3.1.1. The concept of open innovation

The model of open innovation relies on a funnel vision of innovation (innovation funnel), which is related to the stage-gate model (Figure 3.1) often used for the development of new products.

The funnel image translates the idea of progressive fine-tuning of the innovation process: in the upstream phase, the process is fed by ideas and concepts of products. After selection, some of these ideas progress to a development phase, then testing, before their market launch. The challenge is then to choose the “best” concepts, that is to say, those capable of finding a potential market.

In closed innovation, this process is performed in an impermeable manner toward the exterior (Figure 3.2): the frontiers of the firm prevent knowledge leaks to the exterior. This approach is especially translated by an intensive use of industrial secret and defensive patent [LEB 10]. Besides, in this closed model, the firm relies exclusively on solutions and knowledge developed internally. The NIH or “not invented here” syndrome [KAT 82] represents the aversion the R&D teams may have toward knowledge developed outside the organization.

Contrary to closed innovation, open innovation is characterized by the permeable frontiers of the firm (Figure 3.3). R&D teams not only try to use knowledge developed at the exterior of the firm, but also seek to have the knowledge developed internally and valued externally.

3.1.2. The two facets of open innovation

Open innovation as described by Chesbrough (see also [ISC 11, PÉN 13]) then comprises two relatively distinct facets. The outside-in dimension designates the knowledge flow from the exterior to the interior of the firm, and thus represents the use of external knowledge in the internal innovation process of the firm. This does not constitute a real novelty compared with the innovation practices developed during the 20th century. In effect, the outside-in dimension appears every time a firm imitates another or grounds its knowledge in external sources or know-how. For example, innovations based on imitation or even bypassing of patents constitute typical forms of the outside-in model.

The second dimension, called inside-out, stresses the knowledge flows going toward the exterior of the firm. The aim is to value the knowledge which was developed internally through external channels. In a world where competitive advantages are often associated with the exclusive exploitation of a technological patent, this approach is much less widespread than the farmer. Nevertheless, it frequently appears in the pharmaceutical and biotechnological industries [PÉN 13, PÉN 08]. In these industries, numerous firms specialize in the development of medicines. The licenses for exploitation are then yielded to big pharmaceutical firms that possess the capabilities and the means for the industrialization and commercialization phases.

While the dichotomy between the inside-out and outside-in processes is structured, these two sides are often mobilized concomitantly in collaborative innovation projects (Figure 3.4).

3.1.3. Open innovation modalities

The development of the concept of open innovation can be explained through many factors [PÉN 13]. In the context of a globalized economy and increased competition, intensive innovation [LEM 06] becomes imperative for firms. This innovation becomes more and more technical and expensive and it becomes difficult or even impossible for a firm by itself to ensure the activities associated with innovation. The rise of the Internet and of information and communication technologies (ICT) enables easier connection of innovators: inventors, user communities, research laboratories, R&D centers, etc. This connection is not only spatial and temporal, but also cognitive. Indeed, the development of expert systems, collaborative working tools and digital simulation techniques are important levers of knowledge codification and of the sharing of this knowledge.

These tools thus contribute to reducing the cognitive distance [NOO 00, NOO 07] between innovators.

For Jullien and Pénin [JUL 14], the development of ICT has given rise to a specific form of open innovation, designated Open Innovation 2.0 (Table 3.1):

3.1.4. The importance of intellectual protection

Against the common belief, open innovation gives an important place to intellectual protection. Open innovation is sometimes erroneously associated with the Open Source model (see Box 3.2). In this model, the opening without restriction of the source codes of computer software aims, by a collective effort, at the improvement and sharing of software within the developer community.

Differing from Open Source, open innovation does not imply free access to knowledge and technology. Even if it induces, by its nature, a form of knowledge sharing, open innovation is largely inscribed in a classic vision in matter of intellectual property. Thus, the exploitation of licenses generally comes with royalties, and the results of collaborative work are not often openly available to the public. The literature insists on the importance of having a well-established intellectual property strategy from the beginning of the innovation partnership. This strategy is dynamic and must, at the same time, protect and manage the results issued from a collaboration process.

