Exploring the Potential Impact of Fla on National Innovation Systems

Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011

EXPLORING THE POTENTIAL IMPACT OF FLA ON NATIONAL INNOVATION SYSTEMS

Matthias WEBER,* Attila HAVAS,** Doris SCHARTINGER* * AIT Austrian Institute of Technology GmbH, Donau-City-Straße 1, 1220 Vienna, Austria ** Institute of Economics, Hungarian Academy of Sciences, Budaorsi ut 45., H-1112 Budapest, HUNGARY e-mail for correspondence: [email protected] DRAFT Version 2.1, 9 May 2011

Abstract

Keywords: Forward-Looking Activities (FLAs); policy governance sub-systems (PGS); national innovation systems (NIS); fit between a chosen FLA approach, the perceived policy needs/ opportunities, and the PGS; impacts of FLA In spite of several decades of using Forward-Looking Activities (FLAs) in the context of science, technology and innovation (STI) policies, our knowledge of the actual impacts of foresight on national innovation systems is still very limited and based on case-by-case evidence – mainly evaluation reports or case descriptions – rather than systematic comparative analyses. However, it is widely acknowledged that apart from looking forward in substantive terms, an intelligent process design can also contribute to shaping the innovation system for which an FLA is conducted. Ultimately, the purpose of understanding impacts is to contribute to the design of more appropriate and effective FLAs. One of the reasons for this knowledge gap can be found in the great diversity of FLAs in methodological terms. A broad spectrum of practices has been applied ranging from highly participatory to expert-based and from creativity-driven to evidence-based examples. Further, the “objects” to which FLAs are applied are complex in nature. In fact, the innovation system (IS) approach that has come to dominate our current thinking about STI policies and system performance, paints a quite sophisticated picture of the embedding of research and innovation in economy and society. Still, for the purpose of investigating impacts of foresight on IS, the conventional actor-centred IS approach shows a number of limitations, in particular with regard to the policy governance sub-system through which major FLA impacts are mediated. Looking into complementary lines of reasoning for better capturing possible impact chains is thus a promising inroad to follow. Against this background this paper aims at - Proposing an analytical framework and tentative taxonomies of policy needs/problems in innovation systems, of FLAs to address them, and of policy governance sub-systems within innovation systems to mediate them, as a basis for - Exploring hypotheses on the likely “fit” (and thus the potential impact) of different types of FLAs with different types of policy governance sub-systems in IS and typical policy needs/problems. - Illustrating the hypotheses (and thus the framework) by looking ex-post at a number of actual cases of FLAs, Hence, first we look into typical problems and policy needs in innovation systems. A second element consists of an extension of the national innovation systems perspective by adding

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 1 - Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011 insights from governance studies on policy-making processes, and by stressing the importance of networking for system change. While we believe that this approach can be generalised for other than national innovation systems (e.g. sectoral, regional, etc.), we concentrate on the national level in order to limit the complexity of our proposed analytical framework. Thirdly, we develop a typology of FLAs, covering different dimensions such stand-alone vs. embedded activitythe reinforcing vs. transformative character with regard to the existing NIS. The combination of taxonomies of FLAs, of typical policy needs in national innovation systems, and of governance sub-systems of NIS provides us with an analytical framework to systematically study specific FLAs and their potential impacts on NIS, and on policy governance systems in particular. Countries suggested to illustrate the relevance of our framework are rather diverse on purpose, and include Columbia, France, Germany, Hungary, Japan, Sweden and Romania. This analysis would also lead to well-substantiated policy conclusions on the appropriateness of different types of FLAs for different kinds of governance settings, and for improving certain “functions” in innovation systems by way of FLAs.

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 2 - Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011

1 Introduction

1.1 Background In spite of several decades of using Forward-Looking Activities (FLAs) in the context of science, technology and innovation (STI) policies, our knowledge of the actual impacts of FLAs on national innovation systems (NIS) is still very limited and based on case-by-case evidence – mainly evaluation reports or case descriptions – rather than systematic comparative analyses. However, it is widely acknowledged that besides looking forward in substantive terms, an intelligent process design can also contribute to shaping the innovation system for which an FLA is conducted. Ultimately, the purpose of understanding impacts is to contribute to the design of more appropriate and effective FLAs. One of the reasons for this knowledge gap can be found in the great diversity of FLAs in methodological terms. What further stresses the challenge of anticipation is that the “objects” to which FLAs are applied are complex in nature. In fact, the innovation system (IS) approach that has come to dominate our current thinking about STI policies and system performance, paints a quite sophisticated picture of how research and innovation operate in economy and society, thus enhancing the challenge of anticipation. Still, for the purpose of investigating impacts of FLAs on ISs, the conventional actor-centred IS approach shows a number of limitations, in particular with regard to the policy governance sub-system through which major FLA impacts are mediated. Looking into complementary lines of reasoning for better capturing possible impact chains thus seems to be promising. Impacts of FLAs on IS are actually mediated through the policy governance sub-system, which, on the one hand, contributes to identifying and prioritising certain policy needs and problems in the IS, and, on the other hand, translates insights from FLA into policy actions. We thus concentrate on the “fit” between different types of policy needs, FLAs and PGS to analyse the potential effectiveness of FLAs. Overall, analysing actual or exploring potential impacts of FLAs on national innovation systems requires handling a great deal of diversity, both with regard to FLA methods and systems of innovation, in which they are embedded.

1.2 Objectives Hence, we intend to develop a framework as the basis for exploring hypotheses on the likely “fit” between perceived policy needs, FLAs, and policy governance sub-systems; or from a different angle, on the potential impacts of different types of FLAs in different settings. The hypotheses (and thus the framework) shall be underpinned and illustrated by a number of actual cases of FLAs. This framework could be used by policy makers who consider launching an FLA, FLA practitioners designing a new activity, and analysts conducting ex-post evaluations of FLAs.

1.3 Methodology and structure of the paper The paper relies on various theoretical building blocks of evolutionary economics of innovation, as well as on political sciences, most notably on the policy governance literature. The conceptual

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 3 - Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011 framework is introduced in Chapter 2. It covers different types policy needs in IS, but the focus is on developing taxonomies of FLAs, on the hand, and of PGS within innovation systems, on the other. While we believe that our approach can be extended beyond national innovation systems (e.g. to sectoral or regional innovation systems), we concentrate on the national level in order to limit the complexity of our analyses. Chapter 3 discusses the relationships between the dimensions of each of the two taxonomies individually, i.e. what relations exist between the different dimensions within these taxonomies. Chapter 4 extends this discussion to the relationships between the two taxonomies, i.e. to what relations exist across the dimensions of FLAs and PGS. Chapter 5 revisits the question of how the proposed framework and approach could be used for addressed policy needs, for analysing actual and potential impacts of FLAs and thus for better designing FLAs in line with the opportunities offered, and restrictions imposed, by a certain policy governance sub-system. It draws on some case examples of actual FLAs conducted in different innovation systems. Chapter 6 draws a number of conclusions regarding the wider applicability of this framework both for ex-post evaluation and design (or ex- ante analysis) of FLAs.

