This chapter is about the tension between two ideal type modes of learning and innovation. One mode is based on the production and use of codified scientific and technical knowledge namely Science, Technology and Innovation (STI) mode, while the other one is an experience-based mode of learning through Doing, Using and Interacting (DUI-mode). At the level of the firm, this tension may be seen in the need to reconcile knowledge management strategies prescribing the use of Information and Communication Technologies (ICT) as tools for codifying and sharing knowledge with strategies emphasizing the role played by informal communication and communities of practice in mobilizing tacit knowledge for problem solving and learning.
The tension between the STI-and DUI-modes corresponds to two different approaches to national innovation systems: One perspective focusing on the role of formal processes of R&D that produce explicit and codified knowledge and another perspective focusing on the learning from informal interaction within and between organizations resulting in competence building often with tacit elements.
There is, of course, an important body of empirical and historical work showing that both these modes of learning and innovation play a role in most sectors, the role being different depending on the sector characteristics as well as the strategy of the firm (von Hippel 1976; Rothwell 1977; Rosenberg 1982; Pavitt 1984). Recent models of innovation emphasize that innovation is an interactive process in which firms interact both with customers and suppliers and with knowledge institutions (Freeman 1986; Kline and Rosenberg 1986; Lundvall 1988; Vinding 2002).
Despite the broad acceptance of this literature, there remains a bias among scholars and policymakers to consider innovation processes largely as aspects connected to formal processes of R&D, especially in the science-based industries. At the policy level, this can be seen in the emphasis on benchmarking variables related to STI and in the focus on instruments such as tax subsidies to R&D, the training of scientists in high-tech fields such as ICT, bio and nanotechnology and strengthening the linkages between firms and universities in these specific fields. At the level of scholarly research, there is a tendency to expect that the increasing reliance on science and technology in the ‘knowledge-based economy’ will enhance the role played by formal processes of R&D requiring personnel with formal science and technology qualifications.
Our juxtaposition of the extreme X-society and the extreme Y-society should have demonstrated fully that such purely competitive or purely planned societies never have and never will exist. But this is not all: we have also tried to show that both these types of societies are incomprehensible as logical constructs.
One of the most fundamental and recurring discourses in political economy has concerned how society should be instituted in terms of self-organized markets versus conscious government regulations, intervention and planning. In this chapter we join this discourse from a quite specific and original perspective. If we take it seriously that knowledge is the most fundamental resource in our contemporary economy and that learning is therefore the most important process, what are the implications for the institutional set-up of the economy? And what are the implications for economic theory? What are the consequences for the plan / market discourse?
The institutional set-up of modern capitalism may be analysed from two perspectives: what is and what should be. In this chapter we primarily present reflections on what is from the perspective of how it affects the use of knowledge and learning. Starting from assumptions regarding the character of knowledge, learning and innovation, we end up by supporting the stance of the Swedish institutionalist economist Johan Åkerman quoted above. The learning economy is, and must be, ‘a mixed economy’, and it is mixed in a much more fundamental sense than normally assumed.
We cannot totally avoid the discussion of what ‘should be’, however. Specifically we conclude that many of the most common arguments for and against the free market are either mistaken or one sided. There is actually a need to reopen the old discourse on a totally new basis: How does the market mechanism allocate knowledge and how does it affect learning? This is especially interesting in a period characterized by post-F ordism in the West and by postsocialism in the East. The need for open mindedness about how to organize the economy is greater than ever, while adherents to mainstream economics seem to believe that the breakdown of socialism marked the final victory of pure capitalist principles.
This book is about the economics of innovation and knowledge. One of the major conclusions drawn is that the perspectives standard economics imposes on society are biased, incomplete and inadequate. The focus on rational choice, allocation of scarce resources and equilibrium only captures some dimensions of the modern economy, notably short-term and static ones. Alternative perspectives, in which the focus is on learning as an interactive process and on processes of innovation, give visibility and direct attention to other, at least equally important and more dynamic, dimensions.
