “subjective risk”. Therefore, overall, this study could guide other researchers on the right way to locate documents on “objective risk” or “subjective risk”.
As part of helping future researchers on how to use the Scopus database, this report presented original work on how future researchers could benefit from using VOSViewer. Most existing documents on “objective risk” or “subjective risk” do not explain the benefits and limitations of doing bibliometric analysis. As such, the result of this study may contribute new ideas when searching for related literature on “objective risk” or “subjective risk”.

2. Literature Review

Several bibliometric studies on risk assessments and management have been conducted across the disciplines of medicine, engineering, management, social sciences, and other fields.
Wang et al. (2014) used the Web of Science to provide insights into research outputs of the global risk of engineering nanomaterials for the period of 1999–2012 by using the bibliometric method. The results show that number of publications per year has increased steadily since 2006. The most influential countries were the USA followed by China and then by the UK. The most influential journals on these topics are Environmental Science and Technology, Toxicology, and Journal of Nanoparticle Research. The results also show that research on environmental behavior and ecological risk of ENMs is a fast-growing field. Amin et al. (2019) employed the bibliometric methodology to examine the existing literature on process safety and risk analysis for the period 2009 to 2018. The findings of the study show that the USA is the leading contributor, and the collaborative works between industry and academia are rare in the searched topic. The results also showed the field of process safety and risk analysis is of great growth potential with growing numbers of annual publications. Fu et al. (2021) applied the analyzed Arctic shipping risk management using bibliometric analysis and systematic review methods for the years 2000–2019. Most of the papers in this field are focusing on the scenario, methods, data sources, and RIFs. Nobanee et al. (2021) employed a bibliometric method to analyze the existing literature on sustainability and risk management using the VOSviewer software for the period 1990–2020, a reflection of 1233 documents appeared in Scopus on sustainability and risk management. The paper highlighted six major streams, related to topics such as the moral responsibilities and sustainability development, blockchain technology and minimization of risks, social sustainability and supply chain, environmental impacts, safety engineering, and risk identification, optimization and sustainability practices. The paper concluded that sustainability remains an important issue in the global perspective and risk factors were also identified and, everyone must be socially responsible to minimize their negative impact on the economy. Díez-Herrero and Garrote (2020) reviewed the existing literature on flood risk analysis and assessment using bibliometric analysis. They argued that studies that reviewed flood risk analysis and assessment using systematic and symmetric methods are not customary and most of these reviews provide a snapshot of the scientific state of the art of FRA with partial views, and they focused on a limited number of selected methods and approaches. In their study, they employed bibliometric analysis using the Web of Science database. The results show that the US researchers dominated the field, but now they have been overtaken by the Chinese. The results also showed that global warming appears to dominate part of future FRA research production. Braun et al. (2019) employed systematic and bibliometric methods to analyze the literature on sustainable remediation through the risk management perspective and stakeholder involvement using both Scopus and the Web of Science databases. The results showed that sustainable remediation is a recent theme verified by a growing number of research outputs in recent years. The study recommended that the perception of stakeholders and risk management will be better understood within the context of sustainable remediation. Xu et al. (2020) reviewed the literature on disruption risks in supply chain management using bibliometric analysis methodology, they argued that the field of supply chain disruption has received increasing attention on qualifying the risks and enhancing the supply chain performance. A total of 1310 publications were derived from the Web of Science. The paper identified the most influential authors, affiliations, and keywords with the most occurrences, the leading publications, and main clusters are also identified to highlight the key research topics based on content and citation analysis. Ganbat et al. (2018) used the bibliometric method to review the literature on risk management and building information modeling for international construction for the period 2007–2017. The results show that building information modeling for international construction is not only attracting all stakeholders’ interests but also brings some financial risks. Jiménez and Bjorvatn (2018) conducted a bibliometric review on political risk, the paper identified key literature on the sources of political risk, the impacts of political risk on countries, industries, firms, and projects. The paper also highlighted research output on vulnerabilities, capabilities, and responses to political risk. Tavares et al. (2017) used a bibliometric method to review the literature on risk management in scrum projects. The paper relies on Web of Science and Scopus databases to identify the main authors, countries, journals, most cited authors, and the keywords with the most frequencies. The analysis was conducted using CiteSpace® software, and despite the importance of the research topic of risk management in scrum projects, the results show that few scientific studies were identified, which brings the need for more research on the topic. Han et al. (2020) applied a bibliometric overview of research trends on heavy metal health risks for the period 1989–2018. The findings showed there was a significant increase in the concern over heavy metal risks and impacts in the past decade, the results also showed that China surpassed the USA and became the most productive country in 2010. Fuentes Cabrera et al. (2019) used the bibliometric review methodology to analyze the literature on bullying among teens, ethnicity, and race risk factors for victimization, in addition to the bibliometric methodology, the study used systematic review, documentary quantification, and data visualization methods to review the related literature. The study discovered 831 documents for the years 2011–2019. the findings showed that bullying has a negative impact both physically and psychologically on the victims. Nagi et al. (2017) conducted a bibliometric analysis of risk management in seaports, co-citation analysis of documents is performed using the organization risk analyzer (ORA) software CoCit-Score method of calculation. The paper suggested directions for future research on risk assessment and management methods based on the findings of the co-citation analysis. Gómez-Galán et al. (2020) employed the bibliometric analysis methods on musculoskeletal risks and RULA method applications in terms of the knowledge, country, year, and journal categories. The documents were collected from the Web of Science database for the period 1993 to April 2019. The analysis discovered 809 publications refined to 226 documents. the results show that the USA stands out for its greater research output. The paper concluded that RULA can be applied to workers in different fields, typically in combination with other methods. Darabseh and Martins (2020) conducted a bibliometric study and content analysis on risks and opportunities for reforming construction with blockchain. the main findings show that while the number of articles about the use of blockchain in construction has grown during the past years, no studies provided ready-to-use solutions. Instead, most of the studies focused on the technical capabilities of the technology. Da Silva et al. (2020) employed a bibliometric method to review research output on data mining and operations research techniques in supply chain risk management. the paper highlighted the gap found in the literature considering data mining techniques in supply chain risk management, identified the current streams, and proposed suggestions for future research.
The above bibliometric analysis studies of risk management reviewed s research outputs in several fields and disciplines. However, based on the above critical review of the existing literature, we did not find any study that employed the bibliometric method to review existing research output on objective and subjective risk. Based on documents found in the Scopus database, this study will carry out bibliometric analysis using software such as VOSviewer and MS-Excel. While using the Scopus database, the research study objectives of this paper include the following:
To locate and list down documents related to “objective risk” or “subjective risk”;
To carry out bibliometric analysis of related literature using VOSViewer;
To discuss the importance of scientometric when carrying out bibliometric analysis; and
To evaluate the clustering of keywords found in the most cited documents.

The Research Questions Are:

Research questions that will be addressed in this study include the following:
What are the top 20 documents that are most cited in research studies related to “objective risk” or “subjective risk”?
What are the sources of the most cited documents related to “objective risk” and “subjective risk”?
Who are the top 20 authors of documents related to “objective risk” and “subjective risk”?
What are the top 20 countries where researchers could have located the most cited documents on “objective risk” or “subjective risk”?
What are the top 20 commonly used keywords in documents related to “objective risk” and “subjective risk”?
What is the proper way of analyzing the clustering of keywords?

