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

Overall, complexity thinking orients modern innovation and economic growth policies to enhancing collaboration within and among existing and emerging ecosystems across local to global scales, thus leading economies to more robust development in the global multi-equilibrium environment. Proliferation of networks and their ecosystems shapes the modern mode of production, motivating economies to become both more cohesive to meet the challenges of high uncertainty, and simultaneously, more innovative, to enhance their global competitiveness and self-sustainability.

5.2. Implementation of ecosystem approach

Efforts for developing the ecosystem landscape ultimately aim at providing an intensive diffusion of newly emerging technologies and innovations across sectors and regions. Schot and Steinmueller (2016) argue that in developed countries innovation policies are now less frequently concentrated on program support in the way of inputs into R&D, as took place in 1960–1980s, when the research sector was seen as a single birthplace of ideas for innovation. Moreover, such policies are also progressively less focused on building national innovation systems, as was typical for 1980–1990s and for early 2000s, when non-linear innovation was gaining momentum. Instead, since 2010s, governments increasingly have concentrated their efforts on facilitating the formation of numerous localized ecosystems to realize the so called system innovation policy approach aimed at providing a continual transformative change in the economy and society (Schot and Steinmueller, 2016). Plainly speaking, to tackle modern development problems that are mostly systemic in nature, countries are expected to involve manifold domestic and global actors in mutual collaborative activities, or just in ecosystemic format of producing innovations.

In contrast to linear innovation, interactive ecosystemic innovation is compatible with a holistic view of knowledge-based economies, embracing both industrial and social spheres. It expects economies to persistently upgrade their socio-technological structures as means for generating endogenous sources for further growth, and to extend their collaborative linkages further, integrating domestic businesses through new partnerships into new or progressively larger ecosystems formed by transnational and global networks (OECD, 2015a). For example, local cluster ecosystems can serve as multi-faceted tools for upgrading industrial structures of national economies, while collaboration between clusters from different geographical locations leads to the evolvement of global value chains and global production networks that can shape more powerful ecosystems for continual innovation. In effect, the regime of ecosystemic innovation attempts a collaborative institutional environment that goes beyond geography to enable unhinderedintensive and even unintended knowledge spillovers across an economy and around the world.

In non-linear economies, which are far from equilibrium, the traditional practice of achieving sustainability by means of monetary and fiscal macro-stimulators becomes increasingly less effective, giving way to ecosystem-oriented policies focused on organizational incentives for raising productivity. Such policies look for enhancing innovation synergies generated by collaboration within and between clusters (Ketels and Memedovic, 2008; Warwick and Nolan, 2014; WEF, 2013). They aim to accelerate regional clustering by means of institutional improvements and to achieve a certain critical mass of triple-helix partnerships. The global front-runners in imbedding this approach into economic growth strategies are Nordic nations, who have been prioritizing institutional growth stimulators for more than two decades to further develop an ecosystem landscape across all sectors and build robust and technologically advanced economic models (BCG, 2014; BDF, 2011, 2014; Smorodinskaya, 2015).

The ongoing organizational transformation of economies is accompanied by a deconstruction of hierarchies both at micro- and macro-levels of social activity. In a growing number of countries private firms and public bodies are meeting the challenge of restructuring, transforming themselves from vertically built entities into more flexible and horizontally oriented (Smith-Doerr and Powell, 2005; Sölvell, 2012). In many cases, governments of both developed and developing nations (in Europe, East Asia, elsewhere) have yet to launch country-wide structural reforms to adjust the domestic institutional context to the global-wide emanation of ecosystemic environment. Many are still path-dependent on excessive hierarchic linkages. This especially concerns emerging markets, as well as some developed countries (like Japan or Korea), whose markets have been much less liberalized than, say, in USA or Canada (Hill et al., 2012). The context-improving policies are usually accompanied by cluster-supportive programs and other measures for encouraging university-industry partnerships across localities and sectors (Christensen et al., 2011; Ketels, 2015; Smorodinskaya and Katukov, 2016).

