1. Enhancing innovation ecosystems in Latvia through anticipatory governance
Applying anticipatory innovation governance (AIG) approaches to innovation ecosystems allows governments to harness the collective intelligence of diverse stakeholders and lead change. Incorporating strategic foresight and other futures approaches directly in innovation ecosystem management enables governments to stimulate transformative innovation and build up new value chains. This chapter outlines the promise of anticipatory innovation ecosystems and describes how the OECD has worked with the Investment and Development Agency of Latvia (LIAA) to explore how they might be fostered through appropriate public governance.
The fast-paced change occurring in domains as different as artificial intelligence, biotechnology and environmental protection presents governments with both opportunities and challenges. On the one hand, innovations resulting from the rapid development of new technologies have the potential to contribute to national prosperity and address grand challenges such as climate change and inequality. On the other, not all innovations are successful and the impacts of new innovations on society, individuals and the environment are uncertain (OECD, 2018[1]). The consequences of innovation may run counter to established policy objectives and create social and environmental disruption. In this context, the ability of public sector to proactively govern innovation is increasingly important so that it can be directed towards pathways that are likely to deliver collective benefits, and away from negative consequences.
This capacity for proactivity is particularly important for small states, which can lack the critical mass in their research and development (R&D) and markets to test upscaling processes, but which can leverage close networks of stakeholders and less complex administrations to experiment in areas of promising early innovation (Tõnurist, 2017[2]). For small states entering early into emerging value chains, finding critical technology niches is essential for long-term value-added productivity growth. Industry and markets in general spot these opportunities, but for smaller economies this is often not a reliable strategy. Governments often need to provide “patient capital” and direction to overcome uncertainty in early investments to build up new economic sectors (Mazzucato, 2015[3]). In smaller countries, where resources are limited, this relies on the capacity of government to detect viable investments and opportunities in its economy and partner regionally to develop place-based advantages (proximity helps to facilitate trust between partners, lower transaction costs, and make use of system externalities) (Tõnurist and Kattel, 2016[4]). Furthermore, as innovation increasingly depends on global value chains, creating local innovation hubs becomes even more important as embeddedness and existing synergies in local networks predetermines the ease in which companies are able to move from country to country and also the ability of the country to attract more investments (Bergek et al., 2015[5]). In its Guidelines for National Industrial Policy 2021-2027, the government of Latvia has recognised these challenges, opportunities, and strengths small states face. The Guidelines detail Latvia’s ambitions to promote the identification and development of pathways for innovation through a combination of bottom-up business and innovation discovery and more responsive policymaking (Government of Latvia, 2021[6]).
Anticipatory innovation governance (AIG) is a future oriented and opportunity focused approach that can be applied in Latvia to help achieve these aims. AIG sets out mechanisms that allow governments to develop and act on knowledge about the future in order to identify potential opportunities proactively and steward change through ‘anticipatory’ innovation (Box 1.3 describes different facets of innovation). Effective AIG is dependent on the ability of government to leverage the knowledge and experiences of diverse stakeholders through networks and partnerships and public participation. Unlocking the collective intelligence of actors across government, industry, research and civil society can allow a wide range of signals about the future to be collected and interpreted, providing all parties with valuable insights for strategic decision-making. This knowledge can be used to direct innovation towards more inclusive, high-potential outcomes (OECD, 2018[1]). It may also be applied by governments to inform policy in other areas affected by rapid change and uncertainty, such as the labour market (Box 1.1) or policy areas which face complex challenges and are dependent on society-wide solutions (such as aging) (Box 1.2).
The world of work is continuously transformed by the complex interaction of trends such as automation, climate change and an aging population. The changes they precipitate affect the demand for skills: jobs and tasks in one sector may disappear while others emerge which require new combinations of competencies. In addition, these trends alter demands for the provision of learning: new forms of self-employment such as ‘gig-work’ may create opportunities for individuals to learn at times that suit them, but they also challenge expectations about employers’ role in skill development.
Against this backdrop, Finland has recognised the need for a reform of continuous learning to create a system that is able to anticipate and respond to changes in the demand for skills and learning across the labour market and broader society. The OECD worked closely with representatives of the Finnish government to explore how the anticipatory innovation governance (AIG) framework could inform the governance such a system.
Enhancing anticipatory innovation governance through the collective intelligence of diverse stakeholders
The challenge of developing an anticipatory system for continuous learning in Finland was characterised by two key features.
First, the breadth of stakeholders int the continuous learning system (including businesses and trade unions) and the autonomy of municipalities and education providers in Finland was likely to mean that hierarchically imposed changes would experience resistance. Furthermore, central government was unlikely to have sufficient capacity for processing information about the actions and effects of sub-agencies, making centralised management difficult to achieve.
Second, information about future changes to jobs and skills is often contested and subject to different ideological interpretations, for instance about the causes of low levels of engagement in adult learning. This means that deliberation is likely to be required in to create an evidence base for decision making that is perceived as legitimate by a wide range of stakeholders.
The OECD proposed that Finland build on these features by developing a governance system that fosters networks and partnerships between governmental and non-governmental stakeholders and facilitates collective sense-making so that information about the changing context of continuous learning is gathered from a wide range of sources, and that policy decisions are based on consistently understood evidence and perceived as legitimate and realistic.
In this way, an ecosystem-like approach to generating and acting on knowledge about the future is applicable to policy contexts outside of innovation.
Source: OECD (2022[7]), Anticipatory Innovation Governance Model in Finland: Towards a New Way of Governing, https://doi.org/10.1787/a31e7a9a-en.
