Research Policy 40 (2011) 403–414
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Research Policy
journal homepage: www.elsevier.com/locate/respol
A framework for analysis of multi-mode interaction among technologies with
examples from the history of alternative transport fuels in Sweden
Björn A. Sandén a,∗ , Karl M. Hillman b
a
b
Environmental Systems Analysis, Department of Energy and Environment, Chalmers University of Technology, Göteborg 41296, Sweden
Institute for Management of Innovation and Technology (IMIT), University of Gävle, Sweden
a r t i c l e
i n f o
Article history:
Received 31 August 2009
Received in revised form
20 December 2010
Accepted 22 December 2010
Available online 26 January 2011
Keywords:
Technology selection
Competition
Symbiosis
Transition
Lock-in
Technological innovation system
a b s t r a c t
The relationship between technologies is a salient feature of the literature on technical change and terms
like ‘dominant design’ and ‘technology lock-in’ are part of the standard vocabulary and put competition
among technologies in focus. The aim of this paper is to provide an account of the wide range of interaction
modes beyond competition that is prevalent in transition processes and to develop a conceptual framework to facilitate more detailed and nuanced descriptions of technology interaction. Besides competition,
we identify five other basic modes of interaction: symbiosis, neutralism, parasitism, commensalism and
amensalism. Further, we describe interaction as overlapping value chains. Defining a technology as a
socio-technical system extending in material, organisational and conceptual dimensions allows for an
even more detailed description of interaction. The conceptual framework is tested on and illustrated by
a case study of interaction among alternative transport fuels in Sweden 1974–2004.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
The literature on technology interaction has hitherto focussed
on competition. A dominant idea in the literature on technical
change is that following a crisis or a technical breakthrough there
is an ‘era of ferment’ when many novel technologies emerge and
compete to be selected as the new dominant design (Abernathy
and Utterback, 1978; Arthur, 1988; David, 1985; Tushman and
Anderson, 1986; Utterback, 1994). This model is attractive due
to its simplicity but could be too simple to effectively describe
change processes. There is evidence in the literature that besides
competition also other modes of interaction could be of great
importance.1 Rosenberg (1976) observed ‘technological convergence’ where one process developed in one industry is imitated
in others, and ‘technological complementarities’ where two technologies are combined to fulfil or enhance a function. Frankel
∗ Corresponding author. Tel.: +46 31 772 8612; fax: +46 31 772 2172.
E-mail address: [email protected] (B.A. Sandén).
1
Margulis and Sagan (2002) argue that the literature on biological evolution has
also paid too much attention to competition and selection of species and less to
the emergence of species. They put forward a theory that put symbioses centre
stage (instead of accidental mutations). Symbiosis of already existing species creates
new genomes in nature, and is thus the motor in evolution. This has its parallel in
evolutionary economics and the idea of radical change as a recombination of existing
ideas and technologies.
0048-7333/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.respol.2010.12.005
(1955) described the latter phenomenon as ‘technological interrelatedness’. While none of these concepts explicitly address
complementarities between potential substitutes, Islas (1997, p.
64), in his story of gas and steam turbines, saw that “two technologies can be complementary and in competition at the same time,
and in the same market sector”. Geels (2005) discussed hybridisation, such as simultaneous use of steam and sails and also observed
that one technology may catalyse a development and open development pathways for others.2 For economic actors (firms) such
‘spillovers’ are termed positive externalities, i.e. free utilities paid
for by someone else. Marshall (1890) underlined the importance of
economies that are external to the firm but internal to an industry,
and Porter (1990) observed that positive external economies also
often extend to related industries within a nation.
Taking these observations one step further Pistorius and
Utterback (1997) outlined a multi-mode framework for technology
interaction based on similar frameworks in organisational ecology
(Brittain and Wholey, 1988) originating from community ecology
in biology (Odum and Barrett, 2005).3 They pointed out that tech-
2
As an example of a catalysing technology, Geels (2005, p. 692) takes the bicycle
in the transition from horse-drawn carriages to automobiles: “the bicycle led to
processes of change in the socio-technical regime on which the automobile could
later build.”
3
‘Community ecology’ instead of ‘population ecology’ since it is about the interaction of many populations (species).
404
B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
nologies not only compete but could also be in symbiosis or in
predator–prey relationships and provided some examples.
To inform public policy and firm strategy, we believe it is essential to better understand the many possible modes of technology
interaction. It could be of particular importance in policy areas,
such as energy and climate policy, where attempts are being made
to govern long term change processes in large technical systems.
It has been suggested that reaching carbon neutral energy and
transport systems is so radical that a stepwise change process is
required, where ‘hybrid technologies’ (Geels, 2002) or ‘two-world
technologies’ (Kemp and Rotmans, 2001) function as ‘bridging technologies’ (Andersson and Jacobsson, 2000). New technologies need
to make use of the existing system and then gradually transform
it (Clark, 1985; Freeman, 1996). The ‘bridging’ function indicates
that some kind of technological complementarities, or spillovers,
need to be involved. However, others warn that initial investments
in technologies with limited potential could lock out technologies with better long-term prospects (Andersson and Jacobsson,
2000; Arthur, 1988; Menanteau, 2000; Sandén, 2004). Given this
dilemma, policy and firm strategy cannot successfully address technologies in isolation, nor retreat to, so called, ‘technology neutral’
policies (Sandén and Azar, 2005). The relationships between technologies need to be investigated and taken into account. To be
able to differentiate things like ‘bridging technologies’ and dead
ends, there is a need for a framework that in more detail describes
technology interaction.
The aim of this paper is twofold. First, we want to provide an
account of the wide range of interaction modes beyond competition that is prevalent in transition processes. Second, we want to
take one step further and systematize the observations made in
previous literature and develop a more elaborate model of multimode interactions among technologies. While the framework,
described in Section 2, is broad enough to include any technology
interaction, we focus on interactions between different emerging
technologies that in principle fulfil the same function and that in
a simpler model would be viewed as competing alternatives in an
‘era of ferment’.4 In Section 3, the conceptual framework is tested
and illustrated by a case study of interaction among alternative
transport fuels in Sweden 1974–2004.5 Finally, our results are
summarised in Section 4.
2. A model of technology interaction
It should be stated upfront that we in this article put technology
centre stage, rather than people and organisations. A result of this
is that technologies tend to get a life of their own in the text. This
does not imply that we allocate agency to technologies rather than
to people. We take this approach merely since the purpose of the
paper is to analyse relations between technologies.
In a model of technology interaction we need to be able to separate technologies, i.e. establish technology boundaries and define
‘technology’. We arrive at a definition of technologies as sociotechnical systems made up of heterogeneous elements, such as
physical objects, organisations, knowledge and regulation. Further,
these elements are organised in value chains. Any specific technology referred to by a common word or phrase such as ‘car’ or ‘wind
power’ is defined by a set of complementary and alternative value
4
In the terminology of Raven (2005, p. 270) this would correspond to the case
where several ‘niches’ (technologies) develop against the backdrop of one ‘regime’
(sector). The history of alternative fuels in Sweden described in the article also contains elements of the case when multiple niches develop against the backdrop of
several regimes (Sandén and Jonasson, 2005).
5
The full report on the history of alternative fuels is provided in Sandén and
Jonasson (2005). In Hillman and Sandén (2008), we explore future trajectories and
interactions in the same system.
