Governing innovation systems: A Parsonian social systems perspective

Technology in Society 59 (2019) 101174

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Governing innovation systems: A Parsonian social systems perspective

T

Filippo Reale Goethe-University Frankfurt, Institute of Sociology, Theodor-W.-Adorno-Platz 6, 60323, Frankfurt am Main, Germany

A R T I C LE I N FO

A B S T R A C T

Keywords: Innovation systems Innovation policy Systems theory

This article develops the observation that systemic and functional reasoning is pertinent yet tacit in current political economy of innovation systems. Starting from this, it elaborates a distinct Parsonian perspective on innovation systems which treats them as systems in a sociological sense. This means that they possess crucial functions which need to be fulfilled for the innovation system to work and persist. A perspective on “crucial functions” adds fruitful new insights to theories on innovation systems and proposes new aspects for successful innovation policy.

1. Introduction The theories on “innovation systems” in political economy are not without dispute. But generally, speaking of “innovation systems” evokes a clear image of how innovations are embedded into socioeconomic and political structures and causally influenced by the way these structures combine [1]. Speaking of “combination,” then, insinuates that analyzing “innovation systems” has developed exactly out of a frustration with mono-causal, one-dimensional explanations for innovative trajectories [2,3]. The political economy of innovation systems confronts these obsolete perspectives with a multi-causal, combinatory, or “systemic” approach. On the other hand, research on innovation systems always also poses questions of innovative capacity (e.g. Ref. [4]), that is, of successful innovation as a precondition for economic capability and growth. This means that the political economy of innovation systems has always carried along a focus on the (conditions of the) functioning of innovation systems. But, as characteristics as a systemic and a functional perspective are for the political economy of innovation systems, as secret and opaque have they remained. Less tacit a relationship to aspects of systemness and functionality could in fact allow for a more straightforward grasp at pertinent theories of systemic functioning and, consequently, offer a much better understanding of innovation systems. The following will try to provide the political economy of innovation systems with this straightforward access to systemic and functional theory. It shall be demonstrated that one of the oldest and most classical systems theories, Talcott Parsons' general systems theory, has a lot to contribute to examining innovation systems and innovation policy. By no means is it supposed to accuse anybody of functionalism though, like perhaps Wolfgang Streeck's [5] hardly known but quite frank accusation of “subversive functionalism” (subversiver Funktionalismus) towards Hall and Soskice [6] pars pro toto for efficiency-oriented approaches in

political economy (cf. [7,8]). It is only about uncovering and taking seriously functional and systemic reasoning where it has always existed and about embedding it in functional and systemic theory on a more abstract level, hoping to constructively supply fruitful propositions on the functioning of innovation systems. In what follows, a “systemic” approach denotes an argumentative approach that discusses a social phenomenon as being a positive combination of elements. A “functional” approach is any approach that understands a social phenomenon through its outcomes. Systemic reasoning need not necessarily be functional but may remain descriptive about the way that given elements interact. In turn, functional reasoning is not necessarily systemic because it need not identify smallerscale elements of the process in question or of the entity involved; nor is it necessarily functionalist, because understanding phenomena through their outcomes does not necessarily imply explaining their existence or dynamics by these outcomes. The paper proceeds in two steps. First, the political economy of innovation systems will be sketched out and discussed as it has developed from more narrowly Neo-Schumpeterian economics. The section demonstrates the tacit ubiquity of functional reasoning and of a conceptual language oriented on systems in research on innovation systems and innovation policy. A second section recalls Talcott Parsons’ grand systems theory both abstractly and with regard to innovation systems taken seriously as “systems.” The governance of innovation systems must necessarily take fundamental, systemic functions and dysfunctions into account. Afterwards, a brief conclusion is drawn, with special focus on the role of functionalist causality for the argument presented. 2. The political economy of innovation systems Research on innovation systems has a long tradition [9]. Building

E-mail address: [email protected]. https://doi.org/10.1016/j.techsoc.2019.101174 Received 18 April 2018; Received in revised form 12 July 2019; Accepted 5 August 2019 Available online 08 August 2019 0160-791X/ © 2019 Elsevier Ltd. All rights reserved.

