Sectoral Systems of Innovation: a proposal on its microfoundations

Abstract

The intriguing relationship between knowledge and innovation, the persistence of different patterns of innovation across sectors and the multidimensional character of innovation processes, have given rise to the proposal of new analytical frameworks for capturing the specific and differential role of knowledge in innovation processes. This is the case of the Sectoral Systems of Innovation (SSI) framework proposed by Malerba (2002; 2004). However, these kinds of approaches are not free from criticism; one of such criticisms assesses that the innovation systems approach (and thus SSI) is at best heuristic rather than theory (Edquist, 2005). Consequently, the epistemological status of such an approach does not seem to be clear. This paper assumes these criticisms and shows how the SSI approach may be supported by a theoretical basis, a theory that leaves room for the goals pursued by the agents within a system and that links these goals to the development and deployment of their capabilities. From this perspective, any system, any SSI, is a product of the action planned and deployed by the agents that interact within a sector. The properties and characteristics of such systems are the object of historical and empirical research on the basis of SSI. The proposed approach would also provide a criterion for the evaluation of the performance of any SSI.

Full text

Sectoral Systems of Innovation: a proposal on its
microfoundations
Félix-Fernando Muñoz (1) & María-Isabel Encinar (2)
felix.munoz@uam.es & maribel.encinar@uam.es
(1) & (2) Facultad de CC. Económicas y Empresariales
Universidad Autónoma de Madrid
28049 - Madrid, SPAIN
(1) & (2) Instituto de Investigaciones Económicas y Sociales Francisco de Vitoria
Crta. Pozuelo – Majadahonda, Km. 1,800
28223 - Pozuelo de Alarcón, Madrid, SPAIN
Abstract: The intriguing relationship between knowledge and innovation, the persistence of
different patterns of innovation across sectors and the multidimensional character of innovation
processes, have given rise to the proposal of new analytical frameworks for capturing the
specific and differential role of knowledge in innovation processes. This is the case of the
Sectoral Systems of Innovation (SSI) framework proposed by Malerba (2002; 2004). However,
these kinds of approaches are not free from criticism; one of such criticisms assesses that the
innovation systems approach (and thus SSI) is at best heuristic rather than theory (Edquist,
2005). Consequently, the epistemological status of such an approach does not seem to be clear.
This paper assumes these criticisms and shows how the SSI approach may be supported by a
theoretical basis, a theory that leaves room for the goals pursued by the agents within a system
and that links these goals to the development and deployment of their capabilities. From this
perspective, any system, any SSI, is a product of the action planned and deployed by the
agents that interact within a sector. The properties and characteristics of such systems are the
object of historical and empirical research on the basis of SSI. The proposed approach would
also provide a criterion for the evaluation of the performance of any SSI.
Keywords: Sectoral Systems of Innovation; knowledge base; goals; intentionality; capabilities
JEL: B52, O3

