Performance Factors of Czech Companies Identified Using Statistical Pattern Recognition: Interpretation of Results

Abstract

The paper interprets factors of corporate performance identified by means of statistical pattern recognition techniques. A Dependency-Aware Feature algorithm with non-linear regression model ranked 74 potential factors of corporate performance according to their contribution to corporate performance prediction. This paper brings consecutive statistical analyses, which interpret the effects of Strategy, FDI, Share of Export, Top Management Performance Pay, and Workers' Performance Pay on corporate performance. Furthermore, the analyses reveal strong mutual moderating interdependencies. On the national scale, the paper brings evidence that the companies from the industries researched can use the stational techniques to learn about corporate performance factors. On a global scale, the paper introduces the contribution of Dependency-Aware Feature selection in the field of management and confirms the need for a multidimensional contingency approach in researching corporate performance. The results are based on a sample of 432 private limited or joint stock companies located in the Czech Republic operating in the manufacturing and construction industries and employing 50 or more people.