3.10 Strategic niche management and an integrative evolutionary multi-level perspective on technological transitions Strategic Niche Management (SNM) and its related Multi-Level Perspective (MLP) arose out of the sociology of technology and was originally developed to understand technological transitions and regime shifts (Schot et al., 1994; Schot and Geels, 2008; Rip and Kemp, 1998; Kemp et al., 1998; Kemp et al. 2001; Geels, 2002, 2005; Kemp, 1994; Levinthal, 1998; Schot, 1998). As Geels (2002:1259) points out, the Multi-Level Perspective is not meant as ontological description of reality, but as a set of analytical and heuristic concepts to understand the mechanics of socio-technical change. It integrates many of the main concepts which are touched on to varying degrees and with differing emphasis and focus elsewhere in the literature and thus can be seen as a good foundation to approach the broader literature. Within MLP, three interrelated dimensions are important: socio-technical systems: the tangible elements of the technology needed to fulfil societal functions; social groups: the actors who maintain and refine the elements of the sociotechnical systems; and socio-technical rules (or regime): formal and informal rules that guide and orient activities of social groups.
In this framework, actors in social groups are not assumed to act autonomously, as they do in the neoclassical model, but rather, in the context of social structures and by normative, formal, and cognitive rules. These rules form a coordinating context that guides and orients action. On the other hand, rules are reinforced and changed through action and enactment. Rules do not exist individually, but are linked together in semi-coherent set of rules called regimes. Rip and Kemp (1998:340) and Geels (2004) have widened the original concept of technological regimes first set out by Nelson and Winter (1982) to indicate that rules, including cognitive routines (Dosi, 1982) are not only embedded in the minds of engineers in their research and development activities, but are also in elements of the socio-technical system including scientists, users, policy makers and special interest groups.
Rules include the problem agendas (reducing carbon, for instance), guiding principles, rules of thumb, standards, government regulations, sense of identity, and the role of expectations. Social groups interact and form networks with mutual dependencies, resulting in the alignment of activities. This intergroup coordination is graphically represented as the meso-level in Figure 3.7 with the concept of socio-technical regime. System stability can arise from mutually reinforcing established networks and ways of thinking. Although skills can be updated, and contracts broken or changed, core-competencies (Leonard-Barton, 1995) and legally binding contracts (Walker, 2000) can also become sources of rigidity in a shifting environment.
Systems are also stabilized because they become embedded in society through institutions. People change their lifestyles to suit them and formal regulations and infrastructure created to serve them. The alignment between these heterogeneous elements leads to technological momentum (Hughes, 1994) and lock-in reinforced by social relationships of mutual role expectations and embedded interests such as industry associations (Hughes, 1987:76-7; Unruh, 2000). The material aspects of sociotechnical systems also contribute to stability, because of sunk costs – once investments are in place they are not easily abandoned (Walker, 2000). This means, for many reasons, socio-technical systems are characterised by stability, not by any means inertia, but dynamic stability, meaning innovation still occurs but is of an incremental nature.
Because these stabilising mechanisms can lead to lock-in, it can be difficult for radical innovations to diffuse. So how does this model theorise the emergence of new socio-technological pathways? Strategic Niche Management theorists argue that for many new technological innovations market opportunities are not readily available as new technologies may differ radically from incumbent system. In many cases, there may be no established markets and no fixed preferences. For diffusion to take place, consumers must be introduced to new products and associated behaviours, firms and organisations must realign production routines, supporting infrastructure may need to be installed and, most likely, new regulations and policies need to be passed to govern all these 177 interactions, all of which take place within a cultural discourse (Lie and Sørensen, 1996; Nelson, 1994, 1995). To prepare the new technological innovation for the market, a technological niche may provide a protected space or “incubation room” to nuture a specific set of interactions between actors, issues and objects.