1. Introduction
The historical emergence of the so-called knowledge-based economies (Cooke, 2001) ⎯with a
high proportion of knowledge intensive labour, a growing economic importance of informational
flows and a higher share of intangible capital relative to physical capital (Foray, 2004)⎯ has
favoured the emergence of analytical frameworks that classify and analyze the empirical
evidence linked to the conceptual elements of the said knowledge economies.
A starting point for this kind of theoretical research is the recognition and understanding of the
complex processes that underlie the characteristic innovation processes of knowledge-based
economies. These include the knowledge creation, diffusion and organization processes. One
very important issue is the role these processes play in relation to the elements, processes and
dynamical links that configure the innovation processes.
Furthermore, it is apparent that different patterns of innovation exist across sectors and these
patterns differ more among different sectors of the same economy than in the same sector
compared across different economies (Malerba, 2005). This is why some authors consider that
the relevant level of analysis for innovation processes is the sectoral level, rather than the
national or regional level. A comparison among actors, sources of novelty, institutions, and
innovation policies in different sectors shows significant disparities, which suggests that the
sources of novelties and their role of dynamical transformation across sectors is much more
diverse (Mowery & Nelson, 1999) and thus requires a specific explanation.
The study of the relationship between knowledge and innovation and the persistence of
divergent patterns of innovation across sectors demand new analytical frameworks that make it
possible to, on the one hand, capture the multidimensional character of innovation processes
and, on the other, point out the specific and differential role of knowledge in innovation
processes across different sectors. This is the case of the Sectoral Systems of Innovation (SSI)
framework proposed by Malerba (2002; 2004). However, these kinds of approaches are not free
from criticism; one of such criticisms assesses that the innovation systems approach (and thus
SSI) is at best heuristic, rather than theory (Edquist, 2005: 186).1
This paper examines the theoretical foundations of the SSI approach, the role knowledge plays
and the nature of interactions within innovation systems. It explores how and why an economic
1 Some criticisms refer to the locus of SSI with regard to other alternative (or complementary) approaches
as National (Freeman, 2002, 1987; Nelson, 1993) and Regional (Cooke et al., 1997) Systems of
Innovation. Other criticisms emphasize the lack of content of this approach from a quantitative point of
view and because of the policy implications of assuming complex frameworks. Edquist (2005) points out
that the main weakness of the systems of innovation approach is that, despite its usefulness for describing
sector performance, it is “over-theorized” or “sub-theorized” and is thus not sufficiently clear as to its
theoretical basis and epistemological status.
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system evolves in order to identify the causes of such evolution and, therefore, of the
differences across systems. Innovation systems (like SSI) would differ substantially because
there are specific causes at work, apart from the differences in the underlying technologies.
Several of these ‘other causes’ appear in a diffuse way throughout the literature, despite their
great importance for explaining the performance of a system. In particular, we refer to the goal
dynamics of the agents interacting within a system. As will be shown, they are a key element for
explaining the differences among patterns of innovation observed in different sectors or the
differences in performance of the same sector in different geographic areas.
The paper is based on an abstract definition of system as a set of constitutive elements (things)
and the connections among them (the way they are related) (Potts, 2000: 2). Such structure and
its evolution support the analytical description of dynamical phenomena. A key issue for
understanding evolutionary processes is the way dynamical connections are formed. The
characteristic evolutionary processes of selection and retention operate on this basis (Foster &
Metcalfe, 2001). The question is how and which connections activate in an economic system.
We argue that self-transformation processes result from changes in agents’ knowledge as a
consequence of learning and acquisition of new evolutionary capabilities and that these
processes are triggered, among other causes, by the dynamics of generation and hierarchical
change of agents’ goals ⎯linked to agents’ intentionality. To accommodate the latter idea, we
propose the analytical concept of an agent’s action plan.
This paper seeks to contribute to the microfoundation of individual and organizational
capabilities and their consequences for economic change (Loasby, 2008; Felin & Foss, 2006,
2005). It is a theoretical paper that explores the determinants that configure the knowledge base
of an SSI; more specifically, why connections within a system are established.
The paper is organized as follows: section 2 offers a brief description of the SSI approach;
section 3 explores the theoretical foundation of the knowledge base dynamism; in particular,
how connections are established among elements within a system; in section 4, we analyze the
implications knowledge base dynamics have for the organization of knowledge and the resulting
pattern of innovation in SSI. In this section, we also refer to the ‘products’ of innovation
processes and propose a standard criterion for the dynamical performance of a complex
process. The paper finishes with concluding remarks.
2. SSI and its building blocks
It is a fact that patterns of innovation vary across sectors in an economy in terms of their
characteristics, sources of novelty, actors, sector boundaries, processes and the organization of
innovative activities, etc. Moreover, a comparison of actors, sources, institutions and innovation
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policies across sectors shows strong contrasts: the role of innovation in the dynamics of sector
transformation is very dissimilar (Malerba, 2005).
The understanding of processes of structural change within sectors is usually beyond the scope
of industrial organization models. Mainstream economics frequently ignores or is not capable of
accommodating in formal models the role played by organizations other than firms (e.g.
universities, public laboratories, institutions, etc.), the emergence of markets and other
institutions, the role of knowledge and learning processes inside firms and extra-market
organizations and the many ways in which agents can interact (not only in markets, etc.).
A tradition of studies that specifically tackle with these issues and which have proliferated in
recent years allows for a richer perception of the different processes at work within each sector
and of their co-evolution ⎯as well as their influence over the dynamical transformation of the
sector itself. These contributions actually form a varied group in which it is not easy to find and
clearly distinguish the common elements. The SSI framework is part of this tradition: it
introduces an integrated and dynamical multidimensional perspective of the transformation of
sectors that try to identify the common elements to bring out the different ones. The following
are two essential characteristics: firstly, it emphasises a systemic approach to sectors; and
secondly, the adoption of an evolutionary theoretical approach. Evolutionary economics
provides a wide theoretical framework with room for the SSI concept: economic and social
systems are seen as systems that are liable to continuous endogenous change, where
evolution is the result of the self-transformation of systems over time (Witt, 2003). Additionally,
evolutionary economics and the economics of knowledge stress that sectors and technologies
differ in terms of their knowledge base and of the underlying learning processes that are
characteristic of each sector;2 both of them are key issues in the SSI approach. Accordingly, it
is considered that the dynamics of the knowledge base of a sector are one of the main
determinants of its pattern of innovation; however, this is not self-evident.
In our opinion, the usefulness of the SSI framework consists of the fact that it is an open
approach to the analysis of innovation processes on two levels: firstly, because it is a
conceptual framework that provides a multidimensional insight of the dynamic links that are
characteristic of innovation processes. Evidence of this is its emphasis on the importance of
analyzing the co-evolutionary processes that underlie and configure innovation processes; and
secondly, because the SSI framework allows for the localization of the role knowledge plays in
innovation processes.
A sector is defined as a set of unified activities around a group of related products, with a given
or emergent demand, and which share basic knowledge. A system of innovation consists of a
set of agents that interactively deploy a set of market and extra-market activities (Larsen & von
2 See Nelson (1995), Dosi et al. (1996), Lundvall and Johnson (1994) and Cowan et al. (2000).
3

