United States in the 1960s and 1970s. The phenomenon of hackerspaces became global in the 1980s, and was not limited to developed countries.
In this same trajectory, another phenomenon appeared at the beginning of the 2000s in California, participating in the development of the entrepreneurial society, that of coworking, which is also referred to as the “third place” (Oldenburg 1989). Coworking or collaborative workspace has its origins both in the recent evolution of digital technologies (Lallement 2015; Berrebi-Hoffman et al. 2018) and in the desire of many employees to escape the interventionism of the managerial enterprise with its oppressive rules. The aim is to help “coworkers” develop their creativity and imagination in the absence of any authoritative or hierarchical relationships. Although these coworking spaces bring together a wide variety of people in different situations, they generally aim to offer good working conditions in terms of autonomy of organization, and even conviviality. Coworkers are not subject to working hours. They are not salaried employees but entrepreneurs (or even self-employed), masters of their working hours, but rarely of their income, as many of them work under precarious conditions (Hill 2015). The entrepreneur is plural, competition is severe: many are called, but few are chosen (Aldrich 2011).
The genesis of innovation is thus both a multiple phenomenon and the result of the involvement of a growing number of organizations and economic agents, and not only the expression of a heroic entrepreneur. The recognition of this collective nature of innovation has a strong impact on innovation policies, which increasingly target the functioning of innovation systems at different scales.
1.5. Innovation policies and the innovation system
The term “innovation policy” is relatively new. Initiated in the 1960s by the SPRU (Science Policy Research Unit), it was quickly reappropriated by international organizations in the 1990s (Fagerberg et al. 2011). This evolution is part of a process of major economic transformations that occurred after the end of World War II and accompanied the development of the managerial enterprise. It is linked to the understanding of the innovation phenomena. Indeed, if science policies have been oriented towards the creation of academic knowledge, the training of scientists and fundamental research, technology policies are more related to the commercialization or non-academic valorization of scientific knowledge. In this context, innovation policies have often been reduced to activities related to R&D and scientific activity. However, public innovation management policy is compartmentalized into three broad categories (Sharif 2006):
– Science policy, inherent in the promotion and production of scientific knowledge, representing the allocation of resources between different scientific activities.
– Technology policy, which is based more on technologies used, developed and which are strategic within the economy.
– Finally, innovation policy, which is similar to innovation processes as a whole, based not only on the content of scientific and technical innovations but also on the institutional structure of the economy.
The role of the State in the management of an innovation policy is indeed to design institutional arrangements that allow support for innovation activities and the application of basic research. However, this must be done without compromising the selection of projects or the evaluation of research 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
