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This study employs a comprehensive bibliometric analysis, adhering to the PRISMA protocol, to systematically review and map global academic trends in neuroimaging tools for neuromarketing research. Utilising data from the Scopus database spanning January 2007 to July 2023, 104 documents were subjected to analysis, revealing a discernible upward trajectory in publications. The findings revealed that the United States emerges as the predominant contributor, with 19 papers, while influential authors such as Balconi, M., and the most-cited article, "The Neural Mechanisms Underlying the Influence of Pavlovian Cues on Human Decision Making," signify pivotal contributions to the field. A keyword analysis reveals the prominence of key themes, including "emotion," "attention," and "advertising," offering valuable theoretical insights into the field of neuromarketing research. The journal Frontiers in Human Neuroscience is identified as the most productive, with 11 papers published. This comprehensive bibliometric analysis offers insights into the current landscape of neuroimaging tools in neuromarketing, as well as providing a foundation for future research directions. The implications of these findings extend to theoretical advancements, which provide guidance to researchers in refining frameworks and offering insights for strategic decision-making in the use of neuroscientific approaches for effective marketing strategies.

This study is aimed to quantitatively classify the publications about the concept of tribalism and to reveal the reflections of the relevant concept in the literature. A systematic literature review was conducted with bibliometric analysis. The study examined 334 articles published between 2003 and 2023 that used the terms "tribalism", "tribal", or "tribes." The articles were exported from Web of Science database in November 2023. The data set of the study was analyzed using the Bibliometix package in the R Studio program. In this context, content analysis and citation network analysis were performed on the data. As a result of the analysis, it was found that the number of articles published on tribalism has fluctuated over the years. Brand tribalism, culture and customer tribes are the most frequently used keywords along with tribalism. A diffraction occurred in 2016 and the words "community", "brand community", "consumer tribes" and "brand" came to the fore. In the last five years, tribalism, brand, brand tribalism and tribal marketing were the most trending topics. It was found that the most frequently cited articles regarding the concept of tribalism are mainly theoretical articles.

Abstract The genus amaranth is comprised of 60–70 species. This climate hardy plant is grown as a vegetable as well as cereal crop in many areas of the world. Current study multivariate analysis was used to characterize the exotic vegetable amaranth genotypes. The experiment was conducted in the research area of Muhammad Nawaz Shareef University of Agriculture, Multan under RCBD design with three replications of 50 exotic amaranth genotype. 22 different variables were investigated for the principal component and cluster analysis using different software packages in R studio. According to the results of principal component analysis the first five PCs had eigenvalues greater than one and they explained the 84.59% diversity in exotic vegetable amaranth accessions. First two principal components explained 57.31% of variation. Traits that loaded highly for first PC include moisture (-0.892), carbohydrates (0.938), energy (0.920), stomatal conductance (0.803), net photosynthesis (0.795) and dry weight (0.770). The traits which impacted the second PC highly include protein (0.692), fat (-0.571), nitrogen (0.692), calcium (0.545), phosphorus (0.908), zinc (0.960), iron (0.826) and potassium (0.872). For future breeding program the selection based on these results will be effective for crop enhancement drive in exotic vegetable amaranth genotypes. Hierarchical clustering was done and dendrogram split the 50 genotypes into 5 groups using silhouette method. The pairwise clustering revealed that intra cluster distance is smaller than inter cluster distance. The maximum inter cluster distance was observed between cluster I and II (9.88); between cluster I and III (9.47); between cluster II and IV (9.27). Clinical trial number Clinical trial number not applicable.

This paper presents a bibliometric analysis examining investment patterns in diverse economies and their relationship with global financial transformations. The study spans from 1964 to 2024, utilising data from the Web of Science (WoS) and analysed with R Studio and CiteSpace to identify trends and shifts in academic focus on investment strategies. A dataset of 1595 articles from 554 sources was processed, revealing key trends and shifts in investment patterns across different regions. The research highlights a notable rise in academic interest since 2015, with an 8.46% annual growth rate in publications. Methodologically, thematic evolution and network visualisation techniques were used to track collaboration patterns and emerging topics. For instance, 40.94% of the articles involved international co-authorship, emphasising global interest in investment dynamics. The study reveals critical insights into foreign direct investment (FDI) trends, particularly in developing and transitional economies, and how various global financial events influence investment behaviours. The average citation rate of 2.7 per article further underscores the significance of this field. The analysis confirms that investment patterns are crucial to understanding global economic transformations, providing a foundation for policymakers to craft economic strategies aimed at promoting balanced and sustained investment growth.