At the global level, the evolution of the ecosystemic industrial landscape assumes many complex forms, from the overwhelming proliferation of global value chains (Smorodinskaya et al., 2017) to the appearance of so called ‘functional regions’, shaped through collaboration of networked partners over and beyond administrative boundaries of localities or countries. In addition to Silicon Valley (as an early functional region that has been widely published), various similar ecosystems on a variety of scales now appear in North America, Europe, Asia, worldwide. For example, Denmark and Sweden have commonly created a highly innovative Øresund region, organized as a complex ecosystem of duplicate triple-helix linkages among neighboring partners from both countries (Karlsson et al., 2010). The whole Baltic Sea Region is now developing as an integrated macro-region by means of transnational collaboration of triple helix actors from 10 administrative coastal territories (countries and regions), which are engaged in various joint cluster projects launched under the EU Strategy for the Baltic Sea Region (BSR Stars, 2013; European Commission, 2012). Moreover, since 2010s, upon taking the experience of the Baltic Sea Region as a blue print, the EU is progressively transforming its classical model of European integration into a much more flexible and innovation-oriented model, aimed at formation in Europe of several macro-regional ecosystems through trans-border collaboration (European Parliament, 2015; Interact Programme, 2017)5. Recognizing the trans-border nature of innovation ecosystems, the EU also builds new infrastructures across Europe, such as EIT Digital (Still et al., 2014).

The policy approach to enable economy-wide innovation ecosystems has become more complex. Today, such ecosystems are seen not as rigid structures, focused on involvement of a certain critical mass of innovative actors and infrastructure, but rather as holistic and agile social communities able to flexibly reconfigure their structure and assets under new innovation projects. Traditional thinking deals with the development of industries and institutes as such (which at present is typical for emerging market economies), while complexity thinking focuses on building a more agile yet cohesive institutional and business environment (which is expected from all modern economies, both developed and developing). As stems from cluster literature (Ketels, 2012; Porter, 2003; Sölvell, 2009) and from the Global competitiveness index of the World Economic Forum (Porter et al., 2008), countries and regions now need continual improvement of the micro-level business environment to eliminate emerging barriers that prevent the economy from becoming ever more collaborative and horizontally inter-connected. This policy approach encourages the agile origination of new inter-firm networks, new university-industry partnerships, innovation clusters, and further inter-cluster linkages forming trans-border value chains. The crucial aim, also clearly pronounced in the new industrial policy gaining momentum since 2010s (Warwick, 2013), is to obtain and sustain through facilitation of domestic organizational complexity the needed level of innovativeness, enabling national economies to achieve a continual rise in total productivity and thus to meet crucial challenges of both the global competition and the high uncertainty.

6. Conclusion

6.1. Main findings

Innovation ecosystems are not built like traditional systems in a top-down way; rather they emanate spontaneously from deliberate collaborative activities of agentsbased on their market-confirmed motivations. In particular, the design of innovation clusters is evolved through a combination of market forces, organizational efforts of triple helix actors, and value transactions. More exactly, innovation ecosystems constitute special organizational spacesor a sophisticated milieu of actorsassets and linkages, generated by collaborative activities within and among networks. Collaborative networks of various forms, sizes and profiles can play the role of modern-type organizations meant for a collective decision-making and collective action, while innovation ecosystems can be viewed as functionally inseparable organizational continua of such networks, relevant for interactive innovation and dispersed patterns of production.

The literature on complexity highlights the interconnectedness of economic activities across the world, suggesting that the global economy should be seen not as a system of interlinked economic units focused on national states but rather as a complex spacecomprised of networks of networks (Dicken, 2015). This growing complexity of economic space must be taken as a fundamental global trend, driven by ICT-transformation and proliferation of digital technologies.