Tunzelmann, 2006) with the purpose of creating, producing and selling the products of the
sector. The firms that operate within a sector share certain common characteristics and, at the
same time, they are heterogeneous. Thus, an SSI is composed of a knowledge base,
technologies, inputs and a potential (or existing) demand that characterize it; and there is a set
of institutions that circumscribe the environment within the agents of the sector and interact.
These are what Malerba calls the three building blocks of an SSI: (i) knowledge base and
technology; (ii) agents, networks and demand; and (iii) institutions. It is the interaction and co-
evolution of theses building blocks that give rise to (or allow the emergence of) an SSI.
In the evolutionary literature, the knowledge base and learning processes are at the base of
innovation processes. Moreover, knowledge and learning processes differ across sectors both
in terms of their knowledge domains (Malerba & Orsenigo, 2000) and in their features. For
instance, in some sectors science is the driving force of knowledge, while in other cases
learning-by-doing and experience are the main causes of the growth of knowledge; in some
sectors the main agents in knowledge generation are universities, in other, they are firms, etc.
From the point of view of innovation, knowledge reveals three main dimensions: accessibility,
opportunity and cumulativeness (Malerba, 2005: 388-389). The knowledge base and
technologies, as well as the learning processes at work, determine the configuration of a
sectoral system and define its boundaries and pattern of evolution. An SSI is also a set of
heterogeneous agents ⎯both individuals (consumers, entrepreneurs, scientists, etc.) and
organizations (firms, universities, government agencies, industrial associations, etc.) that
interact through a series of links (connections) they establish. Agents interact through
processes of communication, change, cooperation, competency and command in markets, but
also through extra-market relationships. Firms are characterized by beliefs, expectations,
competencies and organizational forms and they deploy learning and knowledge accumulation
processes (Nelson & Winter, 1982; Teece & Pisano, 1994; Teece et al., 1997). Importance is
also placed on the role played by the goals agents pursue according to their beliefs, values and
representations, etc. (Nelson, 2006: 497).
Within an SSI the heterogeneous agents that are linked by means of market relationships
(firms) and extra-market relationships (universities, research departments, etc.) generate
networks. These networks differ across SSIs as a consequence of the different features of the
underlying knowledge base. However, they also differ as a consequence of the relevant
learning processes, basic technologies, characteristics of demand and, of course, as a
consequence of the dynamic links and complementarities that arise as a product of the
interaction between agents and networks.
The third building block is institutions. Institutions are successful former courses of action that
may be products of design or selection; they encompass norms, routines, shared habits,
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practices, rules, laws, standards, etc. and configure the environment within which agents deploy
their activities. Institutions arise for overcoming the practical issues of the problems associated
with decision (and action) in a context of radical uncertainty (Knight, 1921). Such a context
would demand cognitive capacities of agents that are out of their reach; institutions economize
this scarce resource and also introduce a certain stability in the agents’ behaviour, which enable
the formation of ‘reasonable’ expectations.3
The SSI approach to institutions is interesting for the analysis of the establishment of
relationships that emerge from interaction. From this perspective, it is possible to show how the
same ‘common’ institutions (for example the patent system) may have so different effects on
innovation across sectors or countries (Werger, 2003).
Finally, it is worth noting that in this approach innovative processes within sectors have a
relevant systemic character. Accordingly, the elements that configure the sectoral system of
innovation are as important as its dynamics; moreover, it is fundamental to stress the role of
(dynamic) links (Kirman, 1997) and complementarities among its elements in order to explain
SSI dynamics. A sectoral system deploys processes of change and self-transformation by way
of the co-evolution of the different elements implied. Not only do these elements evolve or are
there certain processes of change present, but it is also essential to understand how these
processes interact, reinforcing, limiting or even neutralizing; these are key issues in a dynamic
approach.
Therefore, the interest for SSI should not circumscribe with a view to characterizing or
describing sectors; the SSI approach may be useful for a substantive explanation of the
evolution of sectors. It seems to accomplish a first requirement for any conceptual framework
that deals with the evolution of systems: it gives insights into the role of dynamic links in
multidimensional innovation processes. However, from a theoretical point of view this is not
enough: a substantive explanation for the evolution of the dynamic links demands the
localization and identification of the role of knowledge and the goals of agents in such
processes.
3. Nature and dynamism of the knowledge base (micro level)
The Oslo Manual (2005: 15) recognizes that knowledge in all its forms plays a central role in
economic progress; additionally, the manual remarks on the complex character of innovation
processes. However, what is the causal link between knowledge ⎯as structure⎯ and
innovation ⎯as a complex process? How are connections established between the elements of
3 See, for example, North (2005), Hodgson (2004), Loasby (1999) and Potts (2007).
5

