Modelling the relationship between prior entrepreneurial exposure, entrepreneurship education and entrepreneurial action using neural networks

Abstract

Previous work on the relationships between entrepreneurship education, prior entrepreneurial exposure and entrepreneurial action has resulted in mixed findings. However, this work typically relies on linear models which may not adequately account for the relationships. Therefore, we explore artificial neural networks (ANN) to test non-linear relationships and compare these results with a linear regression model to understand the previous mixed findings. Data from 125 entrepreneurship graduates in Zambia revealed that a non-linear model best explained the variation in entrepreneurial action, whereby the relationship was cubic. These results explain some of the previously mixed findings and demonstrate the importance of educators, policy makers and scholars paying attention to non-linear relationships when aiming to promote and further understand entrepreneurship. Therefore, this paper has implications for educational initiatives aiming to augment entrepreneurship education, while also contributing to the development of theory explicating the relationship between entrepreneurial exposure, education and action.

Artificial intelligence in entrepreneurship education: a scoping review

“…4.1.2 Machine learning. Nine of 13 studies mentioned machine learning (Alqahtani, 2023;Shepherd and Majchrzak, 2022;Xu and Zhang, 2021;Liang et al, 2021;Tkachenko et al, 2019;Botha et al, 2021;Chen et al, 2021;Zhao et al, 2022;Zhu and Zhang, 2022). Except for educational purposes, the findings were coded from data sourcing, sample size, machine learning algorithm and validation of algorithm, summarized from Luan and Tsai's (2021) methodology of machine learning analysis.…”

“…The back propagation neural networks are multi-level neural networks (Zhao et al, 2022). Neural networks are normally combined with other algorithms such as pattern recognition (Botha et al, 2021) and wavelet transform (Xu and Zhang, 2021). In an applied example, Liang et al (2021) optimized an evaluation module partly by generalized regression neural network.…”

“…Alqahtani has identified the impacts of four specific intelligent technologies on entrepreneurship cognition, competence and innovation spirit (Alqahtani, 2023). ( 2) Six papers attempt to solve teaching-relevant problems (Botha et al, 2021;Chen et al, 2021;Zhao et al, 2022;Tkachenko et al, 2019;Liang et al, 2021;Alqahtani, 2023). The procedures or stages of neural networking have been considered as teaching steps to design entrepreneurial tasks and activities (learning from technological logic) (Tkachenko et al, 2019).…”

“…Alqahtani (2023) proposed and verified four hypotheses related to the effectiveness of AI applied in entrepreneurship education. (3) Two papers tried to answer administration/research (Botha et al, 2021;Xu and Zhang, 2021). To maintain high retention rate and avoid dropping out, the neural network was used to predict students' entrepreneurial psychology (Xu and Zhang, 2021) and the relationship between prior entrepreneurial exposure, entrepreneurship education and entrepreneurial action (Botha et al, 2021).…”

Education Reform and Quality Training of Music Majors From the Perspective of Entrepreneurial Education

“…Since the twenty-first century, there are more and more entrepreneurs worldwide with the rapid development of the economy, and the number of young entrepreneurs also increase significantly (Helle et al, 2020;Botha et al, 2021;Shekhar and Huang, 2021). In this case, China has strengthened the cultivation of college students' entrepreneurial ability accordingly.…”