Towards a modular and temporal understanding of system diffusion: Adoption models and socio-technical theories applied to Austrian biomass district-heating (1979-2013)

Published as: Geels, F.W. and Johnson, V., 2018, Towards a modular and temporal understanding of system diffusion: Adoption models and socio-technical theories applied to Austrian biomass district-heating (1979-2013), Energy Research and Social Science, 38, 138-153

AbstractThe diffusion of socio-technical systems is more complex than that of discrete products and cannot be understood solely with adoption models that have come to dominate the diffusion literature. The paper makes two contributions. First, it aims to broaden the conceptual repertoire by distinguishing two analytical families: adoption models and socio-technical theories of diffusion. We distinguish four adoption models (epidemic, rational choice, socio-psychological, increasing-returns-to-adoption) and three socio-technical models (system building, circulation/replication, societal embedding), and discuss their phenomenological characteristics and causal mechanisms. Second, the paper shows that system diffusion is a multi-dimensional process that is best understood with a modular approach that combines insights from different conceptual models. To demonstrate this second contribution and explore the temporal salience of different models, we apply them to the diffusion of Austrian biomass district heating (BMDH) systems (1979-2013). The paper ends with integrative suggestions by temporally positioning different diffusion models in a broader framework.

Keywords: diffusion theory, conceptual models, low-carbon systems, biomass district heating

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1. Introduction

The debate on socio-technical sustainability transitions (Smith et al., 2010; Markard et al., 2012) is entering a new phase, as some green niche-innovations (e.g. renewable electricity, electric cars) are beginning to diffuse in certain countries. It is therefore important to shift analytical attention from the emergence of green niche-innovations (which dominates the literature on strategic niche management or technological innovation systems) to their diffusion. Several papers have already investigated the drivers and barriers for the diffusion of eco-innovations and low-carbon technologies (Tsoutsos and Stamboulis, 2005; Montalvo and Kemp, 2008; Rao and Kishore, 2010; Dijk et al., 2013; Karakaya et al., 2014). But much less attention has been given to the diffusion of green or low-carbon systems such as tram systems, dedicated cycling infrastructures, integrated waste management or district heating systems. The paper therefore aims to investigate the under-studied topic of system diffusion.

Adoption models have come to dominate the diffusion literature, leading some scholars to simply assert: “Technological diffusion is the adoption of a technology by a population over time. It describes the aggregation of adoption decisions” (Kemp and Volpi, 2008: 14). Adoption models focus on decisions by dispersed adopters (individuals or collective actors), who are primarily conceptualised as buyers. Different adopters decide to purchase a technology at different times, depending on many different factors which “may well be over a hundred” (Kemp and Volpi, 2008: 14).

While adoption models have many strengths, they struggle to address complexities in the diffusion of systems such as changes in socio-institutional frameworks (Nakićenović, 1991: 182), interrelatedness, interdependence and cross-enhancing between multiple technology and organizational changes (Grübler, 1991). Freeman (1994) and Grübler (1991) identified the diffusion of systems as an under-studied topic, while Lissoni and Metcalfe (1994: 121) suggest that for energy and transport systems “adoption cannot be seen as a discrete event fully resumed by the purchase of a single item, but must be portrayed as a wider process”.

In their provocative paper, ‘What’s wrong with the diffusion of innovation theory’, Lyytinen and Damsgaard (2000) identified five analytical challenges that system diffusion poses for adoption models: a) systems are not discrete packages, but develop during the diffusion process, b) diffusion rates do not result from push or pull factors, but from co-evolutionary learning and interaction, c) consumer preferences and innovation characteristics are not given beforehand, but articulated during the diffusion process, d) instead of a single diffusion curve, there may be multiple diffusion processes for different application domains, e) time-scales are long (years, decades), which requires a processual approach.

Building on Lyytinen and Damsgaard (2000), we argue that the family of adoption models has two further limitations for understanding system diffusion. First, recent reviews (Meade and Islam, 2006; Rao and Kishore, 2010) suggest that the empirical evidence base of adoption models is relatively narrow, drawing mainly on studies of discrete products that offer better performance or new functionalities, especially in consumer durables (radios, televisions, CD players, video-recorders), white goods (refrigerators, washing machines, ovens) and telecommunication (tax, telephone, mobile phone, email, computers). So, there is limited empirical evidence of the relevance of adoption models for system diffusion. Second, adoption models privilege one kind of actor (adopters) and downplay the importance of other actors such as firms, policymakers, wider publics. This makes it difficult to accommodate the importance of co-evolutionary learning between users and firms which is important when technologies or systems develop during diffusion processes (Fleck, 1988; Windrum and Birchenhall, 1998; Dijk et al., 2013).

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Because adoption models have limitations with regard to system diffusion, the paper’s first contribution is to broaden the conceptual repertoire. Concretely, we distinguish and discuss two analytical families (adoption models and socio-technical models), which offer different conceptualizations of diffusion. Broadly, adoption models understand technological diffusion as the spread of innovations in a population of adopters. Socio-technical models, which draw on STS literatures, offer several complementary conceptualizations to adoption models that are pertinent for system diffusion: 1) diffusion as upscaling of systems from small to large, which is addressed in the large technical systems literature), 2) diffusion as spreading of innovations across space, which is addressed in conceptual models of ‘circulation and replication’, 3) diffusion as societal embedding of innovations, which highlight alignments with existing political, cultural or economic structures.1 The paper’s first aim is to describe the core characteristics and causal mechanisms of different models in each analytical family: four adoption models and three socio-technical models.

The second contribution is the proposition that system diffusion is best understood with a modular approach that combines insights from different analytical models. So, instead of an ‘either/or’ approach or ‘constant-cause explanations’ (Thelen, 2003), in which the same factor or causal mechanism operates constantly through space and time, we suggest that ‘modular explanations’ (Héretier, 2008) are better suited to understand system diffusion as deriving from interactions between multiple delimited causal mechanisms. This second contribution builds on Grübler (1991: 168), who suggested that the diffusion of technology systems “can hardly be reduced to single determining variables, but emerges out of a complex vector of influencing factors”. It is also a response to the call by MacVaugh and Schiavone (2010: 209) who emphasized the “need for academics to study technology adoption through a multi-disciplinary lens”.

The Multi-Level Perspective (Geels, 2004; 2011) already goes some way towards modular positioning of several diffusion theories in a wider framework, which accommodates both endogenous explanations (drivers of niche-development) and contextual influences (alignments with developments at ‘regime’ and ‘landscape’ levels).2 But Geels’s (2004) discussion does not include adoption models (except increasing-returns-to-adoption), societal embedding theories or socio-cognitive circulation and aggregation. While building on the 1 The concept of system diffusion may sound unfamiliar if one privileges adoption models. But if one starts with a broader phenomenological understanding of diffusion, there is no problem, and adoption models appear as one amongst others. The Cambridge Dictionary defines diffusion as “1) the spreading of something more widely, 2) the spreading through or into a surrounding substance by mixing with it (for liquids and gases)”. Our conceptual models resonate with this general definition. In adoption models, technologies spread from factories to purchasers via markets. In LTS-models, infrastructure systems spread through space via building and construction activities (starting locally and gradually combining in large integrated systems). In circulation and replication models, knowledge and system designs can start in one location and then spread to other places where they lead to the building of new systems (which remain separate as in tram systems and district heating systems). The societal embedding model resonates with the Cambridge Dictionary’s second definition, because it emphasizes alignments between new entities and pre-existing structures.

