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«Leveraging complexity for ecosystemic innovation» 
Martha G.Russell, Nataliya V.Smorodinskaya


Ecosystemic innovation focuses on the non-linear complexity of knowledge-based economies, where new values are co-created interactively at the level of collaborative networks.

To meet high global uncertainty and motivate innovation-led growth, modern economies are moving toward ecosystem-based design. Unlike rigid hierarchic systems, agile heterarchical ecosystems display properties of complex adaptive systems (CAS), such as self-organization, self-adjustment, and self-governance.

The CAS concept, a stream of complexity science, helps to highlight the contrast between the modern ecosystem approach and the traditional system approach to innovation and growth.

Business networks with a higher complexity in interaction patterns are able to generate greater innovation synergy effects. Innovation ecosystems are thus generated by networks that have advanced from cooperation to collaboration among agents. Those innovation ecosystems that enable continual innovation, such as innovation clusters, have a more complex, triple helix pattern of collaboration.

To ensure sustainable growth in the age of globalization and non-linearity, countries need active policies facilitating the ecosystem-based transformation of their institutional and industrial landscapes, which also implies measures to accelerate the replacement of hierarchies by collaborative models of governance at micro- and macro-levels.


This paper looks at innovation ecosystems through the lens of complexity science, considering them as open non-linear entities that are characterized by changing multi-faceted motivations of networked actors, high receptivity to feedback, and persistent structural transformations. In the context of the growing organizational complexity of economies, driven by their adaptation to high uncertainty, and the central role of collaboration, we differentiate the innovation capacity of various types of business networks by the complexity of their internal interactions, thus identifying the place of innovation ecosystems in the world of business networks, as well as the place of innovation clusters among other innovation ecosystems. We observe how innovation ecosystems have been viewed in four different research streams: management literature; the inter-firm and business network stream of economic and sociological literature; the innovation policy and competitiveness agenda in economic literature; and the dichotomy of localized and economy-wide innovation ecosystems in policy studies (in economic literature, evolutionary geography, and regional research). We then describe generic properties of innovation ecosystems in terms of complexity science, viewing them as complex adaptive systems, paying special attention to the complexity of innovation clusters. We compare complexity thinking of modern economies, deriving from their emerging ecosystem design, with traditional thinking conceived for industrial era, drawing insights for a better transition to innovation-led growth. We conclude with a summary of key findings, practical and policy implications and recommendations for further study.

Graphical abstract

Image 1



Business network; Collaboration; Complexity; Innovation ecosystem; Innovation cluster; Global economy; Non-linearity

1. Introduction

1.1. Non-linear innovation and the emergence of innovation ecosystems

Under enhanced global competition and global proliferation of information communications technologies (ICT), economic activities have become more knowledge-intensive, and industrial economies have accelerated their transition to knowledge-based systems.

In various sectors, the linear model of innovation (a downstream cascade of knowledge flows from fundamental science to applied research, and further to application) is giving way to a non-linear model, in which ideas for innovation come from many sources and stages of economic activity, and a growing number of institutions have become involved in the production and diffusion of knowledge (OECD, 1999). This implies that innovation is becoming highly interactive and collaborative, often multidisciplinary and multidirectional (National Research Council, 2012).

Driven by global forces of non-linear innovation, the modern systems of production and economic governance are also obtaining a non-linear nature to become decentralized, diffused and dispersed along network nodes (Elsner, 2015; Nieto and Santamaría, 2007; Smorodinskaya, 2015; Todeva, 2013). Their development is increasingly characterized by uneven leaps, multi-vector fluctuations and other manifestations of nondeterministic behavior. In contrast to linear systems, non-linear ones evolve disproportionally: in some cases, minor behavioral changes in a system’s separate elements may lead to large-scale changes in its state, while in other cases, major changes in the state of elements may produce weak or even no impacts on the system as a whole (OECD, 2009).

The objective paradigm shift from linearity to non-linearity brings about a non-equilibrium, constantly changing global environment, which generates a situation of unprecedented high uncertainty, unlike has been witnessed ever before (Kidd, 2008). Facing this challenging situation, businesses and economies in different parts of the world are searching for new ways to enhance their innovativeness, strengthen their competitiveness and adapt themselves to non-linear global realities.