SSI and what are the consequences? The answers to these questions are of great importance
for clarifying the epistemological status of SSI.
Systems, knowledge and connections
Analytically, a system is explained by both its constituent elements and the connections by
which they are related. In a dynamic analysis, the fundamental issues are that connections are
continually changing ⎯which “makes connections the prime variables” (Potts, 2000: 5)⎯ and
that the recombinant process of connections may generate novelties (Loasby, 2001).
Furthermore, these new connections may be intended or unintended (Loasby, 2006).
Knowledge itself is an example of association among elements: which the specific elements are
and how they are connected is knowledge itself. In this approach, knowledge may be
considered as a structure, a system of connections that is also changing.
A feature of any system is modularity (or duality): once a system emerges (as a complex entity),
it can become an element (or subsystem) of a higher-level system. For instance, an
organization or firm is a (complex) system integrated within a sector which, at the same time, is
part of an economic system, etc.). Thus, every system is built with elements that may be lower-
level systems themselves: ‘system’ is a ubiquitous concept.
In this perspective, knowledge is considered as a system itself; and the structures of the human
brain that support it also constitute a system (Fuster, 2003). At the same time, they are parts of
a human body, etc. However, from another point of view, knowledge is embodied in
organizations and sectors, etc. that are higher-level systems. The growth of knowledge consists
of the accumulation of connections between the internal elements of a system, and between
these elements and others belonging to higher or lower ranks. The economic agent itself is a
system. Following Earl (2003), the economic agent is completely reconstructed when all of his
internal and external operational connections have been made completely explicit. However, it
is actually impossible to fully reconstruct an economic agent; economic agents are continuously
establishing (and removing) connections. We refer to such a process as learning.
Learning
The connections that constitute agents’ knowledge are the basis of their economic (and social)
action. Agents make use of their acquired knowledge to draw up theories (Nelson, 2005) on
how the diverse elements that constitute the physical-natural, technological and social systems
within which they deploy their action are causally connected. These theories have a conjectural
value (Popper, 1972) and they are not necessarily true in that they have not been scientifically
contrasted. Theories are models or frameworks that enable agents to anticipate (or form
expectations about) the consequences of their actions in a context of uncertainty, thus defining
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the set of feasible events and weights (‘probability’) attached to them by agents. These future
courses of action have to be necessarily imagined and deemed possible (Shackle, 1972;
Loasby, 1996) since they affect the action of the agents. These models provide frameworks and
procedures which, insofar as they are of common use, may be defined as institutions. Therefore,
the study of economic processes is also the study of institutions (Loasby, 1999: 13).
Furthermore, the connections that configure these frames or structures for action are
necessarily incomplete.4 In a context of true uncertainty, it is impossible or agents to know all
the feasible links between the elements (usually means, but also goals of action) that constitute
a system in the present and future. Learning consists of testing (and eventually retaining) new
connections that prove to be useful for agents to reach their goals. As a result, agents deploy
bounded rationality, which refers to the “reasoning capabilities of an actor who, on the one hand,
has a goal to achieve, and goes after his objective with an at least partially formed theory about
how to achieve it (this is the ‘rationality’ part of the concept), and on the other hand that the
theory is somewhat crude, likely will be revised at least somewhat in the course of the effort,
and that success is far from assured (this is the meaning of the ‘bounded’ qualification to
rationality). Both aspects of the concept seem necessary to capture what we know about human
and organizational problem solving, in a variety of different arenas” (Nelson, 2005: 3). This
approach is also compatible with the emergence of novelty and with the growth of knowledge;
i.e. with the conditions of possibility of true learning processes (Witt, 2007).
Thus, we may assess that knowledge is connections, structure is connections and dynamics is
change in connections. Taking for granted this approach, important questions arise: how does a
system move towards adjacent states? Why is it able to pass from one state into another? How
does knowledge grow, or rather, evolve? And, how does it coordinate efficiently? The
organization of knowledge, its growth/evolution and its eventual efficient coordination are all key
issues for understanding the configuration of the knowledge base itself and the pattern of
innovation of a particular SSI.
Action plans
For evolutionary economics, social and economic systems are considered as systems liable to
continuous change: evolution is the result of the self-transformation of systems over time (Witt,
2003: 12-13). Evolution is seen as the process or set of processes that combine the generation
of novelties with the selective retention of some of these novelties (Loasby, 2001: 1; Dopfer &
Potts, 2008), following the three-phase schema: generation-selection/adoption-retention of
variety (Foster & Metcalfe, 2001). It could be said without exaggeration that evolving systems
are characterized by continuous endogenous change, induced by the generation of novelties
4 An example of a complete system is that of the Walrasian general equilibrium theory. In this kind of
system there is no room for learning or for true dynamics. A ‘paradox’ underlies this argument: knowledge
always implies lack of knowledge (Allen, 2004: 85).
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and subject to selection processes operate on self-organized processes (Rubio de Urquía,
2003; Kauffman, 1995).
However, what changes? What, if any, is the unit of selection in such selection-retention
processes? And what about the causal explanation of renewed variety? There is intense debate
on these issues. For some authors, the unit of selection is routines (Becker, 2004); for North
(2005) and Hodgson (1993), it is institutions; for Boulding (1981), Hayek (1945, 1952) and
Loasby (1999), it is knowledge which evolves ⎯to the extent that they identify the basic
economic problem with that of the social organization of knowledge⎯; for others, it is
capabilities (Dosi et al. 2000), etc. Finally, there are those who, like Dopfer and Potts (2008), on
a more abstract level, consider that it is rules or “it is connections that change” (Potts, 2000: 57).
In order to understand the epistemological status of SSI, we should carefully differentiate the
types of connections that are established between the elements in a system. In particular,
between the different kinds of elements that are connected: means (actions) and goals
(objectives) ⎯which determine the sense of connections.
Economic dynamics may be understood in a complementary way to that previously exposed as
the process of generation, adoption and an attempted interactive deployment of the agents’
plans of action and the resulting ‘products’ (Encinar & Muñoz, 2006; Muñoz & Encinar, 2007).
As Rubio de Urquía (2005) poses, agents’ action plans are the result of a key operation that
consists of agents allocating means/actions projectively in order to reach the
goals/ends/objectives they pursue. In other words, at each instant of time, an action plan may
be interpreted as a template or ‘guide’ for action that projectively connects elements of a
different nature: something the agent wants to achieve (goals or ends) with the actions and
means the agent ‘knows’ afford him/her success.
Agents choose their goals of action on the basis of a myriad of psychological, social, and
cultural factors, as well as ethics and beliefs (Metcalfe, 2004), etc. Agents constitute their action
plans using their imagination (Loasby, 2007), taking into account that the goals they pursue are
located in an imagined future (Lachmann, 1978[1994]). In this sense, it could be said that
agents ‘invent’ the future on which they focus their actions. This idea is valid whether we
consider objectives in the short, mid or long term. The opportunities for acting in a specific way
(entrepreneurial action, for instance) are not hidden somewhere in reality, waiting to be
discovered by entrepreneurs or visionaries, but they ‘emerge’ initially in the mind of agents
regardless of the fact that at some time in the future they may be embodied in a written
document or an organizational form, etc.5
5 It is important to distinguish between this use of projective imagination and the Austrian school’s
traditional approach to ‘entrepreneurship’, which is based on the concept of ‘discovery’ (Kirzner, 1992).
8