Seoul50Plus (“50+”) is an innovative mix of social welfare, employment, and lifelong learning policies geared towards supporting people 50 and older in a rapidly aging society. With an ecosystem of stakeholders from the target group and supporting NGOs, the aim is to explore and anticipate new life models, life transitions and social participation in a centennial society.
The work is coordinated by the Seoul 50+ Foundation that carries out needs assessments and coordinates policy responses aimed to address social security blind spots in a new type of a society. Coordination has been necessary to ensure that gaps and overlaps with existing welfare services are minimised, and to ensure that a full range of services can be provided through partnership with private and civil society organisations. A comprehensive infrastructure comprising ‘50+ campuses’ and ‘50+ centres’ built on multi-sectoral collaboration provide services to members of the 50+ generation, including counselling, education and networking opportunities, but also new types of employment.
Source: OECD (2019[8]), Public Value in Public Service Transformation: Working with Change, https://doi.org/10.1787/47c17892-en; CPI (2018[9]), The Seoul 50+ Initiative in South Korea, https://www.centreforpublicimpact.org/case-study/seoul-50plus (accessed on 1 November 2022).
Informed by action research undertaken as part of a two-year partnership between OECD OPSI and the Investment and Development Agency of Latvia (LIAA), this report explores how governments might employ anticipatory innovation ecosystems as vehicles through which they can prepare for the future by consistently leveraging the collective intelligence of diverse stakeholders, and by orienting their actions towards delivering different types of value through innovation. OECD work on bioeconomy and biotechnology has emphasised the importance of innovation ecosystems in fostering value chains and industrial clusters, focusing on the inter-relation of state-sponsored technology development and firms (Philp and Winickoff, 2019[10]). The current report adopts a broader definition of an innovation ecosystem as an evolving, complex network of diverse organisations who collaborate to achieve shared innovation goals (Granstrand and Holgersson, 2020[11]; Klimas and Czakon, 2021[12]; Russell and Smorodinskaya, 2018[13]). Anticipation describes the act of “asking questions about plausible futures so that we may act in the present to help bring about the kind of futures we decide we want” (David Guston, in (OECD, 2018[1])). In an anticipatory innovation ecosystem therefore, actionable knowledge about the future is consistently generated through ongoing collective consideration of future needs, opportunities and challenges by ecosystem partners. This approach is therefore aligned closely with the ambitions set out in Latvia’s Guidelines for the National Industrial Policy 2021-2027 (Government of Latvia, 2021[6]).
While a review of international cases conducted for this report has found that actors in many innovation ecosystems employ anticipatory techniques such as strategic foresight to explore possible futures and describe compelling visions, none have applied anticipation in systematic way. Similarly, while theoretical and practical work on future-oriented technology assessment (Cagnin, Havas and Saritas, 2013[14]) and the co-creation of policy initiatives (Matti et al., 2022[15]) have recognised how innovation-focused participatory foresight approaches can help governments anticipate and respond to change, little work has been done to leverage innovation ecosystems as a source of anticipatory intelligence for government. The anticipatory innovation ecosystem approach is therefore novel in its aim to apply anticipation as a consistent driver of innovation, and its use of innovation ecosystems to continuously generate strategic futures knowledge to inform government decision-making.
The intended audiences for this report are individuals from government agencies with a mandate to stimulate innovation (such as LIAA), public officials who in their policy fields rely on cross-sectoral input for innovation (for example, to fulfil a mission) or wish to learn more about innovation ecosystem approaches and the impact of employing anticipatory methods. Non-governmental actors working with innovation ecosystems will also benefit from reading this report, though some concepts and viewpoints will require translation to their contexts and needs.
Readers of this report will develop a clearer idea of the benefits and trade-offs of working through an anticipatory innovation ecosystem approach. They will understand governance functions that the public sector can play in initiating, supporting, monitoring and exiting anticipatory innovation ecosystems, based around an empirically-grounded model for ecosystem governance. Importantly, they will also have vocabulary and concepts to hand that they can use to gain buy-in for anticipatory innovation ecosystems from stakeholders in government, industry, academia and civil society.
This project set out to explore how the application of anticipatory approaches by innovation ecosystems might stimulate anticipatory innovation and enhance the capacity of the Government of Latvia to proactively deal with transformative change.
Working in partnership with the Investment and Development Agency of Latvia (LIAA), OECD OPSI undertook practical, evidence informed interventions that engaged government actors and stakeholders in four existing technology networks in Latvia (Table 1.1). Combined with research interviews and a dedicated formative evaluation, this collaborative action-oriented approach served both to reveal insights about the effective public governance of anticipatory innovation ecosystems (with a particular focus on the Latvian context) and developed the capacity of LIAA to develop anticipatory innovation ecosystems. It was informed by extensive literature reviews, interviews with Latvian stakeholders and original case-study research covering 10 innovation ecosystems across Europe. For a detailed methodology, please see Annex A.
The role of innovation in a complex and rapidly changing world
To build the case for the public governance of anticipatory innovation ecosystems, it is necessary to establish why governments wish to stimulate innovation. Innovation is a process through which actors develop, implement and, in the private sector, commercialise new or improved products and processes (known as ‘innovations’) (OECD/Eurostat, 2018[16]). In the public sector context, a core aim of innovation is to create “impact” through public value (OECD, 2022[17]; 2019[8]). In general, public value represents a normative consensus of prerogatives, principles, benefits and rights that can be attributed to both governments and citizens (Jørgensen and Bozeman, 2007[18]), and linked to a variety of values like effectiveness, transparency, participation, integrity and lawfulness. The public sector may be interested in supporting innovation in the private sector to increase a country’s competitiveness for more value-added jobs, sustainable tax revenue, sustainability, health and a host of other public aims.
