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The Influential Knowledge Factors of Companies’ Performance in Slovenia

100%

Rašič K. , Mulej M. , Čančer V.

Business Systems Research Journal

2016

tom 7

nr 1

46-58

Background: Knowledge plays a crucial role in supporting the European Union model based on economic growth, social responsibility, and sustainable development. To improve companies’ performance, one must reflect on new forms of knowledge and develop new indicators to measure them. Objectives: The goal of the paper is to investigate the impact of the selected factors of knowledge on companies’ performance in Slovenia. Methods/Approach: A questionnaire was created and sent to small and medium-sized enterprises in Slovenia. The principle axis factoring method was used to identify the factors of knowledge and of companies’ performance, and a regression analysis was conducted to determine the influence of the selected knowledge factors on companies’ performance. Results: The establishment of scientists’ collaboration with companies has a positive impact on companies’ performance, but the obstacles to the establishment of scientists’ collaboration with companies do not have any impact. Conclusions: The results could be useful for governments and companies in the adoption of measures aimed at strengthening scientists’ collaboration with companies. Further research can be oriented toward the common synergy index (e.g., the knowledge triangle).

A Comparison OfK-Means And FuzzyC-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets

100%

Al-Augby S. , Majewski S. , Majewska A. , Nermend K.

Folia Oeconomica Stetinensia

2014

tom 14

nr 2

19-36

The main goal of this article is to compare data-mining clustering methods (k-means and fuzzy c-means) based on a sample of banking and energy companies on the Gulf Cooperation Council (GCC) stock markets. We examined these companies for a pattern that reflected the effect of news on the bank sector’s stocks throughout October, November, and December 2012. Correlation coefficients and t-statistics for the good news indicator (GNI) and the bad news indicator (BNI) and financial factors, such as PER, PBV, DY and rate of return, were used as diagnostic variables for the clustering methods.