3. Methodology

We used the Scopus database for our bibliometric analysis on objective and subjective risk, which turns in with Elsevier. We explored the Scopus database on 25 October 2020, to obtain the journals and articles related to objective and subjective risk. The bibliographic archive in Scopus had a wide range of subjects (Md Khudzari et al. 2018), which we employed to support the bibliometric analysis centered on the coupling and visualization of bibliometric and scientometric methods (Nobanee et al. 2021). Several similar studies in many disciplines including risk management have been conducted using the Scopus database such as the studies of (Md Khudzari et al. 2018; Yahaya et al. 2020; Moreira et al. 2019; and Khatib et al. 2021). There are three major databases are available for collecting bibliographic information: Scopus® by Elsevier, Google Scholar, and the Web of Science (WoS) by Thomson Reuters (Delafenestre 2019). Each of the above databases has several advantages and disadvantages (Adriaanse and Rensleigh 2013; Delafenestre 2019). Mongeon and Paul-Hus (2016) argue that both Scopus and the Web of Science are valid for bibliometric studies and social sciences disciplines are better represented in the Scopus database, Scopus database includes several conference proceedings and book chapters (Delafenestre 2019). Scopus database has millions of publications online. To locate related documents, main keywords such as “objective risk” or “subjective risk” will be applied in the search engine box of the Scopus database. In general, narrowing down the search for related documents is possible using inclusion/exclusion criteria (Hattingh et al. 2020). As such, Table 1 summarizes the inclusion/exclusion criteria applied in this study (Nobanee 2021). (See Table 1—Inclusion/Exclusion Criteria below)
According to Korom (2019), the use of top-tier related journals or documents is best when it comes to conducting the bibliometric analysis. Therefore, only the top 20 most cited documents, top 20 authors, top 20 countries, and top 20 author-supplied keywords were analyzed in this study. To quantify and analyze the list of related documents, top-tier ranking and descriptive statistics were purposely applied in this study (i.e., frequency/percentage) (Bisdorff 2008).
In the bibliometric analysis, the object of interest is considered as items (Nobanee 2020). To create a visual representation of items, the VOSViewer software was used to create a map that represents the relatedness of each item (, accessed on 25 October 2020). Using distance-based maps, the relatedness of each item is also known as the visualization of two similar items (Delafenestre 2019; Eck and Waltman 2010; Borg and Groenen 2005).
This paper analyzed the bibliographic information of objective and subjective risks obtained from the Scopus database. The network mapping and visualization of the authors, countries, documents, affiliations, and occurrence of words were achieved with the use of the VOSviewer software, (Visualizing Scientific Landscapes), developed by the Leiden University Centre for Science base and Technology Studies in the Netherlands. The VOSviewer software is based on an algorithm called “visualization of similarities” or VOS (Lulewicz-Sas 2017; Sarkar and Searcy 2016) The software can also present the thematic flow of knowledge and identifying information clusters of the analyzed bibliographic data (Moed 2010; Zhu et al. 2009; Khatib et al. 2021). Clustering of bibliographic data aims at conjoining sets of concepts and items possessing common characteristics of authors, countries, documents, affiliations, and occurrence of words (Radicchi et al. 2004; Li et al. 2020). These methods enable us to provide a comprehensive evaluation of the development of objective and subjective risk research from an international perspective and across all disciplines.

4. Results

To locate documents on “objective risk” or “subjective risk”, the researcher had to use the Scopus database. Using keywords such as “objective risk” or “subjective risk” in both “title” and “keywords”, the researcher was able to locate 215 document results. To narrow down the search for related documents, advanced search options like “LIMIT-TO” were used in this study. Since only 215 related documents were found during the initial search for related documents, no limit was set on the year of publication.
In general, researchers can use an online search engine to limit the retrieval of documents to a specific language (Bates 2012). To increase the validity and reliability of the list of related documents, duplicates found in the Scopus database were removed from the dataset. After limiting the search to the English language only and removing all duplicates, 192 related documents were left for the final assessment. (See Table 2—Basic and advance search results).

4.1. Top 20 Most Cited Documents

The top three (3) most cited document related to “objective risk” or “subjective risk” includes documents written by Acerbi, C. (n = 399, 20.2%) followed by Rozendaal, L. (n = 193, 9.8%), and Botzen, W.J.W. (n = 178, 9.0%). Table 3 summarizes the top 20 most cited documents on “objective risk” or “subjective risk” These papers are highly cited given that their citations exceed the average citations per document. (See Table 3—Top 20 Most Cited Documents on “objective risk” or “subjective risk”).

4.2. Top 20 Sources of Most Cited Document

The top three (3) sources of most cited document related to “objective risk” or “subjective risk” include Journal of Banking and Finance (n = 399, 18.0%), Risk Analysis (n = 252, 11.4%), and Ergonomics (n = 211, 9.5%). Table 4 summarizes the top 20 sources of most cited documents on “objective risk” or “subjective risk”. The topic of objective and subjective risk has been published by 160 sources, of which only 9.3 present more than one document on objective and subjective risk, relieving that very few sources are specialized at dealing with this field (Izzo and Camminatiello 2020), (See Table 4—Top 20 Sources of Most Cited Document on “objective risk” or “subjective risk” below).

4.3. Top 20 Authors of Related Documents

The top three (3) authors of related documents include: Acerbi, C. (n = 399, 11.4%), Helmerhorst, Th.J.M. (n = 192, 5.5%), and Kenemans, P. (n = 192, 5.5%). Table 5 summarizes the top 20 authors of related documents. Older documents have more citations than new documents, this will reduce the chance of newer articles being considered and this will affect the order of the top authors in the list (Luther et al. 2020; Van Oorschot et al. 2018). (See Table 5—Top 20 Authors of Related Documents below).

4.4. Top 20 Countries of Related Documents

The top three (3) countries of related documents include: United States (n = 1009, 26.4%), Italy (n = 493, 12.9%), and Netherlands (n = 464, 12.2%). Table 6 summarizes the top 20 countries where the researcher can find related documents when using the Scopus database. (See Table 6—Top 20 Countries of Related Documents below).

4.5. Top 20 Author-Supplied Keywords

Author-supplied keywords are keywords identified by the author of documents related to this topic (i.e., “objective risk” or “subjective risk”) (Gordon 2019). As such, the Top 20 author-supplied keywords include: human (n = 62, 11.6%), article (n = 46, 8.6%), and humans (n = 45, 8.4%). Table 7 summarizes the top 20 author-supplied keywords in this study. (See Table 7—Top 20 Author-Supplied Keywords below).

4.6. Cluster of Author-Supplied Keywords

The most influential papers in objective and subjective risk were categorized into clusters based on the common topics and keywords. clustering of popular keywords is all about the grouping of keywords based on their inter-relatedness or interconnection with one another (Konchady 2006). As such, the author-supplied keywords were clustered into eight (8) groups. Content analysis and future research questions are presented in Table 8. We created the cluster table mainly with eight main streams that include risk and socioeconomic variables, attitude to health, risk factors, decision making, risk optimization, risk analysis, assessments, and management, physiological aspects, and safety, we summarized the purpose of the study, the study findings and we converted the suggestions of future research in each article into research questions (Bahoo 2020).

