1. Introduction

Global restructuring has dramatically restricted the freedom of nation states in designing public policies, while regions are playing new roles in terms of governance and intervention in order to promote learning, innovation, productivity and economic performance at the local level.

The capacity of both individuals and organizations to successfully engage in learning processes has come to be regarded as a crucial determinant of economic performance. The globalization of economic processes has, in fact, increased learning opportunities. This does not imply the disappearance of differences among regions. On the contrary, various types of learning are implicated in the complex interaction between global and local processes. Such ‘learning regions’ are well attuned to the requirements of the new learning economy and may be fostered through the development of appropriate strategies of public governance and intervention.

This monograph is designed for economists not yet familiar with the systems of innovation approach. Given the comprehensive and crucial macroeconomic consequences of innovation, this should also be of interest to policymakers dealing with economic growth and employment issues.

2. Synopsis

Economic growth experienced over the past two centuries needs to be understood as the result of the progressive introduction of new technologies associated with increasingly higher levels of worker productivity and the ability to produce new or improved goods and services. The huge divergence in long-term growth rates over the past two centuries must be attributed largely to the presence or absence of social capabilities for institutional change that facilitate and stimulate a high rate of technical change. Recent literature on national systems of innovation can be described as an attempt to come to terms more systematically with these problems of social capability for technical change.

The development of the notion of an innovation system has largely been the work of non-orthodox economists active in the development of evolutionary growth theory, who have been motivated with the belief that neoclassical growth theory is totally inadequate in its treatment of technological advances. In the late 1970s and early 1980s, there emerged a well-articulated evolutionary critique of neoclassicism, providing a coherent micro-based alternative to the dominant neoclassical paradigm, followed by a very substantial research program and literature during the past 20 years. The links between innovation theory and innovation policy have been part of an evolutionary learning process. This created a niche that permitted the development of a set of diverse and non-orthodox ideas, which pertain not only to the nature and determinants of innovation itself, but wider problems of institutional mismatches locking up the economic potential for technical change.

There has been a significant change in innovation-related policy arenas during the last 20 years. In terms of objectives, innovation policy has become a central instrument for achieving outcomes that lie well beyond the field of research and technology development. The concepts and instruments of policy have shifted, with non-linear models of innovation and the innovation system concept playing a central role in policy discourse, and with a wide range of new policy instruments directed at networking, clustering, and personnel mobility.

Innovating innovation policy is not easily accomplished. It usually requires significant investment of economic, social, and political capital; it will frequently encounter barriers and opposition from vested interests; and it is a risky process that does not always lead to desired outcomes. Despite these difficulties, efforts to modernize innovation institutions and policies continue. Such efforts are accelerating, though success remains elusive.

Innovation is the result of an effective knowledge management process and learning organizations are the locus of innovation processes. The firm is conceptualized as an organism that is able to acquire, develop and accumulate knowledge. Such knowledge is embedded in technologies, individual capabilities, and organizational routines. Organizational learning is interpreted as a process of development and acquisition of new knowledge necessary for solving organizational, manufacturing, marketing problems, and creating platforms for the development of new ideas.

Complex technologies are increasingly being innovated by self-organizing networks. Innovation networks are organized around constant learning, which create, acquire, and integrate the diverse knowledge and skills required for creating complex technologies and bringing them to the market. Self-organizing innovation networks and globalization may be co-evolving. Changes in the organization of the innovation process seem to have facilitated the broadening geographical linkages of products, processes and markets. At the same time, globalization seems to induce cooperation among innovative organizations.

A major change in the US national innovation system has been the rapid increase in strategic research partnerships (SRPs), involving firms, universities, non-profit organizations, federal research laboratories, and public agencies. The growth in SRPs can be attributed in part to several bipartisan policy initiatives. These policy changes include an expansion of programs to support public-private technology partnerships, a relaxation of antitrust enforcement to promote cooperative research and the adoption of various initiatives to promote more rapid diffusion of technologies from universities and federal laboratories.