Innovation, therefore, can be seen not only as a key catalyst of economic growth, productivity and well-being (OECD, 2018[1]) but as a driver for a variety of values. In recent years, a growing awareness of the need to find solutions in an increasingly complex and uncertain world has meant that the hopes for what can be achieved through innovation have expanded and diversified, further prioritising public value. Funding initiatives from the European Union, such as Horizon 2020, show how innovation is expected to “address a number of well-chosen societal challenges and for example contribute to a transition to low carbon and inclusive economy” (Schot and Steinmueller, 2018[19]). As has been seen in the COVID-19 crisis and the rapid development of vaccines, innovation is not only necessary to catalyse social and economic development, but also to ensure that societies are able to adapt and flourish in adversarial environments (McGuire and Paunov, 2022[20]).
At the same time, it has long been understood that the benefits of innovation are not realised without risk or negative consequences, nor are they equally experienced. Past innovation is the cause of many of the ‘grand challenges’ of today, as the development of new processes and products created and sustained the damaging resource-intensive, fossil-fuel based paradigm of production and consumption that has persisted since the industrial revolution (Schot and Steinmueller, 2018[19]). The dominance of certain groups among both innovators and consumers of innovation has exacerbated inequalities (Criado-Perez, 2019[21]). The speed of implementation of an innovation can, intentionally or unintentionally, create shocks in the systems they affect which result in negative second-order consequences. Guarding against and addressing these issues is a priority for the development of more resilient and equal societies.
In short, innovation is increasingly expected to deliver multiple types of value and address complex challenges in an equitable manner. Governments have therefore begun to explore how a range of approaches to shape and intervene in systems for innovation can promote the development of new technologies and other solutions with wide-ranging social and environmental benefits, but limited negative consequences. The OECD’s innovation facets model (see Box 1.3 below) maps out these approaches and their relevance to different contexts. Enhancement oriented approaches are appropriate for more certain environments in which efficiency and effectiveness are prioritised. Adaptive approaches enable government to steward innovation to address the evolving needs of citizens and emerging environmental shifts. Mission-oriented approaches place government in the position of directing innovation to address complex societal challenges. Finally, anticipatory approaches aim to enable governments to address and benefit from change in conditions of uncertainty by promoting an active exploration of the future. In these more uncertain environments, innovation ecosystems have emerged as a vehicle through which governments can leverage the knowledge and resources of diverse actors while stewarding innovation towards delivering greater public value.
The Observatory of Public Sector Innovation has developed an innovation model along two central characteristics – uncertainty and directionality. Based on these characteristics, four different facets of innovation emerge, each of which is suited to a particular type of environment. While the model is focused on public sector innovation, the model can be applied to innovation more generally.
Source: OECD (2019[22]), “Innovation facets and core values: How different forms of innovation can cause different reactions”, https://oecd-opsi.org/blog/innovation-facets-and-core-values-how-different-forms-of-innovation-can-cause-different-reactions/.
Enhancement-oriented innovation is suited to more certain environments in which greater efficiency and efficacy are required. It is focused on upgrading practices, and building on existing structures (e.g. through digitalising services and better process management). An example of this type of innovation is the use of behavioural insights to improve the compliance rate with one-time payments.
Adaptive innovation tests new approaches in order to respond to a changing operating environment and address emerging needs (e.g. co-designing new community responses to emerging challenges such as the COVID-19 pandemic). Governments adopting social media as a channel for citizen interaction is an instance of adaptive innovation.
Mission-oriented innovation establishes a clear outcome and an overarching objective for achieving a specific mission (e.g. setting clear goals and roadmaps towards carbon neutrality). As an example, setting an objective to dramatically reduce greenhouse emissions within a decade is a mission-oriented approach to innovation.
Anticipatory innovation explores and engages with possible future changes that might shape priorities (e.g. conducting experiments to explore the future of work). An example of anticipatory innovation is the use of a sandbox to explore the impact of Artificial Intelligence on service delivery in health.
Source: OECD (2022[17]), Tackling Policy Challenges Through Public Sector Innovation: A Strategic Portfolio Approach, https://doi.org/10.1787/052b06b7-en.
The promise of innovation ecosystems
According to the Oslo Manual of the OECD, “innovation is not a linear, sequential process, but involves many interactions and feedbacks in knowledge creation and use. In addition, innovation is based on a learning process that draws on multiple inputs and requires ongoing problem solving” (OECD/Eurostat, 2018[16]). While innovation can and does occur within individual organisations, the dispersal of knowledge and resources across business and institutions means that its emergence is stimulated by interactions between stakeholders within regional, technological, sectoral and national innovation systems.
Research and practice in policy and business have therefore long sought to better understand how knowledge and resource flows across organisational boundaries can be orchestrated in order to enable opportunities for innovation to be identified and seized (Bogers et al., 2019[23]). This interest has deepened in recent years with the development of systems innovation approaches, which aim shift entire systems towards government priorities through innovation by examining and shaping the interdependencies of a wide range of actors (OECD, 2016[24]). Within this milieu, the ‘innovation ecosystem’ has emerged as a popular way to frame coordinated approaches to stimulating innovation by leveraging the knowledge and resources of a wide range of actors (see Box 1.4 on the role of innovation ecosystems for the circular bioeconomy). It is a more operational concept than national, technological or regional innovation systems as it denotes and concentrates on the act of collaborating for shared innovation goals.
In a report titled ‘Innovation Ecosystems in the Bioeconomy’, the OECD (2019) has previously investigated the potential of innovation ecosystems as policy instruments to catalyse the “transition to new economy that uses less energy, makes less waste, and inspires a more circular approach”.