The set of actions and goals linked projectively by means of an action plan may contain different
kinds of elements: material or immaterial elements, localized at different moments in time (not
all, obviously, at the same time); with a monetary price (in official currency) or without a
monetary price (a subjective level of satisfaction of a need), etc. Action plans are an analytical
open representation of agents’ projective action, in which actions (means) and goals
(objectives) are not given, but rather produced by the agents themselves. These analytical
constructions enable the depiction of any kind of action plan ⎯such as a planned trip, a
business plan, a strategic plan (Day, 2008: 264-265), a plan of the EC to implement the
objectives of the Lisbon agenda, etc.⎯ with structures of hierarchical dependence between
goals and with as many analytical periods of time as necessary. Moreover, these analytical
structures may be used to represent how agents’ action plans configure the economic dynamics
of a society when they are deployed interactively (Encinar & Muñoz, 2005).
Knowledge, intentionality and agents’ goals
Knowledge stands as cognitive networks in the human brain; routines; habits and patterns of
behaviour, cognitive, social and technological rules; institutions; organizations, etc. It is also the
foundation of capabilities. Evolutionary economics describes the evolution of an economy as a
consequence of the growth of knowledge. However, it remains beyond its scope or at least the
goals agents pursue (as well as their internal dynamics of evolution, which alter their
hierarchical interdependence and contents) and their intentionality as elements that encourage
action and knowledge have not been fully integrated. However, the goals and intentionality of
agents play an essential role in explaining the emergence of novelties and evolutionary
capabilities (Langlois, 2006; Cañibano, Encinar & Muñoz, 2006a), institutions (Nelson, 2008: 7)
and learning processes (Dosi et al., 2000: 2-4).
In general, evolutionary economics proceeds in its models and theories as given goals (ends)
pursued by agents. However, until recently the analysis of the role played by the goals pursued
in the development of new capabilities, new patterns of behaviour, etc. has been postponed. 6
However, the theory should consider the real fact that new goals of action may emerge, that the
hierarchical ordering of goals may change, that goals now (or never) reached may be removed
from or replaced on agents’ plans, etc. (Cañibano, Encinar & Muñoz, 2006b). All these changes
involve learning processes, as well as the emergence of completely new actions that cannot be
explained only by means of mere knowledge acquisition. They are special connections that are
established between new goals and means.
6 For example, North (2005) devotes a whole chapter (#4) to this issue. On the other hand, the role of
purposes is not strange to the literature of technical innovation. For example, Arthur (2007: 276) defines
technology in terms of human purposes: “I will define a technology ... quite simply as a means to fulfill a
human purpose. The purpose may be explicit...; or it may be hazy, multiple, and changing... But whether
its purpose is well defined or not, a technology is a means to carrying out a purpose.”
9