4.7. Cluster Analysis

The analysis carried out on the main eight clusters is based on studying the most powerful stream in the eight clusters and finding the most influential paper in that stream. Then analysis will discuss the mainstream, author name, the purpose of the study, and the finding of the study. This analysis will give us a clear insight into the way the clusters are formulated and how they are divided. (See Table 8—Stream Analysis below).
The stream analysis (See Table 8—Stream Analysis Above) helps us to identify the most influential papers par stream and by analyzing these papers we can see that some of the terms are dominant in each cluster such as risk and socioeconomic variables, attitude to health, risk factors, decision making, risk optimization, physiological aspects, and safety.
Each stream discusses our research subject “objective and subjective risk” and analyzes it from the researcher point of view, for example, stream 1 “risk and socioeconomic variables” indicates a complex relationship between the risk of involvement in an accident and the subjective expectation of that risk as written Stülpnagel and Lucas (2020).
Stream 2 “attitude to health”, as Chen et al. (2020) explained in their paper, is the risk optimization process sub-model that takes into account uncertainties and develops an operating model that takes into account two competing goals to reduce flood risk upstream and downstream flood risk.
Stream 3 was “risk factors”, and according to Groves and Varley (2020), risk perceptions and the relationship to safety equipment differ significantly between levels of expertise, along with the contrast between risky behavior and declared action, evidence of optimistic bias and defensive disapproval.
Decision Making and Risk optimization were the number 4 and 5 streams and “Decision Making” explains the relation between the decision and the type of the risk, called “Risk optimization”. The principle of predicting future states based on current states was put forward so that a mathematical model for objective risks was created, taking into account the deviations of the current and future downturn in art as written by Wang et al. (2019).
Stream 6 was “Risk analysis, assessments, and management”. In this cluster, the most dominant keyword was “Risk analysis” and the papers related analyzed the objective and subjective risk.
Stream 7 was “Physiological aspects”. This stream relates the psychological aspects to the risk as written by Liebherr et al. (2018) to implications of decision making under objective risks while performing additional engine requirements.
Stream 8 was “Safety” and the relationship between safety and the objective and subjective risk was studied. Larger risk changes as departures from the baseline risk are found to be significant in explaining choices, according to Thiene et al. (2017).

5. Discussion of Results

Using the ranking method and descriptive statistics, the act of analyzing the results of the top 20 most cited documents, top 20 authors, top 20 countries, and top 20 author-supplied keywords is straightforward. However, the use of the ranking method and descriptive statistics does not provide the researcher a complete insight with regards to how the most cited documents, authors of most cited documents, the country where researchers can find the most cited documents, and author-supplied keywords are interrelated and connected. To better analyze bibliometric results, it is best to carry out a scientometric analysis using the VOSViewer software.
In general, VOSViewer is a useful tool when illustrating the distance between items. As such, Eck and Waltman (2010) explained that a longer distance between items means that the items are not so related to one another whereas a shorter distance between items means that the items are related to one another. For example, Figure 1 presents the visualization map of the top 20 most cited documents on “objective risk” or “subjective risk”. In Figure 1, it was noted with a large blue bubble that the document written by Acerbi, C. is the most cited document on topics related to “objective risk” or “subjective risk”. However, the distance between the documents written by Acerbi, C. and other authors that were clustered into groups is quite far from one another. This strongly suggests that not many research studies have been made with regard to creating a spectral measurement for subjective risk aversion. To address the gap in research, future researchers who are interested in doing a research study on “objective risk” or “subjective risk” should conduct a similar study. This way, the research finding that was presented by Acerbi, C. can be validated by other researchers. (See Figure 1—Top 20 most cited documents on “objective risk” or “subjective risk”).
The same is true with regards to the document written by Golpira, H. The document written by Golpira, H. is about creating an objective risk-based decision-making framework for smart building energy management. The fact that the size of the purple bubble is quite small means that this document has not been cited so much in other studies related to “objective risk” or “subjective risk”. Compared to the document written by Acerbi, C., the document written by Golpira, H. was set further away from the clustering of other documents as shown in this visual map. Therefore, another way to address the gap in the literature is to encourage more researchers to conduct a research study on objective risk related to smart building energy management.
Figure 2 presents the visualization map of the top 20 sources of most cited documents on “objective risk” or “subjective risk”. In Figure 2, Eck and Waltman (2010) explained that the lines that connect two separate bubbles point out a relationship in each source of most cited documents. Complexity in the lines as shown in Figure 2 somehow suggests that there is a connection in each source of most cited documents. It could be that the line that binds two or more sources of most cited documents represents the frequency in co-citation or two documents being cited by a third-party author whereas differences in the color of bubbles symbolize the temporal pattern in the top 20 sources of most cited documents (Zhou et al. 2015). (See Figure 2—Visualisation map of Top 20 Sources of Most Cited Documents on “objective risk” or “subjective risk” below).
With regards to the visual map on the top 20 authors of related documents, the explanation of Eck and Waltman (2010) with regards to the distance between items suggests that the documents written by Kendel, F. are somehow connected to the document written by Leventhal, H. However, documents written by Kendel, F. have no relationship with the documents written by Dislich et al. (2010). (See Figure 3—Visualisation map of top 20 authors of related documents).
According to Eck and Waltman (2010), bubbles in each visualization map stand for the object of interest, and that a bigger bubble means a higher frequency than the other items. With this in mind, the huge green bubble in Figure 4 suggests that the majority of related documents can be found in the United States and not in other countries such as Chile or Greece. (See Figure 4—Visualisation map of top 20 countries of related documents).
Figure 5 shows the visualization map of author-supplied keywords. In this particular map, the clustering of author-supplied keywords is represented by three different colors (i.e., red, blue, and green). As such, the red color represents the first group of author-supplied keywords (cluster 1) whereas the green color represents the second group of author-supplied keywords (cluster 2). The blue color represents the third group of author-supplied keywords (cluster 3). Lines that connect the author-supplied keywords represent how each of these keywords is interrelated with other keywords. (See Figure 5—Visualisation map of the Clustering of Author-Supplied Keywords below).

