International Market Segmentation with the Use of CMS Method and Cluster Analysis

2019 ◽  
Vol 65 (4) ◽  
pp. 492-506
Author(s):  
Marcin Salamaga
Keyword(s):  
Cluster Analysis ◽  
Current Trend ◽  
International Exchange ◽  
Foreign Markets ◽  
Multivariate Statistical ◽  
Goods And Services ◽  
Detailed Assessment ◽  
Constant Market Share ◽  
And Cluster Analysis ◽  
A Current

The segmentation of foreign markets is currently treated as an important element of the strategy of operations of enterprises that participate in international exchange of goods and services. This paper fits squarely into a current trend in research on the matter. The article presents the possibility of combining the model of Constant Market Share developed by Leamer, Stern (1970) with cluster analysis. The CMS method allows for a detailed assessment of the sources of changes occurring in the export of compared countries, and in particular its results allow for answering the following question: To what extent may changes in export be explained by the economic situation in the world trade of individual clusters of commodities and to what extent do they result from the competitiveness of these countries? The application of the multivariate statistical methods for the designated effects will allow for the identification of the clusters of countries of the most similar position in the spatial and commodity arrangement, including countries of similar competitiveness of trade. This approach has been applied to the segmentation of EU countries’ markets.

Segmentation of markets in Central and Eastern Europe with the use of the CMS method

2019 ◽  
Vol 64 (2) ◽  
pp. 5-16
Author(s):  
Marcin Salamaga
Keyword(s):  
Eastern European ◽  
Commodity Trade ◽  
Multivariate Statistical ◽  
Statistical Analysis Method ◽  
Detailed Assessment ◽  
Trade Competitiveness ◽  
Constant Market Share ◽  
Eastern European Countries ◽  
Selected Effects ◽  
Demand Effect

The paper aims at making a comparative analysis of the Central and Eastern European countries in the scope of effects accompanying changes in their export. The Eurostat’s data for 2016 were used in the study. The effects of changes in export of individual countries were separated based on the Constant Market Share (CMS) model developed by Leamer and Stern. The calculated effectssuch as: demand effect, market distribution effect, commodity composition effect and competitiveness effect enabled a detailed assessment of the sources of changes occurring in export of individual countries. They allowed, in particular, for answeringthe following question: to what extent may changes in export be explained by the economic situation in the world commodity trade of individual clustersand to what extent do they result from the competitiveness of these countries? The application of the multivariate statistical analysis method for the selected effects allowed for the identification of clusters of countries with the most similar position in the spatial and commodity arrangement, including countries of similar trade competitiveness.

scholarly journals Application of multivariate statistical analysis in the assessment of groundwater quality of Tan Thanh district, Ba Ria – Vung Tau province

2017 ◽  
Vol 1 (M2) ◽  
pp. 66-72
Author(s):  
Au Hai Nguyen ◽  
Ngan Thi Khanh Phan ◽  
Thuy Thi Thanh Hoang ◽  
Ngoc Nguyen Hong Phan
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Anthropogenic Pollution ◽  
Dry Season ◽  
Wet Season ◽  
Multivariate Statistical ◽  
Rock Interaction ◽  
And Cluster Analysis

In the present study, Multivariate Statistical Analysis (MSA) such as Principle Component Analysis (PCA) and Cluster Analysis (CA) were applied to determine the temporal and spatial variations of groundwater quality in Tan Thanh district, Ba Ria – Vung Tau province. Groundwater samples were collected from 18 monitoring wells in April (dry season) and October (wet season) during the year 2012. Fifteen parameters (pH, TH, TDS, Cl-, F-, NO3-, SO42-, Cr6+, Cu2+, Ca2+, Mg2+, Na+, K+, HCO3- and Fe2+) were selected for MSA. PCA identified a reduced number of mean three latent factors of groundwater quality. Three factors called salinization, water-rock interaction and anthropogenic pollution explanined 70,5% (dry season) and 71.28% (wet season) of the variances. Cluster analysis revealed two main different groups of similarities between the sampling sites. This study presents the necessity of MSA in order to extract more precise information from a huge minitoring data, which will be usefull to groundwater quality management.

