2013
Comparison of the multivariate and bivariate analysis of corporate competitiveness factors synergy
ČÁSTEK, Ondřej; Ladislav BLAŽEK; Pavel PUDIL a Petr SOMOLZákladní údaje
Originální název
Comparison of the multivariate and bivariate analysis of corporate competitiveness factors synergy
Název anglicky
Comparison of the multivariate and bivariate analysis of corporate competitiveness factors synergy
Autoři
ČÁSTEK, Ondřej ORCID; Ladislav BLAŽEK; Pavel PUDIL a Petr SOMOL
Vydání
Ekonomická revue, Ostrava, VŠB - Technická univerzita Ostrava, 2013, 1212-3951
Další údaje
Jazyk
čeština
Typ výsledku
Článek v odborném periodiku
Obor
50600 5.6 Political science
Stát vydavatele
Česká republika
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Označené pro přenos do RIV
Ano
Kód RIV
RIV/00216224:14560/13:00066244
Organizační jednotka
Ekonomicko-správní fakulta
Klíčová slova česky
Konkurenceschopnost; faktory konkurenceschopnosti; finanční výkonnost; vícerozměrné statistické metody; sekvenční dopředný plovoucí výběr; synergie; k-nejbližších sousedů
Klíčová slova anglicky
Competitiveness; competitiveness factors; corporate financial performance; multidimensional statistical methods; Sequential Forward Floating Search; synergy; k-Nearest Neighbours
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 10. 7. 2013 08:52, doc. Ing. Ondřej Částek, Ph.D.
V originále
Corporate competitiveness is influenced by a number of factors. Their impact is not partial, but synergistic. It is necessary to respect the phenomenon of synergy consistently when examining which of these potential competitiveness attributes can really function as these factors. Consequently, feature selection and classification methods of statistical pattern recognition have been used for the multivariate statistical analysis of and search for competitiveness factors. The calculations conducted herein show that the Sequential Forward Floating Search method in combination with k-Nearest Neighbours classification is capable of capturing the synergistic effect of the whole set of factors, providing much better results than simple bivariate analysis methods that test only the partial effects of individual factors.
Anglicky
Corporate competitiveness is influenced by a number of factors. Their impact is not partial, but synergistic. It is necessary to respect the phenomenon of synergy consistently when examining which of these potential competitiveness attributes can really function as these factors. Consequently, feature selection and classification methods of statistical pattern recognition have been used for the multivariate statistical analysis of and search for competitiveness factors. The calculations conducted herein show that the Sequential Forward Floating Search method in combination with k-Nearest Neighbours classification is capable of capturing the synergistic effect of the whole set of factors, providing much better results than simple bivariate analysis methods that test only the partial effects of individual factors.
Návaznosti
| GAP403/12/1557, projekt VaV |
|