2 Conceptual framework FLAs do not have a single, all-encompassing theory to support them, and thus they rely on a range of – somewhat overlapping – theories and methods, including (i) evolutionary economics of innovation; (ii) sociology of science and technology;1 (iii) actor - network theories; (iv) political sciences analyses of policy processes; (v) communication, co-operation, and participation theories; (vi) decision-preparatory, (project) management and future-oriented methods and techniques.2 This list is far from exhaustive, and most likely disciples of these theories would change the grouping, or the wording used here. That might be an interesting discussion in its own right, indeed, for theoretical purposes. Yet, the intention here is just to indicate the ‘eclectic’ – and thus complex – nature of FLA programmes, rather than attempting to provide a meticulous, comprehensive treatise of these issues. In this paper we mainly draw on evolutionary economics of innovation and political sciences, in particular the policy governance literature. The former one provides useful observations to appreciate the relevance of FLAs from different angles. First, forward looking, prospective/ strategic analyses, foresight, future, change, innovation, and uncertainty are closely interrelated notions – and some of these are the underlying terms of evolutionary economics of innovation, too. Second, FLAs are important policy tools, and thus the nature of policy formation processes and the policy rationale of FLAs should be clearly understood. (Havas, 2005) Policy governance sub-systems (PGS) need a close attention as the immediate impacts of FLAs are likely to be on the PGS rather than on the NIS as a whole. Hence, there is a need to apply some methods and results of political sciences. In this section first we consider what role FLAs can play in contributing to various activities (functions) of innovation systems, and identify different key policy needs and problems to be addressed. Then we propose a taxonomy of PGS by applying four dimensions to characterise an actual PGS: oligopolistic vs. distributed structure; antagonistic vs. consensual strategy setting processes; degree and type of legitimisation (process and/ or output legitimacy); systematic vs. sparse, ad hoc use of policy preparation tools (PPTs). It is followed by a taxonomy of FLAs covering different dimensions such as FLAs’ relation to other policy preparation tools (stand 1 A well-established school is known as Science and Technology Studies (STS). 2 The order, in which these disciplines are listed here, does not indicate their relevance in any sense.

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 4 - Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011 alone vs. embedded); degree and types of participation; as well as rationale (reinforce the existing NIS and/ or its sub-systems vs. transform it/ them).

2.1 Theoretical building blocks, implications for STI policies and FLAs In spite of the apparent similarity with biological processes, one should not mistakenly equate evolutionary economics with evolutionary biology. Freeman (1994) highlights two fundamental differences, with obvious relevance to decision-making processes, and hence FLAs as supporting tools. First, selection is at least partly conscious in innovation processes as decision- makers can choose between various ‘mutations’ (that is, new products, processes, and organisational forms). Moreover, their expectations, hopes, plans, and values (as well as those of participants in FLAs) also shape the ‘evolution’ of these ‘mutations’. Ethical and social considerations, therefore, play an increasingly important role in innovation processes, notably in the development and utilisation of nuclear energy, life sciences and nanotechnology, as opposed to the process of biological evolution. Second, selection is taking place at a number of levels in the course of competition: among products, firms (organisations), sectors, regions, countries, and socio-economic systems. There are some autonomous rules and laws of the selection process at these different levels. Strong interrelations and interdependencies, however, can also be observed. Technological innovations are shaping both their natural and socio-economic environment, while the success of innovations strongly depends on their environment, including the quantity, quality and distribution of accumulated capital in the form of production equipment, roads, railways, communications networks, bridges, etc., as well as policies, attitudes and norms, that is, institutions in short. While rational agents in the models of neo-classical economics can optimise via calculating risks and taking appropriate actions, “innovation involves a fundamental element of uncertainty, which is not simply the lack of all the relevant information about the occurrence of known events, but more fundamentally, entails also (a) the existence of techno-economic problems whose solution procedures are unknown, and (b) the impossibility of precisely tracing consequences to actions” (Dosi, 1988: 222 – emphasis added). Thus, the notions of optimisation or maximisation become meaningless. Another important implication of uncertainty concerns the scientific and policy relevance of forecasting, based on the extrapolation of (supposedly) known trends. The space of events, in which forecasting can be meaningful is strictly limited: the only certain – and thus easily predictable – outcome of innovative activities is that most of the underlying technological and business trends can change quite radically even in the space of 10-15 years.3 From a policy perspective, therefore, new methods are required, which can take into account uncertainty during a decision-preparatory process. Certain types of FLAs, most notably foresight, are prominent from this angle, for two reasons. First, it is capable of dealing with uncertainty by devising multiple (fundamentally different) ‘futures’ (visions of future, future states or scenarios). Second, participatory FLAs – again, foresight processes – can reduce uncertainty, too, because participants can align their endeavours once they arrive at a shared vision. To this effect, however, it is a necessary condition to involve the major stakeholders, who can significantly

3 Obviously, there are certain trends, e.g. demographic ones, which are not directly influenced by innovative activities, on the one hand, and their ‘stability’ (predictability) extends to a much longer time horizon (in this case around 40-50 years), on the other. Also, the pace and intensity of innovative activities – and hence their impacts on major technological and business trends – vary significantly across time (different historical periods) and countries (socio-economic systems).

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 5 - Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011 influence the underlying trends by shaping the strategies or policies of their respective organisations (government agencies, businesses, research organisations, NGOs, unions, etc. – depending on the issues in question, as well as the political and decision-making culture of the ‘entity’ conducting a foresight programme: international organisations or regions, nation states, sub-national regions, business associations, groups or individual firms, cities, etc.). A vast body of empirical literature has also clearly shown that innovators are not lonely scientists. While some path-breaking scientific or technological ideas might come indeed from individuals, successful innovations can only be generated by a close collaboration of different organisations such as: university departments, government and/or contract research labs, firms, and specialised service-providers. Forms of their co-operation can also be varied widely from informal communications through highly formalised R&D contracts to alliances and joint ventures. Thus, conscious network-building efforts of participatory FLAs are crucial, indeed – as well as their unintended impacts on networking.4 In a world of uncertainty, policy cannot bring about the optimum either. The policy-maker is not “a perfectly informed social planner correcting imperfect market signals to guide private decisions toward more desirable outcomes”. (Metcalfe and Georghiou, 1998: 94) Of course, this conclusion is not easy to accept, especially for those trained in the paradigm of rationality, maximisation and optimisation: “For obvious reasons, many economists prefer models that provide precise policy recommendations, even in situations in which the models are inapplicable to the world of our existence. Our own view is that, rather than using neo-classical models that give precise answers that do not apply to situations in which technology is evolving endogenously, it is better to face the reality that there is no optimal policy with respect to technological change.” (Lipsey and Carlaw [1998], p. 48) Variety, selection and uncertainty also have repercussions on the very nature of policy and strategy formation, and thus decision-makers – either devising public policies or strategies for firms or RTDI organisations – should take into account these features. The relevant and potentially successful policies and strategies are adaptive ones, relying on feedback from the selection process to the development of further variation. (Metcalfe and Georghiou, 1998) In other words, policy and strategy formation is increasingly becoming a learning process. (Lundvall and Borrás, 1998; Teubal, 1998) This notion underlines the importance of appropriate FLAs: more ‘robust’ policies can be devised when (i) multiple futures are considered, and (ii) participants with different background are actively involved in a decision-preparatory process, and thus bringing wide-ranging accumulated knowledge, experience, aspirations, and ideas in.