Critical thinking is often regarded as one of the vital skills in the 21st century. In recent years, it has become important to combine this skill with other skills such as language learning, creativity, problem-solving, decision making, and reflective thinking. As language and thoughts are intertwined and influence each other, incorporating thinking skills into English Language Teaching (ELT) can enhance academic and professional success. The prime objective of the current study is to conduct a bibliometric analysis of research on Critical Thinking (CT) in ELT from 2012 to 2022 by focusing on publication and research trends such as influential authors, globally cited documents, pertinent journals, leading countries, and significant affiliations. The current study also sheds light on the co-occurrence of keywords and thematic evolution through bibliometric analysis, examining 238 articles from Scopus and Web of Science databases using R studio (Biblioshiny). This bibliometric analysis highlights Thinking Skills and Creativity as the most pertinent journal with high citation count and China being the most cited country in this field. It also accentuates the interconnection between higher-order thinking skills, self-regulation, critical thinking, and critical reading. The thematic evolution of CT shows a shift from collaborative learning and CT (2012–2019) to language teaching and CT (2020–2022). It emphasizes the importance and the rising prominence of CT skills in language teaching. Moreover, this review will guide scholars and academicians to identify future research directions in the field of CT.

The study deals with the important question of how educational reforms affect healthcare competitiveness, especially when it comes to medical education. The primary study goal is to investigate the connection between improvements to the educational system and the calibre of medical care. To provide a thorough examination of trends over previous ten years, the study focusses on a dataset of 2,646 articles published between 2013 and 2023, taken from the WoS and Scopus databases. The necessity to record current and pertinent advancements in medical education and healthcare justifies the selection of this time frame. The CiteSpace and Biblioshiny programs were applied to conduct a bibliometric analysis, enabling the identification and visualisation of significant patterns and trends in the literature. The research hypothesis was verified by the analysis, which showed a strong association between educational changes and increases in healthcare quality. According to the report, health literacy, health disparities, and medical education are the most important topics. Improving healthcare competitiveness depends on these areas. Policymakers, educators, and healthcare administrators can benefit from the research findings because they offer evidence-based perspectives that can direct future medical education reforms to enhance healthcare outcomes and system competitiveness. The results also point to potential prospects for future investigation, especially when evaluating the long-term effects of educational reforms on international healthcare systems.

Perguruan tinggi sangat memperhatikan penerimaan mahasiswa baru sebagai indikator kemajuan dan pertumbuhan institusi. Namun, fluktuasi jumlah penerimaan mahasiswa baru dapat menjadi kendala dalam perencanaan dan pengambilan keputusan. Penelitian ini bertujuan untuk menerapkan metode Autoregressive Integrated Moving Average (ARIMA) dalam menghitung prediksi jumlah mahasiswa baru pada Universitas Papua. Oleh karena itu, penelitian ini menggunakan metode ARIMA untuk meramalkan jumlah mahasiswa baru tahun 2023 dengan memanfaatkan data historis dari tahun 2017 hingga 2022. Hasil analisis menggunakan R Studio menunjukkan bahwa model terbaik adalah ARIMA (11,0,12) dengan nilai error terendah. Penelitian ini memberikan manfaat bagi peneliti dalam memahami metode peramalan menggunakan ARIMA dan juga memberikan informasi yang berguna bagi Universitas Papua dalam perencanaan strategis untuk meningkatkan kualitas dan minat calon mahasiswa baru. Universities pay great attention to the acceptance of new students as an indicator of the progress and growth of the institution. However, fluctuations in the number of new student admissions can be an obstacle in planning and decision making. This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) method in calculating predictions for the number of new students at the University of Papua. Therefore, this study uses the ARIMA method to predict the number of new students in 2023 by utilizing historical data from 2017 to 2022. The results of the analysis using R Studio show that the best model is ARIMA (11,0,12) with the lowest error value. This research provides benefits for researchers in understanding forecasting methods using ARIMA and also provides useful information for the University of Papua in strategic planning to improve the quality and interest of prospective new students.