The bioeconomy is defined as “the set of economic activities in which biotechnology and the life sciences (also chemistry and in particular their smart integration) contributes centrally to primary production and industry through the conversion of biomass into food, materials, chemicals and fuels.” In a circular bioeconomy, biomass is recovered from other processes which would normally result in their waste. Developing circular bioeconomies, therefore, is dependent on the coordination of stakeholders in ways that enable waste to be converted into resources. The focus of ecosystem approaches on stimulating coordination and collaboration makes them of relevance to the circular bioeconomy. Following Kanter (1994) innovation ecosystems are defined as “groupings of companies in different industries with different but complementary skills which link their capabilities to create value for ultimate users.”
Through analysis of bioeconomy ecosystems in Belgium, Canada, China, Finland, France, Italy, Japan, Norway, Sweden and the United States of America, the study sought to understand the types of policies applied to “help to spur technological development and the formation of formation of new types of cross-sectoral value chains, industrial and innovation ecosystems and commercial partnerships to enable the transition to a more sustainable and circular bioeconomy”, and explore how these might differ across bioeconomies.
The report outlines the necessity of a systemic approach to developing viable value chains as the success of individual elements is dependent on other parts of the system. Ecosystem approaches, by promoting the interlinking of actors, can ensure that individual elements do not become ‘stranded’, but are able to build on and contribute to the success of others. Strengthening the system additionally requires policy alignment to ensure that technology push and market-pull are aligned, and that competition for biomass does not hamper innovation.
Four common policy instruments are identified as valuable to stimulate the development of the circular bioeconomy through innovation ecosystems:
Clusters: Publicly funded organisations to support the coordination and connection of organisations from different industry sectors.
Pilot and demonstration phase funding: Demonstration of new technologies is considered essential to promote technology development but high-risk. Government funding can help to de-risk the process.
Joining policy up: Identifying and promoting synergies in policy and regulation are valuable for breaking down the barriers to innovation. The report used the example of poor synergy in which “supply-side policy instruments to encourage using wastes as feedstocks” are blocked by regulatory barriers to prevent this.
Carbon price and taxation: These instruments are identified as opportunities to promote and fund the circular bioeconomy.
Source: Philp, J. and D. Winickoff (2019[10]), “Innovation ecosystems in the bioeconomy”, https://doi.org/10.1787/e2e3d8a1-en.
For this report, the OECD defines the innovation ecosystem as an evolving, complex network of diverse organisations who collaborate to achieve shared innovation goals (Granstrand and Holgersson, 2020[11]; Klimas and Czakon, 2021[12]; Russell and Smorodinskaya, 2018[13]).This broad definition intentionally encompasses a range of multi-stakeholder innovation programmes and initiatives, such as clusters, while extending the types of organisation involved beyond private sector companies cited in a previous OECD definition (Philp and Winickoff, 2019[10]). While innovation ecosystems are often conceived as emerging naturally from relations between stakeholders (used academically or in policy analysis as descriptive tools for pre-existing relationships), a growing body of evidence shows that they can also be consciously promoted and coordinated.
Innovation ecosystems are structures that facilitate the “flow of innovation-relevant knowledge across the boundaries of individual organisations” (OECD/Eurostat, 2018[16]). An innovation ecosystem can also be characterised as an ongoing process to generate innovation “characterized by changing multi-faceted motivations of networked actors, high receptivity to feedback, and persistent structural transformations, induced both endogenously and exogenously” (Russell and Smorodinskaya, 2018[13]), and resulting in continuous evolution of the ecosystem’s boundaries, goals, and the roles participating of stakeholders (Ritala and Almpanopoulou, 2017[25]).
By engaging diverse stakeholders from industry, academia, government and civil society (the ‘Quadruple Helix’) in a participatory, co-creative process, innovation ecosystems are expected to stimulate innovation through a wide range of mechanisms. They build trust and reduce transaction costs by fostering connections between actors who do not normally work together (Guzzo and Gianelle, 2021[26]; Philp and Winickoff, 2019[10]), enable the sharing of knowledge, information and resources that are beneficial for ecosystem actors (Guzzo and Gianelle, 2021[26]; Philp and Winickoff, 2019[10]), facilitate the identification of areas in which the network of ecosystem partners has a comparative advantage (JRC, 2021[27]), enable the selection of priorities and discovery of opportunities for innovation that deliver better collective outcomes (Foray, Morgan and Radosevic, 2018[28]), facilitate coordinated action to bring about transformative change that can address grand challenges (Schot and Steinmueller, 2018[19]) and enable innovation ecosystem partners to achieve outcomes that would be unattainable if they were working alone (Hasche, Höglund and Linton, 2019[29]; Pontikakis et al., 2022[30]).
The high hopes for innovation ecosystems are evident in the European Union’s investment in Smart Specialisation, a policy approach that aims to develop and leverage regional innovation ecosystems to stimulate innovation and catalyse sustainable and inclusive regional growth (Guzzo and Gianelle, 2021[31]) (Box 1.5). While the theoretical basis for Smart Specialisation builds on a firm understanding of the systemic nature of the emergence of innovation, its implementation across Europe has been beset by governance challenges and a lack of capacity to facilitate the bottom-up identification of opportunities for innovation through the ‘entrepreneurial discovery process’ (Guzzo and Gianelle, 2021[31]). To address some of these issues, the anticipatory innovation ecosystem approach outlined in this report places a strong focus on governance.