2.2 Key activities in innovation systems Innovation processes are influenced – induced or hindered – by various factors, characterising a system, in which these processes are embedded. First the notion of “national system of innovation” was introduced by Freeman (1987), and since then has been used widely both by analysts (e.g. Edquist (ed.), 1997; Fageberg et al. (eds), 2005; Freeman, 2002; Lundvall, 1992; Lundvall et al., 2002; Nelson, 1993) and policy makers (working for regional, national, or international organisations). A similar way of thinking has also been extended to systems defined by other boundaries (regional, technological, sectoral), and now it is common to refer to a systemic or systems approach. This ‘popularity’ comes with a price: authors use the same notion, but define it differently: “The SI [systems of innovation] approach has often been used

4 The benefits in this respect include strengthened existing networks, formation of new ones, and more generally, enhanced communication and co-operation.

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 6 - Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011 more as a label than as an analytical tool. […] it has not been used to formulate hypotheses to be confronted to empirical observations. […] it is often presented in too vague and unclear way.” (Edquist, 2005: 2002; cf. p. 183) It is also telling that there is no comprehensive taxonomy of NIS, based on major differences in their structural features, only classifications (clustering) of NIS based on performance indicators. In trying to avoid this trap, we stick to the following definition: an innovation system encompasses all important economic, social, political, organisational, institutional and other factors that influence the development, diffusion and use of innovations, as well as the relations between these factors. (Edquist, 1997: 14, 2001:225, 2005:182-183) In turn, the main function of an NIS is to generate, introduce, disseminate, and exploit “novelty” in society and economy. Thus, knowledge generation, distribution and exploitation is a major factor determining the performance of an innovation system. Innovation systems perform several activities (functions), and various lists of these activities have already been developed. (Bergek et al., 2005, 2007; Edquist, 2005; Hekkert et al., 2007) Edquist (2005: 190-191) has listed the most important ones as follows: (1) Provision of research and development (R&D), creating new knowledge, primarily in engineering, medicine, and the natural sciences. (2) Competence building (provision of education and training, creation of human capital, production and reproduction of skills, individual learning) in the labour force to be used in innovation and R&D activities. (3) Formation of new product markets. (4) Articulation of quality requirements emanating from the demand side with regard to new products. (5) Creating and changing organisations needed for the development of new fields of innovation, e.g. enhancing entrepreneurship to create new firms and intrapreneurship to diversify existing firms, creating new research organisations, policy agencies, etc. (6) Networking through markets and other mechanisms, including interactive learning between different organisations (potentially) involved in the innovation processes. This implies integrating new knowledge elements developed in different spheres of the SI and coming from outside with elements already available in the innovating firms. (7) Creating and changing institutions – e.g. IPR laws, tax laws, environment and safety regulations, R&D investment routines, etc – that influence innovating organisations and innovation processes by providing incentives or obstacles to innovation. (8) Incubating activities, e.g. providing access to facilities, administrative support, etc. for new innovative efforts. (9) Financing of innovation processes and other activities that can facilitate commercialisation of knowledge and its adoption. (10) Provision of consultancy services of relevance for innovation processes, e.g. technology transfer, commercial information, and legal advice. National innovation systems are composed of various sub-systems, differentiated by their main activities: most notably governing policy setting; performing R&D and innovation; connecting actors by transferring information, knowledge, and capital; providing legal, management, incubation problem-solving and other types of services, etc. It is not our intention to develop a detailed ‘map’ of these sub-systems, but it is important to stress that actors can be active in various sub-systems, i.e. there is no one-to-one match between actors, activities, and sub- systems. (E.g. research performers can influence policy formation processes, might transfer information and knowledge, as well as provide services to other actors.)

Of the above sub-systems, we focus on the policy governance sub-systems (PGS) for two reasons. First, policy recommendations of FLAs are mediated directly by PGS, although indirectly and/ or informally by other sub-systems, too. Second, immediate impacts of FLAs can be observed in the products (that is, policy measures) of PGS or their operation, structure and performance. The main function of a PGS is to generate, diffuse, and exploit knowledge on, and knowledge for, science, technology and innovation (STI) policies. In doing so, it is directly dealing with functions (5): creating and changing organisations needed for the development of new fields of innovation; (6): networking through markets and other mechanisms; (7) creating and changing institutions; and (9) financing of innovation processes and other activities. It should be stressed, however, that these activities of a PGS are not performed exclusively by policy making or funding bodies; several other actors are also of crucial importance both in terms of shaping policy directions and implementing jointly devised changes (depending on major features of a given NIS and its policy governance sub-system, cf. section 2.4). Also, private sector actors (both profit-seeking and non-profit ones) pursue their own strategies by creating and changing organisations (e.g. entrepreneurs set up firms, commercial labs or private universities, existing companies establish subsidiaries or joint ventures); they network without taking advantage of policy schemes; business angels, venture capital funds, and banks finance innovation projects. Further, a PGS can also influence the ways in which the remaining six functions of a NIS – (1), (2), (3), (4), (8), (10) – are performed. As already pointed out, STI policies – and thus PGS – are not omnipotent: many other factors affect the performance of a NIS. Moreover, ‘optimal’ or ‘ideal’ NIS does not exist. For these reasons, even carefully designed policy actions, based on a truly systematic approach, and implemented effectively, would not ‘deliver’ an ‘optimal’ NIS: “… just as innovation processes are evolutionary, SIs evolve over time in a largely unplanned manner. Even if we knew all the determinants of innovation processes in detail (which we certainly do not now, and perhaps never will), we would not be able to control them and design or ‘‘build’’ SIs on the basis of this knowledge. Centralized control over SIs is impossible and innovation policy can only influence the spontaneous development of SIs to a limited extent.” (Edquist, 2005: 191) This “limited extent”, however, still can lead to a noteworthy improvement. Hence, it is crucial to identify the key bottlenecks [deficiencies/ deficits] of an existing NIS, and assess which of those can be rectified by what type of policy measures. Another important role for STI policies is to create new opportunities.5 In either case, the way in which STI policies are designed and implemented can make a significant difference, and FLAs can contribute to both processes. In sum, policy needs – either aimed at solving problems or creating opportunities – need to be correctly identified, and policy preparation tools – among them FLAs – should be tailored to those policy needs.