As the pace of technological change has accelerated and global competition has intensified, large centralized bureaucracies emphasizing functional specialization have given way to smaller, leaner organizations in which team-based structures cross functional lines, disrupt traditional hierarchical chains of command, and focus on core functions while contracting with outside firms for other tasks. As an adjunct to internal restructuring, large manufacturers have turned to long-term external supplier relationships for many inputs to the production process as well as a variety of operational and administrative functions. Thus, specialized technological knowledge and innovation increasingly reside in small and medium suppliers whose R&D takes place in a team-based configuration on the shop floor rather than in corporate laboratories staffed with scientists working on long-range basic research.

In order to leverage the benefits of cooperation, inter-organizational linkages have been formed horizontally among similar firms in associations, vertically in supply chains, and with multi-directional links to sources of technical knowledge, human resources, and public agencies. The contribution to the institutional effectiveness of these relationships measured in terms of economic performance and innovative capacity is referred to as social capital. This form of social capital, as powerful as physical and human capital, is the stock that is created when a group of organizations develops the ability to work together for mutual productive gains.
Social capital refers to features of social organization (such as networks, norms, and trust) that facilitate the coordination and cooperation for human benefit. The notion of social capital extends our understanding of cooperation or collaboration in two significant ways: 1) Linking cooperation to the economic concept capital signals to the investment or growth potential of a group’s ability to work jointly; 2) The concept identifies the structure created from collaborative efforts as capital.

The notion of a sectoral innovation system (SIS) provides the integrated and dynamic view of sectors. The main advantage of the SIS approach is a better understanding of the factors at the base of differential performances of firms and regions in a sector. It provides decisionmakers with a taxonomy that may help them to avoid the trap of generalizing policies without taking sector specificities into account.

The innovation system approach has been diversified by studies that recognized the evolution of autonomous systems of innovation at the local, regional, continental, and global levels. The dominance of national institutions is called into question, as institutions at territorial levels below and beyond the nation states become increasingly important for innovative processes. A multi-level approach directs the dynamic reconfiguration of NIS towards the sub-national as well as international level.

In the last two decades of the 20th century, the system of governance for science – the web of institutions that shape the incentives, social norms, and priority of scientific research – has shifted away from being state-dominated to a more distributed model. The former emerged after the Second World War and prevailed until the 1980s. Based on the linear model, it included a dominant role of state support for science and the separation of the scientific community from the rest of the society. The drawing of scientific and technological research into closer interaction suggests an alternative model for the public science system based upon a distributed model of governance, which is in the process of displacing the state-dominated system.

The first academic revolution took off in the late 19th century, by making research a university function in addition to the traditional task of teaching. A second academic revolution then transformed the university into a teaching, research and economic development enterprise. This transition initially took place with respect to industry at MIT. The entrepreneurial academic model was then transferred to Stanford in the early and mid-20th century. An entrepreneurial academic format is currently being fashioned from a variety of historic university systems.

Given that any research collaboration requires the definition of a common research topic, science-industry collaborations may imply significant joint costs and benefits that academic partners integrate into their calculations. The nature of collaborative research may vary considerably according to the characteristics and strategies of the partners involved. In the process of matching academic laboratories and firms R&D funds, the partners may take into account the set of various strategic considerations for preferably interacting with one another.

Recent studies of technological specialization emphasized the continuing diversity of national differences of various sectors and the strong connections between this diversity and variations in institutional frameworks. The funding and governance of public research organizations and the way of establishing and managing research programs continue to differ significantly among countries. These variations remain mostly national due to the dominant role of the state in funding and controlling institutions where publication-focused research has been conducted. National differences in the speed of developing radically novel technologies based on academic research appear especially marked in sectors such as information technology and biotechnology.

The role of the public science system in supporting the growth of new industries with innovative technologies has varied between countries. Two characteristics of public science systems – different levels of reputational competition and intellectual pluralism and flexibility – help to account for continuing differences in the rate at which public science systems produce highly novel intellectual innovations and deal with a variety of problems.

