Multilevels of knowledge creation the "Mode 3" node - Model for a Twenty-first century innovation ecosystem

Knowledge represents an area where the application of systemic concepts (systems theory) promises particularly explanatory benefits. Modern and advanced societies and economies are being understood as knowledge-based. Knowledge is regarded as crucial for sustaining wealth and competitiveness. Global knowledge rankings of societies often correlate, at least to a certain extent, with wealth or competitiveness rankings (IMD 1996, p. 12, 2003a, b, 2004; and World Bank 2002). 

In this article A twenty-first century innovation ecosystem is a multilevel, multimodal, multinodal, and multiagent system of systems.

Introducing ‘‘Mode 3’’ is a multilateral, multinodal, multimodal, and multilevel systems approach to the conceptualization, design, and management of real and virtual, ‘‘knowledge-stock’’ and ‘‘knowledge-flow,’’ modalities that catalyze, accelerate, and support the creation, diffusion, sharing, absorption, and use of cospecialized knowledge assets.

Why Mode3??

University research, in a traditional understanding and in reference to universities in the sciences, focuses on basic research, often framed within a matrix of academic disciplines and without a particular interest in the practical use of knowledge and innovation. This model of university-based knowledge production also is being called “Mode 1” of knowledge production (Gibbons et al. 1994). Mode 1 is also compatible with the linear model of innovation, which is often being referred to Bush (1945). The linear model of innovation asserts that first, there is basic research in university context; gradually, this university research will diffuse out into society and the economy. It is then the economy and the firms that pick up the lines of university research and develop these further into knowledge application and innovation, for the purpose of creating economic and commercial success in the markets outside of the higher education system. Within the frame of linear innovation, there is a sequential “first-then” relationship between basic research (knowledge production) and innovation (knowledge application).

The Mode-1-based understanding of knowledge production has been challenged by the new concept of “Mode 2” of knowledge production, which was developed and proposed by Gibbons et al. (1994, pp. 3–8, 167). Mode 2 emphasizes a knowledge application and a knowledge-based problem-solving that involves and encourages the following principles: “knowledge produced in the context of application,” “transdisciplinarity,” “heterogeneity and organizational diversity,” “social accountability and reflexivity,” and “quality control” (see furthermore Nowotny et al. 2001, 2003, 2006). Key in this setting is the focus on a knowledge production in contexts of application. Mode 2 expresses and encourages clear references to innovation and innovation models. The linear model of innovation also has become challenged by non-linear models of innovation, which are interested in drawing more direct connections between knowledge production and knowledge application, where basic research and innovation are being coupled together not in a first-then but in an “as well as” and “parallel” (parallelized) relationship (Campbell and Carayannis 2012). Mode 2 appears also to be compatible with non-linear innovation and its ramifications.

A “Mode 3” university, higher education institution, or higher education system would represent a type of organization or system that seeks creative ways of combining and integrating different principles of knowledge production and knowledge application (for example, Mode 1 and Mode 2), by this encouraging diversity and heterogeneity and by this also creating creative and innovative organizational contexts for research and innovation. Mode 3 encourages the formation of “creative knowledge environments” (Hemlin et al. 2004). “Mode 3 universities” and Mode 3 higher education institutions and systems are prepared to perform “basic research in the context of application” (Campbell and Carayannis 2013a, p. 34). This has furthermore qualities of non-linear innovation. Governance of higher education and governance in higher education must also be sensitive, whether a higher education institution operates on the basis of Mode 1, Mode 2, or a combination of these in Mode 3. The concept of “epistemic governance” emphasizes that the underlying knowledge paradigms of knowledge production and knowledge application are being addressed by quality assurance and quality enhancement strategies, policies, and measures (Campbell and Carayannis 2013a, 2013b, 2016).

The Triple Helix innovation model concentrates on university-industry-government relations (Etzkowitz and Leydesdorff 2000). In that respect, Triple Helix represents a basic model for knowledge production and innovation application. The models of the Quadruple Helix and Quintuple Helix innovation systems are designed to already comprehend and to refer to an extended complexity in knowledge production and knowledge application (innovation); thus, the analytical architecture of these models is conceptualized broader.

With the comprehensive term of “Mode 3,” we want to draw a conceptual link between systems and systems theory and want to demonstrate further how this can be applied to knowledge in the next steps. Systems can be understood as being composed of “elements”, which are tied together by a “self-rationale”. For innovation, often innovation clusters and innovation networks are being regarded as important. By leveraging systems theory for innovation concepts, one can implement references between the elements of a system and clusters (innovation clusters) and the self-rationale of a system and that of networks (innovation networks). One advantage of this approach is that it makes the tools of systems theory effectively available for research on innovation. Based on original research about the European Union, also the concept of a multi-level hierarchy promises conceptual opportunities.