6. Conclusions and Recommendations

In conclusion, the use of ranking and descriptive statistics in the bibliometric analysis is not enough to show researchers an in-depth analysis of the available literature found in the Scopus database. To get a deeper insight as to how each related documents are linked to one another, it is necessary to create a visualization map using the VOSViewer software.
To narrow down the gap in existing literature, results found in VOSViewer would direct future researchers on what specific topic to write when it comes to “objective risk” or “subjective risk”. For example, a strong or thicker line that connects two items means that there is sufficient literature available on the subject matter. A weak or thinner line means that there is a research gap in the existing documents. For this reason, future researchers who wish to make studies on “objective risk” or “subjective risk” should locate subject areas with thinner lines.
Our research offers interesting insights on objective and subjective risk, nevertheless, like other studies, our paper is affected by some limitations. First, the search technique used in our study was restricted to” objective” or “subjective” risk within the titles, and keywords. However, some research might not refer to “objective” or “subjective” risk within the searching scope. Second, we rely only on documents published on sources that are listed in the Scopus database as it is considered the most dominant database of peer-reviewed articles, conference proceedings, and book chapters (Khatib et al. 2021). Hence, the results of our search query may not cover all publications on “objective” or “subjective” risk. Future research may make a comparison of the outputs from multiple databases such as the Web of Science and Google Scholar. Third, we limited our research to documents that are written in English, some documents on “objective” or “subjective” that are written in other languages are not included in our analysis. Fourth, the top 20 most cited documents on “objective risk” or “subjective risk”, the top 20 authors of documents on “objective risk” or “subjective risk”, the top 20 countries where researchers could locate the most cited documents on “objective risk” or “subjective risk”, and the top 20 author-supplied keywords on subjects related to “objective risk” or “subjective risk” were analyzed in this study. Therefore, the result of this study does not completely represent what researchers can find when using the Scopus database. Therefore, future researchers should consider increasing the number of documents used for bibliometric and content analysis. Fifth, the “Matthew Effect” can also lead to biased findings when highly cited documents are blindly cited without checking their quality (Luther et al. 2020; Ball and Tunger 2005).

Author Contributions

All authors contributed equally to this work. Conceptualization, H.N., M.A., M.A.A. (Mohammed Ahmed Alkaabi), M.M.A., M.A.A. (Mohamed Abdulla Alhassani), N.K.A., S.A.A. and H.H.A.; methodology, H.N., M.A., M.A.A. (Mohammed Ahmed Alkaabi), M.M.A., M.A.A. (Mohamed Abdulla Alhassani), N.K.A., S.A.A. and H.H.A.; validation, H.N., M.A., M.A.A. (Mohammed Ahmed Alkaabi), M.M.A., M.A.A. (Mohamed Abdulla Alhassani), N.K.A., S.A.A. and H.H.A.; formal analysis, H.N.; investigation, H.N., M.A., M.A.A. (Mohammed Ahmed Alkaabi), M.M.A., M.A.A. (Mohamed Abdulla Alhassani), N.K.A., S.A.A. and H.H.A.; data curation, H.N., M.A., M.A.A. (Mohammed Ahmed Alkaabi), M.M.A., M.A.A. (Mohamed Abdulla Alhassani), N.K.A., S.A.A. and H.H.A.; writing—original draft preparation, M.A.A. (Mohammed Ahmed Alkaabi), M.M.A., M.A.A. (Mohamed Abdulla Alhassani), N.K.A., S.A.A. and H.H.A.; visualization, H.N., M.A., M.A.A. (Mohammed Ahmed Alkaabi), M.M.A., M.A.A. (Mohamed Abdulla Alhassani), N.K.A., S.A.A. and H.H.A.; supervision, H.N.; project administration, H.N.; funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Data Availability Statement

