Keywords

19.1 Introduction

More and more organizations are confronted with highly dynamic external organizational environments [1]. The drivers of change are globalization, sustainable development, new technologies, and the aging population. The pressure on organizations forces them to continuously adapt to the environmental shifts [2] and to create organizational forms able to provide faster and innovative response to market threats and opportunities [3]. Therefore in today’s world, innovation is a subject of great importance because it stimulates sustainable growth in a highly competitive market [4].

Theories and definitions of innovation changed during the last century: “The early 1900s witnessed the birth of the first theories of innovations. Since the second half of the twentieth century the concept of innovation started to spread over the different fields of science. The time span between 1960s and 1990s can rightly be called the golden age in the study of innovation. However in the last 10 years the concept of innovation began to gradually shift from strong scientific definitions to management concepts, slogans and buzzwords” [5]. And this is sometimes confusing for executives, practitioners, and researchers. Table 19.1 shows some assumptions and describes the reality and related core principles of today’s successful innovation management.

Table 19.1 Assumption, reality, and core principles [5] and [6]
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Based on modern statistical practice, several types of innovation classification can be distinguished (Multiple classifications, multilayer classifications, and dichotomical classifications) and controversial pairs of innovation types can be identified [5]:

  • User-driven/supply-side innovation,

  • Open/closed innovation,

  • Product/process innovation,

  • Incremental/radical innovation (and other examples of “strong”/”weak” classification of innovation),

  • Continuous/discontinuous innovation,

  • Instrumental/ultimate innovation,

  • True/adoption innovation,

  • Original/reformulated innovation,

  • Innovation/renovations

When we look closer to “closed innovation—open innovation” dichotomy in the context of organizations, the core aspects of closed innovation and open innovation should be clear. On the other hand, the question is, why should it be a dichotomy? Isn’t it possible to bridge the gap between closed innovation and open innovation with a new, hybrid model or framework? The next section highlights the key elements of closed innovation and open innovation.

19.2 Closed Innovation and Open Innovation

19.2.1 Closed Innovation

In the last decades, organizations were primarily concerned with their own ideas, their own manufacturing processes, their own machines, their own scientists and workers. These enterprises couldn’t believe in a network of exchanging information and knowledge among other companies, suppliers, universities, customers, etc. There were strategic partners and alliances with severe contracts, protecting the secrets of the company, like ideas, inventions, or innovations. In this context, research teams should cooperate with development teams for accomplishing the company’s innovation, but problems were revealed from this communication between the two departments. So, many companies put their ideas coming from the research team on the shelf and after a long time perhaps the development team uses these ideas.

Many dangerous factors came from this inventory, ideas sitting on the shelf, such as many scientists, watching their ideas to wait, could not afford it and resigned from their current position and went to another company with better conditions of working. This means a transfer of ideas and innovation from one company to another. By the same way, there were exchanges between the partners, the suppliers even the customers of the companies [7]. But the whole system cannot protect its own parameters and values in closed smaller systems of a company and its supply chain. In this model of closed innovation, firms relied on the assumption that innovation processes need to be controlled by the company [8].

Because of market pressure it was obvious to improve this closed innovation model. The FORA Report [9] highlighted 9 innovation principles, each based on evidence of new innovation behavior: Co-creating values with customers, users’ involvement in innovation processes, accessing and combining globally dispersed knowledge, forming collaborative networks and partnerships, dynamics between large companies and entrepreneurs, environmental concerns drive innovation, needs in developing countries drive innovation, welfare system concerns drive innovation, technology’s role as an enabler of innovation.

In “many organizations, especially those with a traditional approach, innovation is often only seen as valid when it is completely ‘homemade’. The traditional view of managing innovation (closed innovation) completely disregards the growth market of demand-driven innovation” [10]. This is the main reason for creating a new system of exchanging ideas and information, mostly knowledge, even components from products. An expression of this kind of system is the paradigm named open innovation. Today, there are five erosion factors driving the shift to the open innovation paradigm [11]: (1) Increasingly mobile trained workers, (2) more capable universities, (3) diminished US hegemony, (4) erosion of oligopoly market positions, and (5) enormous increase in venture capital. But there are several (different) definitions of open innovation.

