6.3.1. Definition of collaboration

The definition of collaboration is not fixed [HU 08]. It is defined as “the coming together of diverse interests and people to achieve a common purpose via interactions, information sharing, and coordination of activities” [JAS 98]. Here, communication plays a key role in the “sharing of skills and resources” [MEL 00]. Amabile and colleagues reinforce these definitions by describing collaboration as “individuals who differ in notable ways sharing information and working toward a particular purpose” [AMA 01].

The heterogeneity of the actors leads to multiple collaborations. These are based on the expertise of the actors, their affiliations to a specific ecosystem and the expected results of the different phases of the innovation process. If the expectations of collaboration build a bridge between the different “complex entities of actors” [BRU 13] that seek to develop disruptive innovations, it affects their ability to agree and coordinate investments, developments and then commercialization [KAP 13] of the innovations they produce.

6.3.2. Expectations of collaboration

The expectations of collaboration of commercial ecosystem actors have largely been addressed in studies of collaboration performance in terms of innovation and economic returns to build sustainable competitive advantage. Conversely, collaboration between scientific and technological ecosystems, which is one of the drivers of innovation, has been the poor relation in innovation ecosystem studies [OH 16].

In view of the importance of these two ecosystems, our interest lies in the collaboration between university (as a scientific ecosystem) and industry (as a technological ecosystem). Studies on university–industry collaboration have identified expectations and many obstacles such as cultural differences [BJE 10] and facilitating factors by knowledge brokers [VIL 17]. They focus on formal mechanisms of scientific knowledge transfer, such as patents or licenses [CHA 16].

However, work remains to be done, particularly on the process of collaboration in an innovation ecosystem, on the factors that influence this collaboration and the mechanisms for creating the interdependencies that support it. Studies on creativity and innovation show that innovation opportunities arise from the combination of very different points of view [SEN 90].

We postulate that the innovation process is at the heart of collaboration in an innovation ecosystem because the actors of the three ecosystems work together to stimulate it. To do so, they mobilize the knowledge and material assets generated by the actors of these three ecosystems. They aim to achieve the objectives of the innovation ecosystem.

These common goals stimulate the desire for collaboration. These actors are looking for new knowledge, methods and processes that will help the innovation ecosystem achieve its goals. Table 6.1 presents the expectations of the actors in the context of collaboration.

Collaboration cannot be improvised. If these expectations are positive for the innovation process, collaboration is linked to difficulties that the actors will have to overcome for its implementation.

6.3.3. Barriers to collaboration

Collaboration between the different actors of the three ecosystems is characterized by “a cultural gap” [BÄC 15]. It is characterized, for example, by the divergent objectives of scientists, technologists and business people, their motivations and routines. Here, the decision-making process for carrying out this collaboration becomes more complex.

In line with the work of Amabile and colleagues, collaboration no longer focuses on the context of interorganizational collaboration [AMA 01]. Ecosystems create the context for this collaboration. This context is built on motivation, influencing factors and interdependencies. Some studies highlight the prior knowledge of the actors as a risk-reducing factor in collaboration, where trust is a mechanism that stimulates collaborative activities [BÄC 15].

Here, interpersonal skills are essential to overcome obstacles related to the specificities of heterogeneous actors. These skills allow for the detection of overly ambitious goals that may be difficult to achieve with the time and resources available [GUL 12]. Two major barriers hinder collaboration: competence limits and identity limit [SAN 05].

6.4.1. The emergence of “small worlds”

In the innovation ecosystem, without a centralized hierarchy led by a focal firm, actors come together in the form of three communities sharing a common vision, which is to achieve the objectives of the innovation ecosystem through the success of the phases of the innovation process: discovery, invention and innovation.

The limits of competence and identity show that collaboration can be complicated when the actors who cohabit the innovation ecosystem belong to different worlds. This situation, maintained by the cognitive frameworks of each community, means that actors, faced with new knowledge, will naturally interpret it according to their respective cognitive frameworks. Moreover, the diversity of the phases of the innovation process positions and maintains the actors in a specific community where they exploit the knowledge and skills of their small world to bring about discoveries, inventions or innovations.

The situation becomes more complicated when actors are looking for new knowledge and skills to advance this innovation process. It becomes even more complicated because some theories are difficult to apply due to the presence of the three ecosystems. For example, the theory of the product life cycle, which focuses on the stages of introduction, adaptation and obsolescence of the product, is not relevant to the innovation process [DAV 05].

Similarly, the theory of the central role of the user is not relevant in all phases of the innovation process. The user appears in the context of the development of the uses of the innovation. The situation is aggravated when, in a network involving many actors belonging to different communities, the actors do not manage to overcome the obstacles of collaboration.