Complementary chains
a
Alternative chains
b
Hn,11
Hn,1
Pn,1
Upstream
chains
Pn,1
Hn-1,1
Hn-1,2
Hn-1,1
c
Hn-1,3
d
H’n+1,1
Downstream
chains
H’n+1,2
H’n+1,1
Pn,1
H’n,1
H’n+1,3
Pn,1
H’n1,
Fig. 1. Value chain hierarchies. All products and processes P are part of several value
chains forming hierarchies. Hierarchies made up of value chains constitute basic
building blocks in our definition of technology. From any given point of reference
(level n) a distinction can be made between upstream supply chains and downstream application chains. Further, some value chains are complementary while
others are alternatives (indicated by the dotted line). Here we use H as a short for
hierarchies made up of upstream value chains and H for those made up of downstream value chains. The arrows indicate that a lower order product or process is
used in a higher order product or process (closer to end use).
chains. We conclude that interaction emanates from shared elements in different parts of the value chain. These overlaps result
in different modes of interaction ranging from pure competition to
pure symbiosis. We end Section 2 by an outline of how our formalisation of technology interaction could enrich diffusion models.
2.1. Bundles of value chains and technology boundaries
Theories of design hierarchies (Baldwin and Clark, 2000; Clark,
1985) and complex systems (Murmann and Frenken, 2006; Simon,
1962) have dealt with the problem of describing similarities
between technologies and technology delineation and provide us
with a useful starting point. They present technologies as systems
built up by subsystems of lower order, while in turn being subsystems of higher order systems. A kind of pyramidal structure is
implied with many subsystems making up a technology, e.g. different materials are combined into components that are combined
into a car.
Instead of viewing these hierarchies as hierarchies of products,
we would prefer to see them as hierarchies of products and processes, since some differentiating aspects, e.g. the environmental
and ethical characteristics of a technology, are often invisible in
the final product as such.6 As depicted in Fig. 1a, a technology
can then be viewed as a hierarchy Hn,1 made up by complementary upstream supply chains, or sub-hierarchies, Hn−1,1 and Hn−1,2 ,
brought together in the process or product Pn,1 . However, not all
supply chains are complementary; there may also be many alternative supply chains that can fulfil the same function, as Hn−1,1 and
Hn−1,3 , denoted by the dotted line in Fig. 1b. What is meant by a
technology in everyday language, e.g. a ‘car’, normally allows for
many alternative supply chains.
Further, one can observe that the pyramid can be turned upside
down. One product or process can be used in many alternative
downstream applications (Fig. 1d). The car can be used as a taxi or
6
Such intangible characteristics can be made more tangible and visible in products by the use of branding, e.g. eco-labelling. Intangible differences may also result
from different uses of a product. Different brands of jeans may be physically indifferent but associated with different user groups.
B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
H’n+1,1
H’n+1,3
H’n+1,1
H’n+1,3
H’n,1
Pn,1
Ta
H’n,1
Pn,1
Tb
Hn,1
Hnn-1,1
Hn-1,3
n-
Hn-1,1
n
H’n+1,3
H’n+1,1
H’n,1
H
Tc
Td
Hn-1,3
Te
Hn,1
405
Hn,1
Fig. 2. Bundles of value chains. A technology T can be defined by ‘a bundle of value
chains’ that includes many or a few alternative upstream supply chains (Hn−1 ) and
downstream application chains (Hn+1 ). Technology Ta illustrates a wider definition,
while Tb , Tc , Td and Te are more narrowly defined. Tb and Tc overlap downstream
) while Td and Te overlap upstream (Hn,1 ). Complementary value chains are not
(Hn,1
shown in the graphs. For Tc , Td and Te some lower level details are hidden.
as a police car. To obtain symmetry we note that also some applications may be complementary (Fig. 1c) in the sense that they fulfil
multiple purposes or use co-products or non-exclusive goods like
non-patented knowledge.
Any technology is a combination of upstream and downstream
hierarchies as in Fig. 2. We thus come to the more general definition of a technology as a ‘bundle of value chains’ (or more precisely a
“system of socio-technical elements organised in bundles of value
chains”, see Section 2.2), made of complementary and alternative
upstream supply chains (subsystems) and downstream applications (higher order systems). A technology can fully be defined by
how it can be made and what it can be used for.7
Technology boundaries may be more or less inclusive, i.e. the
bundles may include many or a few alternative value chains. There
is not one correct boundary but different boundaries could be more
or less useful in a given situation. The usefulness of a particular
delineation depends on the purpose of making it. In the concept ‘Ta ’
we can for example include all possible ways of producing a device
that are capable of converting solar energy directly to electricity,
i.e. all types of solar cells regardless of semiconductor material and
production process, and include all applications. Ta in Fig. 2 illustrates such a wide definition including all alternative upstream and
]. Alternatively, we could be
downstream chains, i.e. Ta = [Hn,1 , Hn,1
] or T that includes
concerned with a technology Tb = [Hn−1,1 , Hn,1
c
a limited number of alternative upstream supply chains, such as
silicon solar cells only, or Td and Te that includes solar cells used in
a limited number of downstream applications, such as electricity
production in satellites or on roof-tops.
The name given to a bundle tends to be associated with one or
many waists on the bundle. For example, compare the denominations ‘biofuel for transport’ and ‘methanol’, the former bundle being
conceptually thinner (specific) towards the resource and application endpoints while allowing for many chemical compositions and
productions processes in the midpoint (Fig. 3a), while the opposite
7
A similar, but somewhat less general, delineation is provided by Carlsson et al.
(2002). They distinguish between three levels of analysis of technological systems:
first technologies (a generic knowledge field) that are applied in many different
products, second, products (or artefacts) that are combinations of many knowledge
fields and used in many applications, and third applications (or functions) that are
combinations of many related products (compare Fig. 3).
a
b
c
d
Fig. 3. Alternative conceptual boundaries. The name given to a technology, or bundle, tends to be associated with one or many waists on the bundle. A technology can
be defined by being delimited in terms of alternative processes towards upstream
and downstream endpoints, e.g. ‘biofuel for transport’ (a), at some mid-point, e.g.
‘methanol’ (b), mainly far downstream, e.g. ‘transportation technology’ (c), or mainly
far upstream, e.g. ‘nanotechnology’ or ‘bioenergy’ (d). If (d) is ‘nanotechnology’ and
we add a qualifier as in ‘nanotechnology for energy applications’ we get a definition
that more resembles graph (a) inscribed in graph (d).
is true for ‘methanol’, which can be made from many resources
and used for other ends besides fuel combustion (Fig. 3b). Sometimes, a technology is only specified with regard to end use, such
as ‘transportation technology’ (Fig. 3c), and sometimes the delimitation lies mainly upstream such as in ‘nanotechnology’ (Fig. 3d).
If Fig. 3d illustrates ‘nanotechnology’ and we add a qualifier as in
‘nanotechnology for energy applications’ we get a definition that
more resembles Fig. 3a inscribed in Fig. 3d.
2.2. Structural overlap and elements of socio-technical systems
Now we can make the observation that two technologies may
overlap at different levels of the value chain (in different parts of
the bundle), that is, in supply chains (production processes) or in
applications. For example, Tb and Tc in Fig. 2 share the same appli , and T and T share the same production processes
cations Hn,1
e
d
Hn,1 . Two technologies overlap if they at some level in the hierarchy use or can use the same input or process, or fulfil or can fulfil the
same function. This overlap is the basis for technology interaction
of different kinds.