The data is available upon request.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Acerbi, Carlo. 2002. Spectral measures of risk: A coherent representation of subjective risk aversion. Journal of Banking & Finance 26: 1505–18. [Google Scholar]
  2. Adriaanse, S. Leslie, and Chris Rensleigh. 2013. Web of Science, Scopus and Google Scholar: A content comprehensiveness comparison. The Electronic Library 31: 727–44. [Google Scholar] [CrossRef]
  3. Aiken, Leona S., A. M. Fenaughty, Stephen G. West, J. J. Johnson, and T. L. Luckett. 1995. Perceived determinants of risk for breast cancer and the relations among objective risk, perceived risk, and screening behavior over time. Women’s Health 1: 27–50. [Google Scholar]
  4. Amin, Md. Tanjin, Faisal Khan, and Paul Amyotte. 2019. A bibliometric review of process safety and risk analysis. Process Safety and Environmental Protection 126: 366–81. [Google Scholar] [CrossRef]
  5. An, Yinuo. 2020. Subjective and Objective Risk Perceptions and the Willingness to Pay for Agricultural Insurance: Evidence from an In-the-Field Choice Experiment in Rural China. New York: Faculty of the Graduate School, Cornell University Ithaca. [Google Scholar]
  6. Andersen, Steffen, John Fountain, Glenn W. Harrison, and E. Elisabet Rutström. 2014. Estimating subjective probabilities. Journal of Risk and Uncertainty 48: 207–29. [Google Scholar] [CrossRef] [Green Version]
  7. Bahoo, Salman. 2020. Corruption in banks: A bibliometric review and agenda. Finance Research Letters 35: 101499. [Google Scholar] [CrossRef]
  8. Ball, Rafael, and Dirk Tunger. 2005. Bibliometrische Analysen: Daten, Fakten und Methoden: Grundwissen Bibliometrie für Wissenschaftler, Wissenschaftsmanager, Forschungseinrichtungen und Hochschulen. Jülich: Forschungszentrum Jülich, Zentralbibliothek. [Google Scholar]
  9. Bates, Marcia. 2012. Understanding Information Retrieval Systems. Management, Types, and Standards. Boca Raton: CRC Press, p. 306. [Google Scholar]
  10. Bisdorff, Raymond. 2008. On Clustering the Criteria in an Outranking Based Decision Aid Approach. In Modelling, Computation and Optimization in Information Systems and Management Sciences. Edited by Hoai An Le Thi, Pascal Bouvry and Tao Pham Dinh. New York: Springer, pp. 409–18. [Google Scholar]
  11. Borg, Ingwer, and Patrick Groenen. 2005. Modern Multidimensional Scaling. Berlin: Springer. [Google Scholar]
  12. Botzen, Wouter J. W., Jeroen cjh Aerts, and Jeroen C. J. M. van den Bergh. 2009. Dependence of flood risk perceptions on socioeconomic and objective risk factors. Water Resources Research 45. [Google Scholar] [CrossRef] [Green Version]
  13. BPP Learning Media. 2014. Governance, Risk, and Ethics, 8th ed. London: BPP Learning Media Ltd., p. 284. [Google Scholar]
  14. Braun, Adeli Beatriz, Adan William da Silva Trentin, Caroline Visentin, and Antônio Thomé. 2019. Sustainable remediation through the risk management perspective and stakeholder involvement: A systematic and bibliometric view of the literature. Environmental Pollution 255: 113221. [Google Scholar] [CrossRef]
  15. Brewer, Noel T., and William K. Hallman. 2006. Subjective and Objective Risk as Predictors of Influenza Vaccination during the Vaccine Shortage of 2004–2005. Clinical Infectious Diseases 43: 1379–86. [Google Scholar] [CrossRef] [Green Version]
  16. Cameron, Trudy Ann. 2005. Updating Subjective Risks in the Presence of Conflicting Information: An Application to Climate Change. Journal of Risk and Uncertainty 30: 63–97. [Google Scholar] [CrossRef] [Green Version]
  17. Chen, Juan, Ping-an Zhong, Weifeng Liu, Xin-Yu Wan, and William W.-G. Yeh. 2020. A multi-objective risk management model for real-time flood control optimal operation of a parallel reservoir system. Journal of Hydrology 590: 125264. [Google Scholar] [CrossRef]
  18. Constans, Joseph I., and Andrew M. Mathews. 1993. Mood and the subjective risk of future events. Cognition and Emotion 7: 545–60. [Google Scholar] [CrossRef]
  19. Da Silva, Juliana Bonfim Neves, Pedro Senna, Amanda Chousa, and Ormeu Coelho. 2020. Data mining and operations research techniques in Supply Chain Risk Management: A bibliometric study. Brazilian Journal of Operations & Production Management 17: 1–14. [Google Scholar]
  20. Darabseh, Mohammad, and João Poças Martins. 2020. Risks and Opportunities for Reforming Construction with Blockchain: Bibliometric Study. Civil Engineering Journal 6: 1204–17. [Google Scholar] [CrossRef]
  21. Delafenestre, Régis. 2019. New business models in supply chains: A bibliometric study. International Journal of Retail & Distribution Management 47: 1283–99. [Google Scholar]
  22. Díez-Herrero, Andrés, and Julio Garrote. 2020. Flood risk analysis and assessment, applications and uncertainties: A bibliometric review. Water 12: 2050. [Google Scholar] [CrossRef]
  23. Dislich, Friederike X. R., Axel Zinkernagel, Tuulia M. Ortner, and Manfred Schmitt. 2010. Convergence of Direct, Indirect, and Objective Risk-Taking Measures in Gambling. Zeitschrift Für Psychologie/Journal of Psychology 218: 20–27. [Google Scholar] [CrossRef]
  24. Eck, Nees, and Ludo Waltman. 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84: 523–38. [Google Scholar] [PubMed] [Green Version]
  25. Ettouney, Mohammed, and Sreenivas Alampalli. 2017. Risk Management in Civil Infrastructure. Boca Raton: CRC Press, p. 31. [Google Scholar]
  26. Farah, Haneen, Shubham Bhusari, Paul van Gent, Freddy Antony Mullakkal Babu, Peter Morsink, Riender Happee, and Bart van Arem. 2020. An Empirical Analysis to Assess the Operational Design Domain of Lane Keeping System Equipped Vehicles Combining Objective and Subjective Risk Measures. IEEE Transactions on Intelligent Transportation Systems 22: 2589–98. [Google Scholar] [CrossRef]
  27. Frewer, Lynn J., Chaya Howard, Duncan Hedderley, and Richard Shepherd. 1998. Methodological Approaches to Assessing Risk Perceptions Associated with Food-Related Hazards. Risk Analysis 18: 95–102. [Google Scholar] [CrossRef]
  28. Fu, Shanshan, Floris Goerlandt, and Yongtao Xi. 2021. Arctic shipping risk management: A bibliometric analysis and a systematic review of risk influencing factors of navigational accidents. Safety Science 139: 105254. [Google Scholar] [CrossRef]
  29. Fuentes Cabrera, Arturo, Antonio José Moreno Guerrero, José Santiago Pozo Sánchez, and Antonio-Manuel Rodríguez-García. 2019. Bullying among teens: Are ethnicity and race risk factors for victimization? A bibliometric research. Education Sciences 9: 220. [Google Scholar] [CrossRef] [Green Version]
  30. Ganbat, Tsenguun, Heap-Yih Chong, Pin-Chao Liao, and You-Di Wu. 2018. A bibliometric review on risk management and building information modeling for international construction. Advances in Civil Engineering 2018: 8351679. [Google Scholar] [CrossRef] [Green Version]
  31. Gerend, Mary A., Leona S. Aiken, Stephen G. West, and Mindy J. Erchull. 2004. Beyond Medical Risk: Investigating the Psychological Factors Underlying Women’s Perceptions of Susceptibility to Breast Cancer, Heart Disease, and Osteoporosis. Health Psychology 23: 247–58. [Google Scholar] [CrossRef]
  32. Gómez-Galán, Marta, Ángel-Jesús Callejón-Ferre, José Pérez-Alonso, Manuel Díaz-Pérez, and Jesús-Antonio Carrillo-Castrillo. 2020. Musculoskeletal risks: RULA bibliometric review. International Journal of Environmental Research and Public Health 17: 4354. [Google Scholar] [CrossRef]
  33. Gordon, Liahna. 2019. Real Research. Research Methods Sociology Students Can Use, 2nd ed. Thousand Oaks: SAGE Publications. [Google Scholar]
  34. Groves, Matthew R., and Peter J. Varley. 2020. Critical mountaineering decisions: Technology, expertise and subjective risk in adventurous leisure. Leisure Studies 39: 706–20. [Google Scholar] [CrossRef]