Smart specialisation is a policy approach for regional development that aims to orient resources towards building innovative capabilities in a limited number of fields. Within smart specialisation, priorities are based on regional strengths and defined through a bottom-process that engages local stakeholders. The concept was first defined in 2009 and introduced by the European Commission in the EU Cohesion Policy 2014-2020. Each region was required to develop a research and innovation strategy for smart specialisation (RIS3) in order to access funding for research and innovation from the European Regional Development Fund (ERDF).
At the core of smart specialisation is the Entrepreneurial Discovery Process (EDP). The EDP is an interactive process through which actors from across the quadruple helix collaborate to prioritise innovation fields based on the identification of key regional strengths and opportunities. The approach leverages the specialised knowledge of these actors to facilitate the co-creation of policies and identification of relevant actions to stimulate the innovation. This process of ongoing, coordinated action by quadruple helix actors to identify shared goals and collaboratively work towards them can benefit from the effective governance of place-based innovation ecosystems which provide a framework for ongoing collaboration.
The role of government in this process centres on helping other actors sustain coordinated participation in order to generate actionable knowledge. More recently, the policy concept of smart specialisation strategies for sustainable and inclusive growth (S4+) has sought to connect S3 to the more directed mission-oriented policy approach in order to better address societal challenges. Additionally, Partnerships for Regional Innovation (PRI) aim to build on S3 to promote transformative innovation and system level change through multi-stakeholder partnerships focused on achieving agreed impacts.
Source: Foray, D., P. David and B. Hall (2009[32]), Smart Specialisation – The Concept, Knowledge Economists Policy Briefs, “Knowledge for Growth” Expert Group; Foray, D. (2014[33]), “From smart specialisation to smart specialisation policy”, https://doi.org/10.1108/ejim-09-2014-0096; OECD (2013[34]), Innovation-driven Growth in Regions: The Role of Smart Specialisation, OECD, Paris; Pontikakis, D. et al. (2022[30]), Partnerships for Regional Innovation: Playbook, https://data.europa.eu/doi/10.2760/775610; McCann, P. and L. Soete (2020[35]), Place-based Innovation for Sustainability, https://data.europa.eu/doi/10.2760/250023; Morisson, A. and M. Pattinson (2020[36]), Policy Brief: Smart Specialisation Strategy (S3), https://www.interregeurope.eu/sites/default/files/inline/Smart_Specialisation_Strategy__S3__-_Policy_Brief.pdf.
Mechanisms for discovery in innovation ecosystems
Drawing on the research of the economic sociologist David Stark (2009[37]) and others (Winickoff et al., 2021[38]; Schot and Steinmueller, 2018[19]),ecosystems can be understood as instruments to generate breakthrough innovations by facilitating a constructive tension between two counteracting mechanisms for discovery: convergence and dissonance.
Convergence describes the integration of diverse types of expertise, technologies and resources to create novel assets, or innovations. It is achieved by bringing together heterogenous stakeholders in an environment that facilitates collaboration and co-creation. Following the adoption of Rocco et al.’s definition of convergence by Winickoff et al. (2021[38]), ‘Convergence [...pertains to ...] the escalating and transformative interaction among seemingly distinct scientific disciplines, technologies, communities, and domains of human activity to achieve mutual compatibility, synergism, and integration, and through this process to create added value and branch out into emerging areas to meet shared goals.’ (Rocco et al. 2013 in (Winickoff et al., 2021[38])).
In this description, convergence sounds like a harmonious process. However, existing system dynamics, path dependence and vested interests often conspire to create friction and constrain the imaginations of innovation ecosystem partners. Yet far from being a drawback of innovation ecosystems, friction is a key mechanism for generating breakthroughs (Stark, 2009[37]). Friction occurs as distinct frameworks of value, such as economic, social and environmental, are brought into relationship with one another through the interaction of heterogeneous ecosystem partners. These values determine objectives that compete with and may contradict one another, creating ‘perplexing situations’ and a ‘sense of dissonance’ that “disrupts organizational taken-for-granteds, generates new knowledge, and makes possible the redefinition, redeployment, and recombination of resources” (Stark, 2009[37]).
In practical terms, dissonance forces stakeholders to recognise trade-offs inherent in choices related to innovation and find new ways to reconcile them. Through conflict and deliberation, they are able to identify potential negative and beneficial outcomes of innovation pathways. It is through the maintenance of dissonance and the continuous negotiation of different frameworks of value that previously invisible opportunities are uncovered, leading to truly novel innovations.
Consequently, convergence and dissonance are core concepts in coordinating innovation ecosystems in an anticipatory manner. Dissonance is needed to consider variety of futures, both risks and opportunities and avoid lock-in; while convergence is required to take action based on future signals avoiding dominance of status quo and incumbents.
The importance of appropriate governance for innovation ecosystems
As the terms ‘friction’ and ‘dissonance’ suggest, the process of collaborative innovation can be uncomfortable for those engaged in it. Friction can become destructive if actors are unprepared to listen to one another or see their objectives as entirely incompatible. Project leaders and participants in co-innovation programmes may wish to reduce friction by rapidly settling on an agreed direction which prioritises one set of values or principles of evaluation, or by imposing a particular understanding of an issue to be resolved. Yet while such approaches may avoid conflict in the short term (and fit with the timescales and expectations of political leaders), they can weaken the attachment of stakeholders to the ecosystem who feel that their values are not understood or represented. Forcing stakeholders to settle on the an expedient objective, risks missing the identification and capture of opportunities for ground-breaking and transformative innovation (Stark, 2009[37]; Schot and Steinmueller, 2018[19]).