To develop a more elaborate classification of overlaps we make
use of the literature on ‘socio-technical systems’. The bundle of
value chains defines in fact a socio-technical system. The literature
on economics of innovation and science and technology studies
has identified a number of structural elements that constitute a
socio-technical system (see e.g. Bergek et al., 2008a,b; Carlsson
et al., 2002; Carlsson and Stankiewicz, 1991; Geels, 2004; Hughes,
1987).8 Here, we group them into three main categories: artefacts,
actors and schemata. By ‘schemata’ we refer to virtual properties, regularities that can be abstracted from artefacts and actors,
such as knowledge and rules.9 Correspondingly we may say that
the technology, understood as a socio-technical system, extends in
a multidimensional space with material, organisational and conceptual dimensions. Some systems exist as full-fledged systems,
extending in all dimensions, being materially, organisationally and
conceptually well-developed, while others exist as embryos (e.g. as
a piece of knowledge scribbled on the back of an envelope or as an
expectation held by a few individuals). Two systems may overlap
in one or many dimensions.
8
The terms ‘technological system’, ‘technological innovation system’ and ‘sociotechnical system’ are used by different authors that give them a slightly different
content. In some versions, the physical artefacts are not included. As noted by
Markard and Truffer (2008) and Bergek et al. (2008b) there are two general functions of a system that are given more or less attention. The first function is to enable
production and use of a product, the second is to enable growth of production and
use of a product, captured by the term innovation. (An analogy is the metabolic system of a child that enables playing as well as growing.) We will reserve the concept
of technological innovation system for the theoretical model describing the growth
aspect (Section 2.4).
9
This use of ‘schemata’ may deviate somewhat from how the term is used in e.g.
psychology or computer science. Our somewhat broader meaning of the term is
close to how it is used in Sewell (1992).
406
B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
In the material dimension a socio-technical system is constituted
by physical artefacts. Two systems overlap in this dimension when
they use the same physical artefact, e.g. if the same production
plant is used to produce two different fuels, or if the same physical
infrastructure such as roads is used for different types of cars.
In the organisational dimension the system is constituted by
actors. Actors could be individuals, or individuals closely linked into
firms and other organisations, or more loosely linked networks of
individuals or organisations. Organisations include not only firms
but also universities, industry associations, NGOs and government
bodies. There are many types of networks. Learning networks link
suppliers to users, related firms or competitors, or university to
industry (Carlsson and Jacobsson, 1993). They constitute important modes for the transfer of knowledge and perception of what is
possible and desirable. Political networks are of equal importance.
Policy making takes place in a context where advocacy coalitions,
made up of a range of actors sharing a set of norms or beliefs, try
to influence policy in line with those beliefs (Sabatier, 1988). Obviously, one actor can support more than one technology, thereby
creating a systemic overlap, and a constellation of actors (organisation or network) once developed to support one technology may
at a later stage fit another technology.
In the conceptual dimension the system is constituted by
schemata. These schemata define what actors and artefacts are
able to do and what they ought to do. They are not only constraining but also enabling. Schemata may be embedded in actors and
physical artefacts, but they can also exist separately from these,
codified in symbolic systems. Hence, schemata in themselves can
be transferred between systems and two systems can overlap in a
conceptual dimension while not sharing any physical artefacts or
actors. We distinguish between three basic types of schemata: positive schemata (technical knowledge and expectations), normative
schemata (normative and regulative rules) and concepts.10,11
Positive (or descriptive) schemata can be divided into technical
knowledge and expectations, i.e. beliefs that are more or less verifiable. In much of the literature on socio-technical systems technical
knowledge (or instrumental knowledge) is not given the status of
being a separate structural element in the same way as ‘institutions’
or ‘rules’ (see footnote 11). This strikes us as odd. While normative and regulative rules define what actors and artefacts should
or should not do, technical knowledge constrains what actors and
artefacts are able to do. This kind of schemata can be captured by
correlations and cause–effect chains: if A then B. Such a schema
makes a repeatable and verifiable prediction about the future of a
limited system that is stable over time. It does apply not only to the
production and use of artefacts but also to knowledge related to e.g.
marketing and business models. Technical knowledge is found as
competence within actors: as explicit knowledge or more experience based tacit skills within individuals (Polanyi, 1958) or as
10
The first two reflect the classical philosophical dichotomy between how things
‘are’ and how they ‘ought to be’ (Hume, 1740).
11
In the literature on technological innovation systems (e.g. Bergek et al., 2008a),
the term ‘institutions’ is normally used, while for example Geels (2004) prefer the
term ‘rules’. Since we want to fully capture the virtual dimension we are not completely satisfied with any of these terms. In the terminology used here, we believe
‘institutions’ would include not only normative schemata but also concepts and
expectations (‘cognitive’ institutions) but not technical knowledge. An alternative
subdivision is then technical knowledge (codified and tacit) and institutions (regulation, attitudes, expectations and concepts). This also appears to be the definition
of institutions used by Scott (2001) with the small difference that Scott also includes
organisational routines as ‘institutions’. In the literature on technological transitions,
technical knowledge would normally not be included in the ‘rules’ category. For
some reason these literature strands tend to treat technical knowledge as embodied in actors, while other schemata are given a more independent position. There are
of course also those that highlight the role of technical knowledge, see for example
the discussion on ‘design space’ by Stankiewicz (2000).
procedures and routines in organisations (Scott, 2001). A fraction of
this technical knowledge is embedded in physical artefacts. Since
technical knowledge can be codified in symbolic systems (articles,
patents or more general, text, drawings, etc.) it can also exist outside of the actors it informs and the artefacts it describes. Technical
knowledge is constantly transformed from one type to another: in
one direction, general codified knowledge is turned into practical
skills and particular artefact designs, and in the other direction,
practical experience is generalised into codified knowledge.
Knowledge overlaps are dealt with in a rich literature on knowledge spillovers. The focus of the literature appears to be spillover
of codified knowledge between sectors (e.g. Grupp, 1996), or
spillover of skills between firms in regional innovation systems (e.g.
Audretsch and Feldman, 1996). While overlaps in the form of tacit
skills need to also overlap in the organisational dimension (shared
actors), codified knowledge developed for one technology can be
applied in the development of other technologies without any actor
overlap.
Expectations have to do with beliefs about the future performance of sometimes large and complex systems (van Lente and
Rip, 1998). There is not a clear demarcation between expectations
and technical knowledge. Expectations can also be framed by the
schemata “if A then B” but cannot be tested and verified in the
same way as technical knowledge. Expectations of one technology may spill over to other technologies, in particular if they are
conceptually lumped together.
Normative (prescriptive) schemata regulate interactions
between actors and define what actors and artefacts should or
should not do. These rules include hard regulations (controlled
by juridical systems) and normative rules such as norms and
attitudes (controlled by social systems). Like positive schemata,
also normative schemata may be codified in symbolic systems, or
be embedded in people, organisational routines and artefacts (e.g.
as standards). Interaction in this dimension includes for example
regulation adapted to one technology that exclude or suit another
technology and attitudes towards one technology that spill over to
another.
A fundamental type of schemata, that is not always given the
attention it deserves, is the differentiation and meaning of concepts,
i.e. the very structure of symbolic systems. These are schemata in
the sense of conventions and they are the building blocks of all other
kinds of schemata. Concepts go beyond empirical testing, but shape
what can be thought and understood (Polanyi, 1958). Concepts are a
prerequisite for codified technical knowledge and expectations, as
well as for codified normative and regulative rules. Different technologies and products may have an overlap in terms of a shared
label, such as ‘environmentally friendly products’, ‘renewables’ or
‘biofuels’, or depend on classifications needed for theories, technical descriptions, testing procedures, regulation etc.
The different types of schemata are not always easily separated.
Taken jointly they influence decisions and actions in the form of
‘frames’ (Bijker, 1995; Geels, 2002) or ‘paradigms’ (Dosi, 1982).