In particular, to maintain sustainable growth under high uncertainty and manage the growing complexity of technological systems, economies of all levels are simultaneously enhancing their social and organizational complexity, tending to assume agile network-based designs (Smorodinskaya and Katukov, 2015). In fact, since 2000s, the creation of new goods and values is seldom singularly producer-led or user-driven; instead, today’s technological, service and social innovations are increasingly co-created interactively by participants of collaborative networks. Individuals and companies, as well as regions and nations are more and more engaged in the formation of multifold network partnerships, in which actors develop multilateral cooperation and create new values together, thus jointly responding to continuing technological and market changes. Economic advantage now accrues to those entities that can quickly transit from their traditional hierarchic model to a horizontal network structure and start participating in collaborative activities with similar network entities (Friedman, 2005; Seppä and Tanev, 2011; Smorodinskaya, 2015). The emergence of manifold social networks and innovative business milieus is accompanied by the development of shared perceptions and systems for value co-creation (Russell et al., 2011). Network of affiliations bridge social worlds, which were formerly less or not well-connected (Powell et al., 2013).

This organizational transformation of businesses and economies toward a higher complexity and more agility catalyzes the emanation and proliferation of innovation ecosystems. Not just networks as such but rather heterarchical ecosystems formed by interactive activities and collaboration of networked partners are shaping the dynamic industrial landscape of knowledge-based economies. Taking various scales, configurations and profiles, such ecosystems are seen in literature as a new typical way for producing goods and values in the twenty first century (MacGregor and Carleton, 2012).

Today, the idea of promoting the persistent emergence of localized innovation ecosystems and of creating an economy-wide ecosystemic landscape, typical for systems with innovation-led growth, stands high on the policy agenda of many developed and developing nations (Bramwell et al., 2012; Warwick, 2013; WEDC, 2009). The World Economic Forum now directly associates the new model of industrial policy, as introduced recently in different countries for developing advanced manufacturing, with the prospect of building powerful innovation ecosystems in the manufacturing sector (WEF, 2013).

1.2. Motivating questions, focus and logic of analysis

In this paper, we explore organizational foundations and generic features of innovation ecosystems, including innovation clusters as their sophisticated sub-variety, in concert with non-linear development, collaborative mode of production, and the ongoing transition of entities and economies to innovation-led growth. Our aims are to more precisely define the notion of the term «ecosystem” versus “system”, to disclose the origin of synergy effects that make innovation ecosystems and particularly innovation clusters “the new face” of the industrial landscape in the twenty first century, as well as to highlight the emerging ecosystem-based design of modern economies and its key role in facilitating their innovation dynamics. We associate the emergence and evolution of innovation ecosystems with the proliferation of collaborative networks aiming to produce innovation interactively, through a collective action of legally independent actors (Bramwell et al., 2012; Russell et al., 2015).

The quest motivating this analysis is to better understand the organizational setup of emerging knowledge-based systems as compared to the traditional industrial landscape conceived for the linear world, as well as to conceive a supposed interplay between the growing complexity of modern economies and their innovation capacity.

We look at innovation ecosystems through the lens of complexity science, considering them as open non-linear systems that are characterized by changing multi-faceted motivations of networked actors, high receptivity to feedback, and persistent structural transformations, induced both endogenously and exogenously. Such ecosystems rely on the agility of network relationships (Adner, 2017) and the collaborative, non-hierarchic models of governance, which enables their self-adaptability to rapid change. Their further proliferation demands decision-makers of all levels to provide and support a favorable context (social, economic, institutional, etc.) for continual networking, more horizontal linkages, and the enhancement of collaborative cohesive milieu within and among entities and economies.

We situate the analysis of ecosystems in the context of the non-linear world of networks and the central role of collaboration in producing innovation. We then differentiate the innovation capacity of various types of business networks by the complexity of their internal interactions, thus identifying the place of innovation ecosystems in the world of business networks, as well as the place of innovation clusters among other innovation ecosystems. Next, we observe how innovation ecosystems have been viewed in four different research streams: management literature; the inter-firm and business network stream of economic and sociological literature; the innovation policy and competitiveness agenda in economic literature; and the dichotomy of localized and economy-wide innovation ecosystems in policy studies (in economic literature, evolutionary geography, and regional research). We then describe generic properties of innovation ecosystems in terms of complexity science, viewing them as complex adaptive systems, paying special attention to the complexity of innovation clusters. We compare complexity thinking of modern economies, deriving from their emerging ecosystem design, with traditional thinking conceived for industrial era, drawing insights for a better transition to innovation-led growth. We conclude with a summary of key findings, practical and policy implications and recommendations for further study.

