5
Coherence and Interdependencies
Coming together is a beginning, staying together is progress, and working together is success.
Henry Ford, 1863–1947
5.1. Introduction
In the previous chapter, we highlighted the roles of the actors and their search for coherence through their diversified activities. We know that the actors set up mechanisms of co-specialization and dependence that are governed by complementarity, the mobility of the actors and the modularity of their activities that privilege membership as a mode of control for all the actors.
This chapter aims at deepening the links of pool and reciprocal interdependencies that these actors build to understand the design situations in which they find themselves. They seek to reinforce their capacities and to develop new ones, particularly relational capacities. The latter create meaning in these design situations and act upon the actors’ behaviors. The search for coherence in the innovation ecosystem is based on two modes of learning in action that combine pool and reciprocal interdependencies.
5.2. Towards a search for coherence between design situations and capabilities
5.2.1. Design situations and capabilities
Understanding the search for coherence of the innovation ecosystem begins with an understanding of the design situations within the ecosystem and then of its dynamics through the behaviors of actors. The evolution of ecosystems and of the practices of innovation actors means that the innovation ecosystem places more and more importance on the coordination of actors, activities and the transfer of assets and knowledge, beyond the boundaries of each ecosystem [GUL 12].
These actors collectively participate in the orchestration of both their own ecosystem and the innovation ecosystem [VAL 17]. There are several reasons for them to come together to develop innovation and to acquire complementary capabilities that serve innovation and the growth of the innovation ecosystem. Co-specialization brings actors together. It creates interactions and interdependencies that allow the three communities of actors to coexist, collaborate and co-evolve through a complex set of relationships that create synergy among all actors in the innovation ecosystem.
These synergies reinforce their dependencies. These dependencies strongly influence the results of the innovation process. The latter reflect the success of the coordination and commitment of the actors [ADN 10] in achieving the innovation objectives. The variety of resources and knowledge that can be combined to achieve these results is a major asset for the acquisition of new capabilities.
The actors have the common objective of creating innovation. They are in a design situation [HAT 04]. A design situation begins as soon as the knowledge held by each ecosystem does not allow the objectives to be reached alone.
5.2.2. From capabilities held to capabilities to be acquired
In the innovation ecosystem, a design situation is always situated at two levels:
- – at the level of material assets which, as we have seen, is distinct from one ecosystem to another. Here, actors aim to understand the gap between the assets they own and the future assets they wish to develop: discovery, invention, innovation, market;
- – at the level of knowledge, the actors set themselves the objective of understanding the gap between the knowledge they have and the knowledge they need to acquire in order to successfully complete the innovation process.
| Ecosystem | Capabilities held | Capabilities to be acquired |
| Scientific | Key capability: to produce scientific discoveries Scientific material assets Scientific knowledge | Formulate the assets and knowledge related to the discovery to make them accessible to the actors of the technological ecosystem |
| Technological | Key capability: to produce inventions through the integration of assets and knowledge from the scientific ecosystem Material technological assets Technological knowledge | Formulate the assets and knowledge related to the invention to make them accessible to the commercial ecosystem |
| Commercial | Key capability: transforming these inventions arising from the technological ecosystem into innovation in use Material business assets Managerial, commercial and design knowledge | Develop use value for the market by adapting the innovation to the intended targets Disseminate these innovations to end users |
In this way, the actors identify their needs in terms of assets and knowledge. They can develop new capabilities that will make it possible for the process to run smoothly if the actors manage to reformulate these assets and knowledge to make them accessible to other actors in the innovation ecosystem. We have identified two major elements: relational capacities and the combination of pool and reciprocal interdependencies that enter into the dynamics of the innovation ecosystem.
5.2.3. Complementary capacities: relational capacities
Relational capacities invite actors to move from a logic of positioning in their respective ecosystems to a logic of movement [SAÏ 01] towards other ecosystems. Ecosystems are defined as a grouping of contrib-actors and complementary actors who interact to create and capture value [ADN 17]. These actors pool their capabilities and knowledge to develop integrated core competencies [MOO 96] in the innovation process.