2 Geels (2006: 1005), for instance, proposes the following understanding of diffusion in the MLP. “On the one hand, there are internal drivers in the niche, e.g. price/performance improvements, increasing returns to adoption, virtuous cycles of niche-internal processes, and actors with vested interests that push for diffusion of the technology. On the other hand, breakthrough of technologies from the niche-level depends on external circumstances at the regime and landscape level that create ‘windows of opportunity’. There may be ongoing processes or tensions in the regime to which the new technology can link up.” This quote shows that the MLP refers to various diffusion theories, but mainly by mentioning isolated concepts. More can thus be done to elaborate the theoretical rationale.

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MLP, our paper thus develops a more systematic modular approach that accommodates a wider range of diffusion theories.

To substantiate the second contribution, we apply four adoption models and three socio-technical models to the diffusion of biomass district heating (BMDH) in Austria (1979-2013). We thus aim to show empirically that the diffusion of Austrian BMDH-systems is best understood with a modular approach that draws on multiple conceptual models. Using the case, we will also explore the temporal salience of different diffusion models over time and tentatively position them in the MLP.

The paper is structured as follows. Section 2 provides an analytical discussion of four adoption models and three socio-technical models. Section 3 addresses methodological issues and data sources. Section 4 investigates the case through different analytical lenses. Section 5 discusses further implications of the findings. Section 6 draws conclusions.

2. Analytical discussion of diffusion models

Section 2.1 briefly discusses four adoption models, which are relatively well-known in the literature. Sections 2.2, 2.3 and 2.4 use somewhat more space to discuss three socio-technical models. Section 2.5 makes an analytical comparison of the different models. Each conceptual model has nuances and complexities, which we are unable to discuss in depth because of space constraints. Instead, we focus on the main characteristics and causal mechanisms of the models.

2.1. Adoption modelsWe distinguish four adoption models that emphasize different mechanisms and factors.a) Epidemic models, pioneered by Bass (1969), focus on the spread of information through a population, often through face-to-face contacts, mediated by network structures. It is assumed that the new technology is ‘better’ than existing ones, so that people adopt when they hear about it. Alternative dissemination modes are media and opinion leaders.b) Rational choice models assume that costs and relative performance of innovations are the crucial drivers of market-mediated technological diffusion (Mansfield, 1961). Using micro-economic decision-making procedures (e.g. cost-benefit analysis), consumers assess the innovation in relation to their (fixed) preferences and available resources, and decide whether or not to buy it. Members of a population have different preferences and resources (based on income, occupation, education, class, lifestyle), which means they have different thresholds at which it becomes rational for them to adopt an innovation.c) Social-psychological frameworks focus on attitudes, beliefs, and norms of adopters. Examples are the technology acceptance model (Davis, 1989) and the theory of planned behaviour (Ajzen, 1991). Although Ajzen’s framework was developed as a general theory of reasoned and intentional behaviour, it has found wide application in debates on technology adoption (e.g. Yun and Lee, 2015). These frameworks suggest that adoption behaviour is shaped by three factors: 1) attitudes and beliefs towards the innovation, e.g. perceived usefulness and relative advantage, 2) normative beliefs, which may be shaped by social pressures from peer groups and social networks, and 3) control beliefs (e.g. perceived ease or difficulty of using the innovation, trust in the product or suppliers). Rogers (1996) also used psychological characteristics such as ‘innovativeness’ and ‘orientation towards novelty’ to distinguish various adopter categories (innovators, early adopters, early majority, late majority, laggards).3

3 Rogers (1996) also linked insights from rational choice and social psychology models to five characteristics of technologies that influence adoption decisions: 1) relative advantage compared to existing technologies, 2) compatibility with existing values and needs of potential adopters, 3)

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d) The increasing-returns-to-adoption (IRA) model proposes that the technology’s price/performance characteristics improve during the diffusion process as more people adopt. Arthur (1988) identified five sources of IRA: learning by using, network externalities, scale economies in production, informational increasing returns, and technological interrelatedness. Because these mechanisms focus on price/performance improvements, the IRA-model can be characterized as ‘quasi rational choice’ (Nelson et al., 2004).

Adoption models have been widely used in modelling and quantitative explanations of diffusion curves that assess the relative importance of various factors. Nevertheless, they have several limitations in relation to system diffusion. First, they privilege one kind of actor (adopters), sometimes in relation to other actors like peer groups or opinion leaders. But they pay less attention to other actors that may be important for the build-up and diffusion of systems, e.g. firms and supply-side actors, policymakers, wider publics and civil society. Second, many adoption models (except increasing-returns-to-adoption) conceptualize the diffusing entity as relatively static. This goes against repeated suggestions that the diffusing entity continues to develop during the diffusion process. Fleck (1988), for instance, coined the phrases ‘innofusion’ and ‘diffusation’ to highlight ongoing innovation during diffusion. Third, the diffusion environment is statically conceived as network structures through which information flows (in epidemic models), as market structures (made up of adopters with different socio-economic characteristics), or as ‘barriers to adoption’ (Eyre, 1997), which are factors that negatively influence adopter behaviour (e.g. limited motivation, high upfront investment costs, or limited access to capital). The three socio-technical models, discussed below, offer broader, processual views that may complement adoption models on these points.

2.2. Diffusion as system building and up-scalingLarge technical systems (LTS) theory focuses on infrastructure systems as a particular kind of technology with distinctive characteristics that differ from discrete technologies in terms of scale, complexity, materiality, asset durability, capital intensity, spatiality, and socio-political dimensions (Hughes, 1987; Markard, 2011). Focusing on integrated infrastructures (e.g. electricity systems, railroad networks, telephone systems, internet), LTS-scholars typically understand diffusion as a process of physically building systems and up-scaling them. Electricity systems, for instance, started in particular neighbourhoods, were then combined into city-wide systems, which subsequently linked into regional systems, and ultimately into national systems (Hughes, 1987). The diffusion process, which took many decades, thus started with small local systems that were subsequently ‘up-scaled’ and integrated into capital-intensive and spatially-extensive systems. LTS-theorists have advanced several concepts to understand such diffusion processes.

First, large technical systems are perceived as ‘seamless webs’ of heterogeneous components that include not just various technologies (e.g. turbo-generators, transformers, transmission lines), but also “organizations such as manufacturing firms, utilities companies, and investment banks (…). Legislative artifacts, such as regulatory laws, can also be part of technological systems” (Hughes, 1987: 51). So, diffusion is seen as a co-construction process in which systems arise from aligning technologies with social, political and cultural elements.