Intentionality, which is linked to goals, activates the development of capabilities, the testing of
new connections within a system, and, therefore, the generation of new knowledge (learning).
Aligning, coordinating, reordering and even inventing new goals are activities that generate
novelty and are therefore sources of true dynamism in economic processes. For example, the
child’s vague idea of becoming a doctor may allow him to discover an ‘innate capacity’ (or
vocation); this would lead him to want to ‘become a professional doctor’ (a new goal), and thus
to study medicine at university ⎯which finally enables him to work in the profession.
The goals (ends/objectives) of action evolve over time, inducing changes in agents’ capabilities,
which may result in the formulation of renewed goals and intentions and, therefore, in the
development of renewed capabilities. Agents differ in knowledge and capabilities, but also in the
goals they pursue. Agents are heterogeneous because they also conceive different goals and/or
different hierarchies of goals and, consequently, they develop different capabilities, deploying
interactive learning processes to carry out their plans. The result is that agents introduce a wide
variety of changes in their (physical and social) environment by means of their actions, thus
altering the spaces of action (and the plans) of other agents.
To sum up, it is the concern for the inherent dynamic dimension of intentions and goals that
makes the individual and organizational capabilities truly evolutionary. The emergence of new
intentions linked to the conception of new goals renews the capabilities of agents. Therefore,
evolving capabilities open up new possibilities for action that allow the conception of new goals,
generating continuous feedback between capabilities and intentions (Loasby, 2006).
Of course, not all changes in society are the result of intended actions. In fact, not all actions
carried out by agents are intended ⎯as shown in the literature on organizational routines.
Furthermore, not all the consequences of actions are intended (even expected). The
consequences of actions may be, and usually are, very different from what agents pursue.
Interaction in complex situations, un-knowledge, etc. may lead to completely unexpected results.
Moreover, it has been said that evolution is a ‘blind’ process (Vanberg, 2006) because new
properties and unintended consequences emerge within it (Popper, 1948). Nevertheless,
human action, as rational, within human constraints, is intended action: there must be goals
(reasons) for acting (Mises, 1949). From the perspective of action plans, it is possible to analyze
how agents’ cognitive dynamics might, for example, imply the introduction of new (projected)
actions or means in agents’ plans and the discovery (invention) of new relationships between
actions and goals as a consequence of novelties in the agents’ projective space of goals ⎯thus
implying a change in the connections between elements within a system. Consequently,
although not all actions are intended and not all novelties are a consequence or the pursuit of
particular goals, the evolution of agents’ goals and intentions is a key explanatory factor
because it triggers processes that establish and renew the connections within a system.
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4. From knowledge base dynamics to patterns of innovation
The necessarily short reference to the concept of evolutionary capabilities underlines a key
factor that explains what triggers the transformation of the agents’ action space
⎯microeconomic basis of the economic self-transformation processes. The process of
acquisition and development of new evolutionary capabilities is equivalent to a learning process
by means of which the agents that configure an SSI may improve their abilities to reach their
goals and eventually introduce or remove goals or reallocate their hierarchical order and deploy
the necessary means and actions. The deployment of these learning processes is also capable
of modulating institutions, configuring agent networks, changing standards, beliefs and agents’
habits, etc., as well as giving rise to new evolutionary capabilities.
Actions such as producing, consuming, innovating, working and organizing, etc. ⎯even
choosing⎯ are conditioned by the goals/ends designed and pursued by agents, goals that
change continuously. The diversity and intensity of such changes in goals have substantial
value as important factors for explaining socio-economic self-transformation processes because
they trigger searching processes and the establishment of connections with adjacent states of
the system, altering its topology and thus giving rise to new features within the system.
Together with the means agents discover and ‘invent’ to reach them, these changing goals,
constitute the agents’ action plans ⎯plans they then deploy interactively. The product of
interaction is the socio-economic reality that is thus configured.
The argument enables the identification of the analytical locus of this explanatory factor of the
transformation of agents’ spaces of action and, therefore, of the systems they configure.
Coming back to the case of evolutionary capabilities, their constitution by agents within a sector
is what enables a double-rank analysis: on the one hand, the analysis of the constitutive
elements of a system; on the other, the analysis of the connections between those elements.
The evolution of such connections is necessarily associated with the diversity and changes in
the goals pursued by the agents that configure the knowledge base within a sector. Thus, the
dynamics of change of goals/ends (and/or changes in the hierarchical structure) have
substantive value as an explanatory factor of the self-transformation (evolution) of SSI and its
sectoral pattern of innovation.
Let us assume, for instance, the subsystem ‘life sciences’. Learning processes and scientific
knowledge in this subsystem allow for understanding the state and evolution of present
research (implemented on the basis of the capabilities and skills of scientists); however, these
learning processes and current knowledge also generate new research questions that spur the
acquisition of new scientific knowledge. This new scientific knowledge, which would eventually
give rise to new technological knowledge that might be developed in firms, universities,
11

research councils, etc., is the starting point for the emergence and development of new
capabilities within the scientific community itself and, if the conditions for accessibility and
appropriateness so allow, in firm. In other words, the formation of new links between science
and firms follows the implementation of new capabilities on both sides of the system. Thus, the
development process of capabilities as intended learning ⎯a process based on current
knowledge⎯ would configure the connections between several elements that constitute the
knowledge base of the system.
Intentionality, capabilities and dynamics
Therefore, the knowledge base of a sectoral system is not only a set of given elements and their
static links, but also the potential dynamics of its connections via capabilities that emerge as
knowledge grows. Also, these potential dynamics give rise to a network of mutual
interdependence between the elements that form the knowledge base; in addition, this network
evolves in time. The emergence and development of capabilities, especially if they are
evolutionary capabilities, make it possible to weave the network and explain it, where applicable,
from the co-evolution of the very connections of the system, based mainly on the co-evolution of
knowledge.
This approach leads us to establish that the problem to be solved in a system is fundamentally
a problem of the appropriate organization of knowledge (current and emergent). Note that the
element that stimulates the connections between the components (agents) of a system as the
permanent stimulus for the evolution of the system has been identified as the implementation of
capabilities.
What is the explanatory factor that triggers the transformation of the agents’ spaces of action
and, consequently, of the systems to which they concur? Why are connections in a system
continuously being established? As has been said, the emergence and development of
capabilities are induced by intention, by agents’ tendency towards the goals they set up. Goals
are imagined realities, expectations, valued as more desirable states and towards which agents
direct their action. Within a system, there is constant feedback between the intention and the
evolutionary capabilities and this feedback explains the transformation of the system itself.
The pursuit of a new goal may cause new capabilities and new patterns of behaviour to be
developed and learning processes to be activated, giving rise to new actions and new ways of
doing things (process innovations) that may ultimately give rise to (by means of design, or as a
result of selection) institutions and/or modify them.
In other words, the pursuit of new goals allows the emergence of action plans with a new
structure and/or contents. These new structures of connections between new actions (means)
12