This ecosystemic approach encourages actors to adopt a holistic vision and to emphasize the different degrees of complementarities [JAC 18] in a design situation that is not supervised by a focal firm. Here, collaboration is built “at the scale of the actors” [RON 09]. The actors of each ecosystem examine their routines, their capabilities and the use of their technologies [WEB 15] to modify their behaviors.
Value creation and capture induce a close correlation between actors and their success. Actors take advantage of the innovation ecosystem to improve their results through relational capabilities. While actors in the scientific and technological ecosystems quickly build relational capabilities based on a similar knowledge base, these capabilities are more difficult to develop with actors in the commercial ecosystem.
Without a similar or close knowledge repository, they tend to communicate little. Here, the emphasis must be placed on communication capable of creating crossover between their experiences and knowledge so that these relational capacities can capture the value drained [RON 09] by the different ecosystems. As a complement to digital platforms, these relational capacities offer the association of productivity, robustness, vivacity and diversity [IAN 04]. They favor the combination of pool and reciprocal interdependencies that are essential to the co-evolution of actors and the innovation ecosystem.
In the innovation ecosystem, which is made up of three ecosystems, the actors all play an important role in the innovation process. The success of this process depends on the relational capacities and symbiotic links between these different actors [TSU 18]. These relational capacities and links imply several logics of action [CLA 14] because the same actors can be involved, simultaneously, in several ecosystems.
Interdependence describes the strength of the interactions and interrelations between the actors [LEN 05] of these different ecosystems. For example, the scientists who work on discoveries can also be involved in the development of inventions, as shown by the design of the graphene bulb. Similarly, actors in the technological ecosystem can participate in activities in the commercial ecosystem. In this situational context, the “zone” of interdependence is different and depends on the perception that each actor has of it.
5.3. Pool and reciprocal interdependencies
5.3.1. The perception of interdependence
In the presence of a focal firm, the perception of interdependence [ADN 19] is established by the expectations of the focal firm. When the latter is absent, as in this case in the innovation ecosystem, the actors must become aware of the issues related to interdependence. The perception of interdependence invites the actors to understand the roles played by this interdependence in the exploitation of the knowledge produced in the innovation ecosystem by the three communities of actors.
Table 5.2 summarizes the main roles played by interdependence within an innovation ecosystem.
Interactions between all the actors are crucial to the success of the innovation process. This process depends on the execution of the activities of the actors of the three distinct ecosystems that are not under the control of a focal firm.
These interactions give rise to interdependencies. They act on the dynamics of the three ecosystems, thus rooting the activities of the actors and their innovation performance in the innovation ecosystem [JUC 16].
The awareness of the different roles played by interdependence highlights two key interdependencies: pool interdependencies and reciprocal interdependencies.
Table 5.2. Roles of interdependence, adapted from [MAR 15]
| Descriptions | |
| Connection | Interdependence generates connections and amplifies them. It connects individuals to each other, individuals to processes, knowledge to individuals and to processes. It connects all actors to each other and to the innovation process Connections transcend ecosystem boundaries |
| “Infusion” | To act on the cognitive frameworks of individuals, it is necessary to “impregnate” the three ecosystems with knowledge and understanding of the objectives, processes and expected results in order to generate interactive loops between the different phases of the innovation process |
| Learning | Infusion “opens” the cognitive frameworks of the actors Actors are able to learn through the information that is accessible: platform, interface, visualization They learn from each other through interaction: sharing ideas, points of view, methods, etc. They rely on a common structure where knowledge can be codified They thus free themselves from semantic barriers: learn the languages of others, adapt them and develop a common language |
| Dealing with the problems | Some cultures force actors to “stifle” problems rather than make them visible. Dealing with problems encourages sharing the difficulties encountered and the problems that arise during the innovation process Negotiate these problems with the actors of the ecosystem or call upon actors from other ecosystems. Encourage the “displacement” of actors from one ecosystem to another so that the problem is collectively solved |
| Empowerment | To train and accompany the actors entering the innovation ecosystem, whatever their position (scientist, technologist, commercial) Guide new actors; train them in the methods and procedures implemented in the ecosystem to act on their cognitive frameworks and routines Improve the knowledge of all the actors of the innovation ecosystem |
5.3.2. Creation of pool interdependence
In the innovation ecosystem, multiple interdependencies coexist as a result of the creation of relationships within the framework of co-specialization and the coordination of actors. Beyond the specificities and undeniable assets of these multiple interdependencies, it is interesting to approach them from the perspective of collaboration.