Second, Hughes (1987) conceptualized the emergence and diffusion of LTS as a lifecycle process progressing through seven stages: invention (initial scientific ideas), development (translation of ideas into technical designs), innovation (first commercial operation), technology transfer (movement to different places), technological style

complexity (ease or difficulty to understand and use an innovation), 4) trialability (can an innovation be tried before adoption), 5) observability (can potential adopters perceive benefits of adoption by others).

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(adaptations to different contexts result in varying system designs), growth and consolidation (system expansion, increasing scale, linking local systems into larger ones, standardization), momentum (lock-in, institutionalization). LTS-scholars emphasise the importance of ‘system builders’ in moulding and linking heterogeneous elements. While charismatic ‘inventor-entrepreneurs’ (e.g. Edison) are crucial system builders in early phases, ‘manager-entrepreneurs’ gain importance in later phases (Hughes, 1987) as cost-efficiency, administration, accounting, and finance become more salient. So, LTS-scholars emphasise agency and endogenous co-construction as drivers of up-scaling and diffusion. While Hughes highlighted heroic individuals, later LTS-scholars identified other system builders, e.g. regional agencies who performed system building ‘from below’ in Dutch water control systems (Kaijser, 2002), or technical experts, bureaucrats and agencies who negotiate standards and guide diffusion through planning (Van der Vleuten et al., 2007).

Third, while emergence and early diffusion depends on the dedicated ‘work’ from system builders, later diffusion becomes more self-sustaining as the system gains ‘momentum’, which refers to the “mass of technical and organizational components (…) direction or goals and (…) a rate of growth suggesting velocity” (Hughes, 1987: 76). Several endogenous drivers of momentum have been distinguished:a) The increase and alignment of organizations provides social-organizational drivers: “The large mass of a technological system arises especially from the organizations and people committed by various interests to the system. Manufacturing corporations, public and private utilities, industrial and government research laboratories, investment and banking houses, sections of technical and industrial societies, departments in educational institutions and regulatory bodies add greatly to the momentum of modern electric light and power systems” (Hughes, 1987: 76-77).b) Extended infrastructures and complex components provide technological drivers such as “acquired skill and knowledge, special purpose machines, enormous physical structures, and organizational bureaucracy” (Hughes, 1994: 108). The technical components of LTS are continuously improved during diffusion processes, as expansion creates internal problems (‘reverse salients’) that need addressing (Hughes, 1987).c) The accumulation of resources (financial, labour, human) provides economic drivers. To spread the high fixed costs of capital investments over more outputs, LTS-operators will search for high capacity utilization (Nightingale et al., 2003), which can lower unit costs and increase returns on investment. So, once LTS have been constructed, operators will search for multiple uses and markets that enable high ‘load factors’.d) Bureaucratic and institutional momentum arises from managerial complexities and the need for administration, procedures, measurement, and control (Nightingale et al., 2003). The involvement of policymakers and administrative agencies leads to the creation of specialized working groups, expert platforms and standardisation committees that appear impenetrable for outsiders (La Porte, 1994).

The emergence of momentum signals a reversal in LTS-dynamics from flexibility to ‘dynamic rigidity’ (Staudenmaier, 1989): the technology shifts from a possible social option, dependent on the ‘work’ of system builders, to a specified social ‘force’ with a mass of well-aligned elements.

The LTS-approach to diffusion has several characteristics. First, it emphasises endogenous dynamics in which the system and its environment are co-constructed. Second, it privileges supply-side system builders, although some social historians have started emphasizing the active roles of users in the appropriation and societal embedding of systems (e.g. Fischer, 1992; Nye, 1998). Third, it emphasizes relatively tangible activities and elements, e.g. artefacts, infrastructure, organizations, skills, knowledge, investments, resources. This also means that technical specificities may influence diffusion processes.

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2.3. Diffusion as a process of circulation and replication

The second socio-technical model, which draws on relational sociology, perceives diffusion as a process of replication and circulation between local practices. When technologies like a bush pump (De Laet and Mol, 2000), tram system (Latour, 1996) or reinforced concrete building (Geels and Deuten, 2006) work in one location (A), diffusion means getting it to work in another location (B). This requires both circulation (of knowledge, design rules, people, finance, paperwork) between location A and B, and replication of the socio-technical configuration in location B. This replication requires active adjustments of the configuration to fit the characteristics of location B. So, diffusion is conceptualised as a process of circulation across space between local practices. This conceptual model not only addresses spatial dimensions of diffusion4, but also acknowledges the diversity, variety, and specificity across space (Law and Mol, 2001), which means that a technology that works in location A does not necessarily also work in location B.

Circulation and replication do not happen automatically, but require dedicated activities. Latour (1987: 108) criticises adoption models for ignoring these activities and for portraying diffusion as process in which “black boxes effortlessly glide through space as a result of their own impetus”. Instead, he suggests that “the spread in space and time of black boxes is paid for by a fantastic increase in the number of elements to be tied together” (Latour, 1987: 108). Diffusion therefore requires the ‘work’ of thousands of people to create the context in which artefacts can function. So, instead of privileging a certain type of actor (e.g. heroic system builders as in the LTS-approach), this model highlights distributed agency and the many interactions, negotiations, persuasions, and struggles between many actors.

We distinguish two theories as specific instantiations of this conceptual model: actor-network theory (which has a flat ontology, based on socio-material networks), and a socio-cognitive theory (which has a layered ontology, based on interactions between local practices and a ‘cosmopolitan’ level with more abstracted and institutionalized forms of knowledge).

Actor-network theorists propose that the diffusion of artefacts across time and space requires the construction of wider networks of elements such as spare parts, maintenance networks, test apparatus, roles and responsibilities, infrastructure. So, technology and environment are co-constructed during diffusion. Callon et al. (1992) emphasized the circulation of a wide range of ‘intermediaries’, such as written documents (scientific articles, reports, patents), people and their skills, money (contracts, loans, purchases), and technical objects (prototypes, machines, materials, products). The circulation of these intermediaries gives material content to the linkages5 and provides a ‘network space’ (Law and Mol, 2001) within which technologies can function.6 Diffusion of technology thus requires the lengthening and convergence of such networks, so that elements and activities “can easily be tied to each other and there is also a high degree of coordination” (Callon et al., 1992: 223).

While the establishment of a global (or ‘cosmopolitan’) network of circulating intermediaries supports the spatial and temporal diffusion of technologies, the deployment of technologies in specific locations requires the combination of these elements into ‘configurations that work’ (Latour, 1996). The local working of technology is thus seen as an

4 Spatial dimensions also play a role in some adoption models (Hagerstrand, 1968), which investigate how and why certain geographical locations adopt an innovation earlier or later than others.5 An analogy is the relational view of the brain in which movements of neurons constitute neural networks (linked to activities such as memory and thinking).6 The diffusion of technology thus co-creates space. For instance, the network within which sixteenth century sailing ships could function included: “hulls, spars, sails, winds, oceans, sailors, navigators, stars, sextants, Ephemerides, guns, Arabs, spices and money” (Law and Mol, 2001: 611).

