scholarly journals Eine empirische Analyse der Bedeutung der strategischen Erfolgsfaktoren von Hidden Champions für mittelständische Unternehmen

2021 ◽  
Vol 69 (2) ◽  
pp. 67-96
Author(s):  
Klaus Deimel ◽  
Alina Gerke ◽  
Greta Molinski

Die Implementierung strategischer Erfolgsfaktoren rückt zunehmend in den Fokus kleiner und mittelständischer Unternehmen. Vor dem Hintergrund des überdurchschnittlichen Erfolgs sogenannter Hidden Champions (HC) stellt sich unter einer praxisorientierten Perspektive die Frage, welche Bedeutung mittelständische Unternehmen grundsätzlich den von Hermann ­Simon identifizierten Erfolgsprinzipien für HC für den Unternehmenserfolg zumessen. Die empirische Studie analysierte dazu die Bedeutung dieser Erfolgsfaktoren für mittelständische Unternehmen und untersuchte, ob Bedeutungsunterschiede zwischen erfolgreichen und ­weniger erfolgreichen Unternehmen der Stichprobe existieren. Im Rahmen einer explorativen, multivariaten Daten­analyse konnten außerdem zwei Cluster, die „Internationalen Innovatoren“ und die „Nationalen Tradi­tionalisten“, im Datensatz identifiziert werden, die sich hinreichend in der Bedeutungszumessung der Erfolgsfaktoren voneinander unterschieden. The implementation of strategic success factors is increasingly gaining the attention of small and medium-sized enterprises. In light of the above-average success of so-called hidden champions (HC), the question arises which importance medium-sized companies in general attach to the success principles for HC identified by Hermann Simon. This empirical study analyzed the importance of these success factors for small and medium-sized enterprises and investigated whether differences in relevance exist between successful and less successful companies in the sample. An explorative, multivariate data analysis also identified two clusters in the data set, the “international innovators” and the “national traditionalists”, which differed sufficiently from each other in the measurement of the importance of these success factors.

1998 ◽  
Vol 87 (1) ◽  
pp. 374-374
Author(s):  
David Lester

The results of eight studies by Steven Stack on national suicide rates were replicated using a data set for 1980; however, a multivariate data analysis indicated that the many associations found by Stack reduced to one major correlate of national suicide rates—females' participation in the labor force.


2017 ◽  
Vol 27 (4) ◽  
pp. 291 ◽  
Author(s):  
Pham Ngoc Son ◽  
Cao Dong Vu ◽  
Mai Quynh Anh

This report introduces a new computer program, having been developed initially at the Nuclear Research Institute at Dalat, for the multivariate data analysis techniques. In this preliminary version of the program, the size of a given data set to be analyzed is up to 50 variables and thousand observations, and can be used to perform some of the multivariate data analysis techniques such as principle component analysis, cluster analysis and data standardization. In comparison with other statistical analysis software, the same results are highly reproduced with MSAP.


2016 ◽  
Vol 8 (1) ◽  
Author(s):  
József Kovács ◽  
Nikolett Bodnár ◽  
Ákos Török

AbstractThe paper presents the evaluation of engineering geological laboratory test results of core drillings along the new metro line (line 4) in Budapest by using a multivariate data analysis. A data set of 30 core drillings with a total coring length of over 1500 meters was studied. Of the eleven engineering geological parameters considered in this study, only the five most reliable (void ratio, dry bulk density, angle of internal friction, cohesion and compressive strength) representing 1260 data points were used for multivariate (cluster and discriminant) analyses. To test the results of the cluster analysis discriminant analysis was used. The results suggest that the use of multivariate analyses allows the identification of different groups of sediments even when the data sets are overlapping and contain several uncertainties. The tests also prove that the use of these methods for seemingly very scattered parameters is crucial in obtaining reliable engineering geological data for design.


1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.


2021 ◽  
pp. 101106
Author(s):  
Naja Bloch Pedersen ◽  
Faegheh Zaefarian ◽  
Adam Christian Storm ◽  
Velmurugu Ravindran ◽  
Aaron J. Cowieson

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