Proliferation of networks and their ecosystems, in step with digital technologies, accelerates the globally emerging network order that manifests horizontal, peer-to-peer linkages among different agents (MacGregor and Carleton, 2012; Slaughter, 2004; Smorodinskaya and Katukov, 2015). Networks are incomparably more agile structures than traditional hierarchies, and simultaneously, more integrated structures than traditional markets, thus making a functional hybrid out of both (Powell and Grodal, 2005; Thompson, 2003). According to Oliver Williamson, the network order rests upon the driving forces of social communications and interpersonal arrangements, which significantly enlarges the speed and variety of economic exchanges (Williamson, 1993). As a result, this new order opens crucially wider opportunities for developing economies and societies as compared both with the market order, which rests on impersonal exchanges and atomistic transactions, and with hierarchic order that personalizes transactions but demands its own model of governance for each transaction (Williamson, 2005). In other words, economic growth is now connected with formation of a new order based on the ability of individuals and firms to unite themselves in networks and to effectively use information and knowledge in the course of communication (Hidalgo, 2015).

To better understand these new opportunities for innovation and growth, we clarified differences between agile heterarchical ecosystems and rigid hierarchic systems through the lens of complexity science. This lens helped us introduce additional arguments in favor of applying the ecosystem approach to modern economic development as compared to the system approach, as well as to better explain validity of the term ‘ecosystems’ versus ‘systems’ — the issue remaining a point of discussion among scholars (Oh et al., 2016). We showed evident similarities between the holistic and dynamic nature of innovation ecosystems, on the one hand, and complex adaptive systems (CAS), on the other. Evolving and developing as a persistent organizational continuum of collaborative networks, or just as their functionally inseparable environment, innovation ecosystems display such typical properties of CAS as emergence, synergy, self-organization, self-governance, and self-adaptation to a changing context. These properties interconnect the innovation-driven model of growthbased on interactive and continual co-creation of new valueswith the world of non-linearity that generates persistent and highly uncertain changes. One can imagine the cohesive ecosystem design of knowledge-based economies, as well as endogenous sources of their innovation dynamics, evolving as an aggregate result of collaboration within and among networks.

Managerial, sociological and economic underpinnings of innovation ecosystems, covered by our literature review, articulated from various angles the key role of collaboration in generating new ideas and bringing them to market. Particularly, studies on innovation policy and competitiveness agenda have made an indicative terminological drift from “innovation systems” to “innovation ecosystems”, acknowledging that the promotion of collaborative partnerships across the economy matters more for achieving sustainable growth than the support of innovative agents as such. We reinforced this argument by considering the very process of collaboration as the result of growing complexity in interaction patterns among networked agents (Fig. 1).

Upon assuming that networks with more sophisticated patterns of interactions can generate more powerful incentives and capabilities for innovation, we identified the functional place of innovation ecosystems in the world of business networks, as well as the place of innovation clusters among other innovation ecosystems (Fig. 2). The ecosystem approach to innovation manifests an entirely new mode of productiontypical for the age of non-linearity: new goods and values are now co-created at the level of collaborative innovation networks, through interactive relationships of and synergies derived from creative reshuffling (agile assembling and reassembling) of agents’ shared assets, both tangible and intangible, in a complementary way and in various novel configurations. Properties of CAS reveal this collaborative pattern of producing innovations, highlighting a holistic integrity, reciprocity and unique functional interdependencies among stakeholders within an ecosystem. A continual co-production of new values shapes the innovation-driven mechanism of self-supportive growth. Importantly, innovation ecosystems constitute a clear departure not just from linear models of innovation but also from the model of open innovation (Chesbrough, 2003; von Hippel, 2005): the latter doesn’t imply multilateral coordination of inter-firm activities under a shared project, shared goals, shared commitments, and shared identity, as takes place in collaborative innovation networks.

The key argument of cluster literature says that well-organized clusters can co-create innovations on a continual basis. We illustrated this argument by describing ecosystems of innovation clusters in the context of complexity theory, and their aggregate synergy effects, in the context of triple-helix concept. Though collaboration in innovation clusters is ultimately oriented toward the implementation of joint business projects, the success of these projects directly depends on the enhancement of collaborative activities as such. The development of collaboration in clusters, and hence, their competitiveness in the globalized world economy, benefits from effective intermediating efforts of a specialized cluster organization and the discipline of a common cluster initiative. Under the pressures of open global competition, regional innovation clusters are seeking to develop their unique, smart specializations in ways that enable them to become geographically localized network nodes of global value chains (in which new final products are now ultimately assembled and delivered to end users as consumers). By describing a cluster ecosystem (Fig. 3) and its institutional features, we demonstrated the level of organizational complexity to which modern economies aspire in order to generate aggregate externalities that can provide innovativeness, harmonization, and self-sustainable growth.