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achievement that requires adjustments to local circumstances, articulation of roles and responsibilities, development of repair and maintenance skills, etc. While actors can draw on the global network of elements, local deployment is site-specific and contingent (Hård, 1994).

Geels and Deuten (2006) conceptualised local-global dynamics in a simplified diffusion model with a focus on socio-cognitive dimensions. This model distinguishes three related sub-processes: dis-embedding, circulation, and re-embedding.* Dis-embedding (or ‘aggregation’) is the process of transforming characteristics of a local innovation into more abstracted, ‘cosmopolitan’ or institutionalized lessons. Typical aggregation activities include codification, standardization, model building, writing of handbooks, formulation of best practices. Aggregation may be done by intermediary actors such industry associations, standardization organizations, or consultants that engage in multiple local projects.* These general lessons can then circulate or ‘travel’ to other locations. This circulation can be facilitated by the creation of a dedicated knowledge infrastructure, e.g. specialized journals, newsletters, workshops, conferences (Geels and Deuten, 2006).* Application of general lessons in another location requires re-embedding and ‘contextualisation’, such as tailoring of design models to local conditions (e.g. urban structure, population density, socio-economic parameters), the build-up of support coalitions, arrangements of finance, and articulation of a positive discourse.

Using these distinctions, the simplified model distinguishes four phases in spatial diffusion, characterized by different actors, activities, and relations between local and global (or ‘cosmopolitan’) levels (Geels and Deuten, 2006). In the first (local) phase, new technologies emerge in one or more local practices that are relatively independent. Pioneers develop novelties in response to local problems, leading to technical variety.

Gradually, local actors become aware of each other, leading to interactions that give rise to a second (inter-local) phase, characterized by increasing circulation of documents, papers and people between local practices. This leads to some learning and aggregation of more general experiences.

The third (trans-local) phase is characterized by explicit comparisons between expanding local practices, aimed at formulating general lessons for the field as a whole. Intermediary actors and industry associations are created to undertake dedicated aggregation activities. Circulation of people and experiences is facilitated by the creation of a global knowledge infrastructure. Stabilization of generic knowledge facilitates ‘rational’ calculation, which may enable the involvement of large firms (which use more formal procedures to underpin investments decisions). It also enables broader market penetration, because mainstream customers can be reassured with reliable performance and safety standards.

The fourth (global) phase is characterized by a ‘reversal’, because institutionalization and standardization result in stable rules, standards, lessons, regulations, and guidelines that guide or prescribe local activities. Figure 1 schematically represents these four phases.

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L o c a lp r a c t ic e s

C o s m o p o lit anle v e l

L o ca l p h a se In ter-lo ca l p h a se Tr an s -lo ca l p h a se G lo b a l p h a seFigure 1: Phases in the development of shared technological knowledge (Geels and Deuten, 2006: 269)

The circulation/replication model has several general characteristics. First, it makes no strict distinction between innovation and diffusion, which are both seen as processes of creating and expanding linkages in time and space. Second, it includes a wide set of actors and many heterogeneous (often intangible) elements that need to be aligned. Third, it emphasizes endogenous mechanisms and the co-construction of technology and environment. Theories like ANT, which underlie this model, have, however, been criticized for an excessive micro-focus and Machiavellian assumptions (privileging the strategic manoeuvring of actors). More contextual elements of diffusion are emphasized in the next theoretical model.

2.4. Diffusion as a process of societal embeddingThe third socio-technical model focuses on the embedding of innovations in pre-existing environments (Figure 2), including the business environment (industry structures, markets), policy environment (formal rules, regulations), user environment (routines, beliefs, skills, practices), and wider society (cultural discourses, norms, social acceptance). This external fit may be difficult to foresee, as Rosenberg (1972: 14) notes: “(…) the prediction of how a given invention will fit into the social system, the uses to which it will be put, and the alterations it will generate, are all extraordinarily difficult intellectual exercises”.

Newproduct

Userenvironment

Policyenvironment

Widersociety

Businessenvironment

Figure 2: Relevant environments for new products and practices (adapted from Deuten et al., 1997: 134)

The process of societal embedding is conceptualized as an alignment process between the innovation and wider contexts: “Technology adoption is an active process, with elements of innovation in itself. (…) Behaviours, organization and society have to re-arrange themselves

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to adopt, and adapt to, the novelty. Both the technology and social context change in a process that can be seen as co-evolution” (Rip and Kemp, 1998: 389). Because of its processual orientation, the societal embedding model does not conceptualize external contexts as ‘barriers’, but as dynamic environments that can (partially) be shaped. We suggest that four processes are especially relevant for system diffusion into broader environments:a) Cultural appropriation refers to creation of positive discourses, narratives, and visions that enhance cultural legitimacy and societal acceptance of new technologies (Geels and Verhees, 2011). Positive cultural discourses can shape consumer preferences, political support, and access to financial resources. Cultural embedding may, however, also be a contested process with discursive struggles between proponents and opponents (Geels et al., 2007).b) Regulatory embedding entails the articulation of regulations and standards to address safety, reliability, and negative side-effects (Deuten et al., 1997). Regulatory embedding often entails power struggles struggles (over subsidies, strictness of regulations, responsibilities), which means that constituency building is crucial. Powerful political actors may endorse technologies, if these can be linked to broader policy goals.c) Embedding in the business environment refers to firm strategies, supply and distribution chains, repair facilities and technical infrastructures. If radical innovations are pioneered by new entrants, widespread diffusion may lead to the downfall of established firms (Christensen, 1997) or to the reorientation of incumbents.d) Embedding in user environments goes beyond purchase, involving the integration of new technologies into user practices. New technologies have to be transformed from unfamiliar and possibly threatening things to familiar objects embedded in the routines and practices of everyday life. The literature on domestication (Silverstone and Hirsch, 1992; Lie and Sørensen, 1996) distinguishes three relevant activities: cognitive work, which includes learning about the artefact and the development of new competencies; symbolic work, which refers to sense-making and the articulation of new interpretive categories; practical work, in which users adjust everyday routines and practical contexts. So, the domestication literature suggests that technology diffusion often involves the articulation of new competencies, beliefs and daily life routines.

2.5. Analytical comparisonTable 1 compares the adoption models and socio-technical models in terms of their basic conceptualization of diffusion processes, the dominant actors and their understanding of the environment into which technologies diffuse. This comparison shows substantial differences between the models.

Phenomenological characteristics of system diffusion

Locus of agency (dominant actors)

View of ‘environment’

1) Adoption Spread of innovation Independent adopters who Static, pre-existing

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through a population of adopters.

make (semi)rational purchase decisions, informed by attitudes, beliefs and norms.

environment mediates diffusion (via markets and information networks).

