Botha, MelodiPohl, MarthiMubita, Lubinda2021-05-062021Melodi Botha, Marthi Pohl & Lubinda Mubita (2021) Modelling the relationship between prior entrepreneurial exposure, entrepreneurship education and entrepreneurial action using neural networks, Development Southern Africa, 38:2, 264-281, DOI:10.1080/0376835X.2020.1826291.0376-835X (print)1470-3637 (online)10.1080/0376835X.2020.1826291http://hdl.handle.net/2263/79806Previous 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.en© 2020 Government Technical Advisory Centre (GTAC). This is an electronic version of an article published in Development Southern Africa, vol. 38, no. 2, pp. 264-281, 2021. doi : 10.1080/0376835X.2020.1826291. Development Southern Africa is available online at : http://www.tandfonline.comloi/cdsa20.Entrepreneurial actionArtificial neural network (ANN)Entrepreneurship educationPrior entrepreneurial exposureEmerging economy contextNeural networksNon-linear relationshipPostprint Article