engleski

Combining PCA analysis and neural networks in modelling entrepreneurial intentions of students

Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are still not developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA) and artificial neural networks (NNs). PCA was used to perform feature extraction in the first stage of modelling, while neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and NNs in modeling entrepreneurial intentions, and provides some ideas for further research.

classification; entrepreneurial intentions; modelling; neural networks; principal component analysis

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