In the case of collaborative innovation, one of the major challenges is the definition of the rights of the different partners, depending on several parameters [SAU 12]:

1. – contribution of each partner;
2. – protection of previous rights;
3. – sharing of necessary knowledge in the framework of the project;
4. – sharing of exploitation rights issued from collaboration.

It is important for the success of a collaborative open innovation procedure that these questions be settled very early in the partnership process.

“Open” necessarily implies the sharing of tacit knowledge. In particular, innovation partnerships give place to socialization phenomena favorable to the sharing of tacit knowledge (see Chapter 2). Nevertheless, open innovation would certainly not exist in its current form without the existence of solid systems of knowledge protection. The emergence of open innovation is particularly explained by the increasing codification of knowledge and of technologies associated with the expansion of ICT, and by the reinforcement of intellectual property rights [PÉN 13].

3.1.5. Advantages and drawbacks of open innovation

Table 3.2 summarizes some of the advantages and difficulties often associated with collaborative open innovation.

3.1.6. Implementation of open innovation

The concept of open innovation is a relatively general frame of reference that in fact covers a set of relatively heterogeneous practices. In this way, the implementation of this type of procedure requires that certain questions be dealt with during the upstream phase (Table 3.3).

In the rest of this chapter, we introduce in detail some common practices of open innovation:

1. – user innovation;
2. – community innovation;
3. – crowdsourcing.

3.2. User innovation

In his book published in 1962 and re-edited in 1995, Everett Rogers [ROG 95] proposes an analysis of the distribution of innovation that distinguishes various types of users (Figure 3.5). Precocious innovators and adopters, who constitute a minority of the population of potential adopters (see bell curve in Figure 3.5), have a pioneering attitude in the matter of adoption. They are at the origin of the first phase (“emergence”) in the diffusion curve (S curve).

In Rogers’ approach, the diffusion of innovation is not possible with the sole mobilization of early adopters and innovators. The growth of the market share demands the mobilization of a more important part of the population of potential adopters: the early majority and the late majority. While the capability to mobilize a majority of potential buyers is essential for the diffusion of innovation, it is nonetheless uncertain. Thus, the commercial failure of innovative products is particularly explained by the incapability of firms to seduce buyers beyond a hard nucleus of pioneers and early adopters.

In his book Crossing the Chasm, Moore [MOO 91] suggests that for radical innovations, there exists a chasm between the early adopters and the early majority. Early adopters have an attitude similar to that of innovators. They are technology enthusiasts who are inclined to adopt new technologies. On the other hand, the majority is constituted of “pragmatic” adopters. They are the first and the foremost waiting for evidence concerning the relevance of the innovation. Their adoption behavior is characterized by mimesis with regard to other pragmatic adopters. According to Moore, the chasm appears when the market composed of the population of early adopters is tight, and the pragmatic adopters wait for a movement on the part of other pragmatists. This inextricable situation is described by the term Catch-22, with reference to the satirical novel published by Joseph Heller in 1961.

During the nineties, economic theory shed new light on these technology adoption behaviors, especially through the notions of increasing returns to adoption and path dependence [ART 94]. This approach explains how the succession of adoption behaviors that may seem insignificant contributes to the diffusion of radical innovations.

3.2.1. The concept of user innovation

The place of users is not limited to the act of adoption described in the previous section. In a seminal work, Eric von Hippel [VON 88] develops the idea according to which users can be at the origin of innovations. In this User Innovation approach, some users possess competencies, needs and motivations that lead them to play an active role in the production of innovation. These users have been identified in numerous domains (Table 3.4).