Legitimacy is a term used for how well a phenomenon (here technology) fits the dominant frames, e.g. how it appears in relation to
widely accepted performance criteria, legal frameworks and beliefs
about the future. Legitimacy can be built up by a real presence
and proved performance, but also by expectations of future presence and performance. From a practical standpoint, when collecting
empirical evidence of overlap, technical knowledge and regulation
is more easily distinguished, while attitudes and expectations are
often difficult to separate.12
12
Attitudes and expectations can be tracked by studies of written and spoken
language (‘discourse analysis’). Various internet applications are opening up new
possibilities for analysing huge amounts of statements and tracking opinions.
B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
407
Table 1
Two-technology (two-species) population interaction.
Mode of interaction
Technology 1
Technology 2
General nature of interaction
Competition
Symbiosis
Neutralism
Parasitism (and predation)
Commensalism
Amensalism
−
+
0
−
0
0
−
+
0
+
+
−
Inhibition when common resource or market is in short supply
Interaction favourable to both
Neither population affects the other
Technology 2 is benefited and 1 is inhibited
Technology 2 is benefited, 1 not affected
Technology 2 is inhibited, 1 not affected
Based on Odum and Barrett (2005).
2.3. Six modes of interaction
From the previous sections we conclude that interaction
emanates from overlaps, i.e. shared elements in different parts of
the value chain. As stated in Section 1, interaction between technologies is commonly understood as competition only. However,
there is more to it than that. As suggested by Pistorius and Utterback
(1997), a list of two-technology interaction modes can be borrowed
from biology, more precisely community ecology. In a recent textbook, Odum and Barrett (2005) distinguish between nine modes
of interaction between species. In Table 1, we list six categories
we find useful in the context of technology interaction.13 Besides
competition, there are five other modes of interaction, including no
interaction (neutralism). Since these six modes comprise all possible combinations of signs (+, −, 0) they cover all possible interaction
modes and provide us with a useful terminology.
Before providing examples of how overlap gives rise to the interaction modes in Table 1, a distinction between quasi–static and
dynamic interaction needs to be made. In the short term, for mature
technologies, many structural elements can be assumed to be fairly
static (Marshall, 1890). When the overlapping part of the bundles
is assumed to be fixed, we use the term ‘quasi–static interaction’
between technologies. For example, a resource flow used by two
technologies is assumed to be constant, or the size of a market
shared by two technologies is assumed to be fixed. We use the
term ‘dynamic interaction’ when technologies interact via structural change in overlapping parts of the bundles. Such interaction
takes into account effects of changing demand, production systems
and knowledge pools. Since our starting point in this article is technical change and the relationship between emerging technologies,
our main concern is dynamic interaction. However, quasi–static
interaction is in focus in much of the (neoclassical) economic literature and can be also categorised with our terminology.
Quasi–static interaction comes in the form of one of the first
three symmetric interaction types in Table 1: competition, symbiosis and neutralism.
Competition could be not only competition for markets, but also
competition for resources. Fig. 2 illustrates how, due to overlap,
technology Tb competes with Tc for the common market (or down , and how T and T compete for a common
stream system) Hn,1
e
d
input (or upstream system) Hn,1 .
Symbiosis exists between technologies defined at different levels in the same value chain. The bundles ‘microprocessors’ and
‘computers’ clearly overlap in a symbiotic way. Such symbiosis
could be necessary, or beneficial but not necessary. Also complementary products (defined at the same level in a hierarchy) could
13
Pistorius and Utterback (1997) focus on competition, symbiosis and predation
(here parasitism and predation). While we keep neutralism, commensalism and
amensalism, we also leave out competition by direct interference, i.e. fighting, combine the two categories mutualism (interaction favourable to both and obligatory)
and protocooperation (interaction favourable to both but not obligatory) into the
category ‘symbiosis’ and combine parasitism and predation into one category. In
the following we will use the term parasitism since we think it better reflects the
types of relationships we will describe.
be mutually dependent in an application, such as cars and petrol
(Fig. 1a). This kind of overlap in applications have been described
through the concepts ‘enabling technologies’ (Utterback, 1994) and
‘technological complementarities’ (Rosenberg, 1976). Upstream,
co-products are more or less dependent on each other, as captured by the term ‘economies of scope’ (Panzar and Willig, 1981).
At one end of the spectrum, where one co-product is a by-product
of low economic value, the interaction borders on commensalism.
Our interest here is focused on technologies that are potential substitutes. In this case too, we may find examples of static symbiosis.
Upstream, substitutes may be produced in the same process (they
are co-products), such as ethanol and biogas in some fermentation
plants (Fig. 1c). Downstream, potential substitutes may be used
in combination to increase performance, e.g. when electricity and
petrol is used in plug-in-hybrid vehicles. This phenomenon has
been captured by the term ‘hybridisation’ (Geels, 2005).
Neutralism typically occurs when two technologies deliver different services and use different resources (no overlap) or when a
common resource is a non-exclusive good, such as (non-patented)
knowledge, or is in abundant supply. Another example is when two
technologies are separated geographically, and thus may coexist
while they in principle compete for the same market (Maréchal,
2007). In biology, this kind of separation is called the Gause principle or competitive exclusion principle (Odum and Barrett, 2005).
Competition, symbiosis and neutralism are given a slightly different meaning when we look at interaction dynamically and take
structural change into account. This is in particular important for
the interaction between two emerging technologies. Two emerging technologies could compete to structure the downstream part
of value chains as well as upstream production chains. As a result
structures involving artefacts, actors and schemata could be created
that fit one technology while the other is locked out. On the other
hand, two technologies can together create a new market or new
supply chains and share the burden of structural change – dynamic
symbiosis. They may also evolve in different directions to avoid
competition, creating new niches and differentiating resource supply (Windrum and Birchenhall, 1998). They may thus develop a
neutral relation.
When we look at interaction dynamically we also find examples
of the asymmetric interaction types in Table 1. We use the term parasitism for the situation when an emerging technology enters the
market space developed by a more entrenched technology or can
make use of the same upstream supply chains that were developed
by the older technology. If we for example view Tc in Fig. 2 as a new
technology, it can make use of the downstream structure Hn,1
that
was developed by Tb . Parasitism occurs when the new technology
gains from the existence of the old, while the old loses market share
or resource supply to the new. On the other hand, the old technology may also benefit from a resource or market niche developed
by the new technology or feed on more intangible assets such as
expectations. When the resource that is developed by one technology and made available for a second technology is a non-exclusive
good such as non-patented knowledge, we instead have a situation
of commensalism. In a situation when an emerging technology is
structurally locked out and does not fit into the system developed
B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
408
H’2,1
2
Told
1
0
P1,1
P0,1
P1,2
P0,3
P0,2
Tnew1
Tnew2
H0,3
-1
H-1,1
H-1,2
H-1,3
Fig. 4. An illustration of a bridging technology. A technology Tnew1 = [H−1,2 , P0,2 , P1,1 ,
] shares a first order downstream application (P1,1 ) with the established technolH2,1
] and an upstream value chain (H−1,2 ) with Tnew2 = [H0,3 ,
ogy Told = [H−1,1 , P0,1 , P1,1 , H2,1
] and can thus function as a bridging technology. For clarity, only alternative
P1,2 , H2,1
value chains are depicted.
around the old technology, we have amensalism where the new is
inhibited, while the old is not affected. Another case of amensalism
is when a bad reputation spills over from one technology to a less
known related technology or when one technology benefits from
the decline of a second technology, which in turn is not affected by
the first.
Bridging technologies can now be defined in terms of parasitism (or commensalism). A bridging technology parasitizes on an
established technology, while a third technology parasitizes on the
bridging technology. In this way the third technology could grow
even if it is in pure competition with the established technology
and should be structurally locked out.
