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The Process of Institutionalization of Innovation and Production Ecosystems

Innovation and production ecosystems are emerging forms of the organization of economic activities. The abundance of research dedicated to this topic evidently shows that this is a relevant theme supposed to provide suitable answers for the issues faced by present-day societies in terms of innovation, growth and employment. The main feature of the analysis of localized innovation lies in the contributions made by several knowledge domains: geographic economics emphasizes the benefits of agglomeration economies, institutional economics outlines the path of local learning, studies focusing on governance underline the need to share responsibilities between private and public actors, and approaches based on social capital rely on proximity and the communication channels of tacit knowledge. Finally, the knowledge economy models the interactions required by the innovation process, whose intrinsic nature is prominently collective, open and built on dialog.

This chapter is situated at the interface of these different contributions. The process that leads to the institutionalization and sustainability of these types of organization relies on learning paths that may be hindered by several elements. In particular, the constraints that limit actors in the context of globalization and the impact of the choices made in terms of location immediately reveal the existence of obstacles and the flexible and adaptable characteristics of these ecosystems.

1.1. Technologies, rules and learning dynamics

These types of organization are in line with a long-term perspective. The effective development and use of technological assets requires investments that also affect other categories of assets, justifying a systematic approach that is too often ignored: human capital, transmission channels for knowledge directed at businesses of all sizes, intellectual property rights, industrial structure (well-organized product chains) and so on. By definition, diversity implies interaction between elements. We need to conceive public policies that can structure effective interactions between public and private actors in order to turn this range of assets into a system: university-industry collaborations, production of qualifications of different kinds, creation of data centers and so on. Within the context of radical innovation, sending new messages about the technological, economic and natural environment is supposed to bring about changes in the behaviors of individuals and organizations and, consequently, to modify individual and collective cognitions so as to turn them into new connected forms of organization.

Rather than relying on the preferences and predictions of economic agents, we have to admit that present-day challenges cannot be dealt with by the market forces: markets are blind and, even if they do not fail in Pareto's sense, they are unable to provide a renewed and qualitatively different vision of economic development [MAZ 14a]. More precisely, the signals of the market are limited in terms of their ability to guide technological-economic development. Economic development does not result from natural, exogenous and existing competitive advantages, but from an endogenous creation of new opportunities that lead us to define and establish new competitive edges [ROD 11]. Nonetheless, once a direction has been identified, the signals sent by the market affect the innovation rate.

A recent research work [POW 12] analyzed the appearance and transformation of ecosystems over time by using three types of arguments. First, the diversity of organizational forms suggests the existence of different selection environments and constitutes a repository rich enough to enable the emergence of practices, standards and rules. Second, the process whereby different organizations are assembled and connected requires the presence of an Anchor Tenant [AGR 03], whose role is not to compete or dictate, according to Powell et al. This actor is situated in such a position within the system of relationships that it can gain access to other actors, and it is acknowledged as legitimate enough to act as a catalyst, direct efforts toward collective action and facilitate the growth of common resources1. Therefore, we admit that not all actors are in the same position in terms of critical resources (influence, network of relationships, reputation) and legitimacy to promote and institutionalize new practices. This may be a university, a research organization, a private company and so on. Finally, taking part in multiple activities facilitates the transposition of ideas and models from one domain to another and creates new possibilities that lead the system toward recombination or a changeover.

This means that, leaving the creation of complementarities aside, we should attach the greatest importance to the mechanisms through which public and private actors interact. The diffusion of new practices belongs to learning dynamics structured in three phases:

• - framing. This phase involves elaborating new concepts (cognitive mechanism) and new representations of an activity, creating legitimacy and promoting agreement. In this context, laboratories (companies, universities) tend to direct their R&D efforts toward the formulation and hierarchization of problems rather than their solution. Complex problems require a theorization that needs an organizational environment favoring the exchange and recombination of knowledge [FEL 14b];
• - the resources and complementary actors involved in a process are combined by establishing new norms and professionalizing the actors in relation to the new dynamics;
• - the progressive coordination of the activities based on rules facilitates the creation of a network, organization of skills and adoption of good practices. This last aspect raises the issue of governance and, in particular, the question of sharing and using aggregate information.

The fact that innovative practices may be regarded as public goods within an ecosystem or, in other words, that the innovation made by an actor does not decrease the possibilities offered to the other actors implies that the collective performance is improved when information about these practices is shared. Even if we assume that this information is shared, nothing allows us to claim that there will be a convergence toward optimal practices [LAZ 11]. The type of learning needed in a changing environment is based on the idea that the actors of an ecosystem have multiple connections and a "limited attention span". If innovative practices can be easily observed, the individual ability to process information will be limited in relation to the quantity of information available. As the authors of this article point out, everything depends on visibility ("A can emulate B if and only if A observes what B is doing") and, consequently, on the nature of innovation [LAZ 11, p. 315]. Innovative practices can be more or less easily observed and, even when this is the case, they tend to spread without entailing the production of firmly established information about what is actually working well. Inter-organizational relationships are therefore necessary.

The creation and diffusion of innovative practices is summed up in Figure 1.1.

We distinguish between the R&D phase, the problem solving phase and the phase involving the implementation of the new practices, as they belong to opposed approaches [NIG 14]. As for research - and, more precisely, basic research - the laws of nature allow scientists to rely on known initial conditions (the causes) to reach an unknown result (or effect). On the contrary, when we deal with technology, the desired result is known, whereas the starting conditions (the specific configurations of the components) are unknown. A wide range of notions may lead to the desired result. Technological functions are imposed upon rather than part of a unique relationship between some causes and a result. More precisely, technology is produced by making choices about operational principles that will define the way it functions. As for radical innovations, operational principles are chosen at the top of the hierarchy. This choice concerns the definition and design of the project and expresses social choices and value judgments. On the contrary, incremental changes are often reduced to their technical dimension and concern lower levels of the hierarchy. Moreover, as Nightingale aptly pointed out, innovative practices integrate tacit knowledge, which plays the role of active integrator and is not involved in inference or deduction processes. This element makes it difficult to observe innovative practices. This remark is somehow comforting in terms of innovation: the decreasing complexity of a problem is proportional to an easier dissemination of information about practices and a higher chance for the forces of conformity to prevail over creativity. On the contrary, as Lazer and Bernstein pointed out, the increasing complexity of a problem is associated with a trickier dissemination of information due to its tacit nature, while the agents will be gradually led to explore more wildly and delve deeper into the field of the problems in order to innovate. The lack of visibility about the practices increases creativity to the detriment of conformity.

Figure 1.1. Path followed by innovative practices2

This approach to the problems leads us to wonder about the boundaries of the notion of national innovation system3 [LUN 92]. The...