Predicting Entrepreneurial Performance Through the Lens of Entrepreneurial Orientation and Digital Adoption: A Machine Learning Approach

It is critical to clarify the specific causal mechanisms and boundary conditions explaining why and how entrepreneurial orientation (EO) enhances firm outcomes. However, despite more than 40 years of research, a theoretical gap is evident in many studies linking EO to firm performance outcomes. Therefore, since it is not unexpected that the diffusion of digital technologies creates a promising field for entrepreneurs and motivates them to take advantage of the adoption of technologies in their business, in this research, the moderating role of digital adoption in the relationship between entrepreneurial orientation and entrepreneurial performance has been investigated. Therefore, the aim of this research was to predict entrepreneurial performance from entrepreneurial orientation with regard to the moderating role of digital adoption. The data for this study has been derived from the Global Entrepreneurship Monitor (GEM) of 20 countries between 2008 and 2018, as well as the World Bank’s Digital Adoption Index (WB DAI). Based on EO, a predictive model of entrepreneurial performance was constructed using machine learning and the XGBoost algorithm. According to the findings of this study, it is evident that the various dimensions of EO exhibit differential impacts on the prediction of entrepreneurial performance. Specifically, innovativeness and risk-taking demonstrate a much greater influence compared to pro-activeness. The inclusion of digital adoption as a moderating role significantly enhanced the impact of innovativeness and risk-taking, whereas it did not have any discernible effect on pro-activeness. This research also has a valuable theoretical and managerial contribution and has provided important suggestions for future research.