S&T policy actions derived from neoclassical and evolutionary frameworks are neither necessarily different nor antagonistic: Many policy actions are apparently common to both frameworks. However, much more detailed analyses would obviously reveal differences in the practical applications of these principles. This common policy action is justified on different grounds according to each framework as it is set up for different purposes.

Public-private partnerships are an integral element of the new paradigm in technology policy characterized by private sector and market-pull cooperative ventures rather than government-led technology push programs. For government, the benefits of partnerships between industry and universities, research institutes and public laboratories include higher social returns from the exploitation and commercialization of public R&D as well as a diversified source of funding and improved training of graduates. Besides reducing risk and cost sharing, partnerships can help firms access skills, monitor new developments, and undertake exploratory research in areas outside their core business. However, partnership policies and schemes should not be designed solely on the notion that cooperation between industry and public research is intrinsically good. Just as industry enters into public-private partnerships to achieve specific goals, government and public research institutions should also set clear goals and time horizons for inputs and outputs.

Technology foresight is defined as systematic attempts to look into the long-term future of science, technology, economy and society with a view to identifying emerging generic technologies likely to yield the greatest economic and/or social benefits. Its exercises need to be carried out at several levels, ranging from bodies responsible for the coordination of overall national S&T policy down to individual firms or research organizations. Some foresight exercises need to be holistic in scope, others more micro-level. Foresight activities at different levels should be fully integrated. Successful foresight involves counter-balancing intrinsic tensions: A balance between technology push and demand-pull; a balance between top-down and bottom-up approaches; allocation of responsibility for foresight among interested parties and a neutral third party (in funding, performing research, and exploiting the results).

Evaluation methods and practices have been developed alongside the evolution of technology and innovation policy and the understanding of the innovation process. Since the 1980s, demand for evaluation has been fueled by the desire to understand the effects of research and technology development (RTD) programs, to learn from the past, and to justify the continuation of such programs. Another important influence has been the growing tendency to associate science with competitive performance and the search for more effective ways to achieve university-industry linkages.

A critical issue in evaluation is the criteria to be used for judging programs and policies. The basic rationale for government initiatives to stimulate technological development in the first place is the market failure rationale. But the preponderance of government failure has forced evaluators to become more careful in accounting for costs of programs as well as for benefits, including the costs associated with distortions to economic incentives. Moreover, the realization that the benefits of individual programs or policies can often be understood only in the context of their impact on a complex innovation system has given rise to the notion of systemic failure as a basis for policy. It has also forced evaluators to recognize that identifying social benefits in diffusion policies involves a dynamic analysis that looks at the development of new capabilities.

The increasing globalization of economies must not be mistaken for the leveling of all regional characteristics. Corporate globalization strategies are meaningful only if national and regional differences exist and can be harnessed on a global scale. Regions will be reconstituted on a global scale – in instances this may arise only because a globally active corporation has observed a certain region in terms of its potentially exploitable economic advantages over other regions for present and future investments. This process also changes the challenges with which regional actors are confronted.

As economic activities are increasingly based on notions of collective learning and competition increasingly involves partnerships and interactive innovation, the regional level of economic organizations has become more important in the wake of the profound economic restructuring of the 1980s and 1990s. Furthermore, with the relative decline in national economic sovereignty, regional administrations are strengthening support policies for their industries, encouraging inter-firm interactions, and evaluating and monitoring clusters.

There are three key reasons for focusing on regional learning/innovation systems: Massive growth in the externalization of production of goods and services by corporations; the increasing specialization of regional economies as a consequence of externalization; and the regionalization of industrial policy, enterprise support, and promotion for inward investment.

It is important to differentiate regional learning systems from regional innovation systems. Learning is a first step to innovation. A regional learning system can be developed towards a regional innovation system when upstream (close to the point of origination of the invention) and downstream (near-market) innovation capability is integrated into regional industry.