scholarly journals Application of multivariate statistical methods in the assessment of mountain organic soil transformation in the central Sudetes

2017 ◽  
Vol 54 (1) ◽  
pp. 43-59
Author(s):  
Bogna Zawieja ◽  
Bartłomiej Glina
Keyword(s):  
Cluster Analysis ◽  
Statistical Methods ◽  
Science Research ◽  
Organic Soil ◽  
Soil Science ◽  
Organic Soils ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Discriminant Variable ◽  
And Cluster Analysis

Summary In studies of organic soil degradation and transformation, alongside the conventional methods used in soil science, an increase in the importance of advanced statistical methods can be observed. In this study some multivariate statistical methods were applied in an investigation of organic soil transformation in the central Sudetes. Andrews curves, linear and kernel discriminant variable analysis and cluster analysis were used. The similarities among peatland soils and their layers were determined. It can be stated that the application of statistical methods in soil science research related to organic soil transformation is a valuable tool. The use of various statistical methods (such as Andrews curves, linear and kernel discriminant variables and cluster analysis) can with high probability confirm earlier laboratory or field observations. This is particularly justified in the case of organic soils derived from varied geobotanical peat materials, different types of peatlands and water supply types, which impact the primary properties of the soil.

Hydrochemical variation during groundwater mixing: a case study with multivariate statistical approach

2013 ◽  
Vol 8 (3-4) ◽  
pp. 399-408
Author(s):  
Linhua Sun
Keyword(s):  
Cluster Analysis ◽  
Coal Mine ◽  
Multivariate Statistical ◽  
Mass Balance Calculation ◽  
Hydraulic Connection ◽  
Mixing Ratios ◽  
Groundwater Mixing ◽  
Multivariate Statistical Approach ◽  
And Cluster Analysis ◽  
Mixed Samples

Identification of groundwater mixing and calculation of the mixing ratios between aquifers are important work for hydrological studies and safety of coal mining. In this study, multivariate statistical methods including factor and cluster analysis have been presented for identification of groundwater mixing status in the Renlou coal mine, northern Anhui Province, China. The methods include three steps: identification of hydraulic connection between aquifers by using factor score plots in combination with Q-mode cluster analysis, selection of end members and mass balance calculation for revealing mixing ratios. The hydraulic connection between loose layer and limestone aquifers have been identified in the Renlou coal mine, and three representative end member water samples, as well as mixed samples have been identified. Moreover, the mixing ratios for mixed samples are also calculated. The results indicate that the methods can be used for identification of mixing and quantification of mixing ratios in groundwater systems.

scholarly journals Multivariate statistical characterization of groundwater quality in Fesdis, East of Algeria

2018 ◽  
Vol 37 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Safia Khelif ◽  
Abderrahmane Boudoukha
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Statistical Characterization ◽  
And Cluster Analysis

AbstractThis study is a contribution to the knowledge of hydrochemical properties of the groundwater in Fesdis Plain, Algeria, using multivariate statistical techniques including principal component analysis (PCA) and cluster analysis. 28 samples were taken during February and July 2015 (14 samples for each month). The principal component analysis (PCA) applied to the data sets has resulted in four significant factors which explain 75.19%, of the total variance. PCA method has enabled to highlight two big phenomena in acquisition of the mineralization of waters. The main phenomenon of production of ions in water is the contact water-rock. The second phenomenon reflects the signatures of the anthropogenic activities. The hierarchical cluster analysis (CA) in R mode grouped the 10 variables into four clusters and in Q mode, 14 sampling points are grouped into three clusters of similar water quality characteristics.

scholarly journals Multivariate statistical analysis applied to the evaluation of groundwater quality in the central-southern portion of the state of Bahia - Brazil

