Predicting social entrepreneurship competence level and its factors:a machine learning approach

Abstract

Social entrepreneurship competences promote training aimed at generating projects that create value. This study aimed to predict the perceived Social Entrepreneurship competence level and its factors employing explainable Machine learning models and using data samples of 408 students who were administered a social entrepreneurship competency instrument and the subcompetencies personal, leadership, social innovation, value and management. Our experiment results findend that explainable machine learning models such as Decision Trees and Random Forests can perfectly predict the perceived Social Entrepreneurship competence level and explain the factors that influence the perception of entrepreneurial competence. Entrepreneurial Management dimension was a prominent feature to predict the level of perceived competence in both models. These findings are intended to be of value to entrepreneurs, decision-makers and change agents in the academic, governmental, business and social sectors.