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

Social, economic and political developments in the globalising world have necessitated a re-examination of the concept of democracy. The concept of organisational democracy is a process that expresses the inclusion of individuals in the management processes of organisations and allows freedom of expression in organisations. In this study, it is aimed to reveal at which stage the concept of organisational democracy is included in the international literature. In this context, it is desired to evaluate the development stages of the concept in terms of literature and to set an example for future studies by filling the gaps in the literature. In line with the main purpose, 99 publications related to the concept of “organisational democracy” between 1990 and 2023 in the Scopus database were analysed using R Studio and VOSviewer softwares. The analyses were evaluated by considering the studies conducted in the fields of “Social Sciences”, “Business, Management and Accounting” between 1990 and 2023. The concept of organisational democracy is often associated with concepts such as participation, hierarchy, employee participation organizational culture, organizational commitment and communication. As a result, it has been determined that organisational democracy plays a role in the satisfaction of internal stakeholders.

Combined sewer overflows (CSOs) are a source of microbial contamination of drinking water intakes located downstream from their discharge. To safeguard the quality of the source water, it is essential to evaluate the risk levels associated with these municipal structures. This study compares two risk assessment approaches to test their applicability for assessing the risk of CSOs to drinking water intakes in a highly urbanized watershed. The first approach was based on a deterministic equation that combines the characteristics of an overflow structure allowing the risk to be rated as very low, low, medium, high, or very high. The second probabilistic risk assessment approach yielded findings that are probabilistically distributed across the five levels of risk. This approach was developed by constructing a novel Bayesian network to probabilistically link the different factors defining the exposure of water intakes to the hazards of CSOs. The comparison between the results of these two approaches highlighted the importance of simultaneously considering many scenarios for assessing the risk of contamination of source waters. It was possible to use the Bayesian network rather than the deterministic equation, which only supports one scenario at a time. It was also shown that the deterministic approach often overestimated risk levels for CSO outfalls close to the water intake. This occurred because the assessment process emphasized the distance factor between the discharge point and the water intake, while neglecting other crucial characteristics of the overflow, such as duration and frequency. In particular, the deterministic approach tended to underestimate risk for CSOs associated with low overflow frequencies as it did not support scenarios of overflow duration, unlike the probabilistic approach. The validation and sensitivity analysis of the Bayesian model revealed that the population residing in the CSO's drainage basin, along with the frequency and duration of the overflows, exerted the greatest influence on the resulting risk levels. These factors outweighed other variables utilized in the risk assessment, including vulnerability of the drinking water intake, the type of overflow recorder, pipe diameter, and variables defining the exposure of the water intake to the discharge. In the context of implementing action plans, the Bayesian network is estimated as a cost-effective technique as it prioritized overflow structures needing special attention in a highly urbanized watershed, where the same CSOs were deterministically rated as having the same risk level. The results also demonstrated the effectiveness of the Bayesian model in addressing data gaps faced by water managers and stakeholders. The Bayesian model proved capable of assessing risks with uncertainties for CSOs, even with limited input data available. These findings can assist managers in identifying problematic structures by considering various scenarios, unlike the deterministic approach, which left almost half (n = 42) of the study site's overflow structures unassessed due to data limitations.

