Predicting Entrepreneurial Intentions and Behaviour: A Machine Learning Approach Using Latent Constructs from Australian GEM Data

Accurately predicting entrepreneurial behaviour is critical for supporting innovation and designing effective entrepreneurship policies. This study introduces a novel modelling framework that integrates latent psychological constructs derived from Exploratory Structural Equation Modelling (ESEM) with multiple machine learning algorithms (MLAs) to predict entrepreneurial intention (EI) and outcome behaviour (EOB) using the 2019 Australian Global Entrepreneurship Monitor (GEM) dataset. The predictor set includes socio-demographic variables, regional classifications, and latent attitudinal and perceptual factors derived from GEM’s behavioural survey items. Seven MLAs were evaluated and compared to a standard logistic regression model. Among them, Multivariate Adaptive Regression Splines (MARS) achieved the highest classification accuracy, displaying sensitivity accuracies for both EI and EOB of around 83% to 84%, while outperforming traditional logistic models, other tree-based models, and newer neural network approaches in terms of sensitivity, interpretability, and parsimony. MARS identified self-efficacy (or internal perceived behavioural control), attitude, external perceived behavioural control, and age as the most important predictors of EI, while work status, age, self-efficacy, and intention were key predictors of EOB. These findings demonstrate that combining latent variable modelling with MLAs can produce robust, theory-informed predictive structures. While the Theory of Planned Behaviour (TPB) provided a useful guide for variable selection and latent construct identification, the results suggest that bounded rationality offers a promising lens for understanding entrepreneurial behaviour – emphasising heuristic decision-making and the limits of rational-choice models in entrepreneurial contexts. Beyond technical accuracy, the results illustrate how predictive modelling can both refine theoretical understanding and offer practical tools for designing more targeted entrepreneurship support.