2) System building, upscaling

Expansion of a system from small to large (upscaling) through physical construction.

Supply-side system builders weave ‘seamless webs’ by combining many heterogeneous elements.

Environment is co-constructed in tandem with expanding system.

3) Circulation and replication

Geographical spread through circulation (of knowledge, people, designs) between local practices. System is replicated in different locations.

Distributed view on agency; many heterogeneous actors interact, negotiate, struggle, persuade each other.

The creation of new networks shapes environments and relations between local practices.

4) Societal embedding

New system is embedded in pre-existing environment via co-evolution process.

Multi-dimensional interactions between new entrants and incumbent actors (large firms, policymakers, politicians, mainstream consumers).

Environment consists of pre-existing structures and ongoing dynamics with which innovation needs to align.

Table 1: Main characteristics of adoption models and three socio-technical models of diffusion

The diffusion models also vary in their conceptualisations of the diffusing entity (static or developing) and diffusion environment (static or dynamic). Most adoption models have a relatively static view of the diffusing entity and diffusion environment. IRA-models are a partial exception, because they accommodate changes in the diffusing entities. The three socio-technical models offer more processual and dynamic understandings in which both the diffusing entity and the environment change during diffusion processes. In system building and circulation/replication models this takes the form of endogenous ‘co-construction’ (in which the innovation and its environment are shaped simultaneously). In the societal embedding model this takes the form of contextual ‘alignment’ and co-evolution (in which the innovation links up with existing contexts).

This discussion and Table 1 suggest that that adoption models and socio-technical models can usefully complement each other. The three socio-technical models highlight some aspects that receive less emphasis in user-oriented adoption models: 1) the system building model highlights the importance of supply-side dynamics in diffusion processes and the inseparability of innovation and diffusion, 2) the circulation/replication model addresses the spatial dimension of system diffusion and the ‘work’ that enables spatial flows, 3) the societal embedding model highlights that new entities do not diffuse into an ‘empty’ world, but encounter existing structures with which they need to align.

Vice versa, adoption models highlight aspects that are less developed in socio-technical models. While socio-technical models usefully draw attention to a broader range of actors and contexts, they often ‘bracket out’ the buyers of new technologies (except for the domestication literature). But adopters are, of course, crucial in determining the economic viability of systems: they do not adopt systems as such, but they do purchase the services that systems provide (e.g. heating, mobility service, power, waste disposal). Adoption models

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therefore usefully emphasize the importance of adopters and their decisions, motivations, and rational calculations (which are sometimes backgrounded in socio-technical models).

On a deeper level, the differences between adoption and socio-technical models can be related to ontological traditions. Adoption models stem from an ontological tradition that starts with individual adopters and then understands broader outcomes (such as diffusion curves) as the aggregate result of decentralized (purchase) decisions.7 Socio-technical models, in contrast, start with collective entities (such as seamless webs, systems, environments, structures, network circulations), which configure individual actors. The socio-technical models understand the diachronic dynamics of these collective entities in processual terms (system building, circulation, alignment, co-evolution). Actors are still important, but embedded in and shaped by these collective processes. In terms of the long-standing individualist/collectivist dichotomy, this means that adoption models and socio-technical models have different analytical foci that may usefully complement each other.

3. Methodology

System diffusion is a complex, multi-faceted process, which is best understood using insights from multiple diffusion models. To demonstrate this point, we conducted a longitudinal case study of the diffusion of Austrian biomass district heating systems, which emerged in the late 1970s, began diffusing by the mid-1980s and experienced accelerated growth by the mid-2000s (Figure 3).

Figure 3: Annual heat production from Austrian biomass district heating, in TWh (Statistik Austria, 2015)

We selected this case, because district heating is a complex technical system, using heat plants (combustors, boilers), heat distribution infrastructures, and heat exchangers in target buildings (houses, schools, hospitals). We focus on biomass district heating (BMDH), because this fits our interest in low-carbon systems. Strictly speaking, biomass is a fuel input 7 Social influences of other actors can be introduced via information transfer or peer pressure, but the orientation remains towards (interactions between) independent actors.

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that is not intrinsically linked to district heat systems. In Austria, however, BMDH-systems became dedicated configurations that developed their own diffusion trajectory, which was separate from large-scale district heating systems, which were constructed in the 1950s and 1960s for large, high-density cities, often using coal or oil as fuel inputs. BMDH-systems differ from traditional district heating: 1) they emerged in rural areas with relatively low population densities, 2) they were pioneered by new entrants (sawmill owners, agricultural cooperatives), 3) biomass feedstocks (waste wood, wood processing by-products) are dispersed and require complex, expensive logistics; many BMDH-systems are therefore relatively small-scale and proximate to forests, especially in eastern provinces (Figure 4). By 2015, Austria had approximately 3,100 BMDH-systems. The majority (about 2,500) are relatively small-scale (<1MWth).

Figure 4: Spatial distribution of biomass heat-only and BMDH-CHP plants (Cogeneration of Heat and Power) in Austria, in 2010 (Austrian Energy Agency, 2012: 13)

We selected Austria, because district heating diffused substantially (accounting for 22% of household space heating in 2012) and more recently than in Sweden, Finland, Denmark, and the Baltic countries. Since the mid-1980s, BMDH-diffusion accounted for much of district heating growth (Figure 5). In 2013, biomass and renewable waste accounted for 43% of fuel inputs in Austrian district heating, generating about 10.5 TWh heat.

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Figure 5: Annual heat production (in TWh) of Austrian district heating, 1990-2013 (data from Statistik Austria, 2015)

The research strategy is to provide several case descriptions that highlight the salient aspects of the four adoption models (section 5.1) and three socio-technical models (sections 5.2, 5.3 and 5.4). The aim is not to present new data or provide an exhaustive account of Austrian BMDH (see Madlener, Rakos and Seiwald for in-depth descriptions). Instead, the goal is to demonstrate the usefulness of describing the case through different lenses.

A methodological problem is that the conceptual models also represent different epistemologies. Particularly one instantiation of the third model, actor-network theory, is associated with complex epistemological and methodological debates.8 Because our paper has different aims, we do not use our limited space to enter into these epistemic debates. To circumvent this problem, our empirical analysis below draws on the second instantiation of the circulation/replication model (which focuses on knowledge flows between local practices and underpinning socio-cognitive activities).