Fig. 4 provides a stylised example. An established technology, for example petroleum based transportation (Told ) is defined
by the value chain petroleum (H−1,1 ), gasoline (P0,1 ), combus ). The figure
tion engine drive train (P1,1 ) and car transport (H2,1
also show two emerging alternative technologies, a biofuel,
], and renewable electricity propulTnew1 = [H−1,2 , P0,2 , P1,1 , H2,1
]. The biofuel (T
sion, Tnew2 = [H0,3 , P1,2 , H2,1
new1 ) shares the first
order downstream application P1,1 (the internal combustion drive
train) with gasoline (Told ), which is not the case for electricity
(Tnew2 ) that shares only the second order downstream application
H2,1
(car transport) and hence share fewer complementary supply
chains.
It is difficult to develop a car propelled by renewable electricity
in a situation dominated by gasoline. However, if the biofuel parasitizes on gasoline benefiting from the shared drive train (P1,1 ),
renewable electricity can benefit from the development of H−1,2 , a
common upstream process of biofuel and renewable electricity. In
this example H−1,2 could be cultivation and gasification of biomass
that can generate a biofuel or renewable electricity. In this case, the
biofuel acts as a bridging technology between gasoline and renewable electricity. Tnew1 parasitizes Told and Tnew2 parasitizes Tnew1 . In
this way, two emerging technologies can compete internally while
at the same time helping one another (symbiosis), or one helping
the other (commensalism or parasitism), to grow at the expense of
an established technology (Fig. 5). Taking the example in Fig. 4 one
,
step further, renewable electricity use ([P0,3 , P1,2 ] or broader H0,3
not visualised in the figure) could form a bridge between bioelectricity production (H−1,2 ) and solar electricity production (H−1,3 )
completing a shift from a gasoline powered car [H−1,1 , P0,1 , P1,1 ,
] to a solar electric car [H
H2,1
−1,3 , P0,3 , P1,2 , H2,1 ].
There is also the risk (from the perspective of the electric car)
that biofuels further strengthens the position of the internal combustion drive train P1,1 at the expense of the electric drive train P1,2
and thus contributes to locking out renewable electricity propulsion. Hence, one emerging technology may have a positive effect
Fig. 5. Simultaneous competition and symbiosis. Two emerging technologies, Tnew1
and Tnew2 , compete for markets or resources, while at the same time expanding their
combined system (new markets or supply chains) at the expense of an established
alternative Told .
on another technology in one part of the value chain and a negative
effect in another part. The end result is not easy to foresee.
2.4. Technological innovation systems – towards a
multidimensional model of diffusion
Our primary interest is the diffusion of novel technologies and
how that diffusion is affected by technology interaction. This far we
have outlined that technologies can be described on the one hand as
bundles of value chains and on the other as socio-technical systems
extending in material, organisational and conceptual dimensions.
Taken together, these descriptions can be used to specify points
of overlap, i.e. points of interaction, e.g. a shared knowledge input
in a supply chain or a shared group of users. But to understand
the dynamic processes during which interaction takes place, we
also need a model of technology diffusion. We suggest here some
basic building blocks of such a model, while leaving the detailed
elaboration (and illustration) for future studies. Again, our starting
point is population ecology, complementing this with ideas from
transition literature and an innovation system framework.
The population ecology approach to diffusion observes that
the growth of technological and biological populations is governed by positive and negative feedback. The size of the population
affects the growth rate. Technology diffusion in an early phase
stimulates further growth by increasing returns to adoption, i.e.
positive feedback due to, for example, economies of scale and learning (Marshall, 1890, p. 265).14 When resources dwindle, market
potentials are exhausted or major drawbacks with the technology
become apparent, negative feedback (decreasing returns to adoption) starts to outweigh positive feedback and growth comes to a
halt. Combined positive and negative feedback generates the classical s-shaped curve of diffusion (Fisher and Pry, 1971; Geroski,
2000). A more general dynamics is formalised in the Lotka–Volterra
functions, which also can take into account that growth rates may
depend on other species (Porter, 1991, pp. 187–199). Variants of
the Lotka–Volterra functions have been used to model technology
competition and substitution (Marchetti and Nakicenovic, 1979).
Pistorius and Utterback (1997) point out that the same framework
can also be used to model symbiosis and parasitism/predation by
changing the signs in the formulas (see signs in Table 1).15
14
See for example Arthur (1988) and Sandén and Azar (2005) for lists of feedback
mechanisms.
15
Ahmadian (2008) models three-technology interaction in a Lotka–Volterra
framework. He also explores the possibility of developing a formal model in a system
dynamics framework based on a conceptual innovation systems model.
B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
The simple population (community) ecology approach treats
technologies as a one dimensional entity described by population
size. Taking the socio-technical systems approach we note that
not only artefacts multiply, but also dedicated actors, networks,
terminology, technical knowledge, expectations, positive attitudes
and adapted regulations accumulate and expand. Observation of
such multidimensional diffusion is of particular importance in the
formative phase of socio-technical systems when the diffusion of
products has barely started. This phase can extend over many
decades and decisive interaction between emerging alternatives
is bound to take place at this stage. The literature on technological innovation systems has identified several feedback loops that
do not directly involve production and use of products. One example relates to knowledge formation: an early finding may lead to
actor entry, further knowledge accumulation, increased expectations and a mobilisation of resources enabling further actor entry
and knowledge production (Kamp, 2008; Sandén et al., 2008). A second example involves legitimacy and regulation: expectations and
positive attitudes can lead to actor entry, which lead to increased
legitimacy and lobbying for changed regulation, which enable more
actors to entry (Bergek et al., 2008b; Jacobsson and Bergek, 2004;
Sandén, 2005). Suurs (2009) identifies four generic types of such
feedback loops that he calls ‘motors of innovation’.
Not only endogenous forces (governed by feedback), but also
exogenous forces, i.e. structures and events that do not depend on
the socio-technical system in focus may affect the development. In
the Lotka–Volterra model, exogenous factors are treated as constants. However, they may change over time. For example, the
emergence of an international debate on climate change may affect
attitudes and expectations around a renewable energy technology
such as tidal power. It is reasonable to treat it as an external variable since the development of tidal power has little influence on
the climate change debate.16 Similarly, Rip and Kemp (1998) and
Geels (2002) describe system innovation or technological transitions as a multilevel reconfiguration process, involving the three
levels niche, regime and landscape. Novel technologies grow at the
niche level. Their growth is governed by internal feedback but also
by exogenous influences from the entrenched technological system
residing at the ‘regime level’ and from the even broader societal or
‘landscape level’.17
Systemic growth can now be described by some ‘innovation system functions’ that relate the growth (or decline) of the elements of
the system (and emergent system properties) to the system itself
and to external forces (Bergek et al., 2008b; Hillman et al., in press;
Hillman and Sandén, 2008; Sandén and Jonasson, 2005). This system model of change has been termed a technological innovation
system.18 The overlaps between different socio-technical systems
as described in previous sections can be inserted in this model.
When actors within socio-technical system Tx develop a system
element that is shared with socio-technical system Ty , or consumes
an element (e.g. a limited resource) that is also required in Ty , this
will affect other elements of Ty . The final outcome of the interac-
16
In a closed and interlinked world the only causes of change that are perfectly
exogenous are related to independent natural events such as radioactive decay and
meteorites from outer space. However, there is a scale from the more endogenous
forces created within the studied system and exogenous forces that give an impulse
from the outside.
17
It is very difficult for niche actors, and even regime actors, to affect the landscape level. On shorter time scales the impact only goes in one direction. We may
observe that there is an even less mouldable level that has an effect on the evolution of socio-technical systems: the laws of nature. By this we do not mean our
current formulations of the laws of nature, which can be considered to be part of
the landscape.