2.3 Tailoring FLAs to different types of policy needs It would go beyond the scope of this paper to offer a comprehensive analysis of different types of policy needs, and hence we only hint at different ways to classify them:

5 Obvious examples include using public procurement, as well as changing standards or introducing new ones [performing functions (4) and (7) in the above list] to promote innovation, that is, ultimately performing function (3).

 by the overall functions of a NIS (is it mainly in the generation, diffusion or exploitation of knowledge and innovation where improvements are needed or new opportunities can be created?);  by the above detailed lists of functions of a NIS (which of the ten functions need to be better performed?);  by the composition of a NIS. For our main argument the last one seems to be pertinent, and thus we elaborate on this by distinguishing the following key categories of policy needs: A) the system has all the major components (the required organisations/ actors and institutions), working efficiently and effectively on the whole, and the relations among them are appropriate; in this case some prospective orientation/ guidance is needed to improve the performance of the actors, prepare them to exploit new opportunities (avoid lock-ins, nurture diversity, switch from one trajectory to another, etc). Dealing with emerging societal challenges, for instance, requires an effective NIS to be in place; otherwise any attempt to provide guidance will suffer from structural inefficiencies; B) the system has all the major components, but some organisations or institutions (policy schemes, IPR laws, other formal or informal rules of the game) do not perform satisfactorily, and thus need to be overhauled (solely by public bodies/ actions, jointly with private actors, or enticing them to act) C) the system has all the major important organisations (actors), but the relations among the actors are insufficient, and thus need to be improved e.g. in terms frequency, quality (building trust, creating conditions for mutually beneficial co-operation, etc.); D) some important elements (organisations or institutions) are missing, and thus need to be introduced (solely by public bodies/ actions, jointly with private actors, or enticing them to act); in case of creating new organisations new types of linkages are to be nurtured, too (relations between the existing and the new organisations).

The key issue is then what type of FLA would be relevant to tackle policy needs A) – D).

2.4 A taxonomy of the policy governance sub-system (PGS) of NIS When studying national innovation systems from a functional perspective, it is the orientation function for which FLAs have the highest relevance. The results of FLAs by definition refer to future developments and raise implications with regard to actor strategies in the NIS. This applies to private sector as well as to public sector actors, and other intermediaries and stakeholders. Within a NIS, the orientation function is ensured by what we call policy governance sub-system, i.e. that segment of the NIS where priorities, rules and regulations are defined that guide or provide incentives for decision-making6. Whether FLAs can exert a major influence on the NIS is thus dependent on how well FLAs are connected to the governance sub-system.

6 There are different ways of sub-dividing innovation systems. Apart from the aforementioned functional differentiation, it is very common to distinguish research performance, production of innovative goods and services, finance, intermediation, and policy governance. See for instance Edquist in Fagerberg (2005)

Governance sub-systems differ from country to country, and therefore also the appropriateness of different forms of doing FLAs – assuming that they should have an impact – differs. PGS can be characterized along several different dimensions which – for reasons of simplicity – we define on in binary terms: - Oligopolistic vs. distributed: We distinguish PGS where the political power is centralised in a few hands, with quite centralised control, from PGS where power is distributed among many actors. As examples of the former, some of the former CEEC countries can be mentioned (i.e. until the early Nineties!), but also some Asian countries with autocratic and strongly hierarchical features. In contrast to this oligopolistic model, several Western European countries have developed systems of distributed governance, with many different players and different policy levels and in different policy areas contributing to the definition of innovation policy (Ref. Kuhlmann). - Antagonistic vs. consensual: The antagonistic governance model is characterised by strong majority-led elements that is balanced by regular changes in power. The consensual model is on the contrary driven by a permanent involvement of all relevant stakeholders in decision-making, even if they are not formally in power. Changes in government do not lead to major ruptures in policy, as positions of all actors have already been integrated in existing policies. The distinction between the two extremes can be exemplified nicely by the British model of governance on the one hand, which relies on antagonistic and majority principles (“winner takes all”), and the . Dutch and Scandinavian democracies, which have strong elements of consensus orientation. Countries with a stronger corporatist tradition like Austria or Germany fall also within the consensual category, but the corporatist model can also tends to show more oligopolistic features than the “open” democracies. (Ref. Rhodes ?) - Output vs. process-based legitimacy: Governance systems differ in terms of the extent to which rely on different types of mechanisms to create political legitimacy for their decisions and action. In principle, one can distinguish output-based legitimacy (i.e. the final output and outcome justify policy ex-post) from process-based legitimacy (i.e. independently of the outcome, the processes leading to a decision are regarded as the appropriate way of reaching such a decision). With regard to innovation this means that innovation performance as measured in indicators plays an important role for output- based legitimacy, whereas the process-based legitimacy would recur, for instance, to participatory forms of technology assessment to generate the generalised support for specific innovation-related policies. Obviously, in countries where process-based legitimation prevails, the question will be raised at some point in time whether the substantive progress has been achieved or not. As an extreme case, one might even consider illegitimate governance systems, where neither process- nor output-based legitimacy is generated. In principle, FLAs could also be used in this type of governance system, for instance for mere planning purposes. - Systematic vs. sparse use of differentiated policy preparation tools (PPTs): FLAs are one among other types of tools used to prepare policies. Policy preparation tools (PPTs) include, for instance, evaluation and monitoring activities, formal and informal consultation mechanisms, system analysis, and FLAs. It seems useful to distinguish countries with rather sophisticated PGS that rely on a whole spectrum of PPTs to support the governance process and use them systematically and consciously, and countries that

are still in a development phase characterized by the emergence of governance institutions for their innovation systems, and thus use PPTs rather sparsely. These four dimensions of PGS are not entirely independent from one another. In fact, there are some important correlations to observe, which will be discussed in further detail in Chapter 3.

2.5 A taxonomy of FLAs There are several taxonomies of FLAs (e.g. in terms of generations of FLA and/or their focus on S&T or socio-economic issues),7 but with regard to the impact of FLAs, it seems more appropriate to concentrate on the nature of the process and the role it can play in the NIS. We suggest using the following three dimensions: - Embedded vs. stand-alone activity: If different FLAs are run in parallel to one another without major linkages, we speak of stand-alone FLAs. Stand-alone FLAs can have a visible and clearly identifiable impact, if appropriately geared to the conditions of the PGS, but a stand-alone situation also means that synergies with other FLAs are not fully exploited. The same argument holds if the use of FLAs is rather limited in general, i.e. the visibility and impact of the few existing FLAs can obviously be very high if conducive conditions prevail. If, on the contrary, a broad and integrated repertoire of FLAs exists in a NIS (“embedded FLA”), certain functions that sometimes are attributed to one FLA could equally be attributed to other types of FLAs (e.g. the balancing of tensions/imperatives of different actors in the NIS), i.e. it is more difficult to identify or anticipate impacts, while the potential for synergies is higher. - High vs. low level of participation (and type): Highly participatory FLAs can exert an influence on the NIS through a range of channels. They allow establishing an interface between different types of actors in the NIS. In other words, they contribute to “wiring-up” the NIS because they are inclusive and participatory. They also enable holistic learning about the system (membership, system characteristics, different backgrounds and views, tensions), facilitate the implementation of policy proposals (foresight participants are owners of a shared vision, as well as policy proposals derived from that vision; and they are also actors in the NIS: they can act upon the proposals). In contrast to this, several FLAs are much less participatory and are constructed as policy advisory processes. They are usually more targeted exercises in support of policy and relying on a small group of experts, but can often have a strong policy impact by giving strategic orientations to key policy-makers, by helping set agendas, or by offering back-up solutions/contingency plans in case of certain events happening). - Reinforcing vs. transformative with regard to the existing NIS (and/or its sub- systems): the role of FLAs with regard to a NIS can be very different. Many examples of FLAs aim to “improve” the NIS, in the sense of incrementally changing the S&T base, the demand conditions or the structural features of the NIS to the better. However, the purpose can also be to induce or cope with very significant structural changes in the NIS or more generally, on the national policy level. We define transformative FLAs as i) taking