Innovation ecosystems can provide a structure in which friction can be organised and made productive through effective governance. The governance structure proposed to achieve this is termed ‘heterarchy’ (Stark, 2009[37]; Russell and Smorodinskaya, 2018[13]). Unlike hierarchy, which supposes the management of interacting stakeholders through top-down leadership, heterarchy describes an organising model in which different principles of evaluation and worth are held in equal relationship to one another. As a result, heterarchies enable stakeholders engaged to make sense of challenges, opportunities and innovations by drawing on the multiple perspectives of others. Heterarchies facilitate convergence by ensuring that a single type of expertise does not dominate, and a single view of the application of a new technology does not occlude the identification of others.
The challenge of ‘organising dissonance’ through heterachy demands much of stakeholders engaged in innovation ecosystems. For government, it necessitates a shift from governmental leadership to collaborative or distributed leadership (Foray, Morgan and Radosevic, 2018[28]). Ecosystem partners must trust that they are all committed to finding solutions that result in shared benefits, in spite of regular disagreement. It necessitates faith in the process of innovation ecosystem development, in spite of friction that may slow down the identification of opportunities (JRC, 2021[27]). Participants in a heterarchy must be prepared to question their assumptions and revise their mental models based on different ways of understanding the world. This shift in mindset can be time consuming to achieve.
The roles of government in innovation ecosystems
Government and agencies, as independent convenors with the power to shape regulation, have important roles to play in order to create contexts in which innovation ecosystems can flourish. This report centres on the role of government stakeholders to initiate and support the development of four processes that sustain a productive tension between convergence and dissonance. At the level of individual innovation ecosystems, the ‘micro-governance’ processes (Wegner and Verschoore, 2021[39]) can be co-designed by government stakeholders and ecosystem partners in order to facilitate collaboration.
Government must also learn to listen to the insights generated by innovation ecosystems and coordinate to act on them. Through their continual search for opportunities, innovation ecosystems can identify gaps and barriers in the systems that prevent or inhibit the discovery and development of innovations (Philp and Winickoff, 2019[40]). By attending to these, governments can design better policy and accelerate innovative processes. Chapter 2 of this report offers a framework of seven government functions to enable this to be done in a systematic manner.
Towards anticipatory innovation ecosystems
While innovation ecosystems enable the identification of new opportunities by leveraging the collective intelligence of their members, a bias towards present concerns and a lack of strategic thinking can mean that even the most diverse set of organisations can generate narrow and short-sighted ideas.
Anticipatory approaches aim to address this challenge. They allow actors to build on information in the present to create knowledge about the future, enabling them to make choices that take into account future trends, opportunities, threats and values. These approaches include the 31 selected qualitative and quantitative ‘traditional foresight methods’, listed by Tõnurist and Hanson, including horizon scanning (‘Systematic monitoring and examination of a broad range of data sources about the phenomenon about which one aims to gain foresight, in order to identify perspectives and trends, premature signs of potential upcoming developments, as well as how they may affect the future.’), scenarios (‘Scenarios are ‘stories’ illustrating visions of possible futures or aspects of possible future. Scenarios are constructed by starting with the present and past and projecting into the future and usually presented in a range of possible futures.’), backcasting (‘Backcasting is a normative scenario method that analyses how events could develop from the present into an imagined future.’) and participatory foresight (‘…a method usually used in normative foresight analysis, in which citizens state their visions and preferences for particular futures and provide comments on scenarios and solutions presented by experts.’) (Tõnurist and Hanson, 2020[41]).
Anticipation, as distinct from strategic foresight, describes the way in which the knowledge generated by these methods is used in practice to make choices about innovation pathways to pursue and actions to undertake. It can allow actors operating together as part of innovation ecosystems to not only prepare for the future, but to shape it, “creating and implementing new, potentially value-shifting innovations in environments of deep uncertainty” (Tõnurist and Hanson, 2020[41]). This is known as anticipatory innovation. In small states such as Latvia, anticipatory innovation has particular value as a way of leveraging limited resources to identify opportunities to enter into and develop emerging value chains (Tõnurist and Kattel, 2016[4]).
Key benefits of anticipatory innovation ecosystems
There are four key benefits of anticipation to innovation ecosystems: the identification of future technological opportunities, the exploration of consequences of technological development, the facilitation of convergence, and the organisation of dissonance (see Box 1.6 for the case study of the Baltic Sea Ro-Ro Shipping Ecosystem).
First, by exploring the future through anticipatory approaches, innovation ecosystem members can develop a better understanding of the types of innovations that are likely to deliver positive outcomes across a range of possible futures. The goal here is not to predict winners: this is not possible. Instead, anticipation stimulates the identification of a wide range of options that may be beneficial for the innovation ecosystem to explore. The objective of this is to prevent the pursuit of innovation goals that risk becoming irrelevant, and to allow the ecosystem to develop more robust strategies for the development of anticipatory innovations. In this way, anticipatory approaches turn uncertainty into an asset for entrepreneurial discovery as opposed to a risk to be guarded against.
Second, anticipatory approaches enable the exploration of the consequences of (technological) development. Their value for this purpose has been recognised in the U.S Nanotechnology initiative and through the Responsible Research and Innovation (RRI) pillar of the EU’s Horizon 2020 programme (OECD, 2018[1]). The engagement of diverse ecosystem partners in the Quadruple Helix enables impacts to be mapped across multiple domains, from the environment to the labour market. This approach allows innovation ecosystem partners to negotiate the benefits and drawbacks of options for technological development and choose to pursue those which generate preferred types of value and are likely to have limited or manageable negative consequences. In this way, an anticipatory innovation ecosystem becomes a vehicle for anticipatory governance, providing the government with an opportunity to promote the selection of beneficial options and generating information that can inform the development of policy and regulation.
