This chapter looks at successful knowledge transfer of products or processes from public research organizations to private sector firms for commercialization. Through six national case studies (Germany, the Republic of Korea, and the UK for high-income countries, and Brazil, China, and South Africa for middle-income countries), contextual conditions that influence success are discussed. Over time, the conceptual model behind policies to support knowledge transfer has shifted from a mode 1 linear pipeline model to a mode 3 model. In the linear model, basic research conducted by universities is followed by applied research, either by public research organizations or firms. In a mode 3 model, multiple actors – such as different types of public research organization, knowledge intermediaries such as knowledge transfer offices, and private businesses – are involved in an innovation system; there is a reverse knowledge flow whereby firms provide public research scientists with information on their needs, which influences the research projects of public research scientists. Best practice includes policy support for research and development and other innovation-related activities and incentives for firms to work closely with public sector researchers for problem solving and commercialization.

Commercialization of public research to support economic growth involves the transfer of knowledge produced by public research organizations to private sector businesses or government agencies. This chapter describes the diverse range of national and institutional policies and practices implemented across countries to encourage knowledge transfer between public research organizations and firms. The chapter largely focuses on the IP licensing model, highlighting its advantages and disadvantages, and discusses how the costs of IP-mediated knowledge transfer can be minimized. It outlines the main reasons for collecting knowledge transfer metrics for licensing – for benchmarking, for identifying factors that support or hinder knowledge transfer, and for informing policy. The chapter also identifies the most commonly used methods for collecting knowledge transfer metrics, and discusses basic metrics that all countries should collect on the IP licensing model, plus supplementary metrics of relevance to specific policy issues.

Several policies and practices are involved in the successful transfer of knowledge from public research organizations to private sector firms for commercialization. To evaluate the effectiveness of these policies, metrics are used for benchmarking changes in performance over time. Most of the existing metrics focus on IP-mediated knowledge transfer, such as the number of patents produced by universities and the amount of license income earned. Thus, non-IP-mediated knowledge transfer gets viewed as unimportant and of low value. This chapter identifies data for measuring non-IP-mediated methods and recommends collecting metrics for other formal (collaboration, contracts, consultancy, etc.) and informal (from surveys of academics and firms) channels of knowledge transfer.

Introduction

The innovation literature has long recognized the role of research and development (R&D) and skilled scientists and engineers in successful innovation in science-based sectors. More recent works within the national innovation systems perspective highlighted the importance of other factors to successful innovation, particularly in low-and medium-technology sectors, where formal R&D frequently plays a secondary role. These other factors include interactions with suppliers and customers, other forms of ‘open innovation’ and feedback mechanisms from the market. These interactions frequently form within localized networks creating unique innovation systems at the regional or national level (Lundvall 1988; Nelson 1993).

Both innovation strategies based on science and on interactive networks require learning in order to develop competences and to be able to rapidly exploit external and internal change. In such a ‘learning economy’, the speed of the innovation process is a critical factor in economic performance. Using Danish data, Jensen et al. (2007) show that innovation performance is significantly enhanced when firms combine science-based learning with experiencebased learning. One possibility is that how firms organize the production and distribution of responsibilities among their workforce could have a significant effect on learning and hence on innovative capabilities.

Some of the early contributions to the innovation literature evaluated the effect of organizational structures on the success of innovation. The Sappho study pointed to the importance of interactions between different divisions of the same firm (Rothwell 1972). Indirectly, Kline and Rosenberg's (1986) ‘chain-link’ model of innovation points to the importance of feedback loops and interactions between agents within the same organization but operating at different stages of the innovation process. Freeman's (1987) analysis of the Japanese innovation system partly explained the success of Japanese innovation performance by the specific organizational characteristics of Japanese firms, while Gjerding (1992) looked at the role of organizational change in national innovation systems. More recently, there have been several systematic attempts to evaluate the effect of specific modes of work organization on national innovation performance (Lundvall 2002; Lam 2005; Lam and Lundvall 2006; Lorenz and Valeyre 2006).