Connections become difficult when it comes to interactions between actors of scientific, technological or business communities. They are weak, or even non-existent, due to the cultural gap that separates these communities. Here, the innovation process becomes a collaborative research process where new knowledge must be interpreted and assimilated in order to be integrated into the development of new assets.

Aware that innovation will succeed if everyone knows how to take advantage of the knowledge and resources coming from other communities, the actors develop networks, called “small worlds” [KAS 10], to overcome the limits of competence and identity that hinder collaboration. These small worlds are “human-sized” networks [KAS 10]. They characterize a specific community. They support innovation [FLE 06] because by definition they are short [KOG 01], facilitating connections between actors.

These networks consist of a set of nodes and connections [BRA 06]. Nodes are the actors in the innovation ecosystem. Connections are formed when one individual identifies the other as an actor with whom they can interact in the innovation process.

These small worlds facilitate the identification of individuals with strong relational capacities, which makes the nodes of the network more visible and quickly reachable. For some authors [WAT 03], these small networks appear spontaneously through a series of random connections [SCH 07] without any marked hierarchical connection.

In innovation ecosystems, the expectations of collaboration and the barriers that actors must overcome to collaborate mean that these small worlds are born from the intention of the actors. They take specific forms according to the needs of collaboration. Here, spontaneity and randomness have no place:

• – an interaction network emerges when actors are regularly in contact with each other;
• – an information network, often digitalized, is created when actors need to access different types of information;
• – a network of ideas is created when actors share ideas at all stages of the process;
• – a problem-solving network appears when actors encounter difficulties during the development of the innovation process.

All these small worlds improve the efficiency of the links between communities of actors who have different ways of interpreting new knowledge [KAS 10]. They interlock to form the iterative network of the innovation ecosystem. Through these small networks, actors have an easier time interacting and sharing their resources and knowledge.

These small worlds play a crucial role in the understanding and development of innovation [JAC 06] and the different stages of its process. However, these small worlds will be effective if the actors perceive the roles of interdependence, discussed in Chapter 5, and if they are able to create interdependencies that serve these small worlds.

6.4.2. Interdependencies and collaboration

Chapter 5 highlighted the co-existence, in the innovation ecosystem, of multiple interdependencies induced by the creation of relationships between these different small worlds that populate the innovation ecosystem. Beyond the specificities and undeniable assets of these multiple interdependencies, it is interesting to approach them from the perspective of collaboration. These interdependencies bring the small worlds closer together by encouraging them to rely on a common knowledge structure, to translate knowledge from one world to the other, to develop a common language that can be shared and finally to learn and adapt the language of others.

In the innovation ecosystem, if pool interdependence goes hand in hand with reciprocal interdependence, in practice, the actors know how to combine these interdependencies more or less well, which has enabled us to highlight a typology of collaborations.

6.5.1. The three types of collaborations

Practitioners and researchers of innovation ecosystems agree that the obstacles to collaboration appear at the very beginning of the process [SAN 05]. For us, these obstacles appear at the beginning of the innovation process. At the start of the process, there is what practitioners call a wavering period during which the actors lose their bearings and must find new ones. This loss of reference points can be caused, for example, by the creation of an innovation ecosystem, the arrival of actors in an existing ecosystem, the obsolescence of their routines, questioning of their cognitive frameworks or the presence of an unknown situation. It leads to misunderstandings between the different actors. It arises as obstacles to collaboration.

In practice, obstacles to collaboration appear throughout the innovation process: a new discovery, such as carbyne, creates a complex situation for the actors of the technological ecosystem in charge of developing inventions from this discovery. They must understand this new technology, its potential evolution and its usefulness for the design of inventions in order to determine the functions of the future technological product.

Once the invention has been made, the salespeople must, in turn, understand the technology and the invention in order to transform it into an innovation whose uses will meet the needs of the future market. The uses transform the invention into an innovation. They may require modifications by removing or adding product functions. Here, connections are created between the actors of the two technological and commercial ecosystems.

During the launch of the innovation and then its diffusion, users will have to understand these uses and the advantages of this innovation, integrating almost unknown technologies, in order to appropriate it and get satisfaction from it.

These different expectations go beyond the simple “delivery” of the result of each phase of the innovation process. They generate a strong tension that serves as a driving force to overcome the obstacles of collaboration or, on the contrary, erect barriers such as those we have already identified.

6.5.2. Strong collaboration: pool and reciprocal combination

Actors use this tension as a driving force for both the innovation process and the creation of pool and reciprocal interdependencies that they combine to overcome obstacles to collaboration. In practice, this situation arises when certain actors know each other. They have already collaborated together and often have a successful track record in one or more ecosystems. These actors develop mechanisms for overcoming barriers by identifying the limits of competence and identity. They start by developing a pool interdependence. They create, via a digital platform, a common knowledge structure that they can use and then enrich.