Social science is about human action and interaction, and it differs from natural science in several respects. It does not have access to laboratories where it is possible to organize controlled experiments. In spite of this, standard economics has gone far in adopting criteria and ideals from natural science, more precisely ideals that originate from Newtonian physics. This is reflected in standard economists’ conception of equilibrium as an ideal reference state and their tendency to focus exclusively on quantitative relations, also paired with in its excessive use of mathematics.
In this book, I insist that economics should remain a social science while also taking into account the complexity of the strivings and hopes of human beings. People cannot be reduced to algorithms or automatons. The basic assumption about rational behaviour in economic models (in which individuals and firms act as if they know everything about the future) is absurd and leads to equally absurd conclusions and to dubious policy recommendations.
Taking a departure from more realistic assumptions about how and why people act as they do in society has implications for what constitutes a theory in social science. In social science, a theory should be regarded as a focusing device – no more and no less. This book presents two sets of theories or focusing devices – the innovation system and the learning economy – that differ from those used in standard economics. These alternative focusing devices help us to see the core institutions in the economy (such as the market, the competition regime, the firm, the law, etc.) in a different light than that cast by mainstream economic theory.
This chapter focuses on the interactive nature of the process of innovation. The analysis takes as its points of departure two important characteristics of an industrial economy: the highly developed vertical division of labour and the ubiquitous and all-pervasive character of innovative activities. It analyses the implications from the fact that a substantial part of innovative activities takes place in units separated from the potential users of the innovations.
Here we shall argue that the separation of users from producers in the process of innovation, being ‘a stylized fact’ of a modern industrial society (capitalist or socialist), has important implications for economic theory. When we focus on innovation as an interactive process, the theoretical and practical problems tend to present themselves differently than in mainstream economic theory.
The interactive aspects of the process of innovation can be studied at different levels of aggregation. First, we discuss the ‘microeconomics of interaction’. Second, we present some preliminary ideas on how the understanding of a national system of innovation can be developed.
The Micro-Foundation: Interaction between Users and Producers
In standard microeconomics the agents – firms and consumers – are assumed to behave as maximizers of profits and utility. Perfect competition with numerous buyers and sellers, and the flow of information connecting them encompassing nothing but price signals, is the normative and analytical point of reference of the theory. Monopolistic structures and complex client relationships are regarded as deviations from this normal and ideal state.
The kind of ‘microeconomics’ to be presented here is quite different. While traditional microeconomics tends to focus on decisions made on the basis of a given amount of information, we shall focus on a process of learning, permanently changing the amount and kind of information at the disposal of the actors. While standard economics tends to regard optimality in the allocation of a given set of use values as the economic problem, par preference, we shall focus on the capability of an economy to produce and diffuse use values with new characteristics. Moreover, while standard economics takes an atomistic view of the economy, we shall focus on the systemic interdependence between formally independent economic subjects.
The purpose of this chapter is to demonstrate the usefulness of applying a user– producer perspective to innovation. A set of analytical and normative propositions – which are neither trivial nor conventional – is developed by focusing on the relationships and the interaction between users and producers of innovations.
The ideas presented here reflect a collective effort. Since 1977, the research program on Innovation, Knowledge and Economic dynamics (the IKE group), consisting mainly of economists but also attracting other social scientists and engineers, at the Department of Industrial Production, Aalborg University, has been working on problems relating to industrial development, international competitiveness and technical change. The approach has been heretic rather than mainstream and eclectic rather than dogmatic. It was developed partially by importing and borrowing from some different new schools with quite disparate origins.
One of the main imports came from France, where Francois Perroux and his followers have put great emphasis on the analysis of vertically organized systems of production. Another came from the United Kingdom, where Christopher Freeman and others at the Science Policy Research Unit (SPRU) have focused on industrial innovations. In Aalborg, a new combination has been tried. Innovative activities within vertically organized units, as verticals of production, industrial complexes and national systems of production, have been analysed.
The empirical work pursued so far should be regarded as exploratory. The hypotheses tested have been crude, reflecting a certain vagueness in the theoretical framework. This chapter represents a modest attempt towards a clarification. Empirical work from the IKE group will be referred to occasionally, but no comprehensive presentation will be attempted.