Our descriptions use material from several data sources: academic publications, reports from European research projects (e.g. Roos, 1998; Kalt and Kranzl, 2009; JRC 2012 Background Report on EU-27 District Heating and Cooling Potentials, Barriers, Best Practice and Measures of Promotion) and from industry associations such as EuroHeat & Power (District Heating and Cooling country by country, 2013), Austrian Energy Agency (e.g. the 1998 Lessons learned from the introduction of biomass district heating in Austria; the 2009 Country study on political framework and availability of biomass; Austria 2012: Basic Data Bio-energy), and statistical data from the Austrian statistics agency (Statistik Austria), provincial governments and Chambers of Agriculture and Forestry. We used these 8 ANT-scholars often claim to ‘follow the actants’ (human and non-human), but rarely explicate how this is done and how they prioritize amongst the thousands of actants. ANT-scholars have also been criticized for not offering explanations (Collins and Yearly, 1992). In response, they argue that good descriptions are good explanations (Callon and Latour, 1992).

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data-sources to construct initial case descriptions. We also conducted eight semi-structured expert interviews with ten organisations involved in Austrian BMDH (Table 2) to develop deeper understandings for topics insufficiently covered by other sources.

Interview number

Organisation Date interviewed

1 Academic researcher 14/04/20152 National industry association 06/05/20153 National industry association 11/05/20154 National energy agency 06/05/20155 Provincial Chamber of Agriculture &

national industry association05/05/2015

6 Provincial Chamber of Agriculture; provincial energy agency & developer

11/05/2015

7 Provincial energy agency 08/05/20158 Provincial government 07/05/2015Table 2: Organizations interviewed (anonymized; two interviews were conducted with two organizations represented)

4. Analysis of Austrian biomass district heating through four theoretical lenses

This section analyses the diffusion of Austrian BMDH-systems, using the four analytical lenses provided by the different diffusion models. Before doing so, it is important to note that Austrian BMDH-systems exist as three different socio-technical configurations: 1) small to medium-scale village heating systems, 2) medium- and large-scale CHP systems (co-generation of heat and power, and 3) small-scale micro-nets. These configurations vary in scale, location, operators, and customers (Table 3).

Village heating systems Co-generation of heat and power (CHP) systems

Micro-nets

Scale and thermal capacity

Small- to medium-scale (400 kWth to 1 MWth, although most are smaller than 1 MWth) .

Medium-scale (up to 10 MWth) and large-scale (up to 65 MWth).

Small-scale (between 100- 400 kWth).

System builders, operators

Agricultural cooperatives, sawmills, Energy Service Companies (ESCos), some municipalities.

Energy utilities, ESCos, industrial firms (paper and pulp, sawmill).

ESCos, agricultural cooperatives, and some energy utilities.

Adopters (function and service)

Heat-only :- public buildings (schools, town halls, hospitals, swimming pools)- private homes and commercial buildings.

Focus on electricity production for the grid. Heat for:- internal industrial processes- semi-urban district heating.

Heat for limited number of closely situated buildings:- housing associations (rented houses, flats)- public buildings- hotels.

Table 3: Configurations of Austrian BMDH-systems (adapted from Seiwald, 2014a:52)

This implies that there is not one diffusing BMDH-system, as suggested by the cumulative diffusion graph in Figure 3, but several BMDH-configurations with different diffusion curves. This is visible in Figure 6, which shows national-scale data on annual heat production

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from BMDH-systems and CHP-BMDH systems, which are divided into ‘public’ and ‘private’ categories.9

Figure 6: Diffusion curves of different BMDH-systems in TWh (total annual heat output) (data from Statistik Austria, 2015)

Based on Figure 6, we tentatively distinguish three phases in the overall diffusion process. The first period (1979-1986) is characterized by the emergence of village heat-only systems, the first of which was installed in 1979 (Roos, 1998). These systems emerged without dedicated policies through the initiative of sawmill owners, monasteries, carpenters and agricultural cooperatives. The second period (1986-2002) is characterized by the growth of village-scale BMDH, because of subsidies and other policy support measures. The third period (2002-2013) is characterized by rapid growth of CHP-systems, because the Green Electricity Act (2002) established a feed-in-tariff for electricity generated from biomass CHP. This linkage of BMDH to the electricity regime also stimulated the involvement of incumbent actors like energy utilities. The third period also saw considerable growth in micro-grids, which were relatively easy and cost-effective to install, and often operated by ESCos and agricultural cooperatives.

4.1. Adoption modelsTable 3 indicates that a broad range of adopters were involved in different BMDH-configurations, both individuals (households, firms) and collective actors (municipalities), which for village heating systems led to two-level adoption decisions: 1) municipalities were

9 The ‘public’ and ‘private’ categories refer the production of heat for collective or private purposes. Public plants, which sell heat to households and other customers, include utility companies (e.g. BMDH-CHP) and farmer cooperatives or other enterprises (e.g. village BMDH). Private plants include micro-grids and auto-producers (pulp and paper, sawmill, chipboard firms), who produce heat for own purposes, but may sell surplus heat to final consumers (Seiwald, personal communication 03/03/2015).

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crucial initial adopters, as owners of public buildings and sometimes as project initiator (Madlener, 2007), 2) once the basic infrastructure system had been created, private homes and commercial buildings could also decide to connect to the scheme and purchase heating services. Different adopters had different motivations, as we discuss below. Generally, however, CHP-systems and micro-nets were primarily economic propositions with a strong role for cost arguments. Decisions for village heating systems had broader motivations, oriented towards supporting farmers, environmental protection, and local self-sufficiency. We briefly discus how the mechanisms, highlighted by the four adoption models, played out in the case.1) The spread of information via word-of-mouth communication (epidemic model) was important in the early period of village heating (1980s) when residents from different villages exchanged experiences and visited BMDH-schemes to see for themselves (Madlener, 2007). In later periods, centrally organized information dissemination (media campaigns, brochures, public events) by provincial energy agencies, chambers of agriculture, and the Biomass Association became more important (Seiwald, 2014a; Schilcher and Schmidl, 2009). Information dissemination was rarely sufficient, however, to trigger adoption of capital-intensive systems like BMDH. Information dissemination could also have negative effects when news of poorly performing BMDH-systems “spread rapidly and created a lot of suspicion” (Rakos, 1995: 884).2) Rational choice models have some relevance for early periods, because village heating offered residents some advantages compared to traditional stoves (which burned biomass, coal or oil), such as greater comfort and convenience. BMDH provided continuous heat without smoke emissions and removed the need for storage space and manual fuel handling (Rakos, 2005). Cost optimality and prices were not the dominant consideration, however, as many households were more sensitive to supply quality issues (Rakos, 1995). Early village-BMDH was slightly more expensive than traditional heating modes. And adoption decisions by rural municipalities often had political motivations, such as providing support for farmers and revitalization opportunities for the region (Rakos, 1995), based on a broader rationality than cost considerations.