2. The world of business networks and the appreciation of innovation ecosystems

2.1. The concept of collaboration and its role in producing innovations

An interpretation of non-linear innovation in modern literature points to its direct connection with the development and proliferation of networks. One of the first descriptions of networks as innovative entities appeared in the early 1990s in the “New Society of Organizations” by P. Drucker (Drucker, 1993), in which he underlined the ability of such organizations for continual “creative destruction” and predicted their future global domination. According to Chesbrough (2003)and Tassey (2008), in order to sustain their competitive advantages, firms move to collective creation of innovation. According to Powell and Grodal (2005), the most effective way to produce innovation involves network interactions of firms with other firms, research institutes and other organizations. The ongoing further proliferation of networks worldwide implies that innovative goodstechnologies and values will be increasingly co-created by networked actors that collaborate with each other to form a certainrelatively sustainable ecosystem of actorsassets and linkages (Gloor, 2006; Russell et al., 2016; Wessner, 2005).

The term “collaboration” (from Latin ‘working together’) has no universal definition in literature: this term is used both in a broad and in a narrow meaning by different lines of research, each of which applies its own language. For example, some experts argue (MacGregor and Carleton, 2012) that collaboration is important for both R&D and non-R&D innovation but each type uses different networks. Others admit that collaboration involves active and interactive exchange of ideas between two or more people who acknowledge that such exchanges can result in the joint production of co-constructed ideas, some of which may be novel (WEF, 2015). Taken in a loose definition, collaboration denotes various forms of interactive communication between networked actors.

By a more exact definition, accepted in economic literature, collaboration is described as “the process of formal and informal negotiations between autonomous actors, during which they create common rules and organizations to regulate their interactions and fields of activity, or tackle common issues cohesively, with these common rules shared by all stakeholders, while negotiations taking place continually” (Thomson and Perry, 2006, p. 23). By another exact definition, coined by multidisciplinary analysis of networks, collaboration is seen as a process in which mutually engaged participants share information, resources, responsibilities and risks to jointly plan, implement, and evaluate a program of activities aimed at achieving a common goal (Camarinha-Matos and Afsarmanesh, 2008b). Taken together, both definitions introduce a certain concept of collaboration that speaks of networked actors, their shared objectives, and their continuous negotiations to harmonize mutual interests and coordinate mutual activities.

Keeping to this concept, we use for the purpose of this paper a strict interpretation of collaboration, viewing it as the most developed form of interactive communication. Particularly, more mutual activity and higher levels of intentional integration differentiate collaboration from other types of relationships, such as networking and cooperation, implying that co-creation of new values is a sophisticated stage of interactions, demanding actors to have a common strategy, joint identity, joint goals and joint responsibilities (Fig. 1). This definition corresponds to collaborative innovation networks able to achieve dynamic sustainability in a non-linear environment. It should be noted that in practice, complex types of relationships may emerge in a non-linear way at varying stages of interactive activities, not necessarily moving in a strict progressive way through all the stages presented in Fig. 1.


Fig. 1


Fig. 1. The growing complexity of interactions and integration of activities from networking to collaboration.

Source: adapted from (Camarinha-Matos and Afsarmanesh, 2008b, p. 312).

Importantly, collaboration within an ecosystem by no means excludes competition between its actors and with the rest of the world. To increase mutual benefits, the ecosystem firms cooperate on certain business projects, while remaining simultaneously in fierce and open competition on other business projects, which illustrates a new business reality known as “co-opetition” (Drucker, 1993; Porter, 1990). Co-opetition implies a dynamic, continually changing balance between cooperation and competition of legally independent agents, thus constituting a much greater complexity in relationships as compared to linear innovation models of the past (Baldwin and von Hippel, 2011). At the same time, within a long-term common project on developing the whole ecosystem (which is typical only for ecosystems with a joint institutionalized identity, such as innovation clusters), the participating actors collaborate with each other relying on relational contracts and coordinating their activities under a joint strategy. The aim is to commonly meet the challenges of a vigorous global competition. The stronger the direct and feedback linkages in the ecosystem, the higher are the mutual benefits in terms of co-created value added, and vice versa (Porter, 1990). So, collaboration implies several types of complex relationships as well as specific dynamic balances within an ecosystem.

