Interactive Learning for Innovation

The challenge for Europe after the global economic and financial crisis is substantially different from the scenarios envisaged by the 2000 Lisbon Strategy. Then, optimistic perspectives of Europe catching up with the United States and becoming the most competitive region during a ten-year period were opened up, and the means of achieving this vision were to spend at least 3% of GDP on R&D, as stated in the Barcelona Declaration of 2002, following what Lundvall and Lorenz (2006) call the STI mode of innovation (Science, Technology, Innovation). Globalization having been identified as the basis of an understanding of the dynamics of contemporary capitalism, there was strong agreement that innovation was the key factor in promoting competitiveness in a globalizing knowledge economy (Porter, 1987; Lundvall, 2007). It is about twenty years since eminent academics acknowledged the role of innovation as the main driver of competitiveness, opening up the global market to new firms and developing countries capable of producing at very low cost (Drucker, 1985; Freeman, 1987; Porter, 1987; Dosi et al., 1988; Pyke and Sengenberger; 1992; Lundvall, 1992; Nelson, 1993; Asheim, 1994; Cooke, 2004). The solution points to leaving the low-road type of competition to enter markets for more sophisticated, specialized or niche products that can be produced by selected firms and/or systems/clusters of firms. For these reasons, these market segments are less price competitive and potentially highly remunerative.

Recent work on innovation performance within the national systems of innovation (NSI) framework has sought to go beyond the focus in the seminal contributions on science-industry links and the R&D activities of industrial firms (Nelson, 1993) or on the nature of user-producer relations promoting interactive learning (Lundvall, 1992). The more recent work has sought to widen the NSI perspective by exploring the way labor market institutions and systems of social protection impact on employee learning and innovation outcomes (Lorenz and Lundvall, 2006; Holms et al., 2010). A focus on the institutional determinants of the innovative performance of firms is also a hallmark of research on the varieties of capitalism (VoC) by Hall and Soskice (2001), who were perhaps the first to emphasize the way labor markets and vocational training systems shape differences in innovation style across nations.

In Jensen et al. (2007) we documented the existence of two different modes of learning and innovation in Danish firms. One, the DUI (doing, using, and interacting) mode, is based on informal processes of learning and experience-based know-how. The other, the STI (science, technology, and innovation) mode, is based on the production and use of codified scientific or technical knowledge. We also showed that firms which were able to combine these two modes (usually DUI firms that introduced elements of STI or STI firms that introduced elements of DUI) were more innovative than firms which relied on only one of the two modes.

The complexities of where and why economic growth occurs nowadays are difficult to pin down, especially by the use of growth models that are not adequate for dealing with the exigencies of the knowledge economy. Hence the first task is to seek to show how ‘knowledge economy’ conventions create different and distinctive demands of people and places from the prominent — century-long and more — effects of industrialization and what came to be called the ‘Industrial Age.’ It is one of the surprises to many observers of the rise of globalized, web-based communication technologies that work and communities have not spread out as a result of the growth of the information society and its attendant ‘footloose’ locational potential for people and jobs. Rather, as globalization has proceeded, regions have become more prominent economic governance actors than they were, because many have evolved science- and technology-based (and creative) clusters requiring elements of localized policy support.

This chapter introduces the concept of combined and complex innovation (CCI). The concept intends to describe complex innovation processes in regional clusters, where different kinds of knowledge are combined in innovation activities. The combination occurs inside firms and in collaboration between firms and knowledge organizations found in and beyond the regional cluster. The conceptualization of the CCI mode builds on main arguments in the regional innovation system (RIS) literature, which underline that complex innovation processes most often include collaboration among many different actors (such as different kinds of firm and research organization) in order to solve technological, organizational, and other challenges. Different kinds of knowledge are used and combined in innovation processes, and collaboration and knowledge flow are stimulated by geographical and other types of proximity (Boschma, 2005). The combination of the different types of knowledge is, thus, a complex process as it includes actors in different firms and organizations, who need to develop cognitive proximity.