Remote sensing techniques based on soil spectral characteristics are the key to future land management; however, they still require field measurement and an agrochemical laboratory for the calibration of the soil property model. Visible and near-infrared diffuse reflectance spectroscopy has proven to be a rapid and effective method. This study aimed to assess the suitability of multispectral data acquired with the agricultural digital camera in determining soil properties. This 3.2-Mpx camera captures images in three spectral bands – green, red and near-infrared. First, the reference data were collected, which consist of 151 samples that were later examined in the laboratory to specify the granulometric composition and to quantify some chemical elements. Second, additional soil properties such as cation exchange capacity, organic carbon and soil pH were measured. Finally, the agricultural digital camera photograph was taken for every soil sample. Reflectance values in three available spectra bands were used to calculate the spectra indices. The relationships between the collected data were calculated using the independent validation regression model such as Cubist and cross-validation model like partial least square in R Studio. Additionally, different types of data normalisation multiplicative scatter correction, standard normal variate, min–max normalisation, conversion into absorbance] were used. The results proved that the agricultural digital camera is suitable for soil property assessment of sand and silt, pH, K, Cu, Pb, Mn, F, cation exchange capacity and organic carbon content. Coefficient of determination varied from 0.563 (for K) to 0.986 (for soil organic carbon). Higher values were obtained with the Cubist regression model than with partial least squares.

Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.

Objetivo: Esta investigación presenta un análisis estadístico de la radiación solar en la ciudad de Cúcuta, con el objetivo de brindar una descripción detallada de su variabilidad entre los años 2005 y 2015. Esta información representa una herramienta evaluativa en el estudio del potencial solar de la región para el diseño de sistemas fotovoltaicos, partiendo de la necesidad de mejorar la relación costo/beneficio de esta tecnología, y así incrementar su penetración en la matriz eléctrica colombiana. Metodología: Se realizó un estudio de tres bases de datos climatológicas con información horaria, seleccionando aquella con la mayor cantidad de datos disponibles. Por medio del software R Studio, se ejecutaron dos tipos de métodos estadísticos: análisis de la varianza de un solo factor (ANOVA) y test de Bonferroni. A partir de esto, se obtuvieron gráficas que representan el resumen estadístico de los valores de radiación solar en la última década. Resultados: El análisis ANOVA arrojó un valor p de 6,28x10-7, indicando que existe una diferencia estadísticamente significativa de la media muestral entre los diferentes años de estudio. Asimismo, se identificaron los años y meses con mayor desviación y las posibles causas de la variabilidad de este parámetro. Conclusiones: A pesar de tener un comportamiento estable, la radiación de la ciudad de Cúcuta requiere de un análisis muy específico para su uso en aplicaciones que necesiten una alta sensibilidad en el manejo de esta información, ya que hay variaciones estadísticamente significativas que se pueden presentar para su uso. Financiamiento: Universidad Francisco de Paula Santander.

BackgroundThere have been no researches assessing the research trends of the application of artificial intelligence in glioma researches with bibliometric methods.PurposeThe aim of the study is to assess the research trends of the application of artificial intelligence in glioma researches with bibliometric analysis.MethodsDocuments were retrieved from web of science between 1996 and 2022. The bibliometrix package from Rstudio was applied for data analysis and plotting.ResultsA total of 1081 documents were retrieved from web of science between 1996 and 2022. The annual growth rate was 30.47%. The top 5 most productive countries were the USA, China, Germany, France, and UK. The USA and China have the strongest international cooperative link. Machine learning, deep learning, radiomics, and radiogenomics have been the key words and trend topics. “Neuro-Oncology”, “Frontiers in Oncology”, and “Cancers” have been the top 3 most relevant journals. The top 3 most relevant institutions were University of Pennsylvania, Capital Medical University, and Fudan University.ConclusionsWith the growth of publications concerning the application of artificial intelligence in glioma researches, bibliometric analysis help researchers to get access to the international academic collaborations and trend topics in the research field.

Numerous studies have highlighted the utility of glycan microarray analysis for the elucidation of protein-glycan interactions. However, most current glycan microarray studies analyze glycan binding protein (GBP)-glycan interactions at a single protein concentration. While this approach provides useful information related to a GBP’s overall binding capabilities, extrapolation of true glycan binding preferences using this method fails to account for printing variations or other factors that may confound relative binding. To overcome this limitation, we examined glycan array binding of three galectins over a range of concentrations to allow for a more complete assessment of binding preferences. This approach produced a richer data set than single concentration analysis and provided more accurate identification of true glycan binding preferences. However, while this approach can be highly informative, currently available data analysis approaches make it impractical to perform binding isotherms for each glycan present on currently available platforms following GBP evaluation. To overcome this limitation, we developed a method to directly optimize the efficiency of assessing association constants following multi-GBP concentration glycan array analysis. To this end, we developed programs that automatically analyze raw array data (kdMining) to generate output graphics (kaPlotting) following array analysis at multiple doses. These automatic programing methods reduced processing time from 32.8 h to 1.67 min. Taken together, these results demonstrate an effective approach to glycan array analysis that provides improved detail and efficiency when compared to previous methods.