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processes and power constellations that have established it [75,76]. However, an influential share of the political economy of innovation systems continues to follow both a systemic and a functional intuition [77]. Unfortunately, these intuitions remain widely implicit and neither of them is spelled out clearly and systematically against a more explicit background of functional and systemic reasoning. It is also a noteworthy development that some recent contributions have transcended the unidirectional causality rooted in most of the political economy of innovation systems – namely, analyzing the embeddedness of innovations into an institutional environment [78], thereby focusing on the influence of policy and institutions on technology and innovations but not vice versa. In doing so, these contributions have renewed interest in the co-evolution of innovations and policies (or institutions) which, it turns out, had already been advocated very early by some evolutionary economists in the field after all [79,80]. Here, technological and institutional change are seen to reciprocally influence each other to the effect that technological innovations can be typified by their “transformative capacity” [81]: the type and degree of political disequilibrium they may cause in innovation systems [82,83]. Consequently, specific constellations of mutually constitutive technologies and institutions are described as “socio-technical systems” similar to the vocabulary in Geels [84] rather than as “innovation systems.” Although this argument claims to be conceptually rooted in “historical institutionalism” [85–87], it explicitly conceptualizes stability as equilibrium and change as a remedy for “mismatch” between technologies and institutions. It is clear that this essentially amounts to a functional rather than an historical argument [88]. Although perhaps again disappointing from a materialist or power resource perspective, the development of these approaches underscores yet again the tacit tendency of institutional approaches to understand innovation systems (or socio-technical systems, in these cases) from a functional systemic perspective and the need to develop this view more systematically. Simultaneously, more policy-oriented approaches to innovation systems display similar tendencies as well. Some contributions openly recommend that effective innovation governance consists in unveiling and then correcting, by policy, “systemic problems” or “systemic failures” of the innovation system at stake [42,89,90]. This pinpoints malfunction as a crucial starting point for governance, implying immediately that there exists something like successful systemic functioning of innovation systems. A couple of contributions have even chosen to speak of “functions” instead of “policy goals” when they ask what exactly an innovation system must fulfill to work properly [91–93]. Some hope to formulate functions basic enough that they theoretically apply to all innovation systems, regardless of their specific institutional and technological context and independently of their territorial scope [94]. Yet, Morlacchi and Martin [95], p. 579) regret that this universalist rhetoric of systemic problems and basic systemic functioning has not yet turned into a general system theory applicable to the governance of innovation, writing:

essentially on Schumpeter's seminal ideas on innovation and entrepreneurship [10–12], it has developed through evolutionary economics pioneered most notably by Nelson and Winter [13,14] (cf. [15–17]) and is situated in a broader framework of neo-Schumpeterian political economy [18–20]. Its conceptual roots date back to the 1990s when it was formulated originally by Nelson [21], Lundvall [22], Edquist [23], de la Mothe and Paquet [24], as well as Pavitt [25] – but the framework remains important until today [26,27] (see Ref. [28] for a comprehensive overview of the literature). Meanwhile, innovation systems research has gradually spilled over from more narrowly economic circles and has had major influence on political science, sociology and geography [29,30]. First, comparative political economy, sometimes designated “comparative capitalisms” [31,32], has adapted and adopted the major propositions of innovation systems theory [6,33,34]. It has combined a more systematic, more sociologically informed institutional analysis [35] with the crucial propositions of innovation systems research in order to account for the “comparative institutional advantages” [36–38] of different types of production regimes. Secondly, policy studies and governance theory have started to investigate “innovation policy” as a policy field of its own, distinguishing it from technology policy and research policy by its specific, idiosyncratic phenomena [3,39,40]. Major protagonists of the innovation systems approach have taken the opportunity to contribute to this innovation policy literature (e.g. Refs. [39,41–45]). Thirdly, and with regard to geography, proposing “regional innovation systems” [24,46–48] has changed the scope of innovation systems from administrative (national) to spatial (regional) dimensions. This has invited geography to the discussion on innovations since spatially structured innovation systems, then, come naturally as an essentially geographic topic [49]. In sum, and including ever more input from neighboring fields, it seems that the innovation systems approach has grown from a specific thread within Schumpeterian economics into a comprehensive comparative political economy of innovations [50,51]. This political economy is still diverse and differentiated but it also seems generally characterized by a strong focus on the governance [52] of innovation [1,53], a broadly rationalist ontology and a transaction-cost based epistemology borrowed from new institutional economics [54], and an evolutionary approach to institutional and policy change [55,56]. All approaches on innovation “systems” obviously suggest a system at least in the widest sense, that means, at the very least they assume a constellation of “elements” which combine more than just randomly and synergistically produce or sustain a “whole” (cf. [9], p. 210). For instance, the most institutionalist among these approaches typically underscore the structural “complementarity” among institutions of different realms of the political economy [57–59]. For example, a common conclusion is that shareholder-oriented corporate governance, finance-based corporate finance, and weak employment security are complementary because they all incentivize short-term, high-risk investments [60,61]. Although they do vary by the details of their arguments [59], the do also concur in assuming that institutions within a given political economy tend to converge in the logics they follow and the rational incentives they set for actors [62–64]. This perspective is certainly far from uncontested: In fact, some tend to treat complementarity rather as an empirical phenomenon [65] and others point out that complementarity implies fragility as much as it suggests stability [66,67] which others yet have taken to mean that “hybrid” constellations are in fact more flexible and thus resilient [68,69]. Yet again, the majority of contributions seems to find explanatory power in what they see to be efficiency gains or comparative advantages attributable to institutional complementarity [33,36,70]. This essentially turns into a teleological as well as functional(ist) explanation [71,72] in the sense that convergence among institutions is causally explained by their increasing incentive compatibility [73,74] and, eventually, by the gains in efficiency from their complementarity. Here, a materialist critique might perhaps insist that the causal reasons for the shape of any institutional constellation should instead be found in the political

“[S]ince the 1970s we can identify a shift to a more systemic way of thinking in our policy research […]. Systems thinking, since the early developments with systems dynamics within the fields of operational research and engineering, has gone through many subsequent developments. However, it has thus far failed to produce a ‘general system theory’ that describes the behaviour of all systems whatever their type, and at the same time is empirically relevant.” Devine [96] has readily picked up on these shortcomings and has offered a convincing synthesis of the theory of “national innovation systems” with the theory of “viable systems” [97].1 He shares the observation that systemic reasoning has remained tacit or, where explicit,

1 My special thanks goes to an anonymous reviewer for pointing these approaches out to me.

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meaning attached to them is ordinarily general purpose, or some specific technical meaning, and if so, which.

inconsistent, in Schumpeterian innovation research and suggests that a more straightforward systems theory would help this approach substantially. These suggestions have afterwards inspired a small number of studies and applications [98,99]; however, resonance has remained limited which suggests that the underlying claim needs repetition. What stands out as both a strength and a shortcoming of the viable systems model is its strong emphasis on adaptation and stability. Adopting an evolutionary, “quasi-ecological” perspective ([96], p. 497), it explains social systems by their ability to “match” ([96], p. 494) with their environment. Innovation systems, then, are successful if they adapt to the external constraint most crucial to them: the expectation to innovate. The major tension of this perspective, however, is that innovation is not easily modeled as a form of adaptation. Instead, faithful to the idea of “creative destruction” that underlies the political economy of innovations, what is characteristic for innovation is that it is rather a form of creation and that it is not mainly the environment that pressures the innovation system to adapt but, to the contrary, the innovation system pressures its environment to adapt to its innovations. What remains of adaptation is the need for the innovation system to exchange resources with its environment, whereby adaptation arguably is one aspect of functioning innovation systems but not anyhow the most important. The viable systems model also assumes that systems adapt mainly through variation or, where variety is insufficient, through rational, centralized management. “Management” constitutes a superordinate subsystem with “higher-level capability” ([96], p. 495) with regard to governing cultural norms and strategic goals likewise. As Devine admits, this injects into the approach an instrumental, reflexive rationality and a substantial hierarchy in governing innovation systems that corresponds with earlier conceptions of innovation processes but is incompatible with recent, late modern approaches to heterarchical and soft governance, the contingency of its goals and principles, and the open-endedness of its outcomes [100]. The need remains for a theory of social systems that is formally concrete about structures in relation to function but, at the same time, as abstract as possible as not to presume any knowledge, reflexivity, or hierarchy which indeed are effectively contingent. What is absolutely astonishing is that none of these contributions more than marginally mentions Talcott Parsons who is famous for pursuing an abstract, general systems theory almost with manic rigor [101,102]. Jacobsson and Jacobsson [103] remain among the few who discuss him (in passing) in order to (disputably) distinguish their “technological innovation systems” approach from his functionalism. Therefore, it ought to be demonstrated now that Parsonian theory can in fact contribute some essential points to innovation systems theory.