As Rosenberg (1976:195) notes: …most inventions are relatively crude and inefficient at the date when they are first recognised as constituting a new invention. They are, of necessity badly adapted to many of the ultimate uses to which they will eventually be put. Within the niche, different selection criteria and public support for the technology can exist separate from broader market forces. SNM work conceptualises a bottom up process in which novelties emerge in technological niches, then conquer market niches, and eventually replace and transform dominant technologies and regimes (Schot and Geels, 2008). Within the literature three crucial internal niche processes have been identified (Schot et al., 1994; Kemp et al., 1998; Kemp et al., 2001; Hoogma et al., 2002): Technical learning processes relate to research and development into the technological object; the assessment of new markets and the nurturing of new user preferences; policy experimentation to facilitate a new regulatory regime and also investigating what complimentary infrastructures are available or are necessary for the niche to develop; Figure 3.7 Multiple levels in a nested hierarchy The building of socio-technological constituencies involves the formation of social groups which may include: scientists, engineers, entrepreneurs, financiers, early adopters amongst consumers, policy makers and new societal coalitions (lobby groups) to support the new innovation and invest in its development; and thirdly, The articulation of visions and expectations to provide direction to learning processes, attracting attention and legitimising protection from the broader market.
Expectations of the future need to be built and shared by actors and their content gradually substantiated through project developments. Figure 3.8 A dynamic (phased) multilevel perspective on system innovations In this framework it is argued that niche innovations are rarely able to bring about great transformations without the help of higher order forces and processes. Thus, for niches to develop they should also ideally be responding to changes at the landscape, or macro level, in a way that the established regime cannot. This broader largely exogenous environment is formed by the socio-technical landscape. The context of this landscape is heterogeneous across different locations and may include aspects such as economic growth (or decline), broad political coalitions, cultural and normative values, environmental problems and resource scarcities (or abundances).
It also includes the large-scale material context of society, for example, the material and spatial arrangements of cities, factories, highways, and electricity infrastructures (Rip and Kemp, 1998). Such landscapes are generally beyond the influence of actors, and cannot be changed at will, usually taking decades to shift. The three conceptual levels in Figures 3.7 and 3.8 can be understood as a nested hierarchy, but the key point of the MLP is that transitions come about through the interplay of processes at different levels in different phases (Table 3.2). Both internal niche dynamics and external development at the regime and landscape level are important for wider breakthrough and diffusion. Having tapped into innovation theory to investigate the mechanics of change in the economic system we now move onto complexity theory which is another related research programme attempting to bring many of these heterodox research currents under a coherent epistemological banner.
As will be discussed, this research agenda has been identified as holding great promise for building on the natural affinities of physical and social systems, which has been identified as a ‘constant dream of geography’ (Thrift, 1999:32). Table 3.2 Dynamic phases in the Multi Level Perspective 3.11 What are complex systems and why is ‘complexity theory’ important? The study of complex systems has been increasingly put forward since the 1960s as an alternative scientific paradigm to what has been termed the mechanistic approaches such as the “clockwork” theories of Newtonian physics in the physical sciences and neoclassical economics in the social sciences. In contrast to such “reductionist systems”, as say, a rubber ball rolling around into the bottom of a bowl (as described by Newton’s Laws of Motion), complex systems exhibit unpredictable emergent properties. Common examples include the co-ordinating ability of ants to form giant colonies capable of food gathering, distribution and defence; the flocking of birds or schools of fish that use simple rules to travel and avoid predators; the neural interconnections which underpin the development of thoughts and personality in the human brain; and the herding behaviour and other “anomalous” patterns exhibited in stock markets that cannot be explained by the underlying value of an individual stock. The unifying logic behind these phenomena is that the relationships between their elements come together in a way which can only be classified at higher levels than the individual units (Coveny and Highfield, 1995:7).
What is more, change in a complex system can result in observations that are very difficult if not impossible to predict, especially in cases where phenomena are highly sensitive to initial system conditions. One of the most famous expressions of this notion was put forward by Edward Lorenz in a talk delivered to the American Association for the Advancement of Science in 1972 titled, “Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas,” which later gave rise to the popular notion of the butterfly effect, related to chaos theory. In this case, the inherent difficulty in predicting weather events was highlighted. In one seminal work, Warren Weaver (1948) describes two types of complexity: Disorganised complexity: arises from a system of many parts, where the random interactions of elements of the system mean that the system as a whole has properties that can be identified with statistical methods, for example, gas molecules in a container. Organised complexity: arises due to non-random or correlated interactions between the elements in a system.