The pool interdependence will give meaning to this situation by the actors adapting to a common and shareable language and accessing the knowledge and resources of the common structure. It offers the possibility of analyzing the elements of the situation to begin to create meaning in order to develop reciprocal interdependencies.

In order to interpret unknown situations, which do not call upon their already-mastered routines and knowledge, these actors combine pool and reciprocal interdependencies to create meaning for both the innovation process and the ecosystem. To do this, they integrate uncertainty and the potential for failure of the innovation process.

To counter this potential for failure, they seek to reduce the equivocality created by uncertainty. Equivocality characterizes the lack of understanding of a situation that is uncertain. It creates difficulties of interpretation because each actor interprets a situation according to their own cognitive framework: the same situation can have several possible meanings of interpretation [WEI 95].

The reduction of equivocality depends on the behaviors of all actors and their ability to adapt their cognitive frameworks to others. To reduce equivocality, actors will focus on understanding what works and what does not work, or does not work well, rather than seeking, in a purely rational way, to interpret the situation. The reduction of equivocality is achieved, first, through pool interdependence, notably through the common structure of knowledge, the common and shareable language that the actors develop through learning in action. Then, this pool interdependence is combined with reciprocal interdependence through which the actors develop a common understanding of the situation or the problem. This common understanding allows them to find or invent solutions that will unblock the situation and solve the problem. The construction of the solution will be the result of a fully collective collaborative work.

Actors seek to develop new capacities to deal with complexity, change and hostility [JOF 92] by relying on collective learning oriented towards the intelligence of exploration.

6.5.3. Medium collaboration: pool and reciprocal articulation

Not all actors act in the same way. For some, this tension is a driving force that motivates them and pushes them towards collaboration. For others, this tension is a hindrance to their involvement in the innovation process. In practice, this situation arises when the actors do not all know each other. They are not yet used to working together or they lack experience. The actors, slowed down by this tension, operate in “stimulus-response” mode. Here, the reflection is shortened due to lack of time. The usual interpretations and justifications tend to prevail [KOE 96].

These actors have a low tolerance for uncertainty. Failure is not accepted. The questioning of their cognitive frameworks is not systematic because they prefer a learning mode based on the management of their acquired knowledge. This cohabitation between motivated and “slowed down” actors leads to a lack of understanding of the situation in which they are involved. It generates obstacles that the actors must overcome in order to collaborate.

In this situation, the motivated actors create pool interdependencies to establish a common structure and a shareable language that will foster dialogue between these two types of actors. Once the semantic barriers have been broken down, experienced actors will enlist hesitant actors in a mentoring role. Actors who are experiencing competence limits can bridge them through collaborative work. They rely on the strong experience of the mentor actors to gain confidence. The competence limit is compensated for by a willingness to explore the knowledge of others in order to appropriate it, step by step, and thus overcome the barriers of misunderstanding of the situation and enter into collaboration.

The experienced actors are “translation nodes” [LAT 87] of the network. They invite others to modify their cognitive frameworks by allowing them to make an additional effort that will serve the innovation process and the actors in the ecosystem. These actors, who gain confidence, gradually move towards learning modes of exploring the knowledge of the other actors. Pool and reciprocal interdependencies can be articulated. There is a strong chance that the innovation process will reach the market.

6.5.4. Weak collaboration: asymmetry between pool and reciprocal

Actors perceive this tension as a powerful brake on their motivation, inducing a strong hesitation to engage in collaboration. In practice, this situation occurs when the actors do not know each other. They have never worked together. They have not had any collaborative experience within an innovation ecosystem or they have had a painful experience.

These actors operate in “stimulus-response” mode. They do not have the time to reflect together and in depth on the different phases of the innovation process. As they are involved in innovation routines, they have a very low tolerance for uncertainty. Failure is not an option.

Routines and a mode of learning anchored in the management of mastered knowledge block any questioning of cognitive frameworks. Actors interpret any situation rationally through analysis. They prefer pool interdependence, which seems the most logical to them.

Connections are difficult to make with actors who are from a different field. They are hostile to knowledge sharing. They do not view learning through exploration positively. In practice, it is not uncommon for actors to favor a single mode of coordination around a leader they have identified or who imposes themselves through charisma and personality. They position themselves under the control of the leader. They reject any mechanism capable of bringing about the emergence of reciprocal interdependencies that could go beyond the boundaries of this community led by the leader, who would lose all control over these actors.

The lack of openness and trust towards other actors in the ecosystem creates barriers that can become almost impassable. These actors maintain a lack of understanding of the situation. Diverging interests and points of view lead to confrontations with other actors in the innovation ecosystem. They create as many barriers to collaboration as there are divergent interests or different points of view. Here, the leader’s objectives will be achieved but not those of the innovation ecosystem. The innovation process is doomed to failure if the actors do not have the full range of knowledge from discovery to market. The actors do not manage to create a common meaning to the process and to the innovation ecosystem. There is a rupture between pool and reciprocal interdependencies, which leads to the failure of the innovation process.