Mills [108], not without spite, has gone as far as to suggest that The Social System [101] needed “translation into English” and could in fact be summed up in just a couple of pages if stripped off its conceptual and abstract language. This, in turn, Hamilton somewhat defends. He at least criticizes how it has unjustly led many people to ignore Parsons' work altogether based – in Savage's words – on “glib dismissal and uninformed polemic” ([109], xi). While Parsons' work is certainly diverse, what interests most in the following is his general systems theory, developed around the publication of Economy and Society with Neil J. Smelser [107], which was written “at the time when the distinctively structural-functionalist character of Parsonian sociology was at its zenith” ([109], p. 192). Parsons defines four distinct functions that must be fulfilled in and for a social system. Any system must develop subsystems that fulfill these functions. He writes ([107], p. 199; my emphasis): “The most general principle is that any system tends toward differentiation of structure in accordance with four functional problems: goal attainment, adaptation, integration, and latent tension management and pattern maintenance. The meaning of these problems is constant from system to system: the goal-attainment function realizes the primary orientation of the system in question; the adaptive function meets certain situational exigencies, either by adjusting in the face of inflexible reality demands or actively transforming the environmental features in question; the integrative function regulates the inter-relationships between the already differentiated adaptive, goal-attainment and latency subsectors, mitigates the level of distinct differentiation of each that obtains, and in general promotes harmonious interaction; finally, the latency function furnishes, maintains and renews the motivational and cultural patterns integral to the interaction of the system as a whole.” In other words [110], the adaptive subsystem is responsible for managing and distributing scarce resources as well as exchanging them with the environment of the system. The goal attainment subsystem translates immediate needs of the system derived from its general “situation” into aims or goals. Especially if scarcity forbids fulfilling every goal at once, the function of adaptation becomes crucial. The integrative subsystem adjusts and aligns the contributions of the other three subsystems with regard to sustaining the system as a whole. The latent pattern maintenance subsystem, then, provides and maintains a shared cultural framework that contains values and meanings which legitimize the structure of the system. It also resolves tensions and contradictions. Adaptation and latent pattern maintenance are functions which generally relate to the sustention of the system as a whole and in relation to its situation or environment – “instrumental” functions [111]. The integrative and goal attainment subsystems are “consummatory” which in Parsons' vocabulary means that they concern the inner cohesion and integrity of the system much different from the other two subsystems [104,111]. The functions combine to constitute Parsons' well-cited “four function paradigm” most popularly known by its acronym “AGIL” ([104], p. 29). It is probably the single most prominent concept publicly associated with Parsons’ work. Parson himself claims that it is among the most important concepts to influence the remaining decades of his reasoning [112]. Characteristic for Parsons' theory of differentiation, then, is the notion that every subsystem is a system in itself, thereby needing to differentiate in its own terms and to develop subsystems of its own to persist. This means that, from the perspective of any subsystem, the other three subsystems of the wider system of which it is a subsystem count as its own environment ([113], pp. 4–5). For Parsons’ “general system of action” as a whole [102], the four functions are fulfilled by (in acronymic order): (A) the behavioral organism; (G) the personality of the individual; (I) the social system; and (L) the cultural system [113]. The