Learning organizations in the regional system (the research laboratories and technology transfer agencies in particular) scan and sensitize the sources and users of innovation. Recently, a new function entered the scene, the reflexivity function – the process of monitoring and evaluating in complex ways the likely implications of the innovation for the regional system. In well-integrated regional systems, reflexivity operates as a means of system guidance. Regional administrations engage in this reflexive process.

Some regions have been strengthening their offensive development capacity since the early 1990s. By feedback loops, advanced regional organizations monitor, engage in reflexive behavior, learn, and implement actions appropriate to the task at hand through associational policy networks. Learning is the selection mechanism, by means of which innovation brings forth the mutation of one path-dependence for another. Such innovations are transmitted and transferred by imitation. Thus, the nexus of processes produced conditions in which economic performance came to depend more and more on a capacity to support and maintain externalized relationships among economic actors of different sizes and types. Such externalization is also accompanied by increasing specialization because of the growth of intra-industry trade or the locational attractions of specific geographic spaces.

Co-location is not enough to give agglomerations a sustained capacity to survive and prosper. An agglomeration may be vulnerable if they are too narrowly focused. If they evolve as relatively self-sufficient islands their risk is that of introversion, deafness to dissent, path-dependence, and lock-in associated with a failure to innovate that may be fatal. If based on small firms, they may be prey to acquisition, concentration, and external control. Localized learning can lead to lock-in and the danger of network and system degradation. Learning regions should be open, information-scanning, sensitizing, monitoring and evaluating systems. There is a distinction to be made between strategic learning and routine learning by firms. Strategic learning is bound almost by definition to be non-proximate where the source is elsewhere and unique. By contrast, in the case of routine learning, the absence of acculturation by the distant user to the nuances prevalent in the producer community is a barrier to successful take-up. This is mirrored in the introverted customer-supplier relationships.

3. Conclusion

The development of the learning economy involves a complexity of economic and social processes: It holds the promise of increased productivity and an improved standard of living, but also implies that individuals and organizations face major challenges in adjusting to new circumstances. The social challenge of globalization is inequality and social exclusion. The average worker has to perform new tasks and develop new skills. Workers become unemployed or secure low-paid employment, if they remain unskilled. Another challenge is sustainability. Innovation policies might contribute to sustainability and can resolve problems such as unsustainable production methods, consumption patterns, and the use of non-environmentally friendly technologies. Despite the uncertainty in policymaking, policy can be used to improve systems of innovation.
From a long-term perspective, the widening gap in a society develops mainly during the crises of structural adjustment, particularly in countries that are leading in technological innovation, and is associated with the unemployment, skill changes, and organizational changes in the production system. The reduction of inequality mainly occurs once a major new technology has become the dominant regime, the economy recovers to full or near-full employment, new skills are widely diffused and socialized, and new movements for social reform have addressed some of the main sources of conflict. Social capital – in terms of social networks, conventions, and norms – affects both individual and organizational learning. Regions need to be able to respond positively to emerging economic and social conditions, especially where this involves the ‘unlearning’ of inappropriate practices and bodies of knowledge left from ‘old’ regional institutions.

1. Economic Growth and Innovation System
　1.1 Research on Innovation System and Policy Learning
　1.2 Innovations and Learning
　1.3 Innovation Networks and Social Capital
　1.4 Sub-National Systems of Innovation

2. Science and Technology Policy
　2.1 The Public Science System
　2.2 Rationale for Science and Technology Policy
　2.3 Public-Private Partnerships and Technology Foresight
　2.4 Evaluation of Research and Technology Development Programs

3. Regional Innovation System
　3.1 Regions as Externalized Learning Institutions
　3.2 Exante evaluation of regional benefits of R&D projects
　3.3 Conditions for Clustering
　3.4 Learning Mechanism in Local Clusters
　3.5 Knowledge Translation Gap and the Intermediary Sector
　3.6 Organization Design for Technology Extension