There exists a distinct connection between harmonization of non-linear systems and collaborative models of governance, which are now gradually replacing the traditional hierarchic model. In forward-looking innovation clusters, collaborative governance relies on regular face-to face communication between stakeholders, their relational contracts, high mutual trust and exclusively collective decision-making, not depending on individual powers of any major cluster participant. Following the cluster literature findings discussed above, we believe that in the coming decades regions and countries will increasingly use the advantages of localized cluster ecosystems for further embedding collaborative model of governance at the region-wide level, among regions, and further extending this model of horizontal consensus-building nationally and internationally, thus getting closer and closer to inclusive economic policy-making, both within countries and across the world. Further proliferation of digital technologies will additionally motivate this direction, increasing interdependence in business relationships – both locally and globally, and enhancing the emergence of ecosystems based on self-organization and peer-to-peer relationships.

We have argued that the ecosystem-based industrial landscape of any economy, from local to global, evolves in a fractal-type way, through increasing organizational complexity. A region-wide innovation ecosystem arises from interactions and feedbacks among many localized collaborative networks. Similarly, a nation-wide innovation ecosystem is the result of a larger complexity of cross-linkages, and so on, that expand through inter-cluster collaboration and transborder networks across localities to encompass new institutional as well as social and geographical connections. However, regardless of the level of an economy, be it a local community (such as Silicon Valley in California or the Basque country in Catalonia) or a transnational macro-region (like European macro-regions now shaped under the corresponding EU strategies), large-scale innovation-led growth will rely on the same collaborative synergy effects that can be observed in a localized innovation cluster.

Table 1 in part 5 generalizes our investigation of ecosystems at the level of new economic thinking. It compares linear and non-linear approaches to the organizational design of economies, and hence, to policies on innovation and growth. The linear, or system approach reflects a traditional, mostly mechanistic perception of economies as static and closed systems. The non-linear, or ecosystem approach perceives economies as open, dynamic and complex adaptive systems undergoing continual transformations. The contrast of ecosystems versus systems is ultimately about the complex non-linearity of the twenty first century versus the more simplistic views of the past.

6.2. Practical implications

In many countries, further innovative and technology-based development is now running into hierarchic barriers built by institutional and political regimes of the past. This is a common challenge for all types of economies, especially for less developed ones. Countries are facing not just a classical market or state failure but rather a systemic failure, concerned with insufficient horizontal interconnectedness of economies to provide smooth knowledge spillovers. To meet this challenge, they need to cultivate more complexecosystem thinking among decision makers of all levels, both in the field of domestic and foreign policies, in organizations dedicated to knowledge creation and dissemination, and in established enterprises as well as in startups. Government bodies, enterprise managers and program directors, all will benefit from adjusting their strategies and practices to the new thinking.

In particular, to support the self-transformation of economies from systems to ecosystems, governments at all levels are now called to nurture a political, economic and institutional environment that enables a smooth process of a continual emergence of new innovative firms, collaborative networks, triple helix partnerships, transborder value chains, and other ecosystems. The enhancement of trust and collaboration between various market actors (among businesses, between business and academia, etc.), as well as within the existing networks, constitutes the primary objective of the new industrial policy of the 2010s (Warwick, 2013).

At this background, the heart of the agenda on increasing the organizational complexity of economies should be seen in promotion of regional innovation clusters and other triple-helix partnerships as key building blocks of knowledge-based economies. To provide a favorable context for the self-emergence and development of strong clusters, national and regional governments are advised to follow a set of widely recognized ‘golden rules’ in their cluster supportive policies (European Commission, 2016; Ketels, 2013; Smorodinskaya and Katukov, 2016). Cluster programs must adapt at the speed of change. Both the internal business environment and the whole institutional context of the region require adjustments that are favorable for inter-firm competition and inter-firm collaboration alike (Porter, 1990).

