This research provides performance metrics for cooperative research centers that enhance translational research through partnerships formed by government, industry and academia. Centers are part of complex ecosystems and vary greatly in the type of science conducted, organizational structures and expected outcomes. The ability to realize their objectives depends on transparent measurement systems to assist in decision making in research translation. We introduce a hierarchical decision model that uses both quantitative and qualitative metrics. A generalizable model is developed based upon program goals. The results are validated through consultation with experts. The method is illustrated using data from the National Science Foundation’s industry/university cooperative research center (IUCRC) program. The methodology provides a basis for a generalizable model and measurement system to compares performance of university science and engineering focused research centers supported by industry and government.

Introduction

Industry-university collaborations conducting multi-disciplinary research are required to solve increasingly complex social problems (Boardman and Gray, 2010). Increased U.S. public policy support for initiatives that enhance translational research has resulted in the evolution of many different forms of technology transfer mechanisms (Boardman and Bozeman, 2015). Today, university-based research centers “are prevalent as both policy mechanisms and industry strategies” [(Boardman and Ponomariov, 2011) pg 76]. Cooperative research centers (CRCs) that involve partnership agreements with actors from three different sectors of government, academia and industry are the most sustainable business models (Lee, 2000). However, supporting these “triple-helix” (Etzkowitz and Leydesdorff, 2000a) or governmentuniversity-industry (GUI) (Carayannis et al., 2014a) collaborations is expensive, driving policy makers to shift their attention towards performance evaluation. Academia, policy makers (Perkmann et al., 2011a) and CRC managers are all invested in understanding the performance and impact of these centers (Bozeman et al., 2013a). A wealth of literature examines program evaluation through primarily qualitative case-based methods or quantitative methods based on traditional indicators such as patents and publications. Despite the effort and many excellent studies, researchers are cautioning that traditional measures are inadequate (Gray et al., 2014a), placing a call-to-arms for further research. A multidimensional-holistic study with a flexible approach that can evaluate both quantitative and qualitative output indicators is needed. This research begins to fill this gap by presenting a generalizable model for CRC performance evaluation.