19.2.2 Open Innovation

Co-creation, user involvement, environmental and societal challenges increasingly drive innovation today. Collaborative, global networking and new public private partnerships are becoming crucial elements in companies’ innovation process [9]. It is against this background that cooperation’s are engaging in forms of open innovation [12–17]. But open innovation today has a much broader application than first proposed by Chesbrough [15], e.g., Reichwald and Piller [18] use the notion “interactive value chain” and the Ministry of Employment and the Economy [19] distinguishes “user innovation” from “user-driven innovation” and “users as collaborators,” which is closer to the idea of von Hippel [12] who used the terms “lead user concept” or “user-centered-innovation.” His consumer survey in the UK found that “8 percent of UK consumers created or modified one or more of the consumer products they use to better address their needs” [20] and 2 out of 100 said that their products had been taken up by other users or adopted and manufactured by producers [21, 22]. But this kind of open innovation—open collaborative innovation—differ from open innovation for organizations (Chesbrough): Chesbrough and von Hippel use different definitions of open innovation [11].

Open innovation according to Chesbrough [13] is “(…) the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively.” Chesbrough and Bogers updated this definition of open innovation [23]: “Open innovation is a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and non-pecuniary mechanisms in line with each organization’s business model.” But for von Hippel open collaborative innovation “(…) is ‘open’ in our terminology when all information related to the innovation is a public good—non-rivalrous and non-excludable, and”… involves contributors who share the work of generating a design and also reveal the outputs from their individual and collective design efforts openly for anyone to use” [24]. The key differences between the definitions are listed in Table 19.2.

Table 19.2 Different definitions for open innovation [23]
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On the one hand, “open innovation” entails purposefully managing knowledge flows across the organizational boundary as well as the associated business model as defining features. On the other hand, “open collaborative innovation” and related notions refer to an innovation model that emphasizes low-cost or free production of public, non-rivalrous, non-excludable goods [23].

Open innovation works from external ideas and knowledge in conjunction with the internal research and development activities. This bidirectional relationship offers new ways to create value. The existence of many smart people outside a company is not a regrettable problem for the prosperity of the company. It indicates also an opportunity for the company. In a better system, the internal research and development occurs awareness, connection, and information from outside research and development. The innovation process is more profitable, valuable and the effort is multiplied many times through the inspiration of the system. It becomes a value creation engine, value according to the customers, so it is essential for a company to learn from its customers and commercialize their ideas through business models.

Some researchers in Europe published an open innovation 2.0 framework, which integrates different perspectives (definitions) of open innovation. The so-called open innovation 2.0 has some fundamental principles, which lead to needs for new skills among all the actors in the innovation process. Modern innovation spaces span beyond clusters mainly in two dimensions: firstly, the traditional triple helix innovation model with enterprises, research and public sector players (being often top-down) is replaced by the co-creative quadruple helix innovation model where users have an active role too, in all phases of the innovation, from the early ideation to the co-creation of solutions. Secondly, the ecosystem drives for multi-disciplinarity rather than clusters, which tend to be quite monolithic [1]. Open innovation 2.0 was published as a new innovation paradigm in a white paper by Curley and Salmelin, at the open innovation 2.0 2013 conference in Dublin. The original paper was elaborated further in the open innovation 2.0 yearbook 2014 [25]. The twenty characteristics of open innovation 2.0 are the foundation of the proposed approach to increase creativity in innovation processes” [1] (Table 19.3).

Table 19.3 20 characteristics of open innovation 2.0 [1]
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For further discussions in this chapter, only the new updated definition of open innovation according to Chesbrough is relevant [23]. In the next section, complexity and uncertainty in innovation process and management is analyzed.

19.3 Complexity, Uncertainty, and (Open) Business Models

19.3.1 Complexity and Uncertainty

Market is no longer a target, it is more a forum [26] to “tap into the knowledge of participants in the social ecosystem to create a freer flow of information, engage people more wholeheartedly, and enable richer, fuller stakeholder interactions” [27]. Further, in such a complex system knowledge is unevenly distributed [28] and the direction of flows of knowledge and information cannot be predetermined [29] (For further information on socio-technical systems, see Chatzimichailidou et al. [30]).

The social world, like most of the biological world and a good part of even the physical world, is populated by highly contingent, context-sensitive, emergent complex systems [31]. Complexity and historicity mean above all that human action inevitably takes place in the face of an uncertain future (Reflexive Modernization) [32]. Haken [33] characterizes complex systems like this: “In a naive way, we may describe them as systems which are composed of many parts, or elements, or components which may be the same or of different kinds. The components or parts may be connected in a more or less complicated fashion. Systems may not only be complex as a result of being composed of so many parts but we may also speak of complex behavior. The various manifestations of human behavior may be very complex as is studied, e.g., in psychology (…). An important step in treating complex systems consists in establishing relations between various macroscopic quantities. These relations are a consequence of microscopic events which, however, are often unknown or only partially known.”