The report “Future Knowledge Ecosystems” (Townsend et al., 2009) posits that within 20–30 years, thanks to development of ICT, production processes will be dispersed among numerous small groups of actors, uniting producers, consumers and intermediaries into flexible, temporary networks formed ad hoc for the period of joint projects. No surprise that the authors call the knowledge-based economy a group economy (Townsend et al., 2009), while other scholars name it a network economy (van Winden et al., 2011). Both definitions are complementary and highlight the collaborative and ecosystemic nature of the future industrial landscape, connecting the new mode of production with a project-based organization of mutual activities by networked actors (Bigham et al., 2015).

2.2. The place of innovation ecosystems in the world of business networks

As literature and evidence suggest, the current world of social and economic networks embraces a very broad variety of network milieus, including networks of different nature, functional specialization, design and scale, extending from local to global entities. A large and well-explored part of this variety belongs to business networks (Smith-Doerr and Powell, 2005; Todeva, 2004), describing interactions both within the business sector (inter-firm networks) and among businesses and other institutional actors (inter-organizational networks).

Literature on networks treats non-hierarchic business networks as relatively stable systems of interactions between legally independent but economically interdependent enterprises. Such networks are numerous and varied; they may be either open-ended or focused on a concrete project task. They can emerge both from value chain relationships (Williamson, 1993) and from agglomerations of co-located companies (Ketels, 2012). Despite widespread opinion, not all business networks rely on collaboration or jointly produce an outcome of mutual benefit (Romero and Molina, 2011; Vargo et al., 2008).

Literature on technical change and social networks explores business networks under different classification criteria, including diversity in pattern and level of mutual activities (Breschi and Malerba, 2005). Networks that can develop more sophisticated patterns of interactions, or display higher organizational complexity, tend to generate more dynamism, agility and innovativeness. According to Ivanova and Leydesdorff (2015), the innovation dynamics of economic systemsand hencethe efficiency of their performance are proportional to their complexity; economic efficiency rises with an increasing number of non-linear network interactions that catalyze self-organization among the system’s elements.

The logic of increasing complexity in interaction patterns gives us grounds to differentiate the numerous and varied business networks in terms of innovation capacity, and hence, in terms of their role in facilitating innovation-led growth. To better understand the origin of innovation ecosystems and their place in the world of business networks, we single out three overlapping varieties, namely, cooperation networks, collaborative networks, and triple helix collaborative networks (Fig. 2).

Fig. 2


Fig. 2. Differentiating innovation capacity of business networks by their internal interaction complexity.

Source: authors’ elaboration based on literature on networks, clusters and innovation.

We refer to cooperation networks a broad variety of business networks in which the development of mutual activities shapes a sustainable ecosystem of interactive linkages. This implies a certain loose coordination of activities but does not necessary include shared responsibility or joint action. Such networks may stay at a relatively low level of organizational complexity in terms of inter-firm and inter-organizational interaction patterns, and hence, may play a supporting or indirect role in facilitating and sustaining innovation-led growth. Cooperation networks enable an environment in which new actors may emerge and abandoned actors may quickly begin again. Sociological literature on networks posits that the formation of a sustainable ecosystem happens at the moment when a spontaneous distribution of horizontal linkages per node in the given network reaches a certain critical level (Barabási, 2002).

The variety of cooperation networks contains a sub-variety of a higher interaction complexity that can be associated with collaboration in its strict definition (as shown in Fig. 1). We regard this sub-variety as collaborative networks and identify their ecosystems with innovation ecosystems, i.e., ecosystems of a higher level, enabling not just support of innovation but co-creation of innovations (new goods, services, assets, etc.). Collaborative networks are usually described in literature as ‘collaborative innovation networks’ to denote typical organizational forms of production in the age of digital technologies. This term was first popularized by P. Gloor (2006) and further explored conceptually (Camarinha-Matos and Afsarmanesh, 2008a) and empirically (Nieto and Santamaría, 2007; Tsai, 2009) by other authors. Such networks may be local, national, transnational or global; they may have different configuration and patterns of collaboration (Camarinha-Matos and Afsarmanesh, 2008a).