In developing the argument, I have leaned heavily on some central works by Nathan Rosenberg and Kenneth Arrow. Rosenberg's analysis (1972, 1976, 1982) of how users interact with producers in specific parts of the economy and under specific historical circumstances has helped to clarify many of the problems involved. Arrow's works (1962, 1969, 1973) on uncertainty and organization theory have inspired essential parts of the conceptual framework.
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).
This book brings together 12 papers written over a period of 30 years (1985–2015). I have added an introduction (Chapter 1) where I indicate the context of the papers and their relationship to each other and an essayistic Postscript (Chapter 14) where I reflect upon normative implications.
Anyone who reads the book as a whole will experience some repetition. This reflects ‘self-citation’ and that the papers appear in their original form. This means, however, that each chapter can be read separately. I am most grateful to Shagufta Haneef who helped me with preparing the manuscript and the editing.
Several of the papers have been co-authored, and I am grateful to Anthony Arundel, Antoine Valeyre, Björn Johnson, Edward Lorenz, Morten Berg Jensen, Rasmus Lema and Shulin Gu for their collaboration and for permissions to republish those papers in this volume.
I am in intellectual debt to many other scholars who have given inspiration to my work. Thanks first to colleagues in the research group on Innovation, Knowledge and Economic Dynamics (IKE group) at Aalborg University, particularly Asger Brændgaard, Bent Dalum, Birgitte Gregersen, Björn Johnson, Esben Sloth Andersen, Gert Villumsen, Jan Fagerberg, Jesper Lindgaard Christensen and many others.
Since 1984, I have become increasingly involved in collaboration with scholars from outside Denmark. I have benefited from cooperation in European projects with Alice Lam, Daniele Archibugi, Edward Lorenz, Giovanni Dosi, Luc Soete, Maria Jao Rodrigues, Mark Tomlinson, Susana Borras and many others.
In the new millennium, I learnt a lot on how to link innovation to development from Globelics colleagues Jose Cassiolato, Judith Sutz, Gabriela Dutrrenit, K. J. Joseph, Keun Lee, Rajah Rasiah, Shulin Gu, Anna Kingiri, Bitrina Diaymett, Mammo Muchie and many others. One of the messages in this book is that apprenticeship learning is important in all domains of knowledge, including research. I have had three ‘masters’ who have inspired my work: Lars Herlitz, Richard R. Nelson and Christopher Freeman.
For the title of this book, I have borrowed the concept ‘the economics of hope’ from Freeman's 1993 book. Christopher Freeman was an intellectual giant who not only called for a better world but also, as a scholar and a world citizen, made contributions to make it a reality.
This chapter addresses global issues regarded through the focusing device of ‘the learning economy’. The form is brief and essayistic. The chapter begins with reflections on the basic concepts and their roots. With reference to the three chapters on Europe, China and Africa (chapter 10, 11 and 12), it is shown that while problems and opportunities are context specific, they often originate from developments in another region. On this basis, the essay points to the need for new forms of global governance that can promote learning worldwide. It ends with some ideas for a research agenda.
The Economics of Hope
The economics of hope alludes to a book with this title bringing together articles written by Christopher Freeman (1992). The essays cover topics related to science policy, innovation and competitiveness linking science and technology to broader social and environmental issues. They are critical to the dominant paradigm in economics and to public policy, but they combine criticism with constructive ideas about where to go. Freeman was critical of how modern capitalism produced inequality and exploited natural resources. He was, however, equally critical of dystopian perspectives where current negative trends were projected into the future and ending in catastrophic scenarios.
The perspective presented in this book is somewhat different. Freeman's starting point was science and science policy, and his most important reference was to the Marxist physicist J. D. Bernal who established analytical links between science and society. Both Freeman and Bernal built their conditional optimism on the assumption that science and technology has a lot to offer in terms of solutions to the world's problems, if the institutional setting allowed it to serve society. In this book the starting point is the learning economy, where human interaction and learning at different levels spanning from the organizations, the regions and the nations shape what is happening in the world. Freeman's conditional optimism was based on the potential that science-based learning could offer, while this book broadens the perspective and gives more attention to the potential of experience-based learning.
The Learning Economy
The learning economy concept has three dimensions – it is normative as well as descriptive and analytical. First, the concept describes characteristics of the current economy where the capacity and the opportunity to learn are crucial for economic performance.