The COVID-19 pandemic has pushed the world towards a digital era and affected the environment significantly. The present study uses a bibliometric approach to provide a comprehensive overview based on existing literature related to COVID-19 and E-learning and its environmental consequences, particularly from the year 2020–2022. In addition to the terrible impact of the pandemic on the world, environmental advantages have also been noticed. The findings show that the use of E-learning reduces the consumption of paper and prevents the cutting of trees which makes the environment more sustainable. The optimum use of technology leads to the conservation of the environment. Second, E-learning ensures developing and less developing countries to provide education at lower levels or remote areas of the society. The findings also suggest that governments and educational institutions should upgrade technology and digital tools in order to enhance E-learning education. Additionally, reviewing 1807 published articles extracted from the SCOPUS database, enrich literature related to COVID-19, E-learning, and the environment. This study also represents a graphical visualization of the bibliometric analysis using VOSviewer and R studio software. A coupling map and three-field plot also have been used for directions for future research.

Today, the importance of reproducible and transparent research has been increased along with the use of large data sets in a multinational environment. Researchers using intermedia agenda-setting theory have also affected this trend by conducting studies using big data and social media data. The Rozelle-Campbell formula is widely used by such researchers to investigate media effects and their direction. “R” is a general purpose statistical software package used in many fields of research. It is licensed for a free and an open-source software. There are many researchers have released applications for general use in “R” after they have been introduced into the literature. The authors have written this informative paper as a reference text for users of R in communication studies. Our primary purpose is to provide users an easy way to learn how to perform using R and R Studio analysis to use in intermedia agenda-setting, without having to navigate through the extensive, and excessive documentation. Another purpose of this study is to simplify and to reduce analyzing steps in complex cross-lagged correlations (synchronous correlations, cross-lagged correlations and autocorrelations) and Rozelle-Campbell Baseline analysis. On the other hand, neither do we claim to provide the most convenient solution for the researchers. We have tried to provide a simple approach which is easily understandable for those with an understanding of statistics. We anticipate that this tool “R” will be particularly useful for intermediate users, who need or want to conduct their studies when retrieving from the big data. In addition, this article will encourage researchers to improve their analytic abilities, by providing a brief manual and tested examples. Following the existing literature, this study re-tests the results of previous four intermedia agenda-settings studies, which had been previously analyzed by SPSS Software Program or Microsoft Excel. The present study’s data analysis 100% has matched with original studies. The results suggest that researchers can use this R Studio scripts to conduct their intermedia agenda-setting analysis.

In this data article, we present the dataset from the RNA-Seq analysis of subcutaneous adipose tissue collected from 5 healthy normal weight women (NW, age 37 ± 6.7 years, BMI 24.3 ± 0.9 kg/m2) and 5 obese women (OBF, age 41 ± 12.5 years, BMI 38.2 ± 4.6 kg/m2). Raw data obtained from Illumina NextSeq 500 sequencer were processed through BlueBee® Genomics Platform while differential expression analysis was performed with the DESeq2 R package and deposited in the GEO public repository with GSE166047 as accession number. Specifically, 20 samples divided between NW (control), OBF (obese women), OBM (obese male) and OBT2D (obese women with diabetes) are deposited in the GSE166047. We hereby describe only 10 samples (5 healthy normal weight women reported as NW and 5 obese women reported as OBF) because we refer to the data published in the article “Transcriptional characterization of Subcutaneous Adipose Tissue in obesity affected women highlights metabolic dysfunction and implications for lncRNAs” (DOI: 10.1016/j.ygeno.2021.09.014). Pathways analyses were performed on g:Profiler, Enrichr, ClueGO and GSEA to gain biological insights on gene expression. Raw data reported in GEO database along with detailed methods description reported in this data article could be reused for comparisons with other datasets on the topic to obtain transcriptional differences in a wider co-hort. Moreover, detailed pathways analysis along with cross-referenced data with other datasets will allow to identify novel dysregulated pathways and genes responsible for this regulation. The biological interpretation of this dataset, along with related in vitro experiments, is reported by Rey et al., in Genomics (DOI: 10.1016/j.ygeno.2021.09.014).

