Metrics for the commercialisation of knowledge produced by public research organisations

The commercialisation of knowledge produced by public research organisations (PROs) such as universities and public research institutes requires the transfer of knowledge to firms or to government organisations. This transfer process can occur through a myriad of informal and formal channels, but research on knowledge transfer tends to focus on formal channels that are mediated through contracts, such as the licencing of intellectual property (IP), because formal channels are easier to identify than informal channels and linked to more valuable knowledge (ref). An important issue is how to promote formal knowledge transfer without hampering other knowledge transfer channels. Many developed and developing countries collect metrics on the knowledge transfer activities of PROs on either on an occasional or annual basis. There are three main reasons for collecting such metrics: 1. To benchmark knowledge transfer activities, for instance to permit comparisons in performance across PROs or over time. 2. For use in analyses to identify the factors that either support or hinder knowledge transfer. 3. Informing policy, such as determining the effect of a change in policy on knowledge transfer outcomes. These three reasons are linked because research on the factors to support knowledge transfer can use benchmarking data as an output measure, for example in a study of the factors that increase the number of patents or the amount of license revenue. Plus, research on ‘what works’ can be of value for developing or improving policies to support knowledge transfer. Metrics include both statistics and indicators. Statistics include the number of invention disclosures, patent applications or license agreements or the total amount of license income earned. Indicators standardize a statistic and include both a numerator (the statistic) and a denominator (the standardizing variable). Examples include the number of patent applications per 1,000 research academics in the sciences or the amount of license income earned per one million Euros in research expenditures. Both statistics and indicators need to refer to a defined time period such as the calendar year. Indicators are essential for benchmarking performance. Using statistics to compare the number of invention disclosures among a group of universities would be seriously misleading if the group included universities with large differences in the number of academic staff or in the types of disciplines. A university that focuses on law and the humanities is likely to have far fewer opportunities for knowledge transfer than a university that focuses on science, technology and medicine. There are four methods of collecting knowledge transfer indicators, three of which require surveys of the participants in knowledge transfer: the managers of KTOs, scientists and other academics employed by PROs that create knowledge, and the firms that are the intended recipient of knowledge. The fourth method is to use publicly available data, for instance on patenting or publications. Most of the available metrics, and consequently research on the factors that support knowledge transfer outcomes, are from surveys of KTOs. However, there are two reasons to collect knowledge transfer metrics from other sources. First, KTO managers are not always aware of all knowledge transfer activities within their institute. This is a significant issue when IP rights can be held by academics or when a substantial share of knowledge transfer occurs through personal contacts between academics and firms. Second, there can be large differences in the opinion of KTO managers, academics and firm managers on the factors that support or act as barriers to knowledge transfer. Relying only on the opinions of only one of these three groups could result in misleading recommendations for how to improve knowledge transfer. This report describes the types of knowledge transfer metrics that can be obtained from each of the three types of surveys and from public data sources and the policy relevance of these metrics. We begin with metrics obtained from surveys of KTOs because these are the most common and consequently the most widely used.