and ends (goals) introduce a ‘renewed genetic material’ which, when interacting, transforms the
network of connections of the system, giving rise to the emergence of all type of novelties within
the system, and fuelling evolutionary processes. The appearance or hierarchical rearrangement
of goals is thus a source of transformation of the agents’ plans and of the subsystems that make
up the economic system.
After a certain time (which may vary greatly depending on the characteristics of the agents and
the institutions involved), depending on how effective they have been to attain the pursued
goals, the new patterns of behaviour and actions might become rules, habits or behaviour
routines. The individuals would follow behaviour patterns learned without conscious deliberation
and certain actions will become regular, foreseeable and routine. The new individuals the
system may incorporate would probably adopt (imitate) these behaviour patterns (routines)
even if they are unaware of their origin or the initial intention that motivated their emergence.
In the example of the ‘life science’ subsystem, much of current research is based on skills,
routines and capabilities already implemented by scientists and whose origin is linked to past
goals that they deliberately tried to reach. Why then does a subsystem continue to develop new
capabilities, as in the case of science, once certain given objectives have been reached? To
answer this question, let us assume that the goal pursued (in the ‘science’ subsystem, for
example) may be reached; in other words, it is technically attainable and the scientific
community has been able deploy the actions required (learning, adapting, developing
capabilities, etc.) to attain its purpose. Once the goal has been reached, there is no apparent
reason for continuing the learning process, concluding the capability implementation process.
However, experience shows that learning processes never come to a halt in a knowledge
economy. As already mentioned, the reason lies in the continuous appearance of renewed
goals of action. Agents ‘invent’ the future towards which they direct their actions.
For instance, for biomedicine it is not enough to discover a treatment for a serious illness: it is
also interested in its mechanisms of propagation, its genetic base, etc.7 The conception of new
goals activates behaviours and actions by means of intention and will, aimed at the pursuit of
that goal. This process generates new knowledge by transforming agents’ capabilities.
Renewed or improved capabilities reduce the gap between the goal the agent intends to
achieve and the real outcome of its actions. Accordingly, the capability building process may
help reduce this gap. Accordingly, intention activates a capability development process. On the
other hand, new or improved capabilities open up the possibility of setting new goals. Actions
aimed at achieving these goals may also imply the appearance of new capabilities or
modifications in previously existing ones. It is partly through the development of capabilities that
dreams or desires may turn into goals. Capabilities can activate intention.
7 An example is Consoli & Ramlogan (2008).
13

The consideration of this constant feedback process between intentions, capabilities and goals
leads us to move from a dynamic to an evolving perspective. This continuous feedback process
is at the basis of the self-transformation (evolution) of agents’ action spaces. This self-
transformation of a system (of its elements and connections) is what makes it an evolving
complex system.
“Products” and efficiency in an evolutionary process
We have explored the microfoundations of agents’ action in order to explain the configuration of
the knowledge base of a sectoral system (meso level). This exploration has showed how the
SSI approach may be supported by a micro basis in which agents’ goals play a crucial role. Let
us explore how the knowledge base is the key determinant of the pattern of innovation of an SSI.
The knowledge base of SSI is characterized by its elements (agents, networks and institutions)
and mainly, by its evolving connections based on the constant feedback between the intentions
and capabilities of the agents that interact within the system. To tackle this issue (the dynamic
link between innovation and knowledge, i.e. between the knowledge base and pattern of
innovation of an SSI)8 implies paying attention to both sides of an SSI: on the one hand, the
new goods and services provided by markets and, on the other, the new discoveries or
inventions produced by scientists and technologists, who work in a place that is ‘strange’ to
markets.
Let us see now that from the perspective adopted in this paper, any SSI may be seen as a
product of the interaction within a sector. In order to transcend from micro to meso level,9 we
assume that the pattern of innovation shown by a particular SSI is the result of the interaction of
agents within a sector (via the continuous implementation of evolving capabilities), i.e. the
originators, disseminators and users of the knowledge inside that sector. To the extent that
agents fulfil their expectations better when interacting in an SSI, it could be said that the
complex system works: the pattern of innovation is efficient if it increases the agents’ potential
for action. Otherwise, the system is more productive as an SSI if its constituent parts (its
building blocks) are connected efficiently ⎯agents achieve their goals, institutions make it
possible, etc. For instance, scientists satisfy their aspirations of wisdom (and social recognition);
‘capitalists’ or venture capital firms achieve a reasonable yield which is an incentive for
investing; governments that funds (public) scientific research obtain a social (and perhaps
political) return; users have better, safer and cheaper products at their disposal or a cleaner
environment; firms and public organizations achieve their social goals, etc. In short, the
8 SSI understood as a ‘product’ or result of such connections.
9 This level of economic analysis is meso because its main characteristic is that it refers to interactions
within a population that carry plans with common expectations, goals and structures, etc. around a set of
commodities and services.
14