The concepts of semantics [RAU 12] offer a renewed approach to the study of collaboration. Here, they help actors to overcome differences in interpretation of design situations and contribute to the creation of pool interdependence. They bring the communities, present in the innovation ecosystem, closer together by encouraging them to rely on a common knowledge structure, to translate knowledge from one community to another, to develop a common shareable language and finally to learn and adapt the language of others.
5.3.2.1. The common knowledge structure
In an ecosystem, the connections between actors are based on the development of a “common structure” in which the knowledge of the actors can be coded [RAU 12]. As mentioned in the previous chapter, this common structure is usually supported by a digital platform. This platform creates a space that can be shared by all. The information, diverse and varied, becomes accessible to all the actors of the innovation ecosystem. This common structure favors connections between actors belonging to the same community and thus participates in the creation of new connections between actors from different communities.
Connections increase through the sharing of scientific, technological and commercial data, to which are added data on suppliers, customers and users. The more this platform is enriched, the more the number of connections increases, making the iterative network more dynamic. Technologies such as blockchain1 secure all the data using asymmetric cryptography, which is then decentralized and stored in a “chain made of several blocks”. The blockchain makes it difficult to lose or modify these data.
This common structure has the effect of transcending the boundaries of each ecosystem and those of the innovation ecosystem. In fact, transcending borders creates new spaces where the communities involved share useful data to create links between all the actors.
5.3.2.2. Knowledge translation
Rau and colleagues advocate the hiring of a translator who presents themself as a mediator capable of transferring knowledge between actors [RAU 12]. However, in view of the acceleration of technological development and the flow of knowledge that circulates and is created around these technologies, a mediator would have to have an inordinate amount of knowledge that is constantly renewed. This may prove difficult.
In reality, digital platforms play the role of translator and mediator. Artificial intelligence technologies, such as machine learning2 or deep learning, combined with “learning” software, offer the possibility to identify terms – the vocabulary – and to categorize them in order to create semantic groups based on different activities.
These technologies create links between different terms and categories. For example, they propose synonyms or clear definitions of each term. These technologies contribute to the development of a common language where each actor can integrate their own vocabulary in the language of their choice. It is also possible to create thematic libraries including academic articles, press articles, etc.
Voice and visual technologies can be integrated to support storytelling [DEN 06]. Experience sharing can be in a narrative form, stories, anecdotes or experience reports. The development of digital platforms facilitates the use of digital storytelling [DUP 19]. This “digital narration” consists of combining images and narrative integrating, at the same time, voice and music [SAD 08]. Storytelling and digital storytelling help actors to “put into narrative” their material assets, their knowledge and their experiences.
These methods promote learning through storytelling that is strengthened by image and sound. Learning is achieved through the art of “telling a story that is strengthened rather than weakened by the media they use, form a learning community so they can share their ideas and talents, meet the educational goals of the project and leverage their imagination and creativity” [OHL 13].
5.3.2.3. The development of a common shareable language
Through the common structure of knowledge and mechanisms of translation supported by appropriate technologies, the development of a “mutually understood” language [RAU 12] allows actors to share and validate each other’s knowledge in a recursive learning process. The techniques implemented are mainly representation by visualization [BAS 14] where drawings, diagrams, graphs and any visualization process are privileged.