7 See for instance the five „generations“ of foresight suggested by Georghiou (200x) which reflect combinations of several different dimensions of FLAs. Well known is also the differentiation with regard to rationales of FLAs, i.e. whether they focus on identifying promising or emerging S&T developments or rather emerging socio-economic needs and challenges. These could be interpreted as sub-types of the reinforcing/transformative dimension.

place in a setting of systemic changes on the national level, or ii) aiming at overcoming sectoral/thematic (e.g. in mobility, or energy supply) or structural (e.g. lack of internationalisation, poor cooperation science-industry) lock-ins and including all possible options in order to overcome that lock-in. A transformative FLA questions the prevailing power structures and challenges the dominant constituencies of actors. This second type of role can be a very healthy one, if – from a societal point of view – a radical change would be desirable, for instance to overcome a lock-in into traditional sectors, to introduce a regime-shifting technology, or to overhaul to PGS itself (e.g. in terms of rationale of STI policies, overall decision-making culture and methods to support it; efficacy of STI policies; efficiency of public spending). High levels of participation tend to strengthen the transformative potential of FLAs by creating new “wires” in the NIS.

2.6 Analysing and relating the taxonomies The main argument of this paper is that the potential impact of an FLA on resolving a policy need is determined at a first stage by the “fit” between the type of FLA and the type of PGS, and at a second stage by the “fit” between the policy need and the FLA-PGS combination. The two taxonomies provide a differentiated view of PGS and the variants that FLAs can take. What we want to learn more about (at the first stage) is what variants of FLAs do best fit different types of PGS. As we do not use a highly generalised typology of PGS and FLAs, but instead look at their key dimensions, a “matching analysis” can only be performed for individual cases, but not at the level of ideal types. We would also regard such an approach as superficial and prefer studying the underlying relationship between the different dimensions of PGS and FLAs respectively. This requires analyzing at a detailed level the fit between the different dimensions of FLAs and the different dimensions of PGS. Given the fact that we distinguish four dimensions for PGS and three dimensions for FLAs, we have to analyse twelve different combinations of dimensions (Chapter 4). As a preparatory step, we also need to look at the compatibility of the different dimensions of PGS and FLAs for each of the two taxonomies individually. This analysis is expected to indicate correlations between different dimensions of PGS and FLAs respectively (Chapter 3). Together, these two levels of analysis of the taxonomies of PGS and FLA deliver arguments which combinations of PGS and FLA dimensions promise to be effective and are likely to have a high impact on policy needs, as an input for both ex-post assessment and ex- ante design of FLAs. At a second stage, the connection between different types of policy needs and PGS-FLA combinations needs to be established. Here, we prefer starting from individual case examples of FLAs (Chapter 5) rather than exploring systematically all possible combinations. Having said that, it must be clear that other contingencies may come into play that do also influence the impact of FLAs on National Innovation Systems in any individual case. We nevertheless propose generalised hypotheses at the level of individual dimensions of PGS and FLAs, which are illustrated by empirical examples from several countries. For considering also different types of policy needs we propose a single example of FLA only, as a kind of test case for our conceptual framework.

3 Analysing the individual taxonomies

3.1 Taxonomy of PGS – some observations

Based on the four dimensions of the taxonomy of PGS, six binary combinations of dimensions can be studied in more depth, as captured in Table 1.

Table 1: Binary combinations of dimensions of PGS Oligopolistic- Antagonistic- Output-process Systematic- distributed consensual based legitimacy sparse PPT use Oligopolistic- PGS1 PGS2 PGS3 distributed Antagonistic- PGS4 PGS5 consensual Output-process PGS6 based legitimacy Systematic – sparse PPT use

Underpinned by empirical examples from our countries, in total six binary combinations of the dimensions of PGS need to be discussed. Here are some examples: - PGS1: Is there a relationship between the oligopolistic/distributed character of PGS and its antagonistic/consensual character? Countries with a distributed governance system can be both antagonistic (e.g. UK) or consensus-oriented (e.g. Netherlands). Countries with an oligopolistic governance structure are not associated with consensus-orientation. - PGS2: Is there a relationship between the oligopolistic/distributed character of PGS and the prevailing form of legitimacy? Oligopolistic governance systems would tend to rely on output-based legitimacy, and is rather unlikely to make use of process legitimacy. In oligopolistic governance systems , a NIS is less regarded as a complex system which has to receive impulses of change from actors with distributed intelligence through interactive processes, but as an enormous coordinated management task where final outputs and impacts justify policy measures. It relies on the governance paradigm of hierarchical steering. - PGS3: Is there a relationship between the oligopolistic/distributed character of PGS and the sparse/systematic use of PPTs? Oligopolistic governance structures may make use of policy preparation tools in a systematic or a sparse manner. Evolving countries (e.g. in Africa) may be characterised by oligopolistic governance which use a selection of policy preparation tools on a trial basis as more coordinated (sub-)systems evolve. Other countries (e.g. China) show an oligopolistic governance structure as a result of an established hierarchy, still policy preparation tools may be used in a systematic way, although the measures taken might differ from Western European countries. We would expect, though, that countries with a distributed governance structure do make use of PPTs in a systematic way.

- PGS4: Is there a relationship between the antagonistic/consensual character of PGS and the prevailing form of legitimacy? A consensual PGS tends to rely on process legitimacy, as the various processes secure comprehensiveness, validation and consensus in prioritisation procedures. At the same time, there is a risk of just achieving a minimum consensus. In antagonistic PGS (like e.g. in the UK), process legitimacy is likely to be combined with output legitimacy, in order to make sure that the party in power cannot be attacked easily for not delivering on the substance of a policy. - PGS5: Is there a relationship between the antagonistic/consensual character of PGS and the sparse/systematic use of PPTs? A consensual PGS is also more likely to be characterised by systematic use of PPTs, because consensus-building tends to require some rational exchange of arguments. However, this assessment is not as clearly cut as it may seem at first sight, because also in antagonistic systems the systematic use of PPTs is likely, but rather to support their respective individual positions, not to seek consensus and compromise. - PGS6: Is there a relationship between the prevailing form of legitimacy and the sparse/systematic use of PPTs? Output-based legitimacy may be associated with a systematic or a sparse use of PPTs. We assume that it is mainly associated with oligopolistic governance, hence the same arguments as PGS2 apply.

In principle, this discussion could be further extended by looking at three or more dimensions of PGS, but in essence, such higher-order combinations should result as logical consequences of transitivity of the binary relationships between dimensions.

