engleski

Decision tree modelling for entrepreneurial intention

Researching entrepreneurial intention has become extremely popular due to the importance entrepreneurial activity has for the economy. Data mining has been used increasingly in the realms of prediction and classification, replacing traditional statistical approaches. The aim of this paper is to analyse the usefulness of decision trees for predicting entrepreneurial intention by comparing the decision tree method with structural equation modelling (SEM). A predictive model was proposed and empirically tested on a sample of 218 business students from Croatia and Macedonia. The results show that both techniques, SEM and decision tree, offer roughly equal theoretical contributions. However, the rules established using decision trees have deepened our understanding of the data by pointing to specific groups of students in the sample and their respective probabilities of having entrepreneurial intentions.

entrepreneurial intention, business students, decision tree, data mining

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