2020 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
Maria Da Conceição Rabelo Gomes ◽  
José Ângelo Sebastião Araújo dos Anjos ◽  
Rafael Ribeiro Daltro
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Water Quality Monitoring ◽  
The State ◽  
Factorial Analysis ◽  
Semiarid Region ◽  
Multivariate Statistical ◽  
Management Actions ◽  
Quality Of Waters ◽  
And Cluster Analysis

 The objective of this study was to identify and evaluate the variables responsible for contributing to possible natural and/or human contamination in groundwater of the semiarid region of the state of Bahia, seeking to subsidize water quality monitoring and management actions in the area. To do so, multivariate analysis techniques regarding factorial analysis in principal components and cluster analysis were used. The factorial analysis allowed the grouping of variables into two principal factors that explained 93% of total accumulated variance. Variables were strongly related to concentrations of metals and salinity in the water. The cluster analysis was used to classify water sources according to the quality of waters into three clusters in each factor. The natural background of the rocks of the municipality of Boquira was shown to influence water resources. A continuous (during dry and rainy seasons) monitoring of water quality from wells and springs located upstream and downstream from contamination sources is recommended, even if these waters are not used for public supply, to determine possible contamination plumes from contaminated material.

scholarly journals Segmentation of Coffee Consumers Using Sustainable Values: Cluster Analysis on the Polish Coffee Market

2018 ◽  
Author(s):  
Grzegorz Maciejewski ◽  
Sylwia Mokrysz ◽  
Łukasz Wróblewski
Keyword(s):  
Cluster Analysis ◽  
Sustainable Consumption ◽  
Computer Assisted ◽  
Purchasing Decisions ◽  
Goods And Services ◽  
Long Time ◽  
The Face ◽  
Coffee Market ◽  
And Cluster Analysis ◽  
Source Of Information

In the face of the ongoing degradation of the natural environment and increasingly worrying climate change, societies and their governments should pay more and more attention to the issue of the development of sustainable consumption and pro-environmental consumer behaviour. It has been known for a long time that producers and retailers are the driving force behind adopting the idea of ​​sustainable development. Unfortunately, many of them, when preparing the offer of their goods and services, still take into account only such consumer characteristics as their wealth, the purchasing frequency and volume. In consumer segmentation, the sustainable values ​​that consumers follow when making their purchasing decisions are rarely taken into account. The purpose of the presented article is to try to fill the research gap in this area. The Polish coffee market, on which this type of research has not been conducted so far, was chosen as an example of segmentation taking into account the sustainable values ​​of consumers. The article’s main source of information is the results of primary research carried out using the CAWI (Computer-Assisted Web Interview) technique on a nationwide sample of 800 coffee consumers in July 2018. Multi-dimensional analyses such as extrapolative factor analysis (EFA) and cluster analysis (CA) were used to describe the results which were obtained from the research and statistical analysis. This made it possible to identify and describe six segments of coffee consumers, taking into account their demographic, social and economic characteristics as well as being guided by sustainable values in their purchases. The conclusions presented in the last part of the article may be used by manufacturing and trade enterprises, operating on the coffee market, in order to respond to the identified needs and expectations of consumers and by governmental and social organisations so as to determine the directions of pro-ecological education of consumers.

scholarly journals Chemical and Microbiological Characterization for PDO Labelling of Typical East Piedmont (Italy) Salami

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
V. Gianotti ◽  
S. Panseri ◽  
E. Robotti ◽  
M. Benzi ◽  
E. Mazzucco ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Biogenic Amines ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Tools ◽  
Multivariate Statistical ◽  
North West ◽  
And Cluster Analysis ◽  
Aroma Components

This study is focused on the characterisation of typical salami produced in Alessandria province (North West of Italy). Seventeen small or medium salami producers from this area were involved in the study and provided the samples investigated. The aim is double and consists in obtaining a screening of the characteristics of different products and following their evolution along ripening. The study involved five types of typical salami that were characterised for aroma components and nutritional features. This approach could provide a basis for a possible PDO or PGI label request. Principal Component Analysis and cluster analysis were used as multivariate statistical tools for data treatment. The overall results obtained point out that the products investigated do not deviate from analogous European products and show the possibility of characterising by specific parameters three main groups of samples:Salamini di Mandrogne,Muletta, andNobile Giarolo; moreover some considerations can also be drawn with respect to the nutritional characterization considering the biogenic amines profile.