3.2 Taxonomy of FLAs – some observations Similar to the taxonomy of PGS, a matrix of binary combinations of the three dimensions of FLAs can equally be constructed, as shown in Table 2.

Table 2: Binary combinations of dimensions of FLAs Embedded vs. High vs. low Transformative Stand-alone level of vs. Reinforcing participation Embedded vs. FLA1 FLA2 Stand-alone High vs. low FLA3 level of participation Transformative vs. Reinforcing

- FLA1: Is there a relationship between embeddedness/stand-alone character and the level of participation? There is no clear relationship between embeddedness and participation: Embedded FLAs may be highly participative or small scale advisory processes, the same holds true for stand-alone FLAs.

- FLA2: Is there a relationship between embeddedness/stand-alone character and the transformative/reinforcing character of an FLA? It is unlikely for embedded FLAs to be transformative: From evaluations of foresight processes it may be derived that the additionality of a foresight process depends on the role and “weight” of the other instruments in innovation policy intelligence and learning. This kind of situation is characteristic of most Western European countries, where more or less differentiated governance mechanisms have been established to develop, monitor and re-orient public policies. TF2 in Sweden (Arnold et al.) corresponds to a foresight exercise in a well established policy governance system with an elaborate set of strategic intelligence instruments. Transformative FLAs take place in a setting of systemic changes on the national level. FLAs may complement and support these systemic changes. Examples would be Central and Eastern European countries in transition where the most important political and economic institutions were re-established in the course of the 1990ies. FLAs in this context have to support the reorientation and building up of a system (e.g. CEE countries, Malta). In these countries, FLAs seem to have the potential to assemble and integrate actors of a system, integrate them into a consensus-oriented dialogue and thereby effectively support policy-learning and unlearning processes. Moreover, it provides the ground for setting up and exploiting the potential of other intelligence approaches by contributing to the shaping of an innovation and strategy culture. In other words, in such contexts, FLAs support already ongoing transformative processes rather than inducing them. An example of inducement of transformative change can be seen in the case of the French Futuris process. In contrast, FLAs in the Swedish context can be considered as reinforcing the NIS in that they are i) of an educative character in creating interest and competences in longterm thinking and foresight, ii) of a steering character in functioning as an arena for broad discussions about technological futures. However, FLAs in such a context also aim at priority setting, and priority setting certainly is a part of transformation processes. Still, the institutional setting is well defined, scopes and competences of actors and distribution of power are well established. Hence, it may be assumed that embedded FLAs are less likely to be transformative. In contrast, transformative FLAs take place in a disruptive systemic setting and due to the associated fragmentation have a major function in assembling relevant actors of the system.

- FLA3: Is there a clear relationship between the transformative character of an FLA and the level of participation? The issue of level and type of participation puts emphasis on the inclusion of stakeholders and their interaction in an FLA process and hence points to a social dimension of learning processes. Participation and interaction in the foresight process are likely to enable new network formation and the building of advocacy coalitions around particular issues of shared concern (Keenan, ETEPS presentation, 2010). Hence, a participative and highly interactive foresight process may contribute to achieve transformation in the long run. In introducing completely new actors to the systemic setting and involving their expert knowledge in the foresight outputs, it may even be possible to achieve some changes in the shorter run. In contrast, inviting only stakeholders who are known in the scene and have established roles which they fulfil also during the foresight exercise will not be able to achieve any major changes and hence will be of a reinforcing character. Non-participative FLAs, which are organised as expert-based advisory processes, may influence policy decisions or priority-setting even in a more visible way than participative FLAs. In this respect non-participative FLAs may also contribute to transformation processes. Obviously, in practice, we can find combinations of process stages with high and with low levels of participation (e.g. start with expert participation only to specify transformative needs, then highly participatory process to discuss transformative needs, then refinement of options in a smaller expert round). In other words, while both participative and more exclusive types of FLAs may have transformative consequences, the mechanisms for achieving transformative effects are quite different. In the first case, they rely on broad involvement of actors in a bottom-up fashion, in the latter case the transformation is rather induced in a top-down manner, after having come to an FLA-inspired decision among key decision-makers. Much depends obviously on the resistance to change, and where it is supposed to reside. If resistance to transformative change is mainly an organisational issue, the involvement of actors in this environment in particular may be necessary, and less so a broad level of societal participation.

4 The role of FLAs in different types of PGS

4.1 Guiding hypothesis The preceding analysis of the FLA and PGS dimensions for each of the two taxonomies individually serves just as a first step towards what is our main objective, namely to relate the dimensions of PGS to the dimensions of FLAs. The objective is to check the fit between these different dimensions, and ultimately between a chosen FLA and the policy governance sub- system in which it is embedded. The underlying hypothesis is that the closer the fit, the higher and more favourable the impacts of the FLA are expected to be. This hypothesis is obviously based on the anchilliary assumption that the FLA is technically conducted in an appropriate way. In this context, the notion of “fit” should not be misunderstood as a static and linear concept. FLAs and PGS influence each other in the course of time. There are interactions (“feedbacks”)

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 16 - Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011 among them. For instance, FLAs influence the type of policy needs and options that are raised in the PGS. The type of FLA is also expected to exert an influence on the nature of the policy governance sub-system: a non-participatory FLA would reinforce the oligopolistic character of a PGS, while a participatory FLA would tend to open up the PGS towards more distributed decision-making. In the longer run, and with ensuing tensions, of course such an effect might even lead to a FLA having a transformative impact on the PGS. In fact, an initial lack of “fit” between PGS and FLA might under specific favourable circumstances enable a transformation of a PGS.

4.2 Specific hypotheses Table 3 gives and overview of the combinations of dimensions from PGS and FLA that will lead to specific hypotheses; in total 12 combinations.

PGS Oligopolistic - Antagonistic - Output – process Systematic – distributed consensual legitimacy sparse PPTs FLA Reinforcing - A1 B1 C1 D1 transformative High – low A2 B2 C2 D2 participation Embedded – A3 B3 C3 D3 stand-alone

A1: oligopolistic/distributed – reinforcing/transformative In an oligopolistic PGS a reinforcing FLA could be launched by the dominant actors (which – if needed – could aim at transforming other sub-systems), and it would further strengthen the position of these actors. If other actors launch an FLA it would be of a transformative nature, targeting the nature of the PGS as such, and possibly also of other sub-systems. However, the barriers they would have to overcome are very high. An interesting example in this regard is the German Futur exercise which aimed to break down borders between established technology and policy domains. It was thus intended to be transformative in many regards, but did not succeed to overcome the established (oligopolistic) coalitions of dominant interests. As a consequence, the impact was rather limited. In a distributed PGS, FLAs are more likely to be successful if they reinforce the distributed character of the PGS, and they could transform other subsystems in order to improve overall performance.