The theory proposed by von Hippel [VON 88], later refined in his work Democratizing Innovation [VON 05], gives a central place to lead users. These users are ahead of the evolutionary tendency of technology, and they have needs that the majority of users will only express later on. Lead users also possess the motivation and often the competence to develop technical solutions that respond to their needs. The innovations that result from this procedure have strong chances of interesting a larger mass of users and of becoming commercially successful. Numerous examples illustrate this process, an emblematic case being that of the GoPro action camera (Box 3.1).

3.2.2. Lead users activities

The 20th century economy is characterized by the development of mass production as the dominant industrial model. Recent years have seen an increasing demand for differentiated and personalized products [PIL 10]. In a context where users’ needs are very heterogeneous, the “a few sizes fit all” strategy [VON 05] leaves numerous consumers or users unsatisfied by the “classic” industrial offer. The users who face problems which the majority of consumers do not have are left with no other option than to develop their own modifications to existing products, or to invent entirely new products. In other words, the users who wish for a specific product will obtain a satisfying result by innovating themselves. In fact, industrial firms are not generally designed to respond to very specific demands (Figure 3.6).

Lead users can intervene in many different ways in the innovation process. In some cases, their role is limited to proposing ideas for product improvement [CHR 97, VON 88]. In a more radical way, the lead users can be in charge of the integrality of the innovation process in order to respond to a need which differs hugely from the existing offer. The development of the GoPro camera (Box 3.1) responds to this logic. In this context, the role of firms consists of continuing the work of the lead users during the industrialization phases when the demand for new products is sufficiently significant. Finally, the involvement of users in the innovation processes can be done through the processes of co-creation and co-development, even by the taking up of supporting activities [NAM 02]. For example, the sports equipment company Salomon (who produce skis, ski bindings, trail running equipment, including shoes, etc.) has benefited from the competence of the professional athlete Kilian Jornet for the development of the S-Lab Sense trail running shoe and the S-Lab equipment line. In this case, the users cannot only be considered as knowledge sources, and open innovation does not only imply an outside-in process type [POE 12]. In a co-creation or co-development procedure, the users actually become avenues of access to the markets (“external paths to market”, [BAL 06]). For Rayna et al. [RAY 15], co-creation with users is possible at many stages of the innovation process: co-design during the conception phase, co-production during the manufacturing phase, and distribution. Co-creation can also be done with individuals or communities [SAW 00, PIL 05].

Co-creation can be associated with the notion of mass customization, which reflects the production of personalized or customized goods at a large scale; however, these are quite distinct concepts. In fact, mass customization does not forcibly imply co-creation with users or open innovation [CHE 12]. For example, when mass customization is translated into the existence of choices among a predefined set of possibilities (color, size, etc.), we cannot define this as co-creation nor even as innovation because customization is not translated into the emergence of a novelty [PIL 10]. Thus, user innovation corresponds to a particular site at the intersection of co-creation, mass customization and open innovation (Figure 3.7).

3.2.3. Competencies of user-innovators

The user innovation paradigm relies on the hypothesis according to which certain users possess a particular competence allowing them to innovate. Thanks to their advanced use of technologies, lead users possess specific consumer knowledge. This knowledge possessed by the user enables him to face problems in use situations, for example, to choose the right product and function for the use situation [BRU 85, MIT 96]. Expert users, who possess deep consumer knowledge, will be more prone to endorsing the status of lead user. In fact, proper knowledge of a product’s characteristics and its use is often considered the prerequisite for creativity and innovation [SCH 08].

With experience, the expert users develop patterns and metacognition that enable them to acquire new knowledge faster and to possess a deeper and more comprehensive vision of the encountered problems. This expertise is the fruit of prolonged experience as well as of deliberate practice [ERI 06, ERI 07]. In fact, while it is necessary, experience alone is not sufficient to become an expert. Research on the psychology of expertise in the domains of sport, music or chess underline the importance of the individual’s engagement in a learning process through the notion of deliberate practice, which describes a permanent attitude of experimentation with the aim of learning.