In our interpretation, innovation ecosystems are essentially the result and derivative of collaboration-type interactions, i.e., they emerge at the moment when cooperating actors have achieved a certain level of integration concerned with a joint identity, joint strategy and joint goals 1. This approach stems from numerous literature findings that highlight the crucial role of collaboration in facilitating innovation in modern economies. As evidence suggests, the development of innovation ecosystems usually rests on formal and informal communication platforms tailored to enhancing open dialogue and collaborative activities; it also often involves special intermediary organizations meant for the same purpose (National Research Council, 2007). Economic literature and business leaders both treat the term “innovation ecosystems” as the pattern of developing interactions between networked actors, the mode of their innovative activities and their interrelationship with operational context (Kelly, 2015; Mercan and Göktaş, 2011).

In its turn, the variety of collaborative networks contains a sub-variety with even a higher complexity of interaction pattern and mutuality of intention, which we refer to as triple-helix pattern of collaborative networks. The triple-helix concept, elaborated by sociologists (Etzkowitz and Leydesdorff, 1995), describes networks developing a simultaneous pair-wise collaboration of legally independent actors from at least three institutionally different sectors, representing business sector, knowledge generating sector (universities, research institutes, other R&D centers) and public sector (government bodies or agencies)2. Due to such diversified interactive relationships, these networks can generate a highly sophisticated ecosystem, through which the exchange of information and knowledgeas well as co-creation of new knowledge and innovationcan be maximized (Etzkowitz and Leydesdorff, 2000). We identify such ecosystems as ecosystems for continual innovation, which follows from the description of innovation clusters constituting the most studied model of triple helix networks in modern economies (Bode et al., 2010; Breschi and Malerba, 2005; Russell et al., 2011; Smorodinskaya and Katukov, 2016; Todeva, 2004). According to Smorodinskaya (2011), in terms of describing the evolution of innovation-driven growth, the triple helix idea is complementary to the cluster idea rooted in M. Porter’s theory of competitive advantage (Porter, 1990).

In particular, as follows from cluster literature (Porter and Ketels, 2009), innovation clusters constitute a special variety of innovation ecosystems, in which triple-helix interactions enable unique economic effects of innovation synergy, or co-creation of innovative goods and services on a continual basis. This literature argues that innovation clusters can develop an ecosystem, or an organizational milieu, in which motives for continual innovation become maintainable, thus leading to a sustainable rise in productivity (Porter and Ketels, 2009), or ‘competitiveness upgrading’ in terms of Porter (1990). In other words, a triple-helix pattern of collaboration can increase mutual interdependencies within an ecosystem in ways that lead to synergy effects of self-supportive growth, less often observed in less complex ecosystems (Porter, 2003; Porter and Ketels, 2009; Smorodinskaya and Katukov, 2015). Accordingly, innovation clusters are considered the most convenient ecosystem model for both continuous co-creation of innovations and for disseminating them across an economy (Sölvell, 2009).

Comprehensive empirical evidence on unique innovation synergy effects in triple helix networks is still very limited (due to measurement and methodological difficulties), yet such effects are confirmed by initial systemic findings on clusters as recognized poles of growth (Delgado et al., 2010). Also, firms and organizations involved in clusters have been found more dynamic and innovative than those outside them (Fitjar et al., 2014). According to our knowledge, comparative innovative advantages of clusters among other collaborative networks can be explained by a combination of several factors. Besides triple-helix relationships, proximity effects matter, since innovation clusters emerge from agglomerations of geographically co-located actors, which is not typical of more dispersed networks such as transborder value chains. Additionally, in terms of collaboration objectives, the majority of networks are focused on achieving solely individual or mutual economic benefits of participants, while innovation clusters are designed for aggregate synergy effects that can persistently improve competitiveness of both the group of participants and the territory of its location (Bode et al., 2010; Ketels, 2012).

