A Predictive Model to Increase Technology Transfer and Transition
Open AccessThere is a failure to maximize returns across Department of Defense research and development (R&D;) investments by way of technology transfer to industry, academia, and the private sector, and consistent technology transition to military users. The establishment of R&D; and science and technology (S&T;) investment portfolios lacks a quantitative method to estimate the probability of technology transfer and transition. This praxis developed a predictive model to aid federal technology managers in the selection of projects that will maximize the likelihood of technology transfer and transition success. The model was built by analyzing 1,737 federal R&D; and S&T; projects with varying instances of technology transfer and transition success to identify the attributes that are critical to this outcome. Based upon the relationship between these key attributes and technology transfer and transition, a predictive model was developed, trained, validated, and tested. This supervised machine learning model predicts, within a 10% margin of error, whether a project will successfully transition to a military user, or transfer to an industry, academic, or private sector partner. This model will enable technology managers to determine the predicted likelihood of technology transfer and transition success when selecting projects for investment funding. This validated Random Forest model will be beneficial to federal technology managers who oversee large and complex investment portfolios and resources.
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