3. Parsonian systems theory The neglect of Talcott Parsons is also surprising since Parsons' influence on sociology can hardly be overestimated. Peter Hamilton [104]; p. 13; original emphasis) writes, “one is immediately struck by the scale of contribution Talcott Parsons made to the development of sociological theory”. He continues to suggest that “Parsons reformulated the nature of sociological enquiry during [his] time, and even gave it a direction, a theoretical programme, which it lacked before.” Yet, Hamilton also underlines that Parsons' goal of pursuing a general theory of society with a maximum of analytical and scientific rigor has also led him to “employ a language all his own,” sometimes incomprehensible and abstract enough for Parsons to be “charged with abstrusity and abstract formalism” by his critics ([105], p. 186). Worswick ([106], p. 700; my emphasis), for instance, reviews Parsons’ collaboration with Smelser [107] and complains: “The outline of the general theory of action provided in this book is extraordinarily abstract, and the style, especially in earlier parts, clumsy and obscure (sic). The frequency of very long words (sic) is daunting, the more so in that one is never quite sure whether the 3

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Table 1 Subsystems of the “general system of action” and of “society,” respectively. (Source: [113]) Function

General system of action

Society

Adaptation (A) Goal attainment (G) Integration (I) Latent pattern maintenance (L)

Behavioral organism Personality of the individual Social system Cultural system

Economy Polity Societal community Pattern maintenance or fiduciary

“physical system” and “ultimate reality” comprise the environment – in the systemic sense – for the general system of action [113]. “Society” then is “the type of social system characterized by the highest level of self-sufficiency relative to its environments, including other social systems” ([113], p. 8). We can again dissemble society into the four crucial subsystems that have differentiated and which sustain it (Table 1). The economy is essentially the adaptive subsystem of society, as Parsons and Smelser [107] most famously and most influentially discuss. The economy as a system of its own, in turn, adapts through the subsystem of “capitalization” whereas it integrates through the “entrepreneurial” subsystem [107]. From a discursive perspective, Parsons’ quite abstract reasoning might be interpreted saying that capitalism as an epistemic order depends on four major ideas: peace (L), progress (G), citizenship (I), and value added (A). Each of them addresses and defines a different aspect of capitalist social order, but only if they persists and combine is capitalism structurally sustainable.

systems. Its main task is, in fact, distribution. If the integrative (or any other) subsystem is neglected or dysfunctional, by definition so is the innovation system as a whole. Contrary perhaps to immediate intuition, this is not only or specifically an issue for innovation systems that are mainly organized through markets and contracts; even the most state-controlled innovation systems may suffer from a lack of social integration and identification. On the other hand, somewhat “arms-length” institutional regimes may be integrated fairly well as long as economic actors basically accept and identify with the regime and as long as it is embedded in a comprehensive model of distribution. This only reiterates that markets and contracts are indeed a mode of integration. However, this aspect of innovation systems as social systems seems quite neglected in pertinent approaches, which demonstrate a tendency to ignore aspects of social integration because they follow a strictly transaction- or incentive-oriented, institutional view. For instance, policy attempts to transplant the Silicon Valley model to other places, for example into the German national innovation system, have overlooked that “Silicon Valley” is a marker that provides identity and a sense of purpose. This is not to say that the lack of venture capital is not an evident factor in the failure of the Neuer Markt [114,115], but that it might have been unconstructive in terms of explanation to ignore completely that there was no comparable “start-up culture” (a goal attainment issue), let alone a common (and be it competitive) identity of radically innovative ICT professionals across Germany (an integration issue). Similarly, there were clear economies of scale involved when the Western European commercial aviation industry reacted to its liberalization by consolidating through major airline alliances [116,117], effectively dividing the sector into three segments of about comparable size. Losing state subsidies in the process of privatization is of course mainly an adaptive issue. But the airlines also took good care to label their alliances in what seems an attempt to develop and emanate a sense of belonging and togetherness. This might have served marketing purposes above all; still, it constituted clear and visible boundaries of membership for each of the specific alliances.2 Moreover, and besides efficiency gains, airline alliances generally redistribute risks and resources. Economic consolidation, in this sense, also resulted in social integration in a number of ways. Or, as yet another example, besides the lack of venture capital, when asked how to potentially improve the innovation systems’ innovative capacities, entrepreneurs in the regional innovation system of the metropolitan region of FrankfurtRheinMain [118] in Central Germany mainly bemoan a missing sense of common identity [119]. Furthermore, for Schumpeter [10], paradigmatically, innovation is effectively a competition. It by definition produces winners and losers. It seems, though, that the political economy of innovation has put so much theoretical effort in explaining how different constellations of institutions combine in incentivizing one innovation or another that it has widely lost sight of those who must necessarily lose the competition for others to win it. This alone might be a source of frustration for the actors involved, but it generally holds for any kind of economic competition, not being at all specific to competition over innovation. However, and more importantly, disappointment is a structural epiphenomenon specifically of innovation as an open-ended process, even

