Determination of Competitive Management Perception in Family Business Leaders Using Data Mining

Abstract

This work seeks to determine competitive management perception of family business leaders, in order to establish working assumptions in new research and propose improvement and consolidation initiatives for these types of companies. This non-probabilistic, intentional study applied an instrument with 10 dimensions and 94 variables to a sample of 133 family business leaders from an intermediate city and a large city in Colombia. Data collection was achieved using supervised machine learning algorithms in the Python programming language, using techniques such as Cronbach’s Alpha Test, KMO, Levene, Bartlett, Discriminant Analysis and Decision Trees. The results allow us to identify four main components in 19 variables: Management and technology, Quality Management, Compensation, and Country competitiveness.