In business organization, it’s about complex/uncertain problem solving for customer. “Complex problem solving (CPS) occurs to overcome barriers between a given state and a desired goal state by means of behavioral and/or cognitive, multistep activities. The given state, goal state, and barriers between given state and goal state are complex, change dynamically during problem solving, and are intransparent. The exact properties of the given state, goal state, and barriers are unknown to the solver at the outset. CPS implies the efficient interaction between a solver and the situational requirements of the task, and involves a solver’s cognitive, emotional, personal, and social abilities and knowledge” [34]. Therefore, knowledge must be applicable to different, new, and complex situations and contexts [8, 35–39]. When we look at activities in innovation processes as highly dynamic, complex, nonlinear and with many positive and negative feedback-loops not only innovation policy is limited in what it can change [40], but analogously organizations are limited too in managing this complex system.

In literature, we can find the statement that “managing uncertainty can be regarded as a core practice of successful innovation management” [41]. If so, we need a deeper understanding of the term “uncertainty.” Ninety years ago, Knight [42] described in detail: “It will appear that a measurable uncertainty, or ‘risk’ proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all. We shall accordingly restrict the term ‘uncertainty’ to cases of the non-quantitative type.” It is this dimension of risk—uncertainty—which is an “under-investigated feature of organizations in late modernity” [43].

Creative work models—like open innovation—are likely to become more prevalent. Such models use a distributed problem-solving approach to tap into large pools of people with unique skills, each of whom can contribute to a final solution [44, 45]. In this new world of work, the barriers between work and life have been eliminated [46]. In this context, it is particularly important that the traditional technology and product-oriented perspective on innovation evolves into a more holistic one in which the key role of people and their working conditions is acknowledged [4].

19.3.2 (Open) Business Models

Chesbrough [16]) states that the first book [15] treated the business model as static and utilized open innovation to find more ways to create and capture value within the given business model. In his second book, the business model itself could be innovated, enabling new ways to obtain more value from the company’s innovation activities. A business model is a framework to link ideas and technologies to valuable economic outcomes. At its heart, a business model performs two key functions: (1) it creates value and (2) it captures a portion of that value [47] (Table 19.4).

Table 19.4 A classification of combinations of open Innovation and open business models [47]
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“Innovation is a paradoxical process, which requires a leap into the unknown and at the same time complex management processes and efforts for rigorous planning. In an innovation ecosystem it is not possible to manage many aspects of the innovation process. Orchestration is needed; this relates to both: The capacity to create conditions where the diverse parties can work together with the right balance of inner and outer focus, and thus reinforcing both their own work and benefiting the ecosystem as a whole; and the provision of supporting service infrastructure to help sustain effective operation within the system” [1].

If self-organization is the answer to complexity, we need competent knowledge worker who are able to handle uncertainty better than technology [36]. If we look at competencies as self-organization dispositions [48] on the individual level, we need a competence model that fits to the need of open business models. The question is: Which competence model can fulfill the requirements named? Grollmann [49] proposes in this case: “The attribution of human capabilities in a universal competence model is a question that research is dealing with since many years also in competition against the traditional intelligence concept. More honest seem to be contributions that have been developed for example within the debate of multiple competence/intelligence. Here various areas can be considered, in which expertise can be developed and in which talents exist. In the model of Gardner for example specific ‘intelligences’ will be differentiated [50]. If somebody would transfer different individual competence profiles on these eight dimensions, it would result in a much differentiated images.” Open innovation can only be successful if the involved partners have sufficient and symmetric degrees of both motivation and competency: Customer competencies (product, technical, leadership) and firm competencies (disclosure, appropriation, integration) [41]. But these competencies do not really fit to manage uncertainty because traditional theories are based on logical–mathematical dimensions and did not take into account individual feelings, impressions, etc. [51]. The concept of multiple competencies on individual/group/organizational/network level integrates these aspects and can be applied for open innovation business model [8, 36].