18
This interpretation differs slightly from earlier conceptualisations of technological innovation systems (e.g. Bergek et al., 2008a; Hekkert et al., 2007). See also
Markard and Truffer (2008) for a review.
409
tion, e.g. entry or exit of actors or changed regulation, depends not
only on this spillover effect but also on the state of other parts of
Ty and on exogenous forces.19
This type of multidimensional diffusion model is obviously complex. However, we believe it can work as a qualitative model that
guides our thinking about the growth of socio-technical systems.
Together with empirical research, it will possibly enable identification of some general patterns of interaction and a limited set of
typical development paths that reduce complexity.
3. Alternative transport fuels in Sweden 1974–2004
The history of alternative transport fuels in Sweden displays
richness in terms of multimode and multidimensional interaction.
This case will here be used to illustrate the actual complexity of
a process that often is viewed simply as a competition between
a few alternative technologies. We also use the case to demonstrate how the terminology developed in this paper can be used
to describe multimode and multidimensional interaction. A more
complete description of the history is provided in Sandén and
Jonasson (2005).
Boundaries between technologies in this case is mainly based
on praxis, i.e. those technologies which are commonly viewed as
separate alternatives in the current discourse and hence are given
different names, are here considered to be different technologies.
This delineation is found to be based on the chemical composition
of the fuel and upstream processes, or more correctly, the type of
primary resource used, e.g. ‘ethanol from wood’ or ‘biogas’.
The case contains interactions taking place between 1974 and
2004. During these years, several alternative technologies were
present with varying intensity in different dimensions. The history
can be subdivided into three periods, each dominated by different external forces that influenced the course of events. In the
first period, roughly from 1974 to 1985, large-scale oil substitution was the primary concern and methanol from gasified coal and
wood completely dominated the alternative fuel stage. The second
period (1986–1997) saw the rise of ethanol, natural gas and biogas in some geographical areas and a short visit of electric vehicles.
The dominant external driving force was a focus on air pollution in
cities. From around 1998, climate change became an increasingly
important issue, in later years complemented by rising oil prices.
A renewed interest in fuels from gasified biomass emerged, while
ethanol and biogas took steps towards large scale diffusion. Hydrogen has been present in all periods as an option for the distant
future.20 All these alternatives to the entrenched fuels, petrol and
diesel, are seen by many as competitors, but as we will see below,
other modes of interaction are also prevalent.
3.1. Methanol and ethanol – parasitism and succession to the
throne
During the 1980s, ethanol gradually overtook the role as the
prime alternative fuel from methanol. This came partly as a result
19
In a mathematical model of the innovation system the functions would be differential equations: dpy,i /dt = fy,i (py,1 , . . ., py,n , z1 , . . ., zm , ε), where py,i represent
internal structural elements of technology Ty , zi represent external influences and
␧ is a stochastic perturbation. When a structural element is shared with technology
Tx (py,i = px,i ), the development of Tx influences the development of Ty . This interpretation opens for system dynamics modeling of innovation systems. Due to the
instability of innovation systems and difficulty with parameterization, such models
are unlikely to add much to the predictive capacity of innovation system studies.
However, formal models are useful for developing conceptual clarity and as tools
for illustration and education (Sterman, 2000).
20
Electricity and battery electric vehicles did not re-enter the scene until after
2004.
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B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
of the shift in the main exogenous force of change from oil depletion
to air pollution, but the ethanol–methanol interaction in the 1980s
also displays a case of commensalism developing into parasitism
where ethanol made use of downstream overlaps with methanol.
Ethanol could benefit from being physically and conceptually
similar to methanol in multiple ways. The choice between the
words ‘methanol’, ‘ethanol’ or ‘alcohol’ seems to have been carefully
made in many texts in the early half of the 1980s, not only for technical reasons. The term alcohol was used to borrow legitimacy in
one way or the other, or to hide controversies, which in one period
benefited both and illustrates a case of symbiosis based on conceptual overlap. The fight to change taxes (regulation) to become
fair or even beneficial to alcohols had been fought by the methanol
advocates but eventually came to benefit ethanol (commensalism).
The standard blends of methanol and petrol (M100, M85, M15, M5
referring to the percentage of methanol) had become well-known
concepts. These concepts were taken over by ethanol (E100, E85,
E5). Furthermore, technical knowledge and experience with alcohol fuel and flexifuel vehicles (that can run on mixes of methanol
and/or ethanol and petrol) had been gained and was kept within
the Swedish car manufacturers Volvo and Saab.21 The diffusion of
ethanol buses in the 1990s was made possible by the overlaps with
the preceding methanol system on the one hand and the differences
related to upstream resources and actor constellations on the other.
Ethanol was considered inherently renewable while methanol was
associated with coal and natural gas.
At the end of the 1990s, when the need for large scale options
was emphasised, resulting in lowered expectations for ethanol and
raised expectations for fuels from gasified biomass (e.g. methanol),
the Foundation for Swedish Ethanol Development (SSEU) was
renamed to BAFF – BioAlcohol Fuel Foundation. This indicates the
renewed credibility for alcohols other than ethanol, i.e. methanol,
even if methanol was not really lobbied for by the organisation. The
existence of methanol as a concept once more benefited ethanol
and as long as ethanol is strong in material and organisational
dimensions the conceptual overlap can be used without risk for
real competition.
3.2. Ethanol of various origins – symbiotic in mind and physically
neutral
The interaction between different kinds of ethanol, mainly from
wheat and from wood, originates in the obvious downstream overlap. The fuels have the same properties in pure form (bus fuel)
and when blended into petrol (E5 and E85). In addition, the same
physical artefacts can be used, such as buses, cars and filling stations. Following from this downstream overlap, upstream actors
representing different kinds of ethanol contributed to the formation of the interest organisation SSEU (the Foundation for Swedish
Ethanol Development). The farmers’ organisation, which supported
the construction of a wheat ethanol plant, recognised a common
interest in ethanol with forest regions, which regarded supplying
the raw material for wood ethanol as an opportunity.
The resulting interaction mode was symbiosis in terms of changing regulation and creating expectations. Already at an early stage,
the expectations of wheat ethanol as a long-term solution were
low. Due to the abundance of forests in Sweden, the expectations
for wood ethanol were higher. On the other hand, wood ethanol initially needed the support from the advocates around the short-term
option. It was argued that wheat ethanol could be used as a bridging technology awaiting the long-term option. Hence wood ethanol
21
Alcohol–petrol blends as well as flexifuel vehicles represent examples of physical downstream overlaps with the entrenched technology that made it possible for
both methanol and ethanol to parasitize petrol.
became a bridge to wheat ethanol by the advancement of the idea
that wheat ethanol was a bridge to wood ethanol! Interestingly,
the interaction mode in the material dimension, in real applications, developed into neutralism. Wood ethanol was used for bus
fuel and E85, while wheat ethanol was used for low concentration
blends (E5).
As the groups around wood ethanol gained strength, it became a
political force of its own. In addition, the need for support from the
wheat producers decreased since ethanol from imported sugar cane
became the short-term option favoured by wood ethanol interests.
The arguments for wheat ethanol as a necessary short-term option
were thus weakened; wheat ethanol was increasingly seen as a
burden in the environmental debate. Possibly, this shifted the interaction mode from symbiosis to parasitism. If, on the other hand,
wheat ethanol had reached a point where it no longer needed wood
ethanol, we instead of parasitism, have amensalism. The bad reputation of wheat ethanol spills over to wood ethanol, while wheat
ethanol due to its organisational and material strength can continue
as before!