For example, these visualizations are implemented to promote the understanding of:
- – the expected results of the innovation process as well as of all the intermediate results that led to discovery, invention and innovation;
- – points or sequences that generated a particular problem;
- – solutions that have been developed in response to difficulties or problems that have emerged during the design process.
Through these representations, elaborated collectively, the actors develop a common language that reinforces the links of interdependence.
5.3.2.4. Learning the language of others
The construction of a common shareable language is part of a collective and shared learning process [BOU 03] between the actors of the innovation ecosystem. The common structure of knowledge, the translation of this knowledge and the common shareable language contribute to create a learning framework for the actors. It makes all the diversified data accessible to new actors entering the innovation ecosystem so that they become familiar with the language of the actors of the ecosystem. For example, tutorials or MOOCs – Massive Open Online Courses – explaining the existing methods, standards, procedures, etc., implemented by the different actors can help new entrants understand how the actors and the ecosystem work.
Sometimes, actors integrate data on their skills into this common structure, with the aim of countering their under-estimation or under-utilization by making them visible to all actors in the ecosystem. New entrants can combine digital learning with peer coaching. In the same way, actors are free to connect with each other, in the context of problem solving, by targeting the actors who have the skills they lack. All these mechanisms, associated with technologies, allow the development of pool interdependencies and create synergies between actors.
5.3.3. Creation of reciprocal interdependencies
Pool interdependencies foster connections between actors through access to contextual data in the innovation ecosystem. In collaboration, if access to translated and shareable knowledge is the pillar of pool interdependencies, it must be complemented by the creation of reciprocal interdependencies.
For reciprocity to be possible between actors with different cognitive frameworks, it is essential that they interpret and understand the cognitive frameworks of the other actors. The pillar of reciprocal interdependence is the creation of meaning [WEI 95]. It allows actors to understand the motivations of scientists, technologists and business people. Sensemaking promotes the understanding of these motivations and the adaptation of the actors’ behaviors in order to share, intelligently, the knowledge accessible through pool interdependencies.
5.3.3.1. Understanding the motivations of actors
Reciprocal interdependence is based on the understanding and interpretation of the knowledge of the actors in the ecosystem. Actors have different motivations depending on their respective interests and objectives. For example, scientists are motivated by academic publications, which play a key role in the valorization of their knowledge in terms of the reputation of their research.
The filing of patents that make the quality of scientific work visible and promote it also motivates them. They create new technological standards that can be exploited in the form of licenses generating royalties. The higher the number of patents and publications, the more public and private subsidies will be granted.
Similarly, for example, technologists, whether industrialists or entrepreneurs, are motivated by patenting and co-patenting with scientists. They play an important role in the development of the reputation of R&D centers of industrial companies or start-ups. The recognition of the actors as “innovators” is important to impose technological standards that constitute a source of income to which are added subsidies to support the development of technological inventions.
Salespeople, whether they are industrialists, entrepreneurs or commercial companies, are motivated by brand development. Brand awareness is based on the number of innovations marketed and the associated market share. These salespeople are also motivated by the desire to be the first entrant in the market to take the lead with all the advantages of this strategic positioning.
5.3.3.2. Interpreting situations
Actors interpret situations according to the reality of the representations they have of them. The interpretation of a situation is the result of the actors’ perception and is based on their lived experiences. However, this interpretation is not always easy in an innovation situation where uncertainty tends to increase. It characterizes a situation in which the actors fear not having adequate representations [KOE 96]. Here, interpretation is a cognitive process that seeks to translate events in order to develop models for understanding the situation [DAF 84] in order to extract meaning.
To do this, actors seek to translate events into learning lessons that will lead to a common and shareable understanding of the events of the situation. Thus, they will then be able to create new methods that can act on their cognitive frameworks and make them evolve.
