Data mining in entrepreneurial competencies diagnosis

Abstract

The aim of the paper is to diagnose the entrepreneurship competency levels among students to identify differences in competencies and their levels regarding gender, material status, and professional situation. In addition, the goal of the analysis is to indicate the competencies that need to be strengthened among individual groups of students. The research was conducted using a questionnaire by The European Entrepreneurship Competence (EntreComp) framework that was sent to students at the Pedagogical University of Cracow and the Rzeszow University. The rule induction method enabled us to discover dependencies that were not obvious among different competencies of respondents and their status. The research revealed that the surveyed women had completely different competencies than men. Good financial status has a positive impact on the self-assessment of competencies and worse-cause difficulties in assessing business ideas. Unemployed students need stimulation to take action, seek funding, share ideas, and protect them. Students running their businesses are able to identify market needs. The results revealed the following implications: It is important to verify the EntreComp methodology to examine how different groups are evaluating their entrepreneurial competencies; the data mining technique enables discover of new knowledge based on regularities hidden in data; and the results can be used to tailor special teaching programs for developing skills that individual subgroups lack.