  35. Haight, Frank A. 1986. Risk, especially risk of traffic accident. Accident Analysis & Prevention 18: 359–66. [Google Scholar]
  36. Han, Ruru, Beihai Zhou, Yuanyi Huang, Xiaohui Lu, Shuo Li, and Nan Li. 2020. Bibliometric overview of research trends on heavy metal health risks and impacts in 1989–2018. Journal of Cleaner Production 276: 123249. [Google Scholar] [CrossRef]
  37. Hanna, Sherman D., and Peng Chen. 1998. Subjective and Objective Risk Tolerance: Implications for Optimal. Available online: (accessed on 20 May 2021). [CrossRef] [Green Version]
  38. Hansson, Sven Ove. 2010. Risk: Objective or subjective, facts or values. Journal of Risk Research 13: 231–38. [Google Scholar] [CrossRef]
  39. Harris, J. 2006. Chapter 10—Total Risk and Reliability with Human Factors. In Fuzzy Logic Application in Engineering Science. Edited by J. Harris. Dordrecht: Springer, p. 129. [Google Scholar]
  40. Hattingh, Marié, Machdel Matthee, Hanlie Smuts, Ilias Pappas, Yogesh K. Dwivedi, and Matti Mäntymäki. 2020. Responsible Design, Implementation, and Use of Information and Communication Technology. Cham: IFIP International Federation for Information Processing, p. 239. [Google Scholar]
  41. Health, Robert, and Henry Dan O’Hair. 2009. Handbook of Risk and Crisis Communication. New York: Routledge, p. 22. [Google Scholar]
  42. Holinagel, Hanne, and Kirsti Malterud. 1995. Shifting attention from objective risk factors to patients’ self-assessed health resources: A clinical model for general practice. Family Practice 12: 423–29. [Google Scholar] [CrossRef]
  43. Houston, David B. 1964. Risk, Insurance and Sampling. Journal of Risk and Insurance 31: 511–38. [Google Scholar] [CrossRef]
  44. Izzo, Filomena, and Ida Camminatiello. 2020. Gaming for Healthcare: A Bibliometric Analysis in Business and Management. International Business Research 13: 1–27. [Google Scholar] [CrossRef]
  45. Jiménez, Alfredo, and Torbjørn Bjorvatn. 2018. The building blocks of political risk research: A bibliometric co-citation analysis. International Journal of Emerging Markets 13: 631–52. [Google Scholar] [CrossRef]
  46. Khatib, Saleh, Dewi Abdullah, Ernie Hendrawaty, and Ahmed Elamer. 2021. A bibliometric analysis of cash holdings literature: Current status, development, and agenda for future research. Management Review Quarterly. [Google Scholar] [CrossRef]
  47. Knight, Frank H. 1921. Risk, Uncertainty and Profit. Boston: Houghton Mifflin. [Google Scholar]
  48. Knuth, Daniela, Doris Kehl, Lynn Hulse, and Silke Schmidt. 2014. Risk Perception, Experience, and Objective Risk: A Cross-National Study with European Emergency Survivors. Risk Analysis 34: 1286–98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Konchady, Manu. 2006. Text Mining Application Programming. Newton: Charles River Media, p. 268. [Google Scholar]
  50. Korom, Philipp. 2019. A bibliometric visualization of the economics and sociology of wealth inequality: A world apart? Scientometrics 118: 849–68. [Google Scholar] [CrossRef] [Green Version]
  51. Li, Songdi, Louise Spry, and Tony Woodall. 2020. Corporate social responsibility and corporate reputation: A bibliometric analysis. Journal of Construction Materials 2: 3. [Google Scholar]
  52. Li, Zhengtao, Henk Folmer, and Jianhong Xue. 2014. To what extent does air pollution affect happiness? The case of the Jinchuan mining area, China. Ecological Economics 99: 88–99. [Google Scholar] [CrossRef]
  53. Liebherr, Magnus, Patric Schubert, Heike Averbeck, and Matthias Brand. 2018. Simultaneous motor demands affect decision making under objective risk. Journal of Cognitive Psychology 30: 385–93. [Google Scholar] [CrossRef]
  54. Lipkus, Isaac M., Barbara K. Rimer, and Tara S. Strigo. 1996. Relationships among objective and subjective risk for breast cancer and mammography stages of change. Cancer Epidemiology Biomarkers and Prevention 5: 1005–11. [Google Scholar]
  55. Lulewicz-Sas, Agata. 2017. Corporate Social Responsibility in the Light of Management Science–Bibliometric Analysis. Procedia Engineering 182: 412–17. [Google Scholar] [CrossRef]
  56. Luther, Laura, Victor Tiberius, and Alexander Brem. 2020. User Experience (UX) in business, management, and psychology: A bibliometric mapping of the current state of research. Multimodal Technologies and Interaction 4: 18. [Google Scholar] [CrossRef]
  57. Mackersie, Robert C. 1989. Intra-abdominal Injury Following Blunt Trauma. Archives of Surgery 124: 809. [Google Scholar] [CrossRef]
  58. Md Khudzari, Jauharah , Jiby Kurian, Boris Tartakovsky, and G. Vijaya Raghavan. 2018. Bibliometric analysis of global research trends on microbial fuel cells using Scopus database. Biochemical Engineering Journal 136: 51–60. [Google Scholar] [CrossRef]
  59. Moed, Henk F. 2010. Measuring Contextual Citation Impact of Scientific Journal. Journal of Informetrics 4: 265–77. [Google Scholar] [CrossRef] [Green Version]
  60. Mol, Jantsje M., W. J. Wouter Botzen, Julia E. Blasch, and Hans de Moel. 2020. Insights into Flood Risk Misperceptions of Homeowners in the Dutch River Delta. Risk Analysis 40: 1450–68. [Google Scholar] [CrossRef] [Green Version]
  61. Mongeon, Philippe, and Adèle Paul-Hus. 2016. The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics 106: 213–28. [Google Scholar] [CrossRef]
  62. Moreira, Joana, Carla Susana Marques, Alexandra Braga, and Vanessa Ratten. 2019. A systematic review of women’s entrepreneurship and internationalization literature. Thunderbird International Business Review 61: 635–48. [Google Scholar] [CrossRef]
  63. Nagi, Ayman, Marius Indorf, and Wolfgang Kersten. 2017. Bibliometric analysis of risk management in seaports. In Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Berlin: Epubli GmbH, vol. 23, pp. 491–521. [Google Scholar] [CrossRef]
  64. Nobanee, Haitham. 2020. Big Data in Business: A Bibliometric Analysis of Relevant Literature. Big Data 8: 459–63. [Google Scholar] [CrossRef]
  65. Nobanee, Haitham. 2021. A Bibliometric Review of Big Data in Finance. Big Data 9: 1–6. [Google Scholar] [CrossRef]
  66. Nobanee, Haitham, Fatima Youssef Al Hamadi, Fatma Ali Abdulaziz, Lina Subhi Abukarsh, Aysha Falah Alqahtani, Shayma Khalifa Alsubaey, Sara Mohamed Alqahtani, and Hamama Abdulla Almansoori. 2021. A Bibliometric Analysis of Sustainability and Risk Management. Sustainability 13: 3277. [Google Scholar] [CrossRef]
  67. Pfeffer, Irving. 1956. Insurance and Economic Theory. Homewood: Richard D. Irwin, Inc. [Google Scholar]
  68. Radicchi, Filippo, Claudio Castellano, Federico Cecconi, Vittorio Loreto, and Domenico Parisi. 2004. Defining and identifying communities in networks. Proceedings of the National Academy of Sciences 101: 2658–63. [Google Scholar] [CrossRef] [Green Version]
  69. Rejda, George E., and Michael J. McNamara. 2021. Principles of Risk Management and Insurance. Global Editon. Hoboken: Pearson Higher Ed. [Google Scholar]
  70. Rowe, William D. 1981. Methodology and Myth. In Risk/Benefit Analysis in Water Resources Planning and Management. Edited by Yacov Y. Haimes. New York: Springer Science + Business Media LLC, pp. 59–88. [Google Scholar]
  71. Rozendaal, L., J. M. Walboomers, J. C. van der Linden, F. J. Voorhorst, P. Kenemans, T. J. Helmerhorst, M. van Ballegooijen, and C. J. Meijer. 1996. PCR-based high-risk HPV test in cervical cancer screening gives objective risk assessment of women with cytomorphologically normal cervical smears. International Journal of Cancer 68: 766–69. [Google Scholar] [CrossRef]
  72. Sarkar, Soumodip, and Cory Searcy. 2016. Zeitgeist or chameleon? A quantitative analysis of CSR definitions. Journal of Cleaner Production 135: 1423–35. [Google Scholar] [CrossRef]
