Predicting art university students’ entrepreneurial intention: A hybrid SEM–ANN approach - Yiliang Cao; Jie Zhang (PLoS ONE, 2025)
In recent years, academics and policymakers have increasingly focused on entrepreneurial behavior among university students. While existing studies have explored the entrepreneurial intention (EI) of students from various academic disciplines, few have specifically examined the EI of art university students. Based on the Diffusion of Innovations Theory (DOI) and the Theory of Planned Behavior (TPB), this study explores the factors influencing art university students’ EI and assesses each factor’s relative importance. This study employed a structural questionnaire to survey 273 students from three universities in Liaoning Province, China, measuring eight constructs: relative advantage (RA), observability (OB), compatibility (CO), entrepreneurial motivation (EM), entrepreneurial attitude (EA), subjective norms (SN), perceived behavioral control (PBC), and EI. Data analysis was conducted using Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN). The results show that, among the direct significant predictors of EI, PBC has the strongest influence, followed by EA, SN, and EM. Additionally, all predictive constructs accounted for 60% of the variance in the EI of art university students. The ANN analysis revealed the following normalized importance ranking of all predictive constructs: PBC (100%), EA (70.8%), SN (57.6%), RA (43.1%), and EM (31.2%). This study not only fills the gap in research on the EI of art university students but also provides valuable insights for developing targeted strategies to foster entrepreneurship among this group.