Many universities engage in academic entrepreneurship, often with funding from external sources. Benchmarking technology transfer performance with external research funding can help universities identify and learn from peers that may possess strategic advantages in productivity. It also can be key for organizational learning and for communicating organizational performance to policy stakeholders and industry partners. In this study, we construct a unique dataset by linking two important data sources, AUTM and UMETRICS, and use stochastic frontier analysis to benchmark university licensing and revenue performance with different federal funding streams. Our empirical results suggest that universities looking to promote commercialization performance might look to National Science Foundation funding, and the universities best at production (i.e., licensing technologies and generating patents) with external funding are not necessarily the best at capturing benefits from generating revenue from entrepreneurial activity and launching start-ups. Our study points to the importance of the differential advantages of sources of federal research funding and offers implications for policy makers and university administrators.

许多大学通常通过外部资金来从事学术创业。利用外部研究资金对技术转让绩效进行基准评估可以帮助大学识别和学习在生产力方面可能具有战略优势的同行。这对于组织学习以及将组织绩效传达给政策利益相关者和行业合作伙伴也很关键。在这项研究中，我们通过链接两个重要的数据源AUTM和UMETRICS来构建一个唯一的数据集，并使用随机前沿分析以不同的联邦资金流为基准，对大学许可和收入表现进行基准测试。我们的经验结果表明，希望提高商业化绩效的大学可能会寻求国家科学基金会的资助，而大学在生产方面表现最佳（例如，使用外部资金获得许可技术和获得专利）不一定是从企业家活动和启动初创企业中获得收益的最佳方式。我们的研究指出了联邦研究资金来源的不同优势的重要性，并为政策制定者和大学管理者提供了启示。