3.3. Biogas and natural gas – from commensalism to symbiosis
Even for biogas and natural gas there is an influential overlap in
that they share the same chemical configuration downstream, i.e.
they both primarily consist of methane.22 This means that drivers
can fill up their vehicles with either gas depending on what is available. From the start in the early 1990s, biogas was able to benefit
from the availability of technology and experience first developed
for natural gas vehicles and filling stations. For technical knowledge this interaction is a typical example of commensalism. Later
on, biogas and natural gas helped one another in the competition
with petrol and diesel through symbiosis regarding expectations
and physical artefacts. In addition, we find a touch of neutrality
here; the choice between the two fuels was usually made in relation
to local conditions regarding distance to the natural gas grid and
availability of biogas (or of raw materials for biogas production).
Biogas was first introduced for vehicles in Linköping because it
was expected that the natural gas pipeline was going to be extended
to that region. The first drivers and vehicles running on biogas were
then supposed to shift to natural gas, i.e. biogas was considered a
bridge to natural gas regarding knowledge, actors and physical artefacts. When it was decided at the national level that the natural gas
expansion was to be stopped, the use of biogas in Linköping was
expanded. Thus, biogas had been introduced due to the physical
overlap with natural gas, and it was further adopted due to the lacking upstream overlap in terms of raw material and regulations. In
retrospect, the relation of biogas to natural gas in Linköping can be
categorised as commensalism in the realm of expectations (biogas
benefited from growing expectations for natural gas), followed by
amensalism in the local setting but commensalism at the national
level (biogas benefited from that natural gas did not materialise
in Linköping at the same time as natural gas materialised in other
cities).
The modes of interaction in Linköping were commensalism
and amensalism, rather than parasitism and competition, since
the development of natural gas was not affected by the biogas in
Linköping at this stage. However, when the focus slowly shifted
from local air quality to climate change at the end of the 1990s, fossil
natural gas needed the connection to its renewable sister. Natural
gas proponents argued both that natural gas was a bridge to biogas
(see e.g. Sandebring, 2004), and that biogas could not survive economically without teaming up with natural gas actors. Conversely,
22
To acquire the quality needed for use in vehicles, so-called ‘natural gas quality’,
biogas needs extra upgrading (not necessary for use in stationary installations).
B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
to increase legitimacy for biogas as being part of a large scale solution, connections to the larger scale and economically stronger
natural gas system were stressed. In addition, biogas could benefit
from using natural gas as a backup in case of a shortage of biogas.
Hence, the relationship between biogas and natural gas was symbiotic in that they complemented each other in terms of attitudes
and expectations and shared downstream artefacts, knowledge and
regulation. However, as the climate change debate was intensified
the connection to fossil natural gas became more of a burden for
biogas, while natural gas still benefited from biogas (a shift towards
parasitism).
411
the development of a group of technologies sharing gasification and
methanol use as downstream processes, in a later period, benefited
a group of technologies sharing biomass supply and gasification as
upstream processes. The latter group gained from the existence
of technical knowledge and a group of dedicated people that had
‘overwintered’ in the electricity domain. Due to the direction of
time, the former group was not affected by the latter, and hence we
regard it as commensalism instead of parasitism. That the former
group did not return in a later period can be explained by exogenous
forces (focus on climate change and carbon neutrality).
3.4. Syngas fuels – lagged commensalism
3.5. Renewable fuels and clean vehicles – concepts leading to
parasitism, symbiosis and neutrality
Another group of interacting technologies are those sharing the
process termed thermal gasification, collecting a large number of
possible value chains at different stages of development.23 In principle, the gasification process can convert carbon containing raw
materials, such as oil, coal, peat or biomass into a ‘synthesis gas’ (or
‘syngas’) composed of hydrogen and carbon monoxide.24 The syngas can be used in several ways. The hydrogen can be separated and
used directly, heat and electricity can be produced through combustion of the syngas or the syngas gas can be used to synthesize
a range of different fuels, e.g. methanol, dimethyl ether (DME) and
Fischer–Tropsch (FT) diesel. Methane and ethanol are increasingly
put forward as other possible products from gasification.
The first activities in Sweden related to gasification and transport fuels appeared in the mid-1970s. Then a strong network of
actors was built up around R&D on gasification of various raw
materials and the demonstration of methanol fuel blends used in
vehicles. The gasification process as well as the use of methanol
represented a downstream overlap for the different value chains.
In the 1980s and 1990s gasification received scarce attention in
the transport sector, and the overlap with the electricity system
became important. The group of actors around gasification could
maintain and increase their competence in the domain of electricity when the fuel domain was closed. At a later stage, when
the large scale gasification of biomass gained increasing attention in the transport sector as a mitigator of climate change,
these actors returned to promote not only methanol but also
other biofuels produced from synthesis gas.25 For these technologies, gasification together with the supply of biomass was
part of an upstream overlap. Physical artefacts from the electricity system could also be used; a gasification demonstration plant
developed for electricity production which had been taken out of
service was rebuilt to produce synthesis gas more suitable for fuel
synthesis.
Regarding fuels and electricity, this would appear to be competition for actors and artefacts. However, considering the dynamics
over time this is rather a case of alternating combinations of
commensalism and amensalism. The decline in expectations for
gasification for fuel production in the 1980s and for electricity production after 2000 was mainly due to exogenous forces. After the
respective declines the group of experts oriented itself towards
the other application. In both shifts, the ‘new’ application was not
responsible for the decline of the ‘old’, while the ‘new’ was dependent on the combined rise and fall of the ‘old’ (compare biogas and
natural gas above).
Regarding the interaction between fuels, looking at the period
as a whole, gasification represents a case of commensalism where
From the 1990s, there have been several examples of interactions stemming from the treatment of alternative fuels as a
group, sometimes including ‘clean vehicles’,26 i.e. vehicles that can
run on any alternative fuel or electricity, including hybrid electric, flexi-fuel and bi-fuel vehicles. These interactions highlight the
importance of concepts and point to overlaps regarding attitudes
and regulations.
In 1991, a political agreement on energy policy created a space
for entrepreneurial experimentation in the form of a national
demonstration programme for biofuels in heavy vehicles. It was
initially launched to investigate (and stimulate) ethanol, but an
increasing share of the funding was used for biogas. The common
biological origin represented an important physical and conceptual upstream overlap between biogas and ethanol that increased
the resources available for biogas (parasitism).27 The unexpected
emergence of biogas as an alternative transport fuel thus depended
on the perfect timing of the commensalism–amensalism relation to
natural gas (outlined above) and the parasitic relation to ethanol.
The national demonstration programme resulted in learning
and changed the legitimacy for renewable fuels as a group among a
large number of actors, including bus transit companies, municipal
administrations and vehicle manufacturers (symbiosis). In addition, consultants were hived off in the process and pushed for and
facilitated continued diffusion of renewable fuels and clean vehicles.
The introduction of cars that can run on alternative fuels was
made possible not only by the relative success of ethanol and
methane in the bus niche, but also by the parallel development of
electric vehicle demonstrations. Electric vehicle tests in the early
to mid 1990s in the larger Swedish cities were considered total
failures. However, they created organisations that could host large
vehicle tests and that were interested in trying new things. This
definitely paved the way for the new organisations clean vehicles
in Stockholm and its sister organisation in Göteborg. Thus, electric
vehicles were a bridge to other technologies in terms of actors and
organisational routines. These city organisations were large enough
to test many types of vehicles and fuels, and lump them all together
under the hat of ‘clean vehicles’.