4. Innovation systems as social systems What does this mean for the task to take innovation systems seriously as “systems” in Parsons’ sense? The most immediate answer is that they do require subsystems that fulfill the four crucial functions. It is most obvious with regard to adaptation, in the sense that an effective innovation system needs a subsystem that guarantees flows of finance, capital, and skills into the innovation system. It needs functioning financial, capital, and labor markets as well as institutions for education and training. Until here, this almost trivially corresponds with orthodox views. Moreover, it is also the task of the adaptive subsystem of an innovation system to manage the outflow of innovations into the subsystems of society for which they are useful. In sum, allocation is the main task of the adaptive subsystem. In terms of goal attainment, innovation systems need a subsystem that designs visions and determines possibilities. This might include, but is not limited to, comparing and evaluating investment opportunities. Seeing that innovation is by definition an open-ended process, it can hardly mean setting clearly defined ends. Other tasks of the goal attainment subsystem are to set standards and norms and to organize research. All in all, valuation is the main task of the goal attainment subsystem. The latent pattern maintenance subsystem of an innovation system defines legitimate actors and actions and manages conflicts, by law or by mediation, that occur over these issues. The latent pattern maintenance subsystem is even more important in heterarchical, differentiated innovation systems where responsibilities are sometimes more than ambiguous and which are prone to either conflict or stalemate. The subsystem is crucial in that it not only manages conflicts but it also manages the overflow of uncertainty and, at times, disappointment as the necessary epiphenomena of innovation. Intellectual property regulation is perhaps the most comprehensible example of latent pattern maintenance in innovation systems. Mediation is the major task of this subsystem. The fourth subsystem of an innovation system is the integrative subsystem. It provides platforms for exchange, it manages flows of information, knowledge, resources, and things within the innovation system, and it provides a common social framework to the innovation system as a social space. Moreover, it regulates mechanisms of inclusion and exclusion, such as expert language. Varieties of employment legislation are perhaps among the most obvious examples of integration in innovation

2 Note, for instance, how it usually says “A Star Alliance Member” rather than “A Star Alliance Airline” or so in the advertisements of Star Alliance airlines.

