Application of VMCM method (Vector Measure Construction Methods) to estimate consumer’s quality of life in EU countries – dynamic perspective
Procedia Computer Science, ISSN: 1877-0509, Vol: 159, Page: 2404-2413
2019
- 6Citations
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In the article is presented methodological approaches of Vector Measure Construction Method (VMCM) used to analyze complex socio-economic process described by many features (indicators). The outcome of the analysis has the most reliable and acceptable representation of the studied process specific to chosen case. In order to solve complex problems (depending on the situation, case under consideration), methods from multidimensional comparative analysis groups are very useful and most often used. In the article will be presented the general methodological assumptions of VMCM method, the advantages of using it in context of consumer issues and there will be presented application of the method to estimate consumer’s quality of life in different EU countries. It will be presented in dynamic perspective comparing changes over 8 years (since year 2010 to 2017).
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1877050919316199; http://dx.doi.org/10.1016/j.procs.2019.09.415; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85076258188&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1877050919316199; https://dx.doi.org/10.1016/j.procs.2019.09.415
Elsevier BV
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