The situations can be interpreted in two ways:
- – A rational interpretation of the situation: if situations are concrete and measurable by indicators or criteria such as the number of market shares and trends in new technologies, a rational interpretation of the situation is possible. The actors collect data that allow the design of coherent solutions adapted to the current situation. Here, the rational interpretation of the situation is dependent on the actors’ ability to collect and analyze data and to develop solutions based on it.
- – An interpretation by sensemaking: the interpretation of a situation becomes complicated when it is difficult to analyze because it may contain knowledge unknown to the actors. A search for understanding of the situation, by implementing the preceding process, can generate contradictions and accentuate the lack of understanding of the situation [KOE 96]. Here, the process of interpretation is based on the critical vision that each actor has of the situation. The interpretation can therefore no longer be purely rational and linear as in the previous process.
The interpretation of the situation is said to be ad hoc. In this context, improvisation plays a determining role in the interpretation of the situation. The actors, who seek to translate the events by taking account of the equivocality of the situation, turn to collective learning in order to create meaning in the situation so as to be able to interpret it rationally.
5.3.3.3. Learning by doing
Actors are confronted with increasingly dynamic situations where the interpretation of situations comes up against problems of collective cognition. The appearance of these cognitive problems leads them to opt for a form of “learning by doing” [KOE 96] instead of learning by repetition.
Learning is distinguished from interpretation by the concept of action [WEI 95]. Learning, in action, incites the actors to take new actions. These actions will be interpreted [ARG 03] in order to give a “comprehensive” meaning to the situation. Here, the interpretation of the situation is assimilated to the learning of a new skill. This action of learning creates meaning in the situation by the production of new information which “will make sense” [WEI 95] for the actors and promote the interpretation of the situation.
In view of the complexity of a situation, learning has two facets: one is oriented towards the management of accumulated experience and the other towards the intelligence of exploration [KOE 96]:
- – experience management is characterized by the experiences lived by the actors. It is based on the accumulation of knowledge and the capacity of actors to collectively disseminate this knowledge;
- – the intelligence of exploration results from a reflection carried out during the action [SCH 84]. It has a collective dimension marked by the reflexive character of the plural interpretations of the situation in order to build new knowledge that will allow us to understand the meaning of the situation.
Learning in innovation ecosystems cannot be satisfied with repetition, given the presence of the innovation process, which by definition induces “novelty” at all stages of its development. Regularities are rare and almost non-existent in the design of radical innovations. This situation induces, in fact, the need for a profound transformation of behaviors.
In practice, the actors, inserted in innovation processes, are oriented towards action, in the form of exploration, experimentation, trial and error, which informs reflection.
The action is contextualized: its results are uncertain and its influencing factors are difficult to control. It disturbs the activities of the actors. It opens the door to reflection and to the search for other methods and tools that will allow the innovation process to move forward.
In the intelligence of exploration, knowledge is produced in and through the acquisition of skills and reflection in action. Promoting learning then requires enriching interpretive work [KOE 96].
With respect to learning in the innovation ecosystem, in Figure 5.1 we adapt Koenig’s model with a link, through the common knowledge structure, to pool interdependence.
In practice, the time spent on this action learning and the efforts made by the actors to develop new knowledge, through new tools, procedures and methods, are capitalized in the common knowledge structure. The fruits of this learning are put at the service of the pool interdependence. These new competences then come to enrich the digital platform. They may participate in problem solving if the situation that arises is close or similar.
Figure 5.1. Two-sided learning adapted from [KOE 96]
In the innovation ecosystem, pool interdependence goes hand in hand with reciprocal interdependence. In practice, actors combine these interdependencies with varying degrees of ease.
