Advancing Measures of Innovation: Knowledge Flows, Business Metrics, and Measurement Strategies
The workshop "Advancing Measures of Innovation" was driven by recent calls for improvements in statistics on research and development (R&D) and innovation, in order to better serve policy needs, advance research on the nature and impact of innovation, and, more broadly, help develop the field of Science of Science Policy.
The workshop brought together participants from both the research and the federal statistical communities to examine new or little known innovation-related data and research and to discuss data development priorities and strategies. Participants set the stage by examining current research challenges and innovation theory and considering the interplay of metrics, research, and analysis and policy. The workshop then went on to address two key questions for the future: Which metrics are most urgent or immediately feasible? What statistical and research activities are likely to advance these priority metrics?
Workshop discussions made clear the continued need for research related to innovation, including inputs to and components or stages of the innovation process, outputs and outcomes, and the social returns of innovation. There was a strong sense that research is currently impeded by several limitations: insufficient data, underutilization of existing data, and insufficient linkage among and integration of existing datasets.
Workshop participants variously discussed the kinds of data that merit greater attention and integration in order to advance our understanding of innovation. These include science and engineering employment and mobility data, international economic data, data on university-industry cooperation, and data collected by industries and firms for their own purposes. The workshop also highlighted the need for closer interaction between researchers and innovators (individuals and firms) as well as among researchers, statistical agencies, and policy makers concerned with innovation.
Workshop participants discussed a number of strategies for data development. These included survey-based methods, including comprehensive innovation surveys; data linking and data integration; nonsurvey-based methods (such as mining of administrative data); and using case studies and qualitative data. This last approach can be especially useful for early identification of trends and structural changes. The sense of the workshop is that the diverse strategies are not mutually exclusive and can be pursued productively in parallel or in combination. Discussants eschewed setting priorities among various approaches, implying that, at this stage, opportunities to advance research on innovation abound and considerable gains are likely to result from any and all the approaches discussed. To some extent, the optimal mix of options will be determined by policy and research needs, resource availability, and the particular risks and benefits inherent in each approach.
The agenda and presentations from this workshop are available at http://www.nsf.gov/statistics/workshop/innovation06/. Workshop papers, along with additional papers consistent with workshop objectives, will be published in a special issue of the Journal of Technology Transfer.