In the last decades cluster initiatives¹ and cluster policy have become central features of policy promoting growth at regional, national, and European level. Many regional and national government’s policies aim at imitating successful clusters in the belief that their local areas may also capture the benefits of new high-technology firm formation and expected economic growth (Cooke, 2001a; Feldman et al., 2005) despite the fact that there is little empirical evidence to support a rationale for such policy, as the link between clustering and economic performance remains under-studied (Stuart and Sorenson, 2003; Maine et al., 2010). Both academic models (Brenner, 2004) and a number of consultant-made guidelines, even guidebooks (such as DTI, 2004; Rosenfeld, 2002), have been developed to assist the policy decision process.² This promotion of high-tech clusters is not confined to urban areas but also often takes place in peripheral regions, such as in the example studied in this chapter, the possible development of a biomedical cluster in the region of North Jutland in Denmark.

The importance of clusters and spatial networks for economic development has been largely acknowledged in economic and business literature (see, for example, Porter, 1998; Martin and Sunley, 2003; Malmberg and Maskell, 2002). Empirical evidence shows that companies in clusters experience stronger growth and faster innovation than those outside clusters, and that clusters attract more start-ups than regions without clusters (Audretsch and Feldman, 1996; Baptista and Swann, 1998; Baptista, 2000; Klepper, 2007; Swann et al., 1998). These characteristics cause clusters to be considered a prerequisite for regional prosperity (Porter, 2003; Bathelt, 2001). Many policy-makers have also recognized this phenomenon and turned to policies based on cooperative networks (Aranguren et al., 2006).

In this chapter we would like to build on recent seminal, extensive work by Jensen et al. (2007), Arundel et al. (2007), and Lorenz and Valeyre (2007) on different modes of innovation and learning across firms and their production systems. On these bases we specify a particular taxonomy of firms based on the learning processes that operate within and across firms in specific local production systems. It is an important research objective as a detailed analysis of local production systems shows the extreme heterogeneity of firms within them, which needs to be taken into account to fully understand their innovation dynamics and to effectively promote their development processes (Boschma and Ter Wal, 2007). Complementarily, we also target the analysis of (nonlinear) sequences of development in the learning pattern of firms; this transcends the critical analysis of typologies, which may create too narrow and rigid boundaries to learning and innovation within firms. We propose the study of actual and foreseeable sequences as a means to giving dynamism to such taxonomy, and as a basis for the discussion of relevant implications for policy-making. With these objectives in mind, we ran a survey on a network of 25 firms associated to a local development forum, called Ezagutza Gunea, which aims to create a basis for joint learning processes based on a common understanding and identification of key issues and solutions to entrepreneurial, organizational, and territorial development problems.

There is no doubt about the fact that innovation is a key to economic growth (Freeman, 1994; Fagerberg, 1994, 2009) and that the development of advanced technologies is an important factor in the competitive advantage of a country, a region, or enterprise (Freeman, 1987; Porter, 1990). So it can be stated that the competitive strength of a state or firm depends, partially, on its innovation capabilities. In this chapter we analyze the improvement of such innovation capabilities in firms that have carried out publicly supported R&D projects. Therefore we study, first of all, the intensity of such learning effects based on internal projects and on the technology transfer flows between partners of the cooperative R&D projects. A second aspect of our analysis is the elaboration of the profile of the firms with higher or lower learning effects. This chapter points out that the firms with a low innovative level have a distinctly lower learning capability than the more innovative firms. Moreover, in some circumstances the firms with a free-rider behavior seem to show, at the same time, a less intensive learning effect. So the main conclusions of this chapter are that the less innovative firms are trapped in a vicious circle and can reduce the technological gap with respect to other firms only by extraordinary efforts. For this reason the improvement of the learning capabilities of a firm should be an important goal of public intervention in the field of technological change and R&D; to initiate a ‘catching-up’ process in the less innovative firms, financial support instruments should be combined with technical support and intensive consultancy services.

Innovation is increasingly regarded as one of the key engines of economic growth and prosperity (Lundvall, 1992; Nelson, 1992; Nelson and Rosenberg, 1993; Verspagen, 1995). One of the most relevant levels for the analysis and policy-making on innovation is the regional level (Lundvall and Borras, 1997) and the most influential strand of literature that deals with them is the regional innovation system (RIS) (Asheim and Coenen, 2005).

The role of universities in society has become a key area of debate in recent years, coinciding with movement towards a more globally integrated economy in which processes of learning and knowledge-generation are widely recognized as central to competitive advantage. Alongside the traditional functions of educating and conducting research, the importance of a broader interface between university and society has been emphasized in concepts such as ‘systems of innovation,’ the ‘triple helix of industry, government, and university’ and the ‘entrepreneurial university.’