A2: oligopolistic/distributed – high/low participation In an oligopolistic setting, high levels of participation are rather unlikely, whereas in a distributed system, high as well as low levels of participation occur. An example for a low participation FLA

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 17 - Forth International Seville Conference on Future-Oriented Technology Analysis (FTA) FTA and Grand Societal Challenges – Shaping and Driving Structural and Systemic Transformations SEVILLE, 12-13 MAY 2011 in a distributed system are the ERA expert groups on the EU level. One out of seven expert groups was the ERA Rationales expert group. Participating experts were invited to rethink the ERA vision proposed in the Green Paper, based on an analysis of the main issues and factors affecting the efficiency, effectiveness and attractiveness of the European research system. A key recommendation of this expert group was the adoption of the Grand Challenges approach (HLG 2008). Examples of highly participative FLAs in distributed systems were UK and Germany. In TF2 in Sweden, stakeholders were invited on a broad basis. However, the reports were largely based on panel discussions between a small number of experts.

A3: oligopolistic/distributed – embedded/stand-alone In an oligopolistic setting, embedded FLAs are possible, but they would most likely rely on an expert-based approach involving a limited range of participants, and aiming to reinforce the positions of the oligopolistic actors. In a distributed system, embedded as well as stand-alone FLAs are possible, but the approach would be rather participatory. Overall, the oligopolistic/distributed category is not making a big difference with regard to the embedded or stand-alone character of an FLA. Embedded FLAs in a distributed system have a lower probability of additionality. TF2 in Sweden may again serve as an example her: There was little sign of direct influence at the decision-making or political level in the aftermath of the FLA. However, there has been a considerable overlap between various undertakings in the domain of research and innovation policy: TF2, the Research Bill and the national innovation strategy Innovativa Sverige were all devised in the same time (Arnold et al., 2005: 23). Interviewed civil servants have argued that the results of TF2 had not been well marketed in the policy-making system, and that the synthesis report had been produced in too late a phase, i.e. after the ‘window of opportunity’ to influence the Research Bill.

B1: antagonistic/consensual – reinforcing/transformative In an antagonistic setting, transformative FLAs are more likely to be launched, but mainly to improve the performance of other subsystems of the NIS than the PGS itself. In consensual settings, it is less likely that transformative FLAs will be initiated, because they would inevitably raise issues that would entail disadvantages to some of the stakeholder groups involved. FLAs could nevertheless be considered to explore options and leave debates about more significant changes to other fora. In the UK, which serves as an example for an antagonistic setting, impacts of the FLA on research agendas and programmes have been noticeable: Ultimate effects have included influence on research agendas of both public (the UK Research Councils, UK government policy) and private actors (industry). Impacts of the foresight exercise on the public domain are evident in the stimulation of new areas of work within existing programmes, rather than the formulation of whole new programmes (PREST, 2006: 18-19). Also the German example of Future and BMBF foresight is instructive here: the Futur process had a limited impact only because it was at odds with the prevailing consensus about future research and technology policy. The time was not yet ripe for Futur results. Some years later, the BMBF foresight could propose similar ideas as Futur, which by then had become more acceptable.

B2: antagonistic/consensual – high/low participation Participatory FLAs can be applied both in antagonistic and consensual PGSs, i.e. the category antagonistic vs. consensual is indifferent with regard to level of participation. Evidence for this hypotheses is provided by the extensive use of participatory FLA is both types of countries. The UK is usually regarded as a typical example of a country with an antagonistic PGS, whereas many continental European countries (e.g. Germany, Netherlands) are characterised by consensual PGS, but equally employ participatory FLAs. Participatory approaches, however, are applied in most of these countries.

B3: antagonistic/consensual – embedded/stand-alone Similarly, both embedded and stand-alone FLAs can be found in antagonistic as well as in consensual PGS.

C1: output/process legitimacy - reinforcing/transformative If there is generally a lack of legitimacy of the PGS (caused either by poor performance of the PGS and other sub-systems, but possibly also due to sparse use of PPTs), there is a need to launch a transformative FLA to change both the PGS and other subsystems. This is a high-risk, high-gain strategy, requiring a very influential, determined, and “bullet-proof” change agent. The case of Hungary is instructive here. At the time when the Hungarian Foresight was launched, the PGS was still in a stage of maturation, and it was characterised by a limited degree of legitimacy. The FLA process was actually meant to contribute to enhancing the process legitimacy of the PGS through its participatory process design. Its main purpose was to support the transformation process of the STI system, and in terms of design the impact potential was quite high. However, the foresight was of limited success for a number of reasons, ranging from external factors like the change of government in the course of the foresight programme to resistance among the established actors.

C2: output/process legitimacy - high/low participation Participatory FLAs are well suited to enhance the process legitimacy of PGS. FLAs with a rather low level of participation, but involving high expertise, may actually deliver better outputs and have thus a higher likelihood to improve output legitimacy, at least in the short to medium term. In the longer term, the participatory element may turn out to lead to an increased effectiveness of implementation of results. Combining process steps with different levels of participation is obviously an option; and it is actually done in many recent projects.

C3: output/process legitimacy - embedded/stand-alone Process legitimacy can be enhanced more easily by visible, standalone FLAs, while embedded FLAs may be less visible and have a lower symbolic impact, but are likely to contribute more to increase output legitimacy, at least in the longer run The 2002-2004 eFORESEE foresight exercise in Malta was conducted in the context of a political system undergoing fast changes in the critical phase of pre-accession to the European Union. The assessment of the exercise revealed that the particularly visible impacts are related to Malta’s Knowledge Futures in ICT and

Education Pilot. The main targeted output in this case was a vision of Malta in 2010, one major objective was also to involve new actors beyond the established players in the field (Cassingena Harper and Georghiou, 2005: 94-97).

D1: systematic/sparse use of PPTs – transformative/reinforcing Where PPTs are used systematically, FLAs could be launched for both transformative and reinforcing reasons. Overall, a systematic use of PPTs is an indication of a reasonably well functioning and mature PGS, which is why it is likely that FLAs are mainly launched to transform specific subsystems of NIS, other than the PGS itself. In both France and Germany the situation under conditions of a systematic use of PPTs can be studied. The German Futur and later BMBF foresight are examples of highly participatory processes that were meant to be transformative also with regard to the PGS, in particular with regard to the definition of priority areas that cut across established technology fields. To achieve this, established thematic actor constellations had to be reconfigured. It may have taken more time than initially expected, but several years after the end of the Futur process, some of the ideas generated at the time found their way into research policy. Also the French Futuris pursued a transformative objective, by aiming to reconfigure the French research and innovation system in order to make it fit for the challenges of the future. The results were taken up in several of the other fora and PPT-like instruments that are used to prepare future research policy in France.

D2: systematic/sparse use of PPTs - high/low participation Where PPTs are used systematically, both high and low levels of participation can be appropriate. Where PPTs are used sparsely, however, high participation is less likely than low level of participation and a strong reliance on experts, because of the less common culture of participation in PPTs in general.

D3: systematic/sparse use of PPTs – embedded/stand-alone If a differentiated and rich a set of policy-preparatory tools is applied systematically in a PGS, the likelihood of realizing embedded FLAs (in plural!) is higher. This situation may however go hand in hand with a rather limited visibility of FLAs within the broad spectrum of PPTs applied; they become invisible and embedded in other PPTs. In other words, FLAs becomes an integral part of the PGS (Georghiou). If, on the contrary, a PGS characterised by ad hoc, sparse use of PPTs, FLAs, once used, are likely to become very “visible” and are likely to have a major impact. The Columbian foresight, but also several other foresight conducted in industrialising economies, have a high impact potential for being not only unique examples of FLA in a country, but often even among the first examples of PPTs.