At the level of major companies, the ecosystem thinking implies a more intensive transformation of traditional hierarchies into agile and dispersed global networks able to enter any local innovation cluster throughout the world (Sölvell, 2012). With a network-based design and networking strategies, businesses can reduce actual and opportunity costs to influence new technological standards (van de Kaa, 2017). According to Karakas (2009), in the world that increasingly celebrates creativity, connectivity, collaboration, convergence, and community, this new thinking provides advantages also inside organizations, calling managers to empower rather than manage employees. It is the expansion of ecosystem thinking that allows companies to tap into the formation of the global brain and bring best global talents together to form cross-disciplinary teams.

Noticeably, the ecosystem thinking puts the development of social capital and interpersonal relationships at the forefront of public and business practices, which serves, inter alia, as facilitator for the global-wide flow of talent, information and financial resources (Russell et al., 2015). Noticeably, the relational perspective of guanxi (connections), which underlies Chinese culture, conceives all entities as coexisting within the context of one another, thus motivating individuals to express alternative views and innovation opportunities (Chen and Miller, 2011).

A special attention should be paid to orchestration of clusters and other innovation ecosystems. As complex projects based on collaboration of legally independent agents, ecosystems can’t be managed in traditional ways typical for classical public or business managers. Rather they require orchestration and leadership, provided by special project leaders. Regarding this experience, the vision of modern economies as complex adaptive systems suggests the following practical approaches:

  • Increase the number of network nodes, considering the basic network effect. Efforts in this direction are likely to have a positive impact on promoting interaction complexity (Autio and Thomas, 2013; Gloor, 2006);
  • Promote quantity and quality of feedback linkages, since these parameters crucially determine the ability of an ecosystem for agile reconfigurations (Sölvell, 2009). Ecosystem orchestrators should refrain from creating an overprotective organizational milieu, but rather support a certain level of non-determinant behavior of agents, encouraging them to act in multiple independent ways (to find its own ‘path’) and thus to enhance their capacity for co-creation of innovation through serendipitous coupling of shared assets and competences;
  • Encourage autonomous relational contracts. Innovation ecosystems rely on relational contracts of agents and require a collaborative (heterarchical) model of governance, which includes shared vision, dispersed patterns of coordination (Mitleton-Kelly, 1997), self-governance, and multiple independent paths (Martin and Sunley, 2007). All these practices suggest that relational contracts must be created autonomously and persistently revised — a way enabling a continual adaptation of the ecosystem to the complex, continually changing global business environment;
  • Facilitate faster and more directed removal of inner and outer communication gaps, since such gaps are seen as key barriers to co-creation of innovations in the ecosystem (Sölvell, 2015);
  • Provide monitoring at the holistic level of the ecosystem. While orchestrating the ecosystem, the leader should focus on its performance as a whole, considering overall knowledge flows and overall cluster goals, rather than measure its results at a granular implementation level;
  • Cultivate shared vision of interdependencies and collective resources. According to Elinor Ostrom, leadership in innovation ecosystems implies, inter alia, the cultivation of a shared vision of collective resources (Ostrom, 1990). It also implies the leader’s focus on functional interdependencies of agents, a continual vigilance to remove barriers impeding the emergence of new fractal-type re-combinations of agents and assets, and a balance of exploration and exploitation (Valkokari, 2015) in promoting growth.

In today’s multi-sector environment, innovation leaders must effectively cross institutional borders to establish partnerships for collaborationas well as master the practice of collaborative governance and intra-organizational leadership. In particular, cluster leadership requires a sense of mutuality and connectedness, as well as facilitation of skills and actions that help a diverse group of actors work together effectively. Specialized cluster organizations and management teams that aim to orchestrate and develop sustainable innovation ecosystems should direct their activities at accelerating mutual learning processes, inducing joint commitments and enhancing trust among partners (Russell et al., 2016; Sölvell and Williams, 2013).

6.3. Policy implications

The collaborative approach of producing innovations within ecosystems alters traditional policies for enhancing productivity and growth, thus having serious implications for effective policy-making.