Overall, the world of networks is much broader than the specialized part of them referred to as innovation ecosystems, and the variety of innovation ecosystems is broader than its special sub-variety presented by innovation clusters and other triple-helix networks enabling continual innovation. Such networks operate within the environment of other kinds of networks; they may be born out of the cooperation and collaborative networks or generate new ones; they can form larger and more robust innovation ecosystems through inter-cluster linkages (BSR Stars, 2013). Elements of innovation ecosystems co-exist in the context of both cooperation and competition. All innovation ecosystems based on collaboration are considered a typical organizational formator just a new business modelfor producing goods and values in the twenty first century(MacGregor and Carleton, 2012).

3. Innovation ecosystems within different research streams (literature review)

The term “ecosystem” was brought to social and economic analysis from biology, through the concept of business ecosystems, coined in mid-1990s by Moore (1996). Over time, this term has been applied within a variety of contexts, including far-reaching impacts of globalization and ICT. The particular idea of innovation ecosystems has been also developing in various complimentary directions to bring about a wide diversity in definitions and approaches (Smorodinskaya et al., 2017). For example, an overview of literature reviews on innovation ecosystems, made by Pilinkienė and Mačiulis (2014), embraces a varied scope of entities with different objectives, which range from industrial and business ecosystems to digital business and entrepreneurship ecosystems. Scholars studying networks of the application programming interfaces (APIs) also use the ecosystem concept in describing the emerging platform economy (Hutamäki et al., 2017). And business consulting literature, while applying scholars’ findings to practical purposes, now uses the term ‘ecosystem’ as not just another management buzzword but rather as an increasingly critical unit of analysis that captures the ongoing innovation-led shift in business landscape and entrepreneurs’ mindsets (Kelly, 2015) - the ecosystem milieu.

Besides pure academic studies (f.e., Fukuda and Watanabe, 2008; Tsujimoto et al., 2017), literature on innovation ecosystems now embraces a variety of research and expert communities. Policy issues are elaborated within Scandinavian countries (Swedish governmental agency on innovation systems VINNOVA, REG X Danish Cluster Academy), in USA (National Research Council, Council on Competitiveness, etc.), and within international organizations such as the World Bank, the World Economic Forum, etc. (Nallari and Griffith, 2013; Napier and Kethelz, 2014; WEDC, 2009).

In this section, we observe conceptions of innovation ecosystems within several somewhat interrelated, yet different academic research streams: management studies on strategic relationships; studies on business and inter-firm networks; and studies on innovation policies and competitiveness agenda. We also consider existing interpretations of localized innovation ecosystems, including innovation clusters, and of economy-wide innovation ecosystems. An overview of this literature is helpful in differentiating innovation ecosystems from other concepts for technology-based and innovation-led development.

3.1. Management literature on strategic relationships

This literature explores ecosystems through the lens of the Moore’s initial concept (1996).

Some management studies advocate a firm-centric vision of innovation ecosystems, which seeks to understand how individual agents can best take advantage of the ecosystem that surrounds them. These studies view an innovation ecosystem as a network of interconnected organizations, linked to or operating around a focal firm or a technological platform. Such networks usually incorporate platform participants from two sides — both producers and users, aiming to create and appropriate new value through innovation (Autio and Thomas, 2013). The agents, who may compete and cooperate simultaneously, come together due to a shared purpose of value creation and stay in alignment due to interdependency stemming from their constant need for maintaining their network’s effectiveness. From a firm-centric perspective, strategies for developing an innovation ecosystem include such competencies as ecosystem creation; ecosystem coordination; optimization of business models to take advantage of ecosystem externalities; and the creation of control strategies to ensure value appropriation (Autio and Thomas, 2013). A computational analysis of key words in business and management literature reveals that core topics of innovation ecosystems focus predominantly on developing and managing innovation, research and knowledge (Hajikhani, 2017).

Other management research advocates a view of ecosystems as “structures”, defining them as a certain cohesive configuration of inter-connections and inter-dependences of multiple actors, which emerges not around a focal firm but around a ‘focal value proposition”, i.e., as coherent alignment of assets or decisions, arising from a joint initiative (project, proposal) for co-creation of value (Adner, 2017). This structure-based approach argues that ecosystems matter when the multilateral relationships that underlie a value proposition are not decomposable into multiple bilateral relationships. In other words, it highlights the factor of a motivation-driven cohesion, thus making a departure from a wider treatment of ecosystems as a certain milieu that spontaneously emerges or is orchestrated (Wind et al., 2008) either at the level of firms, or industrial sectors, or regions.