Expert competence is one of the elements that determine the capability of lead users to identify new uses and technical solutions, allowing them to provide an answer to the problems encountered in practice. According to Amabile [AMA 88], individual creativity is the combination of three factors: expertise, creative thinking skills and motivation (Figure 3.8).

The literature underlines the capability of lead users to anticipate market tendencies and to make decisions “ahead of time” by comparison with the classic R&D actors [LIL 02, MOR 04]. Thus, lead users are often at the origin of new industries. Note that the expert competencies held by users are strongly tacit. They are the fruit of experience which has been interpreted, analyzed and ranked by users throughout time and are at the origin of a fine perception of the problems encountered in the situation and the capability of providing answers. This tacit knowledge ([HIP 88] uses the term “sticky knowledge”) is contextualized and associated with individuals.

3.2.4. Implementation of user innovation

Apart from their competence, lead users benefit from important flexibility in the matter of innovation. In their activity, these actors are not constrained by the traditional stages of the stage-gate model or by the standardized procedures of project management that predominate in firms. Besides, lead users do not cover fixed costs of infrastructures. The perspective of witnessing the emergence of a solution to their problems constitutes a motivation for the necessary (time) investment. Thus, in a similar way to entrepreneurs, these actors possess an internal locus of control. While the main motivation of lead users is not commercial, the capability of users to anticipate market trends is of prime interest for the firms essentially motivated by future profit. The literature tends to show that the innovation produced by lead users carries a strong level of sophistication and that they provide high value to customers.

We note that the mobilization of lead users is very different from approaches based on listening to customers [DAN 04]. Certain authors (see, for example, [CHR 97]) have shown that excessive listening to users could lead firms into an “incremental trap” susceptible to dramatic consequences for their survival in a context of radical evolution of technologies. Lead users are not representative of the majority of users but their practice situates them in advance of the existing market. Thus, by mobilizing lead users, the firms do not risk falling into the innovator’s dilemma as described by Christensen [CHR 97].

Consequently, how is it possible to marry the “classic” model of new product development with the activity of lead users? For von Hippel, the lead user method can be decomposed into four stages (Figure 3.9).

3.3.3. Innovating with external communities: the role of the middleground

The role of communities in the development of open source software (Box 3.2) highlights the innovative potential that lies in these informal organizations. Following Florida’s approach of creative cities [FLO 02], recent work has focused on the place of communities in creative processes [BUR 11a, COH 08, COH 10a, COH 10b, COH 10c, HAR 13]. These authors develop the idea that the communities create an interface between the local actors, the members of networks or collectives and the organizations who seek to pilot innovation projects. Knowledge communities here have a catalyzing role. Thanks to their way of producing knowledge, they create a relation between the relatively disorganized world of ideation and creativity, and the structured world of the organization in project mode. The authors equally use the terms underground, middleground and upperground to characterize these sets (Figure 3.10):

1. – the underground corresponds to the hidden part of creative territories, that is to say, individuals and collectives who carry creative activities (scientific, technological, artistic and cultural) at the exterior of any institution or organization. It is about exploratory or cutting-edge activities, often exercised in a non-profit way;
2. – the middleground comprises communities who have the mission of consolidating and distributing knowledge. In this way, they constitute an interface with the underground, characterized by an informal functioning mode, and the formal organizations of the upperground. Middleground plays a crucial role in the innovation process because it makes knowledge produced in the underground accessible to the upperground;
3. – the upperground is composed of firms and creative institutions (artistic centers, cultural centers, etc.), which possess creativity integration and financing capabilities. These organizations put forward the notion of a project and are constrained by economic considerations. Their role consists of promoting the formalized knowledge produced in an informal way by the underground. By doing this, they generate positive knowledge externalities [ARR 62].