For the later, post-2000 period, rational choice explanations gain more analytical traction, as cost-benefit calculations informed decisions by utilities and large-scale industrial users to switch to CHP-BMDH. These adoption decisions were stimulated by an attractive CHP feed-in-tariff (2002), which boosted CHP-BMDH diffusion. The adoption of BMDH micro-nets by hotels, housing associations, and public buildings was also informed by economic calculations and considerations of convenience (Seiwald, 2014a).3) Social-psychological models were important in early phases, because village adopters were motivated by values such as environmental protection, prestige and local self-sufficiency and the goal of supporting local agriculture (Danielsen et al., 2001). Peer pressure and the views of opinion makers were also important in small villages (Madlener, 2007). As climate change gained salience in the late 1990s, public image reasons started informing adoption choices. For instance, the early 2000 decision by Vienna’s energy utility to add a biomass plant to their existing district heating system was motivated by the desire for a green and innovative reputation (Madlener and Bachhiesl, 2007).4) BMDH-diffusion experienced some increasing returns to adoption such as network externalities (additional connections became easier once BMDH-systems were established), technological interrelatedness (e.g. better pipes, boilers, biomass supply and processing became available as BMDH-systems diffused), informational increasing returns (because of dedicated information dissemination activities) and economies of scale (in component production). But BMDH-diffusion was also hindered by two case-specific instances of diseconomies of scale (Seiwald, 2014a). First, biomass supply is dispersed, which means that

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costs of collection and transportation increase rapidly for large-scale BMDH-systems. Second, longer distribution networks experience greater heat losses which diminish the efficiency of large-scale systems. These particularities still “prevent the final breakthrough to competitiveness” (Seiwald, 2014b: 8) and continue the dependence of BMDH-systems on subsidies (section 5.4).

4.2. System buildingBMDH-systems did not scale up into an integrated national system, as LTS-theory predicts, but remained localized and geographically dispersed. One reason for this is the technical specificity that heat cannot be transported over long distances. Another technical reason, noted above, is the dispersed character of biomass supply. Nevertheless, BMDH-diffusion experienced some instances of up-scaling, for example increasing size of newly constructed BMDH-plants (especially for CHP-configurations after 2002). Additionally, pipes were sometimes added to existing BMDH-systems to connect more customers (Madlener, 2007).

Other aspects of the system building model offer more relevant insights. For instance, different ‘system builders’ acted as change agents in different periods. * Village BMDH-systems were initially pioneered by private sawmill owners, who utilised wood residues to provide heat to nearby houses (Roos, 1998). These early BMDH-systems emerged without policy support, often using boilers imported from Sweden (Rakos, 1995).* From the mid-1980s, farmers, who in Austria often own forests, also engaged in system building because BMDH-plants formed new markets for wood products. Many farmers teamed up in cooperatives to address high investment costs and pool resources such as time, skills, and fuel (Egger et al., 2010). Municipalities and energy utilities also sometimes acted as early BMDH-system builders (Figure 7).

Figure 7: Owners/developers of annually installed BMDH-plants in Austrian villages, 1982-1996 (Rakos, 1998: 2)

* In the early 2000s, energy utilities became important system builders for CHP-BMDH systems, which expanded rapidly (Figure 6) because of favourable green electricity policies (feed-in tariffs). Because these systems linked heat and electricity domains, energy utilities were well placed to develop them. These CHP-BMDH systems were also much larger (up to 65 MW thermal capacity), requiring greater technical and operational capabilities and deeper

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financial pockets. The National Forestry Agency ÖBf, which manages 15% of Austria's woodland, was another financially strong actor that entered CHP-BMDH construction (Seiwald, 2014a).* Throughout the 2000s, Energy Service Companies (ESCos) also acted as system builders. They pioneered new business models like energy service contracting,10 which became popular amongst hotels, housing associations, and public buildings and helped diffuse small heating networks (micro-nets) (Seiwald, 2014a). The entry of ESCos also stimulated further professionalization of the BMDH-field because they contributed technical, operational, and planning skills as well as expertise in subsidy applications. In 2003 an ‘umbrella’ developer (‘Bioenergie NÖ’) was created in Lower Austria to provide administrative and other support for the growing numbers of micro-grid BMDH-schemes (Interviewee 6).

The activities of successive system builders contributed to increasing momentum. Momentum was initially low, because system builders, installers and local plumbers lacked engineering skills and experience, leading to design mistakes in early BMDH-systems, such as technical over-dimensioning and connecting too many dispersed buildings, which lowered techno-economic performance (Madlener, 2007; Interviewees 4 and 5). During the 1980s, technical and operational problems of BMDH-plants gradually diminished (Figure 8) through learning-by-doing and dedicated circulation and aggregation activities (section 4.3).

Figure 8: Operational problems in early BMDH-plants, 1981-1992 (Rakos, 2005: 49)

BMDH-growth in the 1990s stimulated the emergence of dedicated supply chains for biomass, pellet boilers and pre-fabricated heat pipes (Kalt and Kranzl, 2009). This, in turn, stimulated specialization and innovation (Daryl and Harvey, 2006). “…these companies grew up and specialised – pumps, flexi-pipes–…and now we have a standardisation…Some know-how was imported, but we also had a pool of know-how, but not a proper fit to the issues of the new biomass systems – it was technically perfect for coal but not necessarily perfect for pellets so therefore they had to learn” (Interviewee 4). The build-up and specialisation of supply chains gave rise to green energy clusters in Styria and Upper Austria that started exporting. “…we have quite famous makers of biomass boilers… They are big players with

10 In energy service contracts customers pay a monthly rate for the provision of heat (and electricity), leaving the construction and operation of biomass plants, located on the client’s premises, to ESCos.

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quite a lot of employees, producing 40,000 biomass boilers. … The domestic market is quite small…. The rest is for the export market – France, Spain, Italy, Germany” (Interviewee 5). An important technical development was the creation of pre-fabricated heat pipes, which reduced infrastructure installation costs and increased system efficiencies (Daryl and Harvey, 2006; Interviewee 4). So, system-building activities in the 1990s shifted from establishing basic technical functionality towards improving efficiency and cost reduction (Madlener, 2007).

In the 2000s, BMDH-systems diversified technologically, with new system builders developing larger systems (biomass-CHP) and smaller systems (micro-nets). Attention also shifted towards business model innovation (ESCos), larger markets, and financial profitability, as predicted by the system building model. An unintended consequence of BMDH-expansion was that higher demand pushed up biomass prices, which increased by about 7% per year between 2005 and 2011 (Seiwald, 2014a). This caused financial problems for some CHP-plants that consequently reduced outputs after 2011 (Figure 6).

BMDH-systems also gained social-political momentum. Chambers of agriculture along with farmer cooperatives enrolled agribusinesses and the farmers’ bank Raiffeisen. These coalitions, in turn, enacted pressure on agricultural policymakers at federal and provincial levels (Hansen et al., 2013). The expansion of biomass-CHP systems after 2002 created new linkages to the electricity sector and new coalitions with powerful actors (e.g. utilities, National Forestry Agency, policymakers in energy and climate change). BMDH-diffusion thus gained momentum by an expanding ‘mass’ of actors.