These first two benefits of anticipation concern the generation of knowledge which enables innovation ecosystems and governments to act with a better understanding of possible futures. The second two relate to the functioning of innovation ecosystems and reveal anticipation as a key tool for innovation ecosystem governance by organising dissonance.
Third, anticipation opens up the future as a space for convergence. Where organisations may struggle to see how their competing interests or frames of value can be integrated in the present, the future can offer an imaginary backdrop in which reconciliation can occur. As anticipation reveals a variety of options for exploration, ecosystem participants can begin to see how greater collaboration can enable them to access opportunities and overcome challenges. Lithuania’s development of smart specialisation roadmaps (Box 1.7) shows how cross-sectoral priorities were identified through anticipatory approaches.
Finally, anticipation has the power to create the ‘perplexing situations’ that characterise dissonance by confronting innovators with the challenges and opportunities the future may contain, and the values of those who will live in it. This provokes those engaging in anticipatory approaches to think creatively about how innovations can align with or shape potential future priorities. For Schot and Steinmuller, this opens up the possibility of transformative change by “stimulating the ability to look from a distance (this could be an imagined future; or a set of social and environmental challenges) at one’s own deeply embedded routines” (Schot and Steinmueller, 2018[19]) .
Through the application of anticipatory approaches, therefore, innovation ecosystems can vary the tension between convergence and dissonance, enabling the creation of ‘productive friction’ that results in ground-breaking innovation. Furthermore, by promoting innovation ecosystems as a vehicle for the exploration of possible futures and collective action, governments are presented with an opportunity to leverage the collective intelligence of diverse stakeholders to generate futures knowledge that can inform policy decisions across a variety of domains.
The anticipatory approaches of roadmapping and scenario planning were employed as part of an innovation ecosystem programme, ECOPRODIGI, led by Interreg VG Baltic Sea that aims to advance the EU’s strategy for a Sustainable Blue Economy through innovation in Roll-on/Roll-off (Ro-Ro) shipping.
Through an iterative and participatory process comprising preparatory research and analysis, interactive workshops, ‘post processing’ of insights generated by the workshops and digital engagement, the organisers of the foresight process sought to develop and stress-test innovation three innovation ‘roadmaps’ for the future of Ro-Ro shipping. In their paper outlining the case study, Spaniol and Rowland (2022) describe the process for the development of a roadmap for integrated logistics-operations.
Three day-long workshops took place, in November 2019 in Lithuania, in January 2020 in Denmark, and in February 2020 in Norway, which each convened 30-40 local actors in addition to a core team of actors from across the project consortium. Over 100 stakeholders associated with the Sustainable Blue Economy from a wide range of businesses and organisations from across the Baltic Sea Region were therefore engaged in the process of constructing.
The workshops sought to unlock the collective intelligence of participants to build knowledge about possible futures for Ro-Ro shipping and explore how a range of changes might influence these. In each workshop, participants were asked to assess and build on knowledge generated in the previous workshop in order to develop increasingly robust roadmaps and refine futures scenarios against which the roadmaps could be tested. A combination of foresight methods, including backcasting and horizon scanning were used.
The workshop in Lithuania generated content for the roadmap through a broad exploration of the future and resulted in an ‘extensive list’ of potential events and trends that could affect the innovation ecosystem. In Denmark, participants used this information to begin plotting a roadmap of innovation and possible influential events against a timeline. The content generated by this process was subsequently digitised in order to engage a wider range of stakeholders. In the third workshop in Norway, participants refined the online roadmap ‘to further articulate and distinguish events and tasks-to-be-done from generic trends and technologies.’ The resulting roadmap was subject to validation by other ecosystem members and Ro-Ro stakeholders and stress-testing against eight futures scenarios.
Benefits of anticipatory approaches
Spaniol and Rowland find that the application of anticipatory approach enabled ecosystem partners to build consensus and orient around a shared vision of the future. The iterative, participatory and deliberative process of developing and stress-testing roadmaps not only leveraged dissonance among stakeholders to produce a robust anticipatory strategy. It also promoted the development of connections between previously disparate stakeholders and resulted in a ‘shared language’ to talk about the ecosystem, promoting convergence.
Importantly, Spaniol and Rowland find that the participatory approach resulted in distributed ownership of the roadmap that enables the ecosystem to remain coherent in the absence of a ‘focal firm’. This enhances the strength and sustainability of the ecosystem by promoting a more heterarchical and distributed governance.
Source: Spaniol, M. and N. Rowland (2022[42]), “Business ecosystems and the view from the future: The use of corporate foresight by stakeholders of the Ro-Ro shipping ecosystem in the Baltic Sea Region”, https://doi.org/10.1016/j.techfore.2022.121966.
In 2013, anticipatory approaches were used in the process of defining priorities and implementation roadmaps for Lithuania’s Smart Specialisation Strategy 2014-2020. The approach sought to address prior issues relating to innovation in Lithuania by engaging a wide range of stakeholders in the identification of ecosystem priorities.
These challenges included a low level of cooperation between industry and research, fragmentation of research and development (R&D) and research and innovation (R&I) policy priorities, programmes, funds and institutions, and failure to leverage different funds to create synergies between measures. Efforts to concentrate funds and create connections, such as the ‘science valleys’ or clusters, had so far been able to deliver only limited impact.