3.4. Crowdsourcing

The concept of crowdsourcing was formalized by Jeff Howe in 2006 in Wired magazine [HOW 06]. It is a contraction of the terms crowd and outsourcing and it represents the act of externalizing, via a website, an activity to a large number of individuals whose identity is a priori anonymous. This is inscribed as a direct extension of Web 2.0 and is frequently considered as a manifestation of open innovation in its outside-in dimension [PÉN 13]. Howe [HOW 08] identified four major categories on how to appeal to the crowds:

1. – crowd wisdom is generally associated with the field of R&D. It aims at problem solving;
2. – crowd voting relies on Internet users’ opinions, either in the form of deliberate votes or their non-conscious contribution (for example, the page rank algorithm used by the Google web search engine);
3. – crowd funding, which designates participative financing;
4. – finally, crowd creation, which requests the creativity of the crowd, particularly in the marketing field (trademark creation, logos, slogans, designs, etc.).

The literature concerning crowdsourcing (CS) in the domain of innovation management focuses on the crowd wisdom and crowd creation dimensions. In particular, existing typologies [EST 12, PÉN 11, REN 14, SCH 12] make it possible to understand how crowdsourcing feeds the innovation process with knowledge contributions. Indeed, CS concerns different types of tasks (simple, creative or complex tasks) and the nature of the process is equally variable (integrative process vs. selective process; collaborative process vs. competitive process)

3.4.1. A typology of crowdsourcing

The first approach consists of differentiating the types of CS according to the nature of the tasks to be performed. Thus, it is possible to distinguish CS related to simple tasks, creative productions and problem solving [SCH 11, SCH 12].

3.4.1.1. Simple task CS

Simple task CS consists of mobilizing a large number of contributors for low-intensive knowledge information elements, whose production does not take long. The contributions do not possess a significant value per se and the associated remuneration to this CS is feeble, even non-existant. The CS of simple tasks is about handling big volumes of information and its value for the firm relies on its capacity to integrate a multitude of information in order to propose an added-value service. This type of CS is at the core of well-known applications such as Mechanical Turk, reCAPTCHA and Waze. With Mechanical Turk, Amazon mobilizes contributors from around the world for the realization of “micro-tasks”, such as simple translations. With reCAPTCHA (see, for example, [SCH 12]), Google mobilizes Internet users for character recognition activities. Finally, with the Waze application, users feed a system enabling the prediction of traffic jams (see, for instance, [TUC 15]). Mechanical Turk implements a principle of micro-payments, whereas for reCAPTCHA and Waze, users are not remunerated for their contributions.

3.4.1.2. Creative production CS

Internet contributors can also be called upon for their individual creativity, that is to say, for their capability to produce new ideas, which have potential value for the firm [AMA 88]. While these creative tasks, for example, the creation of a logo or a name, sometimes appeal to particular professional competencies, they are equally accessible (with very variable results!) to non-professionals, for example, to customers or users of the brand.

This form of CS enables the firm to have access to professional competencies at a lower cost [POE 12], but equally to approach their customers. In this way, CS is inscribed in the marketing approach of the firm [KOZ 08, WHI 09]. This is particularly implemented by the firm Lego (Box 3.3), or by the creation platforms Creads and eYeka.

In any case, it is a question of soliciting the crowd in the context of creativity contests and choosing the best production. The selected productions sometimes constitute content within a more global offer. Burger-Helmchen and Pénin [BUR 11b] thus use the term “content CS”.

3.4.1.3. Problem-solving CS

In this type of CS, the firm appeals to the crowd to solve scientific or technical problems that appear during the innovation process [BRA 08, JEP 10]. Problem solving has a strong cognitive dimension and crowdsourcing then aims to access distributed expert competencies. The required experts are outside the firm’s boundaries [PÉN 12], even outside its domain of activity [BOU 11, BOU 13]. CS here serves the purpose of exploring new knowledge and Afuah and Tucci [AFU 12] refer to distant search.

Since 2001, the InnoCentive platform (Box 3.4) has become essential for the implementation of this type of CS. This platform connects organizations that face a problem and contributors distributed around the world.