Ecological distribution of moss-dwelling tardigrades on Vancouver Island, British Columbia, Canada

1991 ◽  
Vol 69 (1) ◽  
pp. 122-129 ◽  
Author(s):  
R. Deedee Kathman ◽  
Stephen F. Cross
Keyword(s):  
British Columbia ◽  
Cluster Analysis ◽  
Vancouver Island ◽  
Ecological Distribution ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Moss Species ◽  
Components Analysis ◽  
And Cluster Analysis ◽  
The Relationship

Replicate samples of tardigrades were collected at six altitudes from five mountains on Vancouver Island, British Columbia, Canada, to determine the relationship between species of tardigrades and altitude, and between species of tardigrades and species of mosses in which they were collected. A total of 13 696 tardigrades representing 39 species were collected and identified. Thirty-seven species of mosses were identified. Data were analyzed using principal components analysis and cluster analysis. The results from both multivariate statistical methods indicated that the distribution and abundance of tardigrades were not dependent upon the altitude or moss species.

scholarly journals ESPECTROSCOPIA NO INFRAVERMELHO COMO FERRAMENTA PARA DIFERENCIAÇÃO DE CHÁ VERDE DE AGRICULTURA ORGÂNICA E CONVENCIONAL

2019 ◽  
Vol 10 (2) ◽  
pp. 59
Author(s):  
Kumiko Koibuchi Sakane ◽  
Eiji Nitta Matsuura ◽  
Andreza Ribeiro Simioni
Keyword(s):  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Infrared Spectroscopy ◽  
Green Tea ◽  
Organic Agriculture ◽  
Chemical Components ◽  
Conventional Farming ◽  
Multivariate Statistical ◽  
Conventional Agriculture ◽  
And Cluster Analysis

O objetivo deste trabalho foi utilizar a espectroscopia no infravermelho associada com a análise estatística multivariada para diferenciar as quantidades de componentes químicos presentes em chás verdes de agricultura convencional e agricultura orgânica, observando as diferentes concentrações químicas de cada amostra, provenientes de um mesmo produtor. Os espectros de infravermelho das amostras analisadas demostraram que existem bandas das amostras de chá verde de agricultura orgânica que são mais intensas comparadas com as de amostras de chá verde de agricultura convencional, sendo que a intensidade das bandas se relaciona com a quantidade dos componentes presentes na amostra analisada. A análise multivariada, PCA e análise de grupamentos mostrou que as regiões de número de onda em 1450, 1365, 1320, 1145, 1030, 825 cm-1, que representam principalmente os flavonoides e catequinas, apresentam picos mais intensos nas amostras de chá verde de agricultura orgânica quando comparadas com a de cultivo convencional. A combinação destas técnicas é rápida, econômica e eficaz, possibilitando um resultado seguro e de uso em todo tipo de cultivo.Palavras-chave: Espectro. Análise multivariada. Orgânico. Camellia sinensis.ABSTRACTThe objective of this work was to use infrared spectroscopy associated with multivariate statistical analysis to differentiate the quantities of chemical components present in green teas from conventional agriculture and organic agriculture, observing the different chemical concentrations of each sample, coming from the same producer. The infrared spectra of the analyzed samples showed that there are bands of the organic agriculture green tea samples that are more intense compared to the conventional agriculture green tea samples, and the intensity of the bands is related to the quantity of the components present in the sample. analyzed. Multivariate analysis, PCA and cluster analysis showed that the 1450, 1365, 1320, 1145, 1030, 825 cm-1 wavelength regions, which represent mainly flavonoids and catechins, show more intense peaks in green tea samples. compared to conventional farming. The combination of these techniques is fast, inexpensive and effective, enabling a safe and useful result in all types of crops.Keywords: Spectrum. Multivariate analysis. Organic. Camellia sinensis.

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