Academic-industry engagement, such as contract research, facilitates the development of university-centered entrepreneurial ecosystems (UCEEs). Research implies that the language utilized within contract research proposals is critical in determining whether an academic chooses to engage with an industrial partner or not. However, we know very little about the role of contract research proposal narratives in facilitating successful academic-industry engagement outcomes. Accordingly, adopting an explorative study, we apply machine learning (ML) techniques to predict successful academic-industry contract research outcomes and reveal key linguistic features associated with successful contract research proposals. Our predictive and exploratory ML techniques achieve an 83% accuracy in predicting successful academic-industry contract research outcomes and reveal that the use of concise and field-specific vocabulary repetitively is associated with successful contract research proposals. Our findings develop research and policy relating to academic-industry engagement. At the same time, our ML techniques provide a useful foundation for scholars to further develop theory, practice, and policy within the academic entrepreneurship and entrepreneurial ecosystem fields.