First, public and private sectors have no longer separate purposes in terms of ensuring a sustainable economic growth. Instead, they have to build tools for interactive dialogue and to work jointly, both when elaborating development strategies and when implementing them. For example, such work for establishing nation-wide platforms for communication and networking has been launched in the US by Harvard Business School under M. Porter’s initiative (Porter et al., 2013). Many other countries have also started to introduce similar platforms, seeing them as building blocks for developing regional and national innovation ecosystems.

Second, government programs prioritizing certain groups of businesses, industries or technologies, while quite sufficient in the industrial era, are no longer effective in the age of accelerating technological changes and the growing organizational complexity. In our times, the improvement of institutional and business contexts, in which new technologies are produced and appliedmatters more than targeting the improvement of rapidly changing technologies themselves. Besides, in order to move the economy to the next technological trajectory, governments will be advantaged to not only focus on upgrading technologies for traditional sectors but also on developing new organizational formats for human interactions that can facilitate the institutional adjustments (Ivanova and Leydesdorff, 2015).

Indicatively, OECD’s paper on the future of productivity (OECD, 2015c) calls countries to re-design their institutions. Pursuing this point further, we argue that to meet challenges of global competition, countries need active policies to re-design their institutional and industrial landscape in an ecosystem-based way that acknowledges and leverages global interdependencies. The better an ecosystem-wide landscape is developed, and the greater the number of new collaborative partnerships that may emerge, then the higher is the innovation capacity of that given economy and, hence, its capacity for sustainable growth under high uncertainty. On the contrary, countries that fail to connect their innovation and growth strategies with the elimination of hierarchic barriers and the promotion of collaborative partnerships face simplification of their industrial structure, and as a result, a growing vulnerability to the pressures of global competition (good examples can be found in a number of post-Soviet economies, including Russia and Belorussia).

Thirdly, the very nature of government interventions is drastically changing. In a non-linear and the increasingly ‘flat’ ecosystemic world, governments can no longer be either a supreme administrator or a ‘night watchman’. Though collaborative forms of producing innovations are nucleated by market forces, the competitive advantage goes to those countries and territories, in which government bodies of all levels are aligning new functional roles of facilitators and intermediators of collaborative interactions, both within and among ecosystems (Smorodinskaya, 2015). This implies intentional strategies and programs of various kinds. Noteworthy, even the US, with its highly advanced and liberalized markets, has recently introduced a ‘soft’ industrial policy which supports triple helixes in localities to help the emergence of ‘production ecosystems’ for developing advanced manufacturing (Locke and Wellhausen, 2013).

Additionally, the ecosystem approach demands re-adjustment of the very procedure of strategic planning. Under non-linearity, economies undergo continual transformative change both at micro- and macro-levels, which makes it no longer productive to forecast future development tendencies through traditional extrapolation of previous experience (Kidd, 2008). Policy-makers should instead consider high uncertainty and rely on principles from complexity science. Particularly, as recommended by OECD (2009), it is more reasonable to reveal trends and probabilities than to forecast events, to build dynamic relationships than fix rules or laws, to focus on policy impacts than concentrate on policy control, etc. Policymakers and forecasting analysts are advised to avoid too heavy reliance on traditional models in elaborating decisions. In many institutional-choice situations, even in the linear world, an informed decision often cannot be found between one alternative and the status quo, but rather among a series of proposed alternatives (Heckathorn and Maser, 1987). They are advised to carefully monitor changes in assumptions made during planning and be ready to implement an approach of late-binding decisions for issues involving substantial ambiguity or rapid change. These cautions are especially relevant regarding the increasingly complex and transformative world of networks: though some complex systems scientists (f.e., Holland, 2002) argue in favour of new modelling to anticipate the future, other scholars, also engaged in studying complexity (f.e., Martin & Sunley, 2007), warn that the model-generated economic landscapes may not be realistic in relation to those actually occurring.

6.4. Further study

Further study of collaborative networks and innovation ecosystems offers many opportunities for scholarly endeavours and for experimentation with practical applications. To facilitate this, the advancement of the ecosystem approach needs more interdisciplinary research.