  73. Schiebener, Johannes, and Matthias Brand. 2015. Decision Making Under Objective Risk Conditions—A Review of Cognitive and Emotional Correlates, Strategies, Feedback Processing, and External Influences. Neuropsychology Review 25: 171–98. [Google Scholar] [CrossRef]
  74. Sjöberg, Lennart, and Britt-Marie Drottz-Sjöberg. 1991. Knowledge and Risk Perception among Nuclear Power Plant Employees. Risk Analysis 11: 607–18. [Google Scholar] [CrossRef] [PubMed]
  75. Stülpnagel, Rul, and Jonas Lucas. 2020. Crash risk and subjective risk perception during urban cycling: Evidence for congruent and incongruent sources. Accident Analysis & Prevention 142: 105584. [Google Scholar]
  76. Suerdem, Ahmet K., Burcu Gumus, and Murat Unanoglu. 2013. Determinants of risk perception towards science and technology. In Intelligent Systems and Decision Making for Risk Analysis and Crisis Response. Edited by Chongfu Huang and Cengiz Kahraman. London: Taylor & Francis Group, p. 77. [Google Scholar]
  77. Summala, Heikki. 1988. Risk control is not risk adjustment: The zero-risk theory of driver behaviour and its implications. Ergonomics 31: 491–506. [Google Scholar] [CrossRef]
  78. Tavares, Breno G., Carlos Eduardo S. da Silva, and Adler D. de Souza. 2017. Risk management in scrum projects: A bibliometric study. Journal of Communications Software and Systems 13: 1–8. [Google Scholar] [CrossRef] [Green Version]
  79. Thiene, Mara, W. Douglass Shaw, and Riccardo Scarpa. 2017. Perceived risks of mountain landslides in Italy: Stated choices for subjective risk reductions. Landslides 14: 1077–89. [Google Scholar] [CrossRef] [Green Version]
  80. Van Oorschot, Johannes A. W. H., Erwin Hofman, and Johannes I. M. Halman. 2018. A bibliometric review of the innovation adoption literature. Technological Forecasting and Social Change 134: 1–21. [Google Scholar] [CrossRef] [Green Version]
  81. Wang, Lipeng, Zhi Zhang, and Qidan Zhu. 2019. Automatic flight control design considering objective and subjective risks during carrier landing. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234: 446–61. [Google Scholar] [CrossRef]
  82. Wang, Qiang, Zhaoguang Yang, Yuan Yang, Chenlu Long, and Haipu Li. 2014. A bibliometric analysis of research on the risk of engineering nanomaterials during 1999–2012. Science of the Total Environment 473: 483–89. [Google Scholar] [CrossRef] [PubMed]
  83. Xu, Song, Xiaotong Zhang, Lipan Feng, and Wenting Yang. 2020. Disruption risks in supply chain management: A literature review based on bibliometric analysis. International Journal of Production Research 58: 3508–26. [Google Scholar] [CrossRef]
  84. Yahaya, Ibrahim Suleiman, Aslan Amat Senin, Maryam M. B. Yusuf, Saleh F. A. Khatib, and Amina Usman Sabo. 2020. Bibliometric analysis trend on business model innovation. Journal of Critical Review 7: 2391–407. [Google Scholar]
  85. Zhou, Shiquan, Aragona Patty, and Shiming Chen. 2015. Advances in Energy Science and Equipment Engineering. London: CRC Press, vol. 1, p. 992. [Google Scholar]
  86. Zhu, Shanfeng, Ichigaku Takigawa, Jia Zeng, and Hiroshi Mamitsuka. 2009. Field Independent Probabilistic Model for Clustering Multi-field Documents. Information Processing and Management 45: 555–70. [Google Scholar] [CrossRef]
Figure 1. Visualisation map of top 20 most cited documents on “objective risk” and “subjective risk”.
Figure 1. Visualisation map of top 20 most cited documents on “objective risk” and “subjective risk”.
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Figure 2. Visualisation map of top 20 sources of most cited documents on “objective risk” and “subjective risk”.
Figure 2. Visualisation map of top 20 sources of most cited documents on “objective risk” and “subjective risk”.
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Figure 3. Visualisation map of top 20 authors of related documents.
Figure 3. Visualisation map of top 20 authors of related documents.
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Figure 4. Visualisation map of top 20 countries of related documents.
Figure 4. Visualisation map of top 20 countries of related documents.
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Figure 5. Visualisation map of the clustering of author-supplied keywords.
Figure 5. Visualisation map of the clustering of author-supplied keywords.
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Table 1. Inclusion/exclusion criteria.
Table 1. Inclusion/exclusion criteria.
All types of documents with title or keyword “objective risk” or “subjective risk”; and
Related documents are written in the English language.
All types of documents without “objective risk” or “subjective risk” in title or keyword; and
All related documents are not written in the English language.
Table 2. Basic and advanced search results.
Table 2. Basic and advanced search results.
Scopus Database Search StrategyDescriptionResult
Basic Search(TITLE (“objective risk” OR “subjective risk”) OR KEY (“objective risk” OR “subjective risk”))215 Documents
Advanced Search(TITLE (“objective risk” OR “subjective risk”) OR KEY (“objective risk” OR “subjective risk”)) AND (LIMIT-TO (LANGUAGE, “English”)) 192 Documents
Table 3. Top 20 most cited documents on “objective risk” or “subjective risk”.
Table 3. Top 20 most cited documents on “objective risk” or “subjective risk”.
RankAuthorh-IndexCitationsPercentage (%)
1Acerbi (2002)539920.2
2Rozendaal et al. (1996)411939.8
3Botzen et al. (2009)141789.0
4Summala (1988)1011537.7
5Mackersie (1989)1091085.5
6Sjöberg and Drottz-Sjöberg (1991)173834.2
7Aiken et al. (1995)95824.1
8Gerend et al. (2004)26783.9
9Frewer et al. (1998)66683.4
10Schiebener and Brand (2015)11673.4
11Hansson (2010)33673.4
12Li et al. (2014)4663.3
13Lipkus et al. (1996)49653.3
14Cameron (2005)25643.2
15Holinagel and Malterud (1995)22623.1
16Hanna and Chen (1998)18593.0
17Brewer and Hallman (2006)52512.6
18Knuth et al. (2014)6482.4
19Constans and Mathews (1993)19452.3
20Haight (1986)8402.0
Total 1976100.0
Table 4. Top 20 sources of most cited document on “objective risk” or “subjective risk”.
Table 4. Top 20 sources of most cited document on “objective risk” or “subjective risk”.
RankSourceCiteScore 2020CitationsPercentage (%)
1Journal of Banking and Finance4.439918.0
2Risk Analysis625211.4
4International Journal of Cancer10.11938.7
5Water Resources Research7.51788.0
6Archives of SurgeryN.A.1084.9
7Journal of Risk Research4.3944.2
8Women’s Health (Hillsdale, N.J.)N.A.823.7
9Health Psychology6.4783.5
10Neuropsychology Review10.6673.0
11Ecological Economics9.1663.0
12Cancer Epidemiology Biomarkers and Prevention6.8652.9
13Accident Analysis and Prevention7.8642.9
14Journal of Risk and Uncertainty3642.9
15Family Practice3.8622.8
16Journal of Financial Counseling and Planning2.1592.7
17Clinical Infectious Diseases13.2512.3
18Cognition and Emotion4.8452.0
19Frontiers in Psychology3.5421.9
Total 2215100.0
Table 5. Top 20 authors of related documents.
Table 5. Top 20 authors of related documents.
RankAuthorh-IndexFrequencyPercentage (%)
1Acerbi C.539911.4
2Helmerhorst Th.J.M.511935.5
3Kenemans P.601935.5
4Meijer C.J.L.M.1331935.5
5Rozendaal L.411935.5
6Van Ballegooijen M.531935.5
7Van Der Linden J.C.321935.5
8Voorhorst F.J.501935.5
9Walboomers J.M.M.601935.5
10Aerts J.C.J.H.531785.1
11Botzen W.J.W.141785.1
12Van Den Bergh J.C.J.M.541785.1
13Aiken L.S.951604.6
14West S.G.581604.6
15Summala H.401534.4
16Brand M.901123.2
17Schiebener J.111113.2
18Hoyt D.B.891083.1
19Mackersie R.C.1091083.1
20Shackford S.R. 1083.1
Total 3497100.0
Table 6. Top 20 countries of related documents.
Table 6. Top 20 countries of related documents.
RankCountryFrequencyPercentage (%)