Various actor groups and networks that had been formed around
ethanol and biogas, as well as the farmers’ organisation lobbied for
more favourable policies, such as general tax exemptions that were
to benefit several renewable fuels, mainly ethanol, biogas and RME
(rapeseed methyl ester). According to some, ethanol and methane
vehicles helped one another to stimulate the build-up of a market for ‘clean vehicles’. They were still small compared to petrol
and diesel and were often lumped together as alternative trans-
23
Gasification of biomass is still at a stage of pilot and demonstration projects.
Steam reforming of natural gas is another process used to produce synthesis gas.
25
One of those was dimethyl ether (DME), which had been rediscovered as a diesel
substitute at the end of the 1990s.
24
26
The Swedish term for ‘clean vehicles’ is ‘miljöfordon’, which can be translated
to ‘environmental vehicles’.
27
Within the programme, there were also a few projects and studies concerning
DME and methanol.
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port fuels. In a small market for clean vehicles, the growth of one
type would raise the general awareness and legitimacy and thus
benefit all other types. Thus, the conceptual overlap between all
alternative fuels was important among general car users. However, they did compete for markets and political attention at the
municipal level, as most cities focused on one alternative fuel. Furthermore, the vehicle manufacturers in Sweden looked for one best
alternative to include in their product portfolio.
The division into ethanol and gas municipalities also created
a kind of neutrality where the parallel development of ethanol
and methane (biogas and natural gas) in the 1990s was facilitated
by the existence of different geographical niches. After the turn
of the century, another kind of neutrality emerged. A vocabulary
was developed that grouped renewable fuels into three categories:
first, second and third generations of renewable fuels. This can be
interpreted as an attempt to tame the competition for legitimacy,
and create neutrality based on separation of ‘time-niches’. “They all
have a value, but on different time scales”. Another interpretation is
that the conceptual grouping is part of a competitive relationship.
It could for example be used to argue that the second generation
is better than the first while the third generation is too distant,
and that resources should therefore be focused on the second
generation.
4. Conclusions
The relationship between technologies has been a salient feature of the literature on technical change and terms like ‘dominant
design’ and ‘technology lock-in’ are part of the standard vocabulary. In the growing literature on sustainable innovation, transition
management and climate policy the question of how to escape
from dysfunctional locked-in systems while avoiding new deadends are gaining increased attention, and hence, the understanding
of technology interaction becomes critical. The aim of this paper
is to provide an account of the wide range of interaction modes
beyond competition that is prevalent in transition processes and
to develop a conceptual framework to facilitate more detailed and
nuanced descriptions of technology interaction.
The list of interaction modes between species borrowed from
community ecology provides a helpful starting point. In this paper
we make use of six basic forms of interaction ranging from pure
competition to pure symbiosis. In between, we find neutrality as
well as three forms of asymmetric relationships. As a refinement
compared to earlier work (Pistorius and Utterback, 1997), we make
a couple of distinctions to identify the locus of interaction more precisely. First, the interaction (or overlap) can be localised in the value
chain. Technologies can share upstream production processes or
downstream applications, giving rise to symbiotic as well as competitive relationships. Depending on the purpose of the study, a
technology may be defined by a more or less limited bundle of
value chains. Second, defining a technology as a socio-technical
system allows for an even more detailed description of interaction. There are material, organisational and conceptual dimensions
of socio-technical systems. The socio-technical systems can overlap in terms of physical artefacts, actors, technical knowledge,
expectations, attitudes, regulation and concepts. We further find
that a technological innovation systems framework could provide
a richer model for technology diffusion and technology interaction than for example simple Lotka–Volterra equations. Such a
model could in principle take into account multidimensional interaction between socio-technical systems as well as influence of
exogenous forces.
The case of alternative transport fuels in Sweden illustrates
a great variety of interaction modes, involving all dimensions of
socio-technical systems. The idea of competing designs in an ‘era
of ferment’ is clearly a too simple model of the real process. Also
the interaction modes symbiosis, neutrality, parasitism, commensalism and amensalism appear in different technology relations
from time to time. Two technologies can show different interaction modes in different dimensions at the same time. For example,
they could be symbiotic in terms of knowledge development while
at the same time compete for favourable regulation. It is not an easy
task to foresee the weighted outcome, and disclose the dominant
interaction mode. We further observe that the dominant interaction mode between two technologies tends to change over time.
These shifts are related not only to the growing maturity of the
socio-technical systems themselves but also to changing exogenous forces, such as the shift from concern for oil scarcity to air
pollution and from air pollution to climate change. The emergence
and growth of one socio-technical system can make use of different
elements developed in several parallel systems. Hence, technologies can act as bridging technologies in many ways and the exact
timing is sometimes critical. The example of how biogas emerged is
a case in point. We also find that the apparent mode of interaction
change with the resolution of the observation. An interaction that
can be classified as one mode when taking a longer time perspective
can be broken down into sequences of many different short-term
interactions in different dimensions. What we observe, instead of
pure competition and selection between distinct alternatives, is a
multidimensional transformation process where ‘symbiogenesis’,
the emergence of new species through the merger of ‘genetic material’ of existing species (Margulis and Sagan, 2002), is as prevalent
as selection.
In conclusion, compared to more simplistic single mode interaction models we have now a richer model of technology interaction
that opens for new empirical observations. Such observations can
inform decision making. Even if it will remain difficult to anticipate
which overlaps that will be decisive in specific situations, anticipating that spillovers will occur is a safe bet. Therefore, we believe that
the model itself can inform policy and strategy making at a general
level. The understanding that technologies commonly viewed as
competitors not only compete but also strengthen one another calls
for a policy that attempts to foster many technologies in parallel
and facilitates spillovers in multiple dimensions, rather than policy
that only focuses on creating arenas for competition. The complexity of technology development also calls for policy and strategy
that makes use of the specific dynamics in different socio-technical
systems and takes advantage of ongoing development in related
socio-technical systems. Policy, as well as firm strategy, needs to
be technologically informed.
From these initial findings we see three pathways for further
work. First, there is an empirical path. In this study we have chosen to use only one case to illustrate how the framework can
be used to describe technology interaction. If the framework is
applied systematically on many empirical cases it might be possible to find repeated patterns. From these, more specific policy
and strategy recommendations could be derived. Second, quantitative modelling of technological innovation systems that takes
interaction into account could be explored. Such an approach would
result in a system dynamics model that potentially could be used
to play around with parameters to illustrate possible pathways and
general dynamics. Third, based on new empirical work, a refined
theoretical framework could be developed. Following from our discussion on the arbitrariness of technology demarcation and the
multitude of overlaps between systems, one interpretation of this
text is that technologies separated by names are merely temporary outgrowths on a socio-technical web. Similarly, organisations
are outgrowths on the same web. Possibly, there are other units of
analysis that could be used to describe symbiogenesis and selection in new ways to shed light on the multi-dimensional process of
technical change and its governance.
B.A. Sandén, K.M. Hillman / Research Policy 40 (2011) 403–414
Acknowledgements
This paper has a long history. We would like to thank all the
people interviewed during the collection of the empirical material in 2004 and 2005. Earlier versions have been presented at
conferences and in a report. We thank all those who have commented on various versions and presentations. In particular, we
would like to thank Staffan Jacobsson for intense discussions and
encouragement, Duncan Kushnir and Rickard Arvidsson for valuable comments on late drafts, and two anonymous referees for
insightful and critical comments that substantially improved the
text. We are grateful to the Swedish Energy Agency, Göteborg
Energy Ltd. Research Foundation, CPM – The Competence Centre
for Environmental Assessment of Product and Material Systems,
Chalmers Environmental Initiative, Volvo Research and Educational
Foundations (VREF) and Chalmers Energy and Transport Areas of
Advance which all at different stages contributed with financial
support to the project.
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