Incubator mediated university industry collaboration drives entrepreneurship in Iranian agricultural universities

Entrepreneurial universities are increasingly acknowledged as pivotal catalysts for socio-economic development, particularly in agriculture, by fostering open innovation ecosystems and cross-sectoral partnerships. Anchored in the Triple Helix framework, this study examines how university–industry collaboration (IC) influences academic entrepreneurship (AE), with a focus on the mediating role of incubator-mediated entrepreneurial activities (IEA). Drawing on data from 127 technology-based startups across three Iranian agricultural universities, a hybrid methodological approach integrating Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) was employed to capture both linear and nonlinear relationships among key variables. Results indicate that IC is positively and significantly associated with AE. Furthermore, IEA not only mediate this relationship but also independently contribute to entrepreneurial outcomes. ANN analysis revealed IC as the most influential predictor (100% normalized importance), followed by IEA (81.7%). The SEM model explained 53.4% of variance in AE, while the ANN model improved this to 67.7%, demonstrating the added value of machine learning in capturing nonlinear dynamics. This research contributes to the open innovation literature by demonstrating how combining SEM-ANN enriches analytical depth and methodological robustness in higher education studies. It also highlights the strategic function of university incubators in bridging the gap between academic knowledge and market-ready innovations, especially in agri-tech contexts. Practical implications suggest that transforming universities into entrepreneurial institutions requires curriculum reforms, investment in innovation infrastructure, and increased private-sector engagement. These findings offer valuable insights for policymakers and academic leaders aiming to align agricultural higher education with innovation-driven economic growth. Limitations include reliance on self-reported data and a cross-sectional design. Future research could employ longitudinal or mixed-methods approaches to capture dynamic interactions.