The global economy can be regarded as a complex global adaptive system, which adapts and evolves according to the environment and the behavior of the agents existing on the market. A topic that is topical and can even affect the welfare of a country is the field of cryptocurrencies. Today, the most well-known phenomenon by people in this field is the emergence of bitcoin. Cryptocurrencies or virtual currencies are an emanation of the financial crisis that started in 2008, a crisis that had led to a decline in confidence of traditional bank. In this paper, we discussed the creation of possible speculative bubbles, we presented the virtual currencies, we applied techniques of multidimensional analysis of the data for their analysis, and we evaluated the effects that the appearance of the cryptocurrencies had on the cybernetic economic system in Romania. Also, the paper deals with a section on identifying risks in the field of cryptocurrencies. In the last part of this paper, we focus our attention on the viability of these coins and their future prospects.

The current study established a research mapping of the vaccine design using bibliometric indicators and network visualization. For an analysis of the result, the study retrieved a total of 5379 documents from Scopus from 1983 to 2021. The study used the VOS Viewer and the RStudio tools for data visualization. The findings revealed that there has been significant growth in literature on vaccine design in the last two decades; in the last ten years, the year with the most publications were 2020, with 477 publications, and the highest had a total of 14,145 citations. D.R. Burton was ranked as the most prolific author, with 86 publications and 18,449 total citations and was observed as the most frequently published author in the domain. The National Institute of Health (NIH) was the most productive organization in the domain, with 266 publications. The document entitled “Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae” received a total of 1398 citations, and was the most cited document in the field of vaccine design. In network visualization, an analysis of the co-occurrence of keywords showed that “vaccine” and “vaccine design” occurred the most, which was 761 and 335 times, respectively. The study also observed that there were five clusters of author collaboration with a maximum of 18 authors and a minimum of two authors. The findings of the study will aid scholarly coalitions in the domains of medicine and health, information science and bibliometric professionals to carry out further research in the area of vaccine design.

This paper aims to conduct a bibliographic analysis on the published papers related to anthropogenic deposits. Based on data recovered from the Scopus database, a bibliometric analysis was performed using the softwares R Studio and Vosviwer. As a result, there was an increase in the number of articles published since the year 2006. The Russian Federation is the country that most publishes works in this field of research. On the other hand, the United States of America is the country that most presents papers considered relevant. A number of 445 authors published papers related to this topic, as first author or co-author, being the journal International Multidisciplinary Survey of Scientific Geoconference, with the largest number of publications. The most frequent keywords in the articles were mapped and divided into four groups: Archeology; Geology and Soils; Urban Deposits; Mining, areas of prominence in this field of research.

The article is aimed at insurance markets research for 31 countries based on insurance indicators such as insurance den-sity and insurance penetration and providing hierarchical clustering analysis using RStudio. The clustering is done with k-means and Ward methods and the authors tried to interpret the clustering analysis and prove the reasons why coun-tries refer to the specific cluster and explain further development opportunities for each cluster. The authors suggested the hypothesis that Russia should be in one cluster with Eastern European counties and the hypothesis was rejected as a result of the research. The analysis revealed that 31 countries should be divided into 5 clusters and Russia is referred to the cluster with Turkey and Mexico that have very similar insurance systems and share common problems in insurance industry development. The authors infer that insurance instruments that are used by members of one cluster can poten-tially be adopted by other members within the cluster. The calculations in R are included in the paper and the research can be repeated by students in classes with insurance industry data as well as in other sectors and with different data.