Innovation Economics, Engineering and Management Handbook 1. Группа авторов
work (Metcalfe 1994). In the end, innovation policies are differentiated according to the very conceptualization of innovation: broad or narrow (Edquist 2004). It is interpreted as “narrow” when it is strictly related to science and technology and as “broad” when it encompasses all forms of ancillary policies (educational, social) that indirectly affect innovation processes.
Public institutions and organizations play a key role in generating and promoting innovation. In particular, they strengthen the competitive environment for firms on the international market (Dogson 2009). The development of an innovation policy involves taking into account the evolutionary, dynamic and interactive nature of innovation processes (Leitner et al. 2010). This nature, characterized by the diversity of interactions and collaborations and their intensity, is primarily a result of the conditions of policy definition, but above all of their implementation and evaluation. Secondly, it is a result of the social coordination of various institutions.
The “broad” definition of innovation policy is necessary to better integrate innovation dynamics. Beyond the conception of innovation by its purely technological appearance, the set of policy instruments that influences innovation in one way or another is considered. An innovation policy requires the capacity to coordinate policy choices between different territorial scales (the relative importance of these scales is contingent on the characteristics of economies and their political regimes) in specific areas or sectors. The objective of an innovation policy is to facilitate interactions between technology users, creating different learning situations that massify the mechanisms for imitating and disseminating technologies. The tool for implementing these innovation policies is the innovation system. Concept, approach, work, notion, instrument, object; the innovation system has been discussed both in academic and institutional circles since the late 1980s and as an economic policy instrument for comparing national technological performances (OECD 2002). But whatever the debates in this case, marking the flexibility of the concept according to the empirical field visited, the authors unanimously agree on the theoretical footprints on which the system is based. These are the evolutionary and institutionalist theories, the latter two references being complementary, within an economy based on understanding, learning and knowledge (Freeman 1988; Lundvall 1992; Nelson 1993). Evolutionary theory is based on the reasoning that innovation processes are dynamic, sequential, cumulative and irreversible (Dosi et al. 1988). Therefore, an innovation system never reaches an optimal stage and equilibrium, because learning processes are subject to continuous change, are undetermined and dependent on developmental pathways. Institutionalist theory, inspired by North (1990), shows the importance of institutions as benchmarks or guides for the functioning of these systems. The institutional structure of the economy creates a model of constraints and incentives that shape and channel the behavior of actors. In other words, institutions are the rules of the game of an innovation system.
As with public policy, innovation in the innovation system approach has been rapidly segmented, first of all by its geographical dimension. Indeed, the approach to innovation systems is broad and there are alternative frameworks (Gregersen and Johnson 1997):
– sectoral systems relating to a specific sector or technology (Malerba 2004);
– localized systems, built on spatial proximity and identifiable on several geographical levels, on a local, regional, national or global scale (Lundvall 1992).
For example, the local approach has been adopted in order to identify and understand the different forms of territorialized productive organizations (Courlet 2001). The national approach is based more on socio-economic, cultural and historical elements than the local approach (Chaminade et al. 2018). The processes of creating, disseminating and/or absorbing knowledge from locally produced or imported technologies depend on the institutions, organizations and actors that influence the learning capacity. These actors create a permanent climate of evaluation and criticism of existing processes, making the territory in which they operate more effective. This is the challenge of innovation ecosystems, including processes of learning and routines (Adner 2006; Boutillier et al. 2016; Laperche et al. 2019). These innovation platforms and ecosystems represent a lever for the development of new markets and a new semantic grid of innovation networks.
Usually, it is the field of empirical analysis that defines the boundaries of the system at the conceptual level. In other words, the innovation system is given a specific name appropriate to the objective and context analyzed (Edquist 1997). The national framework is a natural delimitation of innovation systems, although their international dimension has been recognized for many years (Lundvall 1988) and a number of works in innovation economics analyze the way national actors use and absorb knowledge from outside (Casadella and Uzunidis 2017). The segmentation of the approach also aims at conceptualizing innovation in a broad and a narrow approach (Mytelka and Smith 2001). The main strength of a “narrow” national innovation system (NIS) lies in the analysis of the impact of national technology policies on the innovative behavior of firms. This NIS includes only those organizations and institutions necessary for research and exploration activities, such as R&D departments, technological institutes and universities. The broad definition includes, by indexing the components of the narrow NIS, all the political, social, economic and cultural institutions affecting learning, research and exploration activities: the financial system, monetary policies, the internal organization of private firms, the primary and secondary education system, the labor market, etc. At a more microeconomic level, the strength of the NIS lies more in the efficiency of business networks (large and small), in the various interactive learning practices within purchasing, production and sales activities, than in actual R&D activities.
Today, NISs are maintaining increasingly complex collaborative relationships across national boundaries and are more adaptive and scalable (Binz and Truffer 2017). Similarly, they are more inclusive systems where frugal innovation and local knowledge play a key role. The effect of inclusiveness at the heart of NIS is an emerging field of research that comes into its full legitimacy with an understanding of bottom-up dynamics, particularly in developing or transition countries (Chaminade et al. 2018).
Choosing the best tools to implement innovation policies requires an understanding of innovation financing. This financing is problematic, complex and must be understood as the right balance between relevant levers and regulated, controlled spending. Governments therefore have a role to play in proposing coherent and appropriate tools.
Many tools have been implemented to attract innovators to the desired territories. For example, in France, two main types of support have been developed: direct support, in the form of subsidies or national programs, and indirect support, in the form of incentives (particularly tax incentives). In the 1990s, the R&D subsidy policy was oriented towards “Major Programs” dominated by large companies, towards research sectors such as aeronautics, aerospace, nuclear energy and NICTs, as well as, in a more original way, towards support for R&D networks using national or international tools. Gradually, indirect R&D support has taken precedence over direct intervention. They include all those that do not give rise to direct payments to firms from public bodies, but which modify their environment and investment opportunities (Lhuillery et al. 2003). In France, the Crédit d’impôt recherche (CIR – Research Tax Credit) is the major innovation support mechanism (Liu 2013). It is an incentive measure allowing a tax reduction calculated on the basis of R&D expenses incurred by companies. With its last major reform, in 2008, this measure, which is very popular with businesses, became the main tax expenditure of the Ministry of Higher Education, Research and Innovation with a cost of 6.2 billion euros for 2019 (Sénat 2019).
The French system seems to be a judicious one (France Stratégie 2016) and other countries have also followed this path. Canada, for example, proposes a fairly similar program through the SR&ED (Scientific Research and Experimental Development) tax credit. It is a federal tax incentive program designed to encourage Canadian companies to carry out R&D in this country. It is the same with the United Kingdom and its “R&D tax relief”. Spain also uses similar designations: its R&D and Innovation Tax Credit, with the parallel implementation of national and international subsidies for R&D, reductions in social security contributions for R&D personnel or patent boxes for reduced costs related to intellectual