19.4 Cognitive Computing and Managing Complexity in Open Innovation Model

19.4.1 Cognitive Computing

Computing can bring open innovation to new levels—but it is not traditional computing, its cognitive computing. The idea of artificial intelligence as “the science and engineering of making intelligent machines” [52] started 60 years ago. During the last decades, many improvements were made and the public heard about it the first time, when the computer system Deep Blue played chess against world class champions. But finally Watson—a jeopardy-winning computer system—changed the game [53]. Today, artificial intelligence—or better: cognitive computing—is able to solve complex problems (CPS: Complex Problem Solving).

The Cognitive Computing Consortium defined several characteristics of cognitive systems [54]:

  • Adaptive. They must learn as information changes and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time, or near real time.

  • Interactive. They must interact easily with users so that those users can define their needs comfortably. They may also interact with other processors, devices, and cloud services, as well as with people.

  • Iterative and stateful. They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time.

  • Contextual. They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).

It’s obvious, that cognitive computing systems will be able to substitute jobs of today’s knowledge worker in several traditional industries: Finance, retail, health, education, etc. [44–46, 55–58]. But the question is which jobs/competencies will be substituted by cognitive computer systems and how can these systems contribute to innovation process and innovation management?

19.4.2 Cognitive Computing and Innovation

Cognitive computing makes a new class of problems computable. It addresses complex situations that are characterized by ambiguity and uncertainty; in other words, it handles human kinds of problems. In these dynamic, information-rich and shifting situations, data tends to change frequently, and it is often conflicting [54]. For organizations that want to improve their ability to sense and respond, cognitive analytics offers a powerful way to bridge the gap between the promises of big data [59] (Table 19.5).

Table 19.5 Data Science today and tomorrow [60]
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A good example is open evaluation: To handle the huge amount of ideas created by online communities isn’t that easy. Google’s project 10 to the 100 got 150,000 ideas from more than 170 countries, from general investment suggestions to specific implementation proposals. These ideas were evaluated by 3000 Google employees [61], not by the crowd (community), and not by cognitive computer systems [35].

According to Bitkom [57], cognitive computing can also contribute to user-centered design, user experience design, service design, design thinking, and lean innovation. Design thinking for example “(…) is a discipline that uses the designer’s sensibility and methods to match people’s needs with what is technologically feasible and what a viable business strategy can convert into customer value and market opportunity” [62]. Table 19.6 shows elements of design think.

Table 19.6 Design Thinking [63, 64, 65]
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When comparing these elements with the above-mentioned characteristics for cognitive computing systems, it is clear that cognitive computing will bring design thinking on a new level. But not only design thinking, it’s the whole innovation process that can benefit from cognitive computing [57]:

  • Market: Monitoring, Screening, Business-Modelling

  • Trends: Trend scouting, (n) Ethnography, User-Insights, Community-Research, Usability-Testing, …

  • Creativity and Pattern: Ideation, Co-Creation, Crowd-Sourcing, Brand-Naming

  • Technology: Patent, Material screening, Research Projects

But there are some limitations: “While robots are highly efficient at applying math to do routine tasks, humans are able to complement their robot ‘colleagues’ with non-programmable capabilities, such as the ability to be flexible and adaptable, interact effectively with humans, and use judgment and common sense to solve unexpected problems” [56]. As we know from knowledge management systems, they will “organize all the knowledge in a corporation, but they cannot produce imaginative breakthroughs” [56].

When we compare levels of competencies [66] for people and for machines, we have to realize that machines (cognitive computer systems) can work with facts and norms but are limited on the levels proficiency and expertise (Table 19.7). Experts, people with expertise, cannot easily substituted by machines (cognitive computer systems). So workers with advanced degrees—expertise (E)—are together with cognitive computing (CC) an essential starting point for [4] for a new ECC-Open Innovation Model.

Table 19.7 Levels of competencies: People-Machine [57]
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19.5 Conclusion

This chapter explains the key elements of closed and open innovation and pointed to different definitions of open innovation. For further discussions, the new updated definition of open innovation according to Chesbrough and Bogers was relevant. In the next section, complexity and uncertainty in innovation process and management was analyzed because managing uncertainty in innovation process can be regarded as a core practice of successful innovation management. It is argued that the concept of multiple competencies on individual/group/organizational/network level can be applied for open innovation business model and that cognitive computing can bring open innovation to a new level. It is shown that Cognitive Computing (CC) can bring innovation management on a new level (ECC-Open Innovation Model). At the end of this chapter, limitations of cognitive computing are outlined. Further research should analyze the whole open innovation model from the cognitive computing and multiple competencies point of view.