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general assets (weak employment protection) with incentives to invest in specific assets (extensive unemployment benefits). However, public policy in Denmark obviously succeeds in (a) managing latent patterns of discontent in terms of social inequality and (b) sustaining an widely shared and credible concept of social citizenship. A major reason might be that “flexicurity” has a long history in Denmark which gave corresponding subsystems of integration and of latent pattern maintenance time to differentiate alongside a flexible labor market. In contrast, the social policy reforms of the late 1990s and early 2000s in Germany have consequently “liberalized” both employment protection and unemployment insurance. The reforms are generally consistent with regard to incentive compatibility as they support investments in general assets. However, policy has failed to mediate the growing social conflicts emanating from increased inequalities and to sustain the concept of citizenship that used to exist. Here, liberalization has undermined the crucial structures of tri-partite conflict resolution, that is, latent pattern maintenance, and the insurance-based structures of social citizenship, both of which have never differentiated similarly in Denmark [131]. Or, as an example that affects a different subsystem, in relation to a sectoral, not a national innovation system, on a Western European scale: When Western European states decided to liberalize the commercial aviation sector, they quite coherently deregulated the product markets simultaneously to privatizing their former “national champions” [88,132–134]. However, their policies failed to install new, marketoriented purposes and goals in the privatized companies that had been created and then run as part of the public sector for decades. They effectively left behind a dysfunctional goal attainment subsystem in the sectoral innovation system (cf. [135]). Consequently, this suggests a new view on innovation policy. Generally, it redefines what is meant by “dysfunction” in functional policy terms. Scarcity of specialist workers or lack of venture capital, for example, are common policy problems in innovation policy, and the institutional dissonances can be identified that produce them, but they are not what is meant by “dysfunction” in Parsonian terms. Instead, from a social systems perspective, “dysfunction” denotes an innovation system's (temporary) disability to fulfill one or more of the four crucial functions discussed above. The governance of innovation should, in any case, take these basic exigencies of innovation systems into account. This is of course not meant to de-legitimate that innovation policy follows less fundamental policy goals which are oriented on the consistency of institutional constraints and incentives – but to say that innovation policy does need at least some focus on the more basic functional requirements of innovation systems, in addition to pertinent foci in policy-making. Distinguishing these dimensions or levels of governing innovation systems in terms of both analysis and policy is especially crucial because, as has been sketched, reaching more fundamental, functional policy goals has a diverse, indeterminate relationship to reaching more immediate ones. Put differently, pursuing the latter does not automatically involve pursuing the former. It is not generally impossible that there are even some trade-offs as well. A major obstacle to functional innovation policy now seems to be that the subsystems of innovation systems are abstract social phenomena and that the quality of their functioning is therefore, at least immediately, more or less obscure. To make this clear, there are operationalizations and indicators for more immediate policy goals in innovation policy, like for the innovative or R&D capacities of an innovation system, for its value-added, for social inequality, and so on, but the overall functioning of the subsystem of latent pattern maintenance, for example, particularly in relation to the other subsystems, is not so easily measurable. This echoes what the methodological literature bemoans about “mechanisms” [136,137]: they are crucial but unobservable [138,139]. Adapting the methodological solution developed by this literature, one suggestion would be that innovation policy needs to connect the question of functional exigencies being fulfilled or not to “observable implications” [140] by which to observe and then evaluate the functional state of the system as a whole. Starting from this, policy measures

without any competitors [120,121]. That means that patterns of disappointment are latent in innovation systems seen as social systems. The political economy of innovation seems to have been so engaged with explaining or predicting innovative success that it has neglected the social and political aspects of innovative failure or disappointment. For a telling example, Hall and Soskice [36] compare the number of patents in different industries between national innovation systems to prove that there are in fact comparative institutional advantages in international trade [38]. Besides other shortcomings of this approach [122], Hall and Soskice neglect the contrasting number of unsuccessful innovations that would have been a viable indicator for the strains or social costs of disappointment associated with the innovative output they measured. This might have been incomplete but justified as long as they were interested in the institutional effects on the comparative success of innovative activity. From a social systems perspective, however, disappointment might result in frustration, conflict, or exit, each of which is a sign for dysfunctional latent pattern management which effectively jeopardizes the entire social system which, in turn, is why this needs to be managed. That means that a functioning innovation system is not simply one that institutionally incentivizes (enough) successful innovation but rather one that produces innovations in a socially, systemically sustainable way. It follows that, with regard to innovation governance related to latent pattern management, there is no institutional way to guarantee successful innovations, there are only functional ways to manage (or govern) the patterns of disappointment and conflict that inevitably emerge. Once again – this perspective suggests that innovation systems require that subsystems perform certain functions. It does not suggest that these subsystems necessarily be clearly defined entities that possess agency to fulfill these functions, like for example in Niosi et al.‘s perspective [9] who define innovation systems as a system of interacting “units:” for them, firms and agencies. Thereby, they implicitly identify “systems” with “networks” and suggest that the constitution of legitimate “units” is in a sense exogenous to, in that it precedes, the constitution of the social system. Quite differently, from the point of view proposed here, “roles” [107], their relevance, their legitimacy, the corresponding kind and amount of agency, as well as the entities legitimate to fill these roles, are institutions defined by the latent pattern maintenance and its relations to the other subsystems inside the innovation system. Now, what has been said has some direct consequences for the perspectives of political economy and innovation policy. First, the distinction between “liberal” and “coordinated” innovation systems that pertains strongly to the political economy of innovations [6,123–126] does not capture the degree of social integration of the innovation systems in question. “Coordinated” innovation systems are not more strongly socially “integrated,” as argued above. Any other proposition would amount to suggesting that “liberal” regimes are structurally dysfunctional which is evidently not the case. Secondly, as an immediate consequence, the understanding of institutional “complementarity” must be revised or at least amended. It has mainly been discussed as institutions sharing one and the same logic or as institutions setting compatible incentives [59,62,127], particularly with regard to transaction costs and the specificity of assets [63,128]. However, from a social systems perspective, a set of institutions is complementary if they contribute to one or more subsystems of the innovation system fulfilling their functions properly. This aspect is generally independent from the question whether they immediately set compatible incentives to actors or, for that matter, whether they do or do not obviously follow the same logic. Instead, they might appear to set inconsistent incentives but contribute to the functioning of the subsystems nonetheless. On a broader scale, this might explain the success of innovation systems that, from the perspective of incentive compatibility, seem “hybrid,” like Denmark [69]. The decisive dimension of their stability and consistency might be indeed functional, not “institutional.” For example, the Danish “flexicurity” model [129,130] appears incoherent in so far as it combines incentives to invest in 5