5.3.4. A combination of pool and reciprocal interdependencies
Pool interdependence is about the quantitative development of relationships between actors, whereas reciprocal interdependence is based on the deepening of these relationships [KOE 12]. Platforms contribute to the development of pool interdependence. It is useful to actors insofar as “strategy is increasingly becoming the art of managing assets that one does not own” [IAN 04]. These platforms are based on the principles of modularity [JAC 06]. Thus, each contribution made by the actors can be improved independently [MOO 06].
In the communities present in the innovation ecosystem, the contribution of each is distinct and isolable since it is based on the results of the innovation process: discoveries, inventions and innovations. These “knowledge-intensive” communities [MUL 04] characterize reciprocal interdependencies. Together with norms, individual leadership is the basic principle of coordination [MOO 06].
By joining an ecosystem, each actor agrees to commit to the realization of the innovation ecosystem’s objective. Here, the alignment of actors is achieved within the innovation process. It combines the alignment of individual and collective goals [ADN 19]. Control of actors through adherence to and sharing of common goals both induces innovation and drives the growth of the innovation ecosystem.
Beyond the framework imposed by the innovation process, the combination of pool and reciprocal interdependencies offers a new definition of the innovation ecosystem. It becomes a place of knowledge sharing, learning and inspiration [KOE 12] for innovation actors. In these ecosystems, a climate favorable to innovation emerges, which facilitates the enrolment of actors [CAL 06] who do not, however, have the same way of problematizing design situations, nor the same tools, methods and procedures for managing them.
5.4. Towards a search for coherence
The innovation ecosystem’s reason for existence is to manufacture innovation, from discovery to market. In this context, the contrib-actors [IAN 04] are inserted into design situations that force them to develop new capabilities related to their activities. Beyond these shareable design capacities, via technological platforms, actors develop relational capacities that energize the relationships between the different roles of breeders, feeders and niche actors [ZAH 11] so that the activities of each ecosystem converge to create value.
In the innovation ecosystem, composed of three distinct ecosystems, each community of actors has its own points of view, decision-making principles and beliefs, which generate behaviors that affect the decisions and behaviors of the other actors [TSU 18]. Far from energizing the innovation process, these rational behaviors often lead to inertia, where the decisions taken can lead to unexpected results in terms of innovation. Here, the actors do not always succeed in conceiving value-creating innovation despite the pool and reciprocal interdependencies that they try to develop.
This situation can jeopardize the innovation process, the evolution of the ecosystem and the future of the innovation ecosystem. In this context, actors are looking for coherence in the ecosystem. This is defined as “the proportion of the actors whose behavior is naturally fit to their decision-making and behavioral principles in an ecosystem” [TSU 18].
Relational skills have a role to play in understanding and analyzing decision options. Here, the coherence will be of high level because the actors are able to select the decisions and to adapt their behaviors to them. These capacities contribute to the coherence of the ecosystem by identifying the proportion of actors who adhere to the ecosystem and who converge towards the achievement of these objectives.
Coherence is never achieved because of perpetual changes in the actors, the phases of the innovation process, the results that are obtained and, in particular, the gaps between what was planned and what was achieved. This coherence can be lost as well as reinforced because it is closely correlated to the stability and resilience of the innovation ecosystem. The stability of the innovation ecosystem depends on the robustness of the iterative network and the dynamics of its integrated value chain.
5.5. Conclusion
As we have just seen, the actors, both autonomous and dependent, co-evolve in complex design situations that encourage the development of new capacities, the main one being relational capacity. These capacities are reinforced and developed through learning by doing. The actors privilege learning based on the intelligence of exploration, which through reflection and interpretation gives meaning to design situations, to their roles and to their activities.
Semantic concepts allow actors to overcome their problems of misunderstanding by relying on pool interdependence via digital platforms. In order to strengthen innovation and support its process, the actors bring a “complement” to pool interdependence, by developing reciprocal interdependencies.
The combination of these two interdependencies gives coherence to the innovation ecosystem through the adaptation of actors and the modification of their behavior, which in turn affects the construction of the iterative network that is the subject of the next chapter.