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Pregled bibliografske jedinice broj: 652069

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


Zekić-Sušac, Marijana; Šarlija, Nataša; Pfeifer, Sanja
Combining PCA analysis and neural networks in modelling entrepreneurial intentions of students // Croatian Operational Research Review, 4 (2013), 1; 306-317 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 652069 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

Autori
Zekić-Sušac, Marijana ; Šarlija, Nataša ; Pfeifer, Sanja

Izvornik
Croatian Operational Research Review (1848-0225) 4 (2013), 1; 306-317

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Ekonomija, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekti:
010-0101195-0872 - Transformacija poduzetničkog potencijala u poduzetničko ponašanje (Pfeifer, Sanja, MZOS ) ( POIROT)
010-0101195-1007 - Procjenjivanje potencijala rasta malih i srednjih poduzeća (Singer, Vjekoslava, MZOS ) ( POIROT)
010-0101195-1048 - Modeli za ocjenu rizičnosti poslovanja poduzeća (Šarlija, Nataša, MZOS ) ( POIROT)

Ustanove:
Ekonomski fakultet, Osijek

Poveznice na cjeloviti tekst rada:

Hrčak Hrčak www.hdoi.hr

Citiraj ovu publikaciju:

Zekić-Sušac, Marijana; Šarlija, Nataša; Pfeifer, Sanja
Combining PCA analysis and neural networks in modelling entrepreneurial intentions of students // Croatian Operational Research Review, 4 (2013), 1; 306-317 (međunarodna recenzija, članak, znanstveni)
Zekić-Sušac, M., Šarlija, N. & Pfeifer, S. (2013) Combining PCA analysis and neural networks in modelling entrepreneurial intentions of students. Croatian Operational Research Review, 4 (1), 306-317.
@article{article, year = {2013}, pages = {306-317}, keywords = {classification, entrepreneurial intentions, modelling, neural networks, principal component analysis}, journal = {Croatian Operational Research Review}, volume = {4}, number = {1}, issn = {1848-0225}, title = {Combining PCA analysis and neural networks in modelling entrepreneurial intentions of students}, keyword = {classification, entrepreneurial intentions, modelling, neural networks, principal component analysis} }
@article{article, year = {2013}, pages = {306-317}, keywords = {classification, entrepreneurial intentions, modelling, neural networks, principal component analysis}, journal = {Croatian Operational Research Review}, volume = {4}, number = {1}, issn = {1848-0225}, title = {Combining PCA analysis and neural networks in modelling entrepreneurial intentions of students}, keyword = {classification, entrepreneurial intentions, modelling, neural networks, principal component analysis} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • EconLit


Uključenost u ostale bibliografske baze podataka::


  • EconLit
  • INSPEC
  • MathSciNet
  • Zentrallblatt für Mathematik/Mathematical Abstracts
  • Current Index to Statistics, ProQuest, INSPEC, EBSCO Host, Hrcak





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