6.6.1. Definition of the iterative network of the innovation ecosystem

The typology of collaborations shows that, just because collaboration is necessary, the actors are not automatically encouraged to do so. Some will succeed and others will not. Some actors encounter difficulties that they will overcome with varying degrees of ease, while others will get bogged down in a lack of understanding of the situations they face.

Throughout the innovation process, interactions are created between the different actors of the innovation ecosystem. These interactions support scientific research, then the development of inventions, then innovations and, finally, business relationships [ACH 12]. These multiple interactions are dependent on the many collaborative networks that are generated by the small worlds of the innovation ecosystem. These networks are supported by pool and reciprocal interdependencies that combine, more or less, as the innovation process unfolds.

The results of the collaboration become visible in the outcomes of the phases of the innovation process. Collaborations are assumed to have worked well when discoveries, inventions and innovations are actionable by actors in the innovation ecosystem. Numerous connections have been created between the different small worlds that populate the innovation ecosystem:

• – connections between the networks of the small scientific and technological worlds are visible when scientific knowledge is converted into an invention in the technological ecosystem. Here, scientific discovery has been integrated to generate an invention that enhances that discovery;
• – connections between the small technological and business worlds are visible when scientific–technological knowledge is converted into a technological innovation by the commercial ecosystem. Here, uses, driven by specific functions, have been developed in concert between technologists and business people;
• – connections between the small business worlds and the market are visible during the launch and subsequent diffusion of the innovation in the market. Rational analyses are possible in terms of market shares, sales figures, etc. Here, the needs and expectations of users have been developed jointly between salespeople and users.

The connections between all these small worlds have led to the emergence of the iterative network of the innovation ecosystem through the combination of pool and reciprocal interdependencies. Here, the more the actors combine pool and reciprocal interdependencies, the more likely the innovation process will succeed because the iterative network gains in length, irreversibility and convergence [CAL 92].

In this logic, the iterative network of the innovation ecosystem is defined as “a coordinated set of heterogeneous actors […] who collectively participate in the development and dissemination of innovations, and who, through numerous interactions, organize the relationships between scientific and technical research and the market” [CAL 94]. The dimensions of the network then become identifiable.

6.6.2. The “small-world” actors of the network

The iterative network constitutes all of the associations and connections between the actors in the three ecosystems. It also integrates all the strategic alignments at the origin of its emergence and growth.

These multiple associations, which are made and unmade within the ecosystem as the innovation process unfolds, constitute translation chains of the iterative network, giving rise to “translation nodes” [LAT 87] between the small worlds of the innovation ecosystem. Here, these nodes are consolidated through the various assemblages made by the actors who combine their diversified knowledge. They stabilize or disintegrate as the innovation process evolves. Finally, they facilitate the understanding of the interactions between the small worlds and make the results of the innovation process visible.

The iterative network is a way to bring together the actors of these different small worlds. It encourages their interest and facilitates their enrolment in the innovation process. It has a federative character in which the small worlds are constituents of the network [COR 07]. Thus, actors and network are closely intertwined and it becomes impossible to define one without defining the other.

Here, the iterative network, like any network, presents a “dual reality” [CAL 92]. It is characterized by cohabitation within the innovation ecosystem by actors who, as we have seen in the typology of collaborations, adopt different strategic behaviors based on strategies of attachment or detachment.

In attachment strategies, actors try to make themselves indispensable to the development of both the process and the innovation ecosystem. They seek to co-evolve by enlisting new actors whom they consider indispensable to the innovation process. They mobilize the actors of the three ecosystems to increase the network. This network becomes irreversible and gains in convergence.

In detachment strategies, some actors feel trapped in the network, either because they are unable to overcome the obstacles of collaboration or because they are not sufficiently motivated or concerned by all the phases of the innovation process or by the other actors. They do not seek to enlist new actors. They are rather static and wait for the right moment to leave the network. The network becomes dispersed and loses its robustness.

6.6.3. Dimensions of the iterative network

Innovation requires the construction of networks so that actors can acquire resources, skills and new knowledge that they lack. The acquisition and development of new knowledge leads to new connections between actors. This results in a change in the architecture of the iterative network around its three key dimensions: length, irreversibility and convergence [CAL 92].

6.6.4. The evolution of the iterative network

The three key dimensions of the iterative network – its length, its irreversibility and its convergence – play a key role in its evolution. The networks present four types of architectures according to their state of evolution: incomplete network, dispersed network, long network and polarized network [CAL 95]. Table 6.3 presents a typology of the evolutionary architecture of these networks.

The nature of the network depends on the behavior of the actors and their capacity to adapt, or not, to learning modes oriented towards the exploration of the knowledge of others and to the choice of the mode of coordination that will or will not be privileged.