fulfilment of different actors’ goals and the compatibility (coordination) of their plans and
expectations (Hayek, 1937: 37), etc. strengthen the (new) connections within the system. All
this means that the pattern of innovation that is characteristic of the sector is efficient: it gives
rise to an increase in the value of the knowledge base at the disposal of the agents within the
sector.
How does this happen? Let us assume a firm within an SSI. The firm should perceive the
innovative capacity of scientific research as a capacity of the productive subsystem that is
potentially compatible with the potential demand of the market on which the firm operates; in
other words, the ‘entrepreneurial’ knowledge base would contain an entrepreneurial insight of
the ‘scientific’ knowledge base in such a way that ‘both sides’ (scientists and entrepreneurs)
would share the identical and at the same time ‘different’ knowledge base.10 Otherwise, the
dissimilar goals and procedures of each agent, scientist and entrepreneur will make the same
knowledge base, its dissemination and value ‘different’.
The successful way in which an SSI may achieve its (market-oriented) purpose of innovating will
consist of the efficient transmission of the knowledge base as a result of the interplay between
the different agents in a process of innovation. ‘Innovation per se’ and ‘knowledge per se’ make
little sense. The efficiency criterion proposed here refers to the economic valuation of the
knowledge base. Such a valuation is caused by/in each agent/step in which stable connection
to the ‘network’ depends on the accomplishment of expectations. It depends on each agent
assimilating knowledge that comes from other points on the network ⎯because they are
compatible with their own goals. Accordingly, a ‘transmission failure’ or disconnection would
imply a loss of efficiency for the SSI, a loss of value of the knowledge base and an inefficiency
of the pattern of innovation. In other words, value does not come only from the elements
contained in the system (the individuals, the organizations and the capital, etc.) but from the
connections that are forged between them (Foster, 2005: 885); and value generates the
establishment of the connections that make it possible for agents to achieve their goals in a
more efficient way.
The stock of knowledge and the volume of interactions, the constant implementation of evolving
capabilities from that knowledge, its character and change, etc. would show the SSI workings
and the efficiency of its pattern of innovation. Therefore, the analysis of a particular SSI should
consider two points: (i) its performance in terms of how much it innovates, which demands it
satisfies, how many agents, firms and projects arise and how many human and financial
resources it mobilizes, etc.; and (ii) how the different parts (elements) of the system are
connected ⎯if they are actually connected⎯, what is the volume, intensity and character of the
interactions, their continuity and the progressive implication of more agents, which agents are
10 The knowledge base is identical in the sense that, seen from outside the system, it is the same for the
entire sector; it is different from the agents’ point of view, since they perceive and use only a part of it.
15

more dynamic, which goals they pursue and if they are compatible a priori, etc. We may also
consider the ‘institutional return’ of an SSI: how the institutional environment emerges, adapts
and transforms and how this affects the compatibility of the agents’ goals (‘coordination’) within
an SSI.11
Finally, it would be possible to show, from an evolutionary perspective, that the pattern of
innovation of a system is efficient if the pattern increases its potential, i.e. if the system renders
more as an SSI (it increases the value of its knowledge base in the form of innovations) and its
parts (building blocks) generate efficient connections (agents achieve their goals and institutions
facilitate the achievement). The particular form knowledge adopts (in terms of appropriateness,
cumulativeness, etc.), which is path dependent, determines the connections that are more likely
or best tested. The number of registered patents, scientific papers, sales, the level of
penetration and concentration in a market, employment and wages, legal framework and
adopted policies, etc. are only products or traces of the said dynamics and these are the
products that are registered in the historical-empirical studies. However, the theoretical and
dynamical foundations always reside in the goals agents project and endeavour to achieve,
establishing every type of connection in the struggle, connections which constitute knowledge.
5. Concluding remarks
From the above discussion, we may conclude that an SSI is indeed heuristic and enables the
depiction, analysis and extrapolation of the dynamics of a sectoral system. Furthermore, an SSI
may be supported with a theoretical microfoundation: that which performs a precise analysis of
how agents operate, i.e. on a theory of human action. In fact, a particular SSI is a product of the
interactive planned action deployed by agents within the sector. As a product of the interactive
deployment, there are processes that co-evolve, that qualify, or rather, specify each particular
SSI, which becomes a subsequent object of historical and empirical research. This constitutes
the basis for ‘measuring’ the performance of a particular SSI.
In the best evolutionary tradition we should remember that a theoretical analysis cannot be
made without a careful observation of reality: it is a matter of fact that agents plan their actions.
Otherwise, this will be irrational or absolutely erratic (Nelson, 2006). In our opinion, several
problems of empirical and quantifiable indicators have to do with the lack of content of the
underlying theories on agents’ action. Whatever the case, it seems that there is only one
theoretical foundation: the analysis of the interactive deployment of personal action and its
11 The new edition of the Oslo Manual stresses the convenience of considering other links of the system
and their evolution. Observe, also, the essential role the interface organization plays in innovation
processes (e.g. the offices of research results transfer, venture capital firms, etc.). The Triple-Helix model
(Leydesdorff, 2006) is ant interesting approach to these issues.
16

products. From this starting point, it follows that the nature, properties, dynamics and complexity
of connections within economic systems may be established.
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