Favoring a conceptual point of departure, the analysis is carried by three conceptual research questions. First of all, elaborate an interface between concepts of systems and concepts of networks (or innovation networks) by claiming analogies between (1) elements (parts) of a system and clusters and (2) and the self-rationale of a system andthe networks. Just as the self-rationale holds together the elements of a system, a network ties together different clusters (an innovation network, thus, links different innovation clusters). Second, an application of systems theory is encouraged to the “world of knowledge,” by speaking of knowledge systems. Following the logic of a “multi-level” architecture, knowledge should be regarded as an aggregated concept; while innovation represents a highly aggregated term, S&T (science and technology) is already less aggregated, and R&D (research and experimental development) is even less aggregated than S&T. This implies using the concept of multi-level systems of knowledge or when an emphasis should be put on innovation, to apply the concept of multi-level systems of innovation. Through policy, the political system tries to influence the economy (economic system) and the other systems of a society. One can seriously discuss to which extent a more narrow economic policy is being replaced by a broader innovation policy. Third, the term “Mode 3” is being introduced, bridging systems theory and knowledge, thus emphasizing a knowledge systems perspective.

Further integrating systems theory, we can speak of multi-level systems of knowledge (following different levels of aggregation) and multi-level systems of innovation (also following different levels of aggregation). The popular and powerful concept of the national innovation system is being chronically challenged by continuous and ongoing processes of supranational and global integration. Conceptually unlocking the national innovation systems in favor of a broader multi-level logic implies furthermore to accept the existence of national innovation systems but, at the same time, also to emphasize their global embeddedness.

Our suggested catch-phrase of “Mode 3”, therefore, integrates several considerations that want to relate systems theory, knowledge, and innovation more directly to each other, and this should be understood as a contribution to a dynamically evolving general discourse on the topics of knowledge and innovation.

‘‘Mode 3’’ is based on a system-theoretic perspective of socioeconomic, political, technological, and cultural trends and conditions that shape the coevolution of knowledge with the ‘‘knowledge-based and knowledge-driven, global/local economy and society.’’

? Quadruple Helix: Quadruple helix, in this context, means to add to the triple helix of government, university, and industry a ‘‘fourth helix’’ that we identify as the ‘‘media-based and culture-based public.’’ This fourth helix associates with Global Systemic macro level Mode 3 Quadruple helix Democratic capitalism Structural and organizational meso level Knowledge Innovation networks Entrepreneurial university firm Global/local Individual micro level Local clusters Sustainable entrepreneurship Democracy of knowledge Creative milieus Entrepreneur/ employee matrix Academic vi Series Foreword ‘‘media,’’ ‘‘creative industries,’’ ‘‘culture,’’ ‘‘values,’’ ‘‘life styles,’’ ‘‘art,’’ and perhaps also the notion of the ‘‘creative class.’’

? Innovation Networks: Innovation networks are real and virtual infrastructures and infratechnologies that serve to nurture creativity, trigger invention, and catalyze innovation in a public and/or private domain context (for instance, government–university–industry public–private research and technology development coopetitive partnerships). ? Knowledge Clusters: Knowledge clusters are agglomerations of cospecialized, mutually complementary, and reinforcing knowledge assets in the form of ‘‘knowledge stocks’’ and ‘‘knowledge flows’’ that exhibit self-organizing, learning- driven, dynamically adaptive competences and trends in the context of an open systems perspective.

As a conclusion a Twenty-First Century Innovation Ecosystem is exactly this node Mode3 system. The constituent systems consist of innovation metanetworks (networks of innovation networks and knowledge clusters) and knowledge metaclusters (clusters of innovation networks and knowledge clusters) as building blocks and organized in a self-referential or chaotic fractal knowledge and innovation architecture,4 which in turn constitute agglomerations of human, social, intellectual, and financial capital stocks and flows as well as cultural and technological artifacts and modalities, continually coevolving, cospecializing, and cooperating. These innovation networks and knowledge clusters also form, reform, and dissolve within diverse institutional, political, technological, and socioeconomic domains, including government, university, industry, and nongovernmental organizations and involving information and communication technologies, biotechnologies, advanced materials, nanotechnologies, and nextGeneration energy technologies.