Recent press reports suggest that Africa may now be at a turning point in terms of economic growth and development. These reports point out that, although starting from a low base, Africa is now the world's fastest growing continent. However, naive optimism on this ground should be avoided (Karuri-Sebina et al. 2012). The recent growth has been concentrated in particular countries and sectors and the transformation of growth into sustainable social and economic progress will not happen automatically.
There is thus a discrepancy between the reporting of record growth rates for African economies in media and the reality of how people's living conditions have evolved over the last decade in the African high growth economies. The widely shared understanding among development scholars that registered economic growth and development must be seen as two distinct, even if related, processes has become more evident than ever. In this chapter, we will argue that in order to transform the economic upswing as measured by gross domestic product, fast-growing African countries need structural and institutional change across the economic, social and political spheres that bring them closer to what we will refer to as ‘learning economies’.
The widening of the gap between reality on the ground and perceptions based on growth rates reflects partly that the increasing global demand for natural resources – especially for commodities such as oil and minerals – has led to advantageous change in terms of trade, to increased export volumes and raised the rates of GNP growth while the impact on domestic employment has often been limited and sometimes negative. The expansion of the commodity sector does not automatically create large-scale employment directly and so far it has rarely resulted in a substantial increase in job creation in upstream and downstream manufacturing and in knowledge-based services.
It has even been argued that the structural change that occurred in lowincome economies with high rates of growth had a negative impact on the potential for future aggregate economic growth (McMillan and Rodrik 2011).
When the first edition of this book was published in 1992, the concept of ‘national innovation system’ was known only by a handful of scholars and policymakers. Over a period of 15 years, there has been a rapid and wide diffusion of the concept. Giving ‘Google’ the text strings ‘national innovation system(s)’ and ‘national system(s) of innovation’ you end up with almost one million references. Going through the references, you find that most of them are recent and that many of them are related to innovation policy efforts at the national level while others refer to new contributions in social science.
Using Google Scholar (May 2007), we find that more than 2,000 scientific publications have referred to the different editions of Lundvall (1992). Economists, business economists, economic historians, sociologists, political scientists and especially economic geographers have utilized the concept to explain and understand phenomena related to innovation and competence building.
In this chapter we argue that during the process of diffusion there has been a distortion of the concept as compared to the original versions as developed by Christopher Freeman and the IKE group in Aalborg. Often policymakers and scholars have applied a narrow understanding of the concept and this has given rise to so-called ‘innovation paradoxes’, which leave significant elements of innovation-based economic performance unexplained.
Such a bias is reflected in studies of innovation that focus on science-based innovation and on the formal technological infrastructure and in policies aiming almost exclusively at stimulating R&D efforts in hi-tech sectors.
Without a broad definition of the national innovation system encompassing individual, organizational and interorganizational learning, it is impossible to establish the link from innovation to economic growth. A double focus is needed where attention is given not only to the science infrastructure but also to institutions/ organizations that support competence building in labour markets, education and working life. This is especially important in the current era of the globalizing learning economy (Lundvall and Johnson 1994; Lundvall and Borras 1998; Archibugi and Lundvall 2001).
Today the term national innovation system appears in several different domains within social science and engineering, and it is widely used in policy circles all over the world. The concept reflects an assumption that the pattern of innovation differs across countries and that such differences can be explained by systemic features. The components of the innovation system are different, and they are linked differently to each other and such differences in economic structure and institutional set up are reflected in the rate and direction of innovation.
We will take as starting point ideas presented in the very first contributions that made use of the innovation system concept, Freeman (1982) and Lundvall (1985). There is some overlap between them but the perspectives are quite different. Freeman's analysis refers to macro-phenomena and to international trade and development, while Lundvall (1985) refers to the micro level where innovation is seen as shaped by user– producer relationships. We will argue that they are complementary and that they can be used to span and dissect important themes in the more recent literature on innovation systems and global value chains.
The concept national innovation system may be seen as a new combination of two different perspectives, one developed within the IKE group at Aalborg University and one developed at Science Policy Research Unit (SPRU) at Sussex University. The concept came out of bringing together an understanding of innovation as rooted in the production system (Aalborg) and an understanding of innovation as rooted in the science and technology system (Sussex).