1United States100926.4
4United Kingdom3388.9
15New Zealand320.8
Table 7. Top 20 author-supplied keywords.
Table 7. Top 20 author-supplied keywords.
RankKeywordOccurrencesPercentage (%)
7Decision making315.8
8Priority journal295.4
10Major clinical study254.7
13Controlled study224.1
14Objective risk213.9
15Risk analysis203.7
16Psychological aspect122.2
17Attitude to health112.1
18Multiobjective optimization112.1
Table 8. Cluster analysis.
Table 8. Cluster analysis.
StreamAuthorPurposeFindingsSuggestions for Future Research (in the Form of Research Questions)
Risk and socioeconomic variablesStülpnagel and Lucas (2020)The importance of risk perception when driving in urban areas is sometimes overlooked by urban planners. The majority of results suggest that the probability of an event as well as the subjective perception of this risk are dynamic.the correlation between objective danger in a moderate German city (caused by cyclical crashes) and personal risk (caused by people report in a crowdsourcing project)Where do bike riders over-evaluate or under-estimate the specific consequences of crashes as a justification for the construction and promotion of healthy biking infrastructure and services?
These sets of data lead to multiple infrastructures including traffic features considered to be important for cycling protection.
In a specific area, the subjective interpretation of risks can vary greatly from the real collision risksWhy will cyclists exaggerate or overlook the real crash risk, which can provide the foundation for developing healthy cycling facilities and for encouraging biking as a convenient means of transport?
Attitude to healthChen et al. (2020)The model considers the cumulative impact of reservoir inflow, side flow, and flood protection uncertainty.The submodel for risk optimization takes advantage of uncertainties and creates an operational model that takes account of two conflicting objectives for reducing downstream and upstream flood threats.What is the solution planned in the middle reaches of the Huaihe River Basin in China for an actual flood management system?
The sub-model for a risk calculation measures the risk using the stochastic method (SDE)
The sub-model for final improvement integrating a risk management model with an unregulated scanning genetic algorithm III into the risk optimizing operating model (NSGA-III)Do these findings suggest that the MOR established will provide plans which fulfill flood management goals while simultaneously reducing total risk?
Risk factorsGroves and Varley (2020)In current outdoor activity settings, we built awareness, preparation and technologies to keep us better or secure.The soil of Avalanche is such a dynamic ecosystem where the confusion is centralHave semi-structured interviews studied the impact of equipment on participant understanding of danger and action risk?
The Glenmore Lodge, National Sportscotland Outdoor Training Centre, undertook a pilot analysis on a limited group during a 3-Year Transceiver Evaluation.Did there vary considerably between views of avalanche and safety equipment as well as the comparison between dangerous conduct and proclaimed behaviour, proof of positive prejudice and protective disapproval?
Decision MakingMol et al. (2020)Assess possible flood risk misunderstandings in the Netherlands and offer insight into the factors linked to underestimation or over-estimation of the perceived risk of floodingMany Dutch inhabitants overestimate the likelihood, but they underestimate the predicted flood level of the peak water level.What if the risk was massively underestimated by a great many Dutch people on the floodplain yet overstated the maximum predicted flood level?
Heuristic accessibility refers to different persons
Risk optimizationWang et al. (2019)The proposed algorithm provides great carriers landing efficiency as well as enhancement of flight efficiency.Objective danger but subjective risk principles are used in the recovery of transport aircraftWhat is the rule developed by the Automated Conveyor Downward Control Act?
To build a statistical model for objective danger taking into account the variations in art from the present and future decline, the concept of future states dependent on current states was advocated.Have all these ever-changing target weights modified over time for monitoring variations in condition and removing the danger in the process of roll optimization, while the contextual risk is handled by additional risk conditions?
The related model is taken from the pilot’s personal experience of flight simulation studies
Risk analysis, assessments, and managementFarah et al. (2020)Creation of a system for the assessment of the area of the operational architecture of lane carriersThe system of research consists of the quantitative driving risk scale focused on the PDRF and a psychological risk scale focused on driver behavior, trust, and circumstances understanding.Why are conditions beyond the Unusual (i.e., in-hill/off-hill signs) commonly found in an ODD?
The approach can be used with the Automatic Lane Keeping Device of Tesla Model S.Are participants primarily accurately identified by the locations inside the Unusual (i.e., tunnel and curve)?
Physiological aspectsLiebherr et al. (2018)Review the results of objective danger decision-making when meeting extra engine criteria72 players, aged 18 to 30 years, either sitting or standing on one knee, played games of Dice TaskThose who stand on each leg and select the most disadvantaged (Number 1) option?
In the sense of decision-making and motor demand, the participants were required to make comparable attempts. A significant big effect of “option” and an effective relationship between “choice” x “gang”
In the sense of decision-making and motor demand, the participants were required to make comparable attempts. A significant big effect of “option” and an effective relationship between “choice” x “gang”Is the “seating party” chosen more frequently as a valuable four-number combination?
SafetyThiene et al. (2017)Mountain slides have happened frequently in places including Italy throughout history, sometimes contributing to casualties. Policies must also be cautiously formulated that eliminate the potential of death related to landslides.A survey of tourists and inhabitants from a region of Italy, vulnerable to landslide, decides the personal risk of others who might have been dying in a landslide and also the subjective chance of dying.Are there then subjective probabilities used to create attributes relevant to risk in the main architecture variant of the conventional model of selection?
One part of the study provides scientific knowledge and, if you so chose, helps you to update your risk evaluation while checking the function of this information.Does the largest risk shift when anomalies are necessary to clarify options?
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MDPI and ACS Style

Nobanee, H.; Alhajjar, M.; Alkaabi, M.A.; Almemari, M.M.; Alhassani, M.A.; Alkaabi, N.K.; Alshamsi, S.A.; AlBlooshi, H.H. A Bibliometric Analysis of Objective and Subjective Risk. Risks 2021, 9, 128.

AMA Style

Nobanee H, Alhajjar M, Alkaabi MA, Almemari MM, Alhassani MA, Alkaabi NK, Alshamsi SA, AlBlooshi HH. A Bibliometric Analysis of Objective and Subjective Risk. Risks. 2021; 9(7):128.

Chicago/Turabian Style

Nobanee, Haitham, Maryam Alhajjar, Mohammed Ahmed Alkaabi, Majed Musabah Almemari, Mohamed Abdulla Alhassani, Naema Khamis Alkaabi, Saeed Abdulla Alshamsi, and Hanan Hamed AlBlooshi. 2021. "A Bibliometric Analysis of Objective and Subjective Risk" Risks 9, no. 7: 128.

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