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Declaration of interest

may then possibly address the functional problem in question, however relating them once again to, and judging them by, the more immediate institutional consequences the policy might have beyond the functional effects in focus.

None. Acknowledgements

5. Conclusion

I would like to thank Alexander Ebner for his advice on this article, and Fabian Bocek, Timo Seidl, and Martin Hauff for some great controversial discussions on systems theory and functional reasoning.

Most common arguments in the political economy of innovation systems exhibit a strong tendency of functional and systemic reasoning, yet this remains tacit in most cases. The same is true for innovation policy research which discusses malfunction as a policy problem but avoids explicit functional and systemic language, too. Yet, it has been shown that a closer look at Parsons’ AGIL scheme of crucial functions for any kind of social system sheds essential light on the sustainability and success of innovation systems. Innovation policy has a more fundamental, functional dimension in addition to its more immediate, institutional dimensions that has been widely neglected so far although it is vital. The principles of the relation between these dimensions remain however difficult to determine. The next important step in terms of analysis would be to comparatively elaborate the ways that institutional configurations and other structures of the innovation system combine, in their historical and local context, not with regard to compatible incentives, but with regard to the fundamental functions of the innovation system as a social system. In other words, to actually amend the theory of institutional complementarity. In a next step, a viable model for functional innovation policy can be developed. It needs to be pointed out that the argument just presented is functional in that it underscores the functional effects, or functions, of subsystems for innovation systems. It has also suggested that the innovation system is more stable and thus successful the more these functions are fulfilled, either by the state or by industry. Yet, it is not functionalist since it has not meant to imply that the subsystems exist because of the functions they contribute to the persistence of the innovation system. Introducing Parsonian perspectives to political economy is not meant to suggest functionalist policy studies. Quite the opposite: Functional reasoning potentially obscures the political essence of the policy process involved in building and, more importantly, governing innovation systems. One might perhaps follow discursive institutionalism [141–143] far enough to point out that the functioning of an innovation system is generally a matter of discursive construction in the first place or even that “innovation systems” count as desirable components of the political economy only for some ideologies to begin with [100]. Crucial policy actors might be reflexive enough to pick up scientific arguments such as the one just presented and include them into their policy goals, putting functional perspectives on the agenda, perhaps even compromise on their immediate interests because they can anticipate the functional effects from the institutional architecture for which this compromise allows. But policy-making based on scientific or other predictions that are essentially “fictional expectations” [144] depends on a number of cognitive preconditions. Beyond this, explaining constellations post facto by their functional effects, however economically rational for whichever group of actors, also implies that both certain ideologies and specific power relations must have been in place. In sum, demonstrating that a constellation is functional with regard to a certain system does not sufficiently explain this constellation causally, neither directly or by the detour of political rationality, nor does it suggest that the functioning of this system is anyhow normatively desirable – but it contributes to the discussion on what, seeing a certain definition of successful innovation systems, are the essential conditions to be fulfilled. Moreover, especially the function of goal attainment is potentially charged with normative questions, which is why some may find it reasonably necessary to subject it to democratic discourse instead of private decision-making.

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