These elements combine to create differentiated structures which can then interact with other systems. The coordinated system manifests properties which are not carried out or dictated by individual elements – these properties emerge with no “guiding hand”. This second type of complexity, which has long been studied by biologists in the context of evolutionary theory, has attracted particular focus for researchers in the area of complex adaptive systems (e.g. Jencks, 1996). Johnson (2007) outlines the following attributes for such systems: The number of elements, types of elements, and the relationship between the elements in the system are non-trivial (i.e.
it has to be big enough for emergence to occur); The system has a memory, or includes feedback; The system can adapt itself according to its history or feedback; The relationship between the system and its environment are non-trivial and non-linear; The system can be influenced by or adapt itself to its environment; and The system is highly sensitive to initial conditions. Chris Langton, quoted in (Lewin, 1993:12-13) describes complexity in the following terms: From the interaction of the individual components [of a system] … emerges some kind of property … something you couldn’t have predicted from what you knew of the component parts … And the global property, this emergent behaviour, feeds back to influence the behaviour … of the individuals that produced it. Roger Lewin (1993:backcover), a major proponent of complexity theory, postulates: Complexity theory is destined to be the dominant trend of the 1990’s… This revolutionary technique can explain any type of complex system – multinational corporations, or mass extinctions, or ecosystems such as rainforests, or human consciousness. All are built on the same, few rules. As Thrift (1999:34) notes, most writers on complexity theory then go on to usually lay claim to a whole series of fields of study which they assert are part of this impulse, including: chaos theory, fractal modelling, artificial life, cellular automata, neural nets and the like.
This includes a companion vocabulary which can be both technical and metaphorical – chaos, attractors, fractals, emergent orders, selforganisation, implicate order, autopoiesis (a self-creating system), and so on. Thrift 185 thus cautions those who seek to use “complexity theory” as a unified theoretical anti-reductionist response, as – to a certain extent, any definition of complexity is likely to be beholden to the perspective brought to bear upon it. While recognising that exciting academic cross-fertilisation may occur between the physical and social sciences in this area, care also needs to be taken to not naively accept a theory developed in one field for application in another without an independently thoughtout and robust case for doing so. On this point Thrift emphatically states that complexity theory ‘does have interesting and even important things to say’ (Thrift, 1999:32-33, emphasis in the original): Here, furthermore, is a body of theory that is preternaturally spatial: it is possible to argue that complexity theory is about, precisely, the spatial ordering that arises from injections of energy. Whereas previous bodies of scientific theory were chiefly concerned with temporal progression, complexity theory is equally concerned with space.
Its whole structure depends upon emergent properties arising out of excitable spatial orders over time. And here, most of all, is a body of theory which asks questions about `instability, crisis, differentiation, catastrophes and impasses’ (Stengers, 1997: 4) in ways which suggest that there is an obvious affinity between the `natural’ and `human’ sciences, a constant dream of geography. Such a movement is not, however, altogether as new as the recent ‘complexity hype’ of Lewin might suggest. Scholars since at least the time of the Scottish Enlightenment and the work of Malthus, whose ideas helped inspire the work of Charles Darwin, have sought to conceptualise the economy as a spontaneous (or emergent) system – in that it is a result of human action, but not the execution of any human design (e.g. Adam Smith’s invisible hand) with individuals, firms and governments processing information and adapting their behaviour giving rise to spatially distinctive patterns of economic development over time.
For example, the Austrian School economist Fredrick von Hayek in his 1974 Nobel lecture argued: Organized complexity here means that the character of the structures showing it depends not only on the properties of the individual elements of which they are composed, and the relative frequency with which they occur, but also on the manner in which the individual elements are connected with each other… In the explanation of the working of such structures we can for this reason not replace the information about the individual elements by statistical information, but require full information about each element if from our theory we are to derive specific predictions about individual events. In this statement Hayek emphasises that to fully ascertain the working of the economic system, the researcher should look, in addition to quantitative ‘statistical information’, to qualitative ‘full information’ on each element of the system. Here Hayek draws attention to the distinction between the human capacity to observe and predict the behaviour of simple systems (such as in Newtonian physics) and the more complex systems found in economics, biology and psychology. Hayek, (1974) also also highlights the importance of recognising the limitations of prediction when dealing with complex systems where only ‘pattern predictions’ are possible as contrasted with the more precise predictions that can be made of non-complex phenomena. Often all that we shall be able to predict will be some abstract characteristic of the pattern that will appear – relations between kinds of elements about which individually we know very little.
Yet, as I am anxious to repeat, we will still achieve predictions which can be falsified and which therefore are of empirical significance. Next Page – Ch 3: Three Emergent Patterns Previous Page – Ch 3: The Mechanics of Change