3.2. Economic and sociological literature on inter-firm and other business networks

This literature applies the term ‘innovation ecosystem’ to a broad range of networks that either co-produce innovations directly or co-create a favorable environment for their emergence and dissemination. In this interpretation, innovation ecosystems may assume various scales and designs – be they regional innovation hubs, nation-wide innovation communities, local inter-firm networks, small ad-hoc groups of individuals collaborating under a common project, or global-wide value chains, etc. (Bramwell et al., 2012).

Particularly, some scholars view innovation ecosystems as communities whose members combine their resources in a mutually beneficial way, with a shared goal of creating innovative results (Chessell, 2008) (cited in Ranga, 2011). Others associate them with networks of sustainable linkages between individuals, organizations and their decisions, which emerge from a shared vision of desirable transformations, evolve through agile reconfiguration and provide economic context (milieu) to catalyze innovation and growth (Russell et al., 2011). These scholars argue that the term “sustainable” is of key importance in describing network links in the organic nature of innovation ecosystems, since it reflects functional interdependences and a certain level of integrity between legally independent actors.

The economic sociology research on business networks considers the formation of ecosystems as an “emergence” processtypical for complex adaptive systems. In particular, Padgett and Powell (2013) describe the coalescence of separate multi-layer linkages into an integrated network, highlighting the role of personal relationships and business agreements as natural ecosystem infrastructure. Literature on inter-firm networks also analyzes the design of innovation ecosystems as complex network-based structures, with this approach used to study value chain networks and ecosystems in a variety of industries (Adner, 2012; Basole and Rouse, 2008; Rosenkopf and Schilling, 2008).

3.3. Economic literature on innovation policy and competitiveness agenda

This literature is policy-oriented, and hence, directly deals with development of organizational models of innovation. The term ‘innovation ecosystem’ has been evidenced in this stream since the mid-2000′s, as a historical derivative from the previously coined term ‘innovation system’. In the late industrial era, prominent economists engaged in innovation studies, known as the conceptual stream of Lundval, Nelson and Freeman (Freeman, 1995; Lundvall, 1992; Nelson, 1993), elaborated the concept of national innovation systems, aiming to organize an economy-wide technological support for domestic firms that were competing in international markets (Schot and Steinmueller, 2016). From the very start, this concept treated innovation as a non-linear process, the result of network-based cooperation between innovating firms and various other actors (competing firms, universities, public and private research institutes, as well as suppliers and customers), with governments supporting these complex networks with funding and other incentives (OECD, 1999).

In practice, however, the innovation systems of the 1990s, established by national governments (and later on, by regional and local authorities), were designed largely as static government-led structures, consisting of a predefined composition of actors and a program-built infrastructure. In line with the initial vision of scholars, these systems maintained a government-centric and producer-centric focus in innovation process (OECD, 1999; Schot and Steinmueller, 2016), while decision-makers and managers associated their successful performance not with collaborative interactions but rather with a critical mass of innovative firms and designated infrastructure (WEF, 2013). Within the business sector itself, companies were often engaged in similar programs meant for building formal innovation systems, such as the Microelectronics Manufacturing Consortium (Gibson and Rogers, 1994).

As recognized today, the early-built national and regional innovation systems failed to meet the growing complexity of the innovation process, since they were lacking instruments for developing collaboration in its above-described meaning (Schot and Steinmueller, 2016). In order to remedy failures of previous innovation policies, the conceptual stream gave way to the term ‘innovation ecosystem’, mainly as an analytical tool to consider how public policies could facilitate innovation by strengthening interactive linkages within the existing innovation systems (Wessner, 2005). Meanwhile, since the mid-2000′s, the innovation and competitiveness agenda in developed countries has turned to a more complex framing, concerned with so called systemicor continual innovation, oriented toward persistent transformative change in the economy and society (Schot and Steinmueller, 2016). Ever since, relevant studies have assumed a more substantial ecosystem perspective, receiving support from Edquist (2005) and incorporating complementary research streams, such as cluster literature stemming from Porter’s competitiveness theory (Porter, 1990) or triple helix concept of Etzkowitz and Leydesdorff (2000).