Network interactions make the world more cohesive and interconnected, thus allowing it to adapt to the acceleration of technology development and the high volatility of globalized markets. The level of interconnectedness is rapidly increasing, with new and more legally independent actors self-initiating collaborations that persist over time, as well as new and more types of data generated and accessible. In the modern networked world, knowledge dissemination has become much more democratized (von Hippel, 2005), new experimental models of ecosystems for innovation and technology transfer are appearing worldwide (Butler and Gibson, 2011; Gibson and Rogers, 1994), and new connectivity approaches to learning are emerging (Dabbagh et al., 2016). The direct involvement of consumers in production of goods and services within clusters is further reshaping market economies, with their price coordination mechanisms, into a modern information economy largely relying on relational contracts (Hagel and Singer, 1999). Collaborative social and business practices arein many respectsoutpacing the existing economic thinking.

The global-wide transformation of traditional systems into network-based ecosystems needs further research at the intersections of sociology and economics with other studies (business network literature, institutional literature on networks, industrial organization literature, literature on innovation and growth, technology management, etc.). To address the inherent complexity in innovation ecosystems, economists, sociologists, policy analysts, management scholars, and technologists will be advantaged to increase collaboration for joint elaboration of conceptual categories, as well as theoretical and empirical approaches that can better describe emergent phenomena, parameters and patterns.

OECD believes the common policy challenge of affording national economies more resilience and robustness under high uncertainty requires a less mechanistic perception of world order, viewing the global economy and its national sections as complex adaptive systems (OECD, 2015b). This stance is associated with deepening the ecosystem approach in several directions.

To begin, future studies on science, technology and innovation (STI) and the knowledge-based economy must more explicitly recognize that innovation-driven growth and persistent social transformation are companions. Human actors in business networks also participate in social networks. A critical need exists to understand what constitutes the new economic community, the new civic culture, the nested relationships that inform effective systems for social knowledge management at the local and regional scale (Gertler and Wolfe, 2002). What makes multi-level self-governance effective? How will global models of learning, R&D, and collective intelligence influence forecasting and planning in this transformed world? How fast will they evolve? We need to learn more about the interdependence between technological and social changes, on how the growing complexity in technological systems generates complexity in societies and economies, and vice versa. Innovation ecosystems are finally about the social, organizational and cultural shifts that facilitate the formation of the knowledge-based economy.

Second, since multifold innovation ecosystems are becoming typical structural units of modern economies, we need to develop additional criteria for their classification. This especially concerns ecosystems for continual innovation: among them, innovation clusters are the most studied model so far, while various other triple-helix collaborative networks (as well as emerging helices with more pillars) await their researchers. We also need further studies of ecosystems as the transactional spaces for co-creation of innovative value under interactive collaboration and globally dispersed production — for both legally interdependent and legally dependent collaborations. Reconciling empirical instances of innovation ecosystems, innovation clusters and cluster initiatives with theories of institutional change may serve to enrich theoretical perspectives as well as illuminate strategies for ecosystem orchestration. Further theoretical thinking can help scholars identify new variables and elaborate new development models relevant for non-linear environments.

Third, scholars can enrich their perception of the emerging ecosystem-based landscape of modern economies by investigating them through the lens of the evolving complexity economics (Beinhocker, 2006), which embraces adjacent research streams that have connotations with holistic concepts, fractal recursion, interaction of direct and feedback linkages, metabolic pathways, synergy effects, mechanisms for network synchronization and quantization, compensatory structures, and the positive effect of negative interactions, etc. In particular, a promising way to explore collaborative models of governance in various types of innovation ecosystems, and particularly, in innovation clusters, follows from the concept of ‘commons’, developed by neoinstitutional economists (Ostrom, 1990), with important contributions made by cluster literature (Sölvell and Williams, 2013).

<p «>Acknowledgments<p >The authors thank Daniel Katukov, from the Institute of Economics of the Russian Academy of Sciences, for his skillful assistance in elaborating images and his helpful input and conceptual insights on academic literature throughout the preparation of this paper.


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