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COMBINING PCA ANALYSIS AND ARTIFICIAL NEURAL NETWORKS IN MODELLING ENTREPRENEURIAL INTENTIONS OF STUDENTS
Marijana Zekić-Sušac
; Faculty of Economics, University of J.J. Strossmayer in Osijek, Osijek, Croatia
Nataša Šarlija
orcid.org/0000-0003-2600-9735
; Faculty of Economics, University of J.J. Strossmayer in Osijek, Osijek, Croatia
Sanja Pfeifer
orcid.org/0000-0002-7394-3080
; Faculty of Economics, University of J.J. Strossmayer in Osijek, Osijek, Croatia
Sažetak
Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to
model entrepreneurial intentions: principal component analysis (PCA) and artificial neural networks (ANNs). PCA was used to perform feature extraction in the first stage of modelling, while artificial 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 ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.
Ključne riječi
Classification; Entrepreneurial intentions; Modelling; Artificial neural networks; Principal component analysis
Hrčak ID:
97407
URI
Datum izdavanja:
1.2.2013.
Posjeta: 9.434 *