5 The potential of FLAs for addressing policy needs in NIS In the preceding section, bilateral hypotheses regarding the relationship and “fit” between dimensions of FLAs and dimensions of PGS have been discussed and underpinned by empirical illustrations.

This “fit” may be a helpful element to better understand the actual or potential impact of FLAs, but it is not yet sufficient to capture the actual or potential impact on innovation systems, and more specifically on tackling perceived policy needs. For this purpose, we need to look at different typical “problems” in innovation systems that could be addressed by an FLA, and mediated through the PGS. Typologies of such problems can be of different kinds, focusing on S&T issues and matters of industrial specialisation, on socio-economic problems, or on structural deficits in the innovation system that may require transformative changes. As suggested in Section 2.3, we propose distinguishing quality levels the functioning of NIS: - Some important elements of NIS are missing; - The important elements are in place, but the relationships are underdeveloped; - The important elements are in place, but they do not perform satisfactorily - The system works properly in structural and relational terms, but needs guidance on the research and innovation directions to take. Even if we now formulated hypotheses on the “fit” between our twelve combinations of PGS and FLAs on the one hand, and these four types of typical problems on the other (which we cannot do in this paper), in the practice of real-world FLAs, the actual impact could still depend on several contingencies. However, as orientation and guidance for FLA designers and evaluators, such an approach promises to be fruitful. In this section, we will pursue a modest way of addressing these different types of policy needs, by discussing the impact of real FLAs against the background of our framework of FLA and PGS dimensions for a specific case, and taking into account the specific problems and policy needs that the FLA should help resolve. This case serves illustrative purposes only; future work may extend this approach to other examples. When studying such cases, we look at the following aspects: - What policy need was perceived? - What PGS is in place? - What FLA was implemented? - Was the FLA useful with regard to PGS and policy need?

The case chosen is the second Swedish foresight conducted between 2002 and 2004, which has also been evaluated (Arnold 2005).

Box 1: Understanding the impact of the Second Swedish Foresight What policy need was addressed? In general, a well functioning NIS with some structural problems resulting from the shrinking of the traditionally strong enterprise-based research, which was downscaled as a consequence of mergers and acquisition of leading Swedish companies by foreign multinationals. As a result, matters of restructuring the research base had to be addressed. In addition, matters of direction of research and innovation in terms of priorities had to be addressed.

What PGS is in place? The Swedish PGS would fall – roughly speaking – in the categories of distributed and consensual PGS that relies on a combination of process and output-based legitimacy (with considerable weight to process-based legitimacy) and where PPTs are used widely and systematically. What FLA was implemented? Transformative in the sense of exploring new ways to restructure and thematically re-position the Swedish NIS Moderately high participation, at least very diverse participation with non-conventional participants Embedded rather than stand-alone, because there were several other FLAs being conducted on other matters, and there is a kind of forward-looking culture in Sweden. Was the FLA useful with regard to PGS and policy need? With hindsight, the impact appears to be rather limited, mainly for two reasons: extensive use of other PPTs, overshadowing the results of FLA, and the combination of distributed, consensual and process-oriented PGS which requires a “maturing” of debates before specific decisions are taken. The results were certainly taken up, but processed within the wider machinery of policy intelligence. The Swedish case underlines the argument that in a PGS using PPTs systematically, embedded FLAs are likely to be launched, but their impacts tend to be rather limited, not because results are not taken up at all, but because policy recommendations stemming from FLAs need to compete with the results of other PPTs, leading to an integration of FLAs in a wider spectrum of policy intelligence.

6 Conclusions and policy implications A conceptual framework has been proposed for exploring the potential impact of FLAs that combines three important aspects: the types of policy needs in NIS, a taxonomy of policy governance systems in NIS (PGS), and a taxonomy of FLAs. Particular attention has been paid to the linkages between different dimensions of PGS and FLAs. This conceptual framework developed has delivered two strands of observations:  First, observations are made that refer to the internal correlations between the different dimensions of FLAs (e.g. between its rationale and the degree of participation) and PGS (e.g. between consensual/antagonistic and systematic/sparse use of PPTs).  Secondly, we develop observations how different dimensions of FLAs correlate with different dimensions of PGS (e.g. between oligopolistic/distributed PGS and rationale of FLA). This allows identifying which types of FLAs are suitable under which policy governance conditions in innovation systems.  Thirdly, we explore (for a single illustrative case) the influence of a specific combination of PGS and FLA dimensions on the potential to address a specific type of policy need.

The combination of a typology of FLAs and a focussed view on national innovation systems in characterising PGSs thus provides us with an analytical framework to systematically compare specific NIS-centred FLAs and their (potential or actual) impacts. This type of analysis, applied thoroughly and honestly, delivered some well-substantiated hypotheses/insights into the appropriateness or “fit” between a chosen FLA, the perceived policy needs/opportunities, and the policy governance sub-system. The main hypothesis we put forward is that the closer the fit, the higher/more favourable impacts of FLAs can be expected (obviously assuming an appropriate quality and methodological rigour in conducting FLAs). Not all hypotheses formulated are “interesting” in the sense that he set of hypotheses proposes a number of guiding principles for the design of FLAs Yet, it would be a mistake to presuppose linear (sequential) links among these “elements” of a needed fit, as they interact in several ways:  First, an often neglected impact of FLAs is that the understanding of the originally targeted policy needs/options would need to be revised, and new policy needs/options are likely to be identified, which may in turn shift the character of a next FLA (assuming that the PGS remains rather stable).  Second, FLAs would impact on the policy governance sub-system itself, at least in the longer term: a non-participatory FLA would reinforce the “closeness” of a PGS (its reliance on expert-based approaches), while a participatory FLA would open up a “closed”, hierarchical PGS in a longer run, and with ensuing tensions, of course.  Finally, analysts and decision-makers also need to ask themselves a broader question when a lack of “fit” between the above three elements is observed: is this a mistake in the design of an FLA, or has it been designed in this way on purpose, for instance with intention to run it as a transformative FLA.

7 References Arnold et al. (2005) Bergek, et al. (2005) Bergek et al. (2007) Cassingena Harper and Georghiou, 2005 Dosi, 1988 Edquist, 1997 Edquist (ed.), 1997 Edquist, 2001 Edquist, 2005 Fageberg et al. (eds), 2005 Freeman, 1987 Freeman, 1994

Freeman, 2002 Georghiou (200x) Georghiou Havas, 2005 Hekkert et al. (2007) HLG (2008) Keenan Kuhlmann Lipsey and Carlaw (1988) Lundvall, 1992 Lundvall and Borrás (1998) Lundvall et al., 2002 Nelson, 1993 PREST, 2006 Rhodes Teubal, 1998

THEME: BUILDING FTA CAPACITIES FOR SYSTEMIC AND STRUCTURAL TRANSFORMATIONS - 24 -