Process
The process was undertaken beginning in 2013 under the supervision of the Ministry of Education and Science and Higher Education Monitoring and Analysis Centre (MOSTA), with the help of an International Independent Expert Group. It engaged six expert groups, consisting of about 150 experts in total, to analyse trends and identify priorities. As such, it was “a bottom-up process with top-down methodological support” (Paliokaitė, Martinaitis and Sarpong, 2016[43])
The process consisted of three stages, modelled as independent projects. They could only proceed upon the satisfactory completion of the previous stage (stage-gate process). Feedback loops were introduced to adjust the process in response to previous outcomes.
“Stage 0 was devoted to scoping — developing and discussing the methodology, awareness-raising, building consensus on the methodological choices, including the definition of ‘priorities’ and ‘priority areas’, securing the funding and constructing a management system consisting of the coordinating committee (public officials from the key ministries), administrative body (MOSTA), and the implementing bodies (the International Independent Experts Group as well as two separate consortiums of analysts and expert groups' facilitators contracted through a public procurement procedure).” [emphasis added] (Paliokaitė, Martinaitis and Reimeris, 2015[44]).
“Stage one (1) focused on the identification and mapping of the broader priority areas based on a qualitative analyses [sic] of the long-term national challenges, the current research and economy potential [sic]. Further discussions with seven expert panels made up of key stakeholders and representatives from the research and business communities resulted in an all-inclusive six broad priority fields.” [emphasis added] (Paliokaitė, Martinaitis and Reimeris, 2015[44]). Stage Two (2) was aimed at mapping out specific specialisations within six broad priority fields. This involved a more detailed analysis of trends and challenges in each of the priority areas, followed by discussions of six expert groups that comprised business and research representatives in each of the priority areas (about 150 experts and 24 discussions in total). Each of the six expert groups was chaired by two group leaders – one acknowledged scientist and one industry captain. Included in these expert groups were Policy makers from the ministries of interest (e.g. Transport, Health, and Education).” (Paliokaitė, Martinaitis and Reimeris, 2015[44]).
Following a process of consensus-building around key priorities, the potential of collaborative action by public and private actors to address these and the identification of barriers to progress in these areas, policy roadmaps were proposed. These aimed to function as a tool for the coordination of public and private stakeholders to achieve policy priorities.
Benefits and challenges of the anticipatory approach
Paliokaitė, Martinaitis and Sarpong (2016[43]) identify three key benefits of the participatory and anticipatory approach for the identification of priorities and the development of implementation roadmaps. Firstly, it challenged the previous sector-based approach which had dominated innovation policymaking in Lithuania and provided an arena and a purpose for cross-sectoral discussions. Secondly, the collective identification of outcomes by expert groups helped to orient objectives towards broader social needs, provided a framework for better management of the implementation, and created ownership of the priorities. Thirdly, the recommendations of the foresight process were largely transposed into policy when it comes to the priority areas. In addition, the process created a large knowledge base for policy makers.
Nonetheless, the process and outcomes did experience challenges. The expert panels reported receiving interference from interest groups; representatives from the ministries did not readily accept the policy proposals associated with the selected priorities, and there was a mismatch between the experimental and uncertain nature of the proposals resulting from the process and the existing administrative culture, which is “prone to low risk-tolerance” (Paliokaitė, Martinaitis and Sarpong, 2016[43]). These challenges highlight the need for effective anticipatory innovation governance (AIG) to facilitate the translation of futures knowledge into action.
Source: Paliokaitė, A., Ž. Martinaitis and R. Reimeris (2015[44]), “Foresight methods for smart specialisation strategy development in Lithuania”, https://doi.org/10.1016/j.techfore.2015.04.008; Paliokaitė, A., Ž. Martinaitis and D. Sarpong (2016[43]), “Implementing smart specialisation roadmaps in Lithuania: Lost in translation?”, https://doi.org/10.1016/j.techfore.2016.01.005; Reimeris, R. (2016[45]), “New rules, same game: The case of Lithuanian Smart specialization”, https://doi.org/10.1080/09654313.2016.1179722.
Anticipatory governance for innovation ecosystems
It is clear that the coupling of anticipation and innovation ecosystems offers a promising path to impactful innovation and the generation of futures knowledge that is useful for governments. Yet the systematic application of anticipatory approaches within innovation ecosystems requires more than simply knowledge of foresight approaches; it needs effective governance. Anticipatory innovation ecosystems are therefore subject to many of the same governance challenges and limitations as innovation ecosystems (Table 1.2).
Anticipatory governance in general can be considered a type of ‘process governance’, which “shifts the locus from managing the risks of technological products to managing the innovation process itself: who, when, what and how. It aims to anticipate concerns early on, address them through open and inclusive processes, and steer the innovation trajectory in a desirable direction” (OECD, 2018[1]). For governments, this can provide an opportunity to promote public value through innovation as well as generating intelligence for the design of more resilient policies.
With a particular focus on deriving the full benefits of anticipation, anticipatory innovation governance (AIG) describes the structures, practices and mechanisms that enable especially public sector with other stakeholders to explore and act on future opportunities, risks and challenges (Figure 1.2). It involves both creating an environment in which anticipatory knowledge can be generated and applied in practice (the authorising environment) and building capacity of individuals and teams for the selection and application of appropriate anticipatory approaches (agency) (Tõnurist and Hanson, 2020[41]). The AIG framework could provide a structured approach to turn futures knowledge into innovation in a more operational manner by concentrating on the role of government in both establishing relationships and creating new roles to coordinate ecosystems. Chapter 2 details how the mechanisms of AIG can inform the effective multi-level governance of anticipatory innovation ecosystems.
Source: Tõnurist, P. and A. Hanson (2020[41]), “Anticipatory innovation governance: Shaping the future through proactive policy making”, https://doi.org/10.1787/cce14d80-en.
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