The Aalborg approach was inspired by the concept ‘national production systems’ as it was used by French Marxist structuralists such as Palloix (1969) and de Bernis (1968). Esben Sloth Andersen (1992) criticized and developed these ideas by introducing an evolutionary perspective with focus on innovation with the aim to overcome the limitations of what he saw as a too static framework. Another important inspiration for the Aalborg group's work on innovation systems came from Björn Johnson (1992) who linked innovation and learning to the socioeconomic characteristics of national institutions. Lundvall (1985) took inspiration from early works by Andersen and Johnson while studying user– producer interfaces as reflecting economic structure as well as institutional characteristics.
In this chapter we present a conceptual framework to analyse knowledge and learning from an economic perspective. The starting point is the assumption that we are in a knowledge-based economy, but we conclude by proposing that it is more adequate to characterize the current era as ‘a learning economy’. Crucial issues analysed here are distinctions between private/ public, local/ global and tacit/ codified knowledge. While appearing ‘academic’ at first sight, these distinctions have important implications both for innovation policy and for the management of innovation and knowledge at the level of the firm.
It has become commonplace among policymakers to refer to the current period as characterized by a knowledge-based economy, and increasingly it is emphasized that the most promising strategy for economic growth is one aiming at strengthening the knowledge base of the economy. This discourse raises a number of unresolved analytical issues. What constitutes the knowledge base? At what level can we locate and define a knowledge base? What are the specificities of local-and sector-specific knowledge bases? How stable is the knowledge base? In order to approach an answer to these questions, three different themes are introduced: first, basic concepts related to knowledge and learning; second, the contribution of economic analysis to the understanding of the production, mediation and use of knowledge; and third, new economic trends and the formation of a learning economy.
A Terminology of Knowledge
Is knowledge a public or a private good?
Sidney Winter concluded his seminal paper on knowledge and management strategy by pointing out that there is ‘a paucity of language’ and ‘a serious dearth of appropriate terminology and conceptual schemes’ for analysing the role of knowledge in the economy (1987). Since then, the number of relevant publications has grown immensely but little headway has been made in terms of a terminology acceptable to all. There is little agreement on questions such as: What is the meaning of knowledge and knowledge production? What separations and distinctions between different kinds of knowledge are most useful for understanding the interaction between learning, knowledge and economic development?
While the Eurozone was originally designed to protect member countries from economic instability, it has now turned into a major source of instability for the world as a whole. Currently European leaders bring Europe ahead in the direction of a European Federation not because it is part of their vision, but because it seems to be the only way to avoid triggering a global depression.
When the Eurozone was established there were warning voices that a monetary union without a common fiscal policy would be vulnerable to external shocks. The total budget of the EU constitutes only a few percent of the total GNP for member states and therefore it cannot play the same role as the federal budget in the United States as automatic stabilizer. This is especially problematic for a currency union bringing together countries at very different levels of economic development. There were elements in the Lisbon Strategy that could have reduced the gaps between Northern and Southern Europe. But the turn towards more neoliberal solutions that took place around 2005 undermined its capacity to function as a scaffold for the Eurozone (Lundvall and Lorenz 2011).
In this chapter I show that the countries in the Eurozone now most exposed to financial speculation are the ones that have the weakest industrial structure with the biggest proportion of workplaces directly exposed to competition with emerging economies. On this background I will argue that, standing alone, neither Austrian austerity nor Keynesian policies can help establish a sustainable Eurozone. There is a need to design Keynesian policies coordinated at the European level in such a way that they promote deep institutional change in education, labour market and industrial policy in Southern Europe.
Public expenditure needs to be allocated to stimulate the learning capacity where it is weakest – this is why the solution may be referred to as a ‘new new deal’. It is about redistributing learning capacities.
Innovation and the Division of Labour
The following analysis builds upon a simple theoretical model linking to each other ‘innovation as an interactive process’ and the dynamics of the division of labour (Lundvall 2006). According to Adam Smith the extension and deepening of the division of labour is the major mechanism behind economic growth.
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