Measuring the Commercial Potential of Science
We develop an ex-ante measure of commercial potential of science, an otherwise unobservable variable driving the performance of innovation-intensive firms. To do so, we rely on LLMs and neural networks to predict whether scientific articles will influence firms’ use of science. Incorporating time-varying models and the quantification of uncertainty, the measure is validated through both traditional methods and out-of-sample exercises, leveraging a major university’s technology transfer data. To illustrate the methodological contributions of our measure, we apply it to examining the impacts of university reputation and university privatization of science, finding that firms’ reliance on reputation may lead to foregone opportunities, and privatization (i.e., patenting) appears to increase firms’ use of the science of one university. We make our measure and method available to researchers.
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