4.3. Circulation/replication modelBMDH-systems spatially diffused through successive replications in different locations. Each village-level BMDH-system entailed both specific technical designs and the creation of support coalitions, which included “well-respected citizens” (Madlener, 2007) and other local opinion leaders such as innkeepers, teachers, priests, and influential employers (Rakos, 1995). Competing interests and views sometimes gave rise to local conflicts, however. Chimney cleaners and coal dealers often opposed BMDH-systems, because these threatened their business (Rakos, 1995; Interviewee 5). Neighbours of BMDH-plants often complained about noise and smoke. Rakos (2005) found that the “majority of villages” with early BMDH-systems (before 1993) experienced some conflicts. Some conflicts were particularly serious (Figure 9) and were “discussed for months at the village inns” (Danielsen et al., 2001: 17).

Figure 9: Resistance against early BMDH-projects in Austrian villages (Rakos, 2005: 54)

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The circulation of people, information, and ideas became the object of dedicated activities. The early 1980s were characterized by ‘spontaneous’ (word-of-mouth) circulation of information between neighbouring villages and circulation of people through site-visits (Madlener, 2007). Early schemes attracted the attention of farmers in surrounding areas, keen to replicate schemes (Interviewee 5 and 6). From the mid-1980s onwards, dedicated intermediary organisations were created to facilitate the circulation of information and knowledge. The newly created Austrian Biomass Association organized workshops, compared local experiences, and formulated generic lessons (Interviewees 3 and 5). Pioneering provinces also launched energy agencies that provided training and financial support for BMDH-developers, assisted with heat mapping exercises, and advised in BMDH-construction via ‘technology introduction managers’ (Egger et al., 2010; Rakos, 2003). These organisations also provided advice for private households and enabled communication between component suppliers and BMDH-operators (Rakos, 1995). Some of these intermediary organizations also engaged in externally-oriented activities such as lobbying policymakers for funding and support (Madlener, 2007).

BMDH-diffusion had a good fit with the four phases in the disembedding-circulation-reembedding model. The first (local) phase (until mid-1980s) saw local tinkering by sawmill owners, carpenters, monasteries, agricultural cooperatives and municipalities, without dedicated policy support. Plants operators did not share much information and were secretive about operational problems (Danielsen et al., 2001). There was limited feedback to technology suppliers and no institutionalised performance evaluation (Interviewee 4).

The second (inter-local) phase (mid-1980s to mid-1990s) saw enhanced mutual awareness, increasing interest from agricultural policymakers, and the creation of intermediary actors who facilitated circulation processes and feedback to equipment manufacturers (Rakos, 2003). “We started to give seminars to the operators – we asked what are the biggest problems in managing a heating plant and worked out answers…we had about 150 participants, because they all had the same problems” (Interviewee 5). Intermediary actors also began developing ‘global’ knowledge through benchmarking and quality control (Weiss, 2004), which contributed to performance improvements in the 1980s and early 1990s (Figure 8). The proliferation of provincial and national BMDH-support policies, however, resulted in a complex and “chaotic” policy environment (Reiche and Bechberger, 2004).

The third (trans-local) phase (mid-1990s to early 2000s) was characterized by dedicated aggregation, standardization and stabilization activities. The creation of the federal Environmental Promotion Fund (1995) streamlined the complex policy environment by harmonizing eligibility, application and payment procedures for BMDH capital grants (Madlener, 2007). The increasing involvement of independent consulting organisations, planning offices, project developers and ESCos (Seiwald, 2014b) also contributed to aggregation and stabilization, because these actors compared experiences and developed general lessons. In 2000, technical performance guidelines were introduced as de facto standards, with the aim of improving technical efficiency and economic viability (Madlener, 2007). Stabilised guidelines, standards and design rules were disseminated via seminars and training courses for BMDH-planners and operators (Rakos, 2005).

In the fourth (global) phase (early 2000s onwards), institutionalized rules began to guide local BMDH-activities. BMDH-operators had to meet certain performance standards to qualify for subsidies. Stabilized rules stimulated the involvement of financially powerful actors (energy utilities, National Forestry Agency), which “decide on their involvement in a project on the basis of sophisticated calculations” (Seiwald, 2014b: 10). Such calculations became more feasible once rules and standards had stabilized. The involvement of large

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professional actors was also stimulated by the Green Electricity Act (2002), which established a nation-wide CHP tariff to replace the various provincial schemes. So, by the mid-2000s, ‘reversal’ had occurred and stabilised rules guided local BMDH-deployment.

4.4. Societal embeddingThe ‘societal embedding’ model offers explanations that focus on alignments with external environments.

Regulatory embedding was crucial for BMDH-diffusion, which benefitted from policy support from the mid-1980s to the present. Policymakers supported BMDH, because it aligned with their pre-existing concerns and policy objectives. Agricultural policymakers had socio-economic concerns about rural communities, e.g. unemployment, declining industrial base, limited opportunities, depopulation (Madlener, 2007). BMDH-systems positively aligned with the policy goal of regional revitalization, offering opportunities for alternative incomes in agro-forestry. “…the rural areas saw themselves in a more and more bad condition …Lower Austria and Styria had the most difficult situation in their agriculture and they had a lot of forestry and they [Chambers of Agriculture and Forestry] started to think that their farmers should become energy farmers ... So this was initially started by two federal states…not by energy economics but by agriculture and forestry” (Interviewee 4). In the early 1990s, the federal Ministry of Agriculture complemented provincial support, which led to subsidies and capital grants that could amount to 60% of investment costs (Interviewee 6).

By the mid-1990s, financial support policies were harmonized by the €90 million/year Environmental Promotion Fund (1995), which increasingly framed BMDH as a potential response to climate change. The Austrian Energy Liberalisation Act (2000) enabled provinces to subsidise CHP-plants that served public district heating systems (IEA, 2014), which gave rise to policy variation between Austria’s nine provinces. The Green Electricity Act (2002) harmonized this variety by establishing a single CHP feed-in-tariff (Reiche and Bechberger, 2004). This policy instrument triggered rapid deployment of CHP-BMDH (Figure 6) and facilitated the entry of energy utilities which, following liberalization, were looking for new business opportunities.

BMDH-diffusion further accelerated because of additional policy support measures. In 2006, micro-grids were included in environmental funding schemes. The 2009 CHP-Law (KWK-Gesetz) provided subsidies for new or modernised CHP plants that offered CO2 reductions or energy savings. The 2009 Law for the Expansion of District Heating and Cooling Networks provided federal aid (up to €60 million/year) for the construction of new infrastructures that reduced CO2-emissons and enhanced energy efficiency. By 2011, about half of the Federal Environment Fund was spent on BMDH-systems (IEA, 2014).

BMDH increasingly aligned with broader national strategies such as the Biomass Action Plan (2006), which aimed for a 75% increase in biomass energy by 2010 and a doubling of renewable energy (including biomass) to 45% of total consumption by 2020. The ‘Austrian Energy Strategy 2020’, introduced in 2010, developed further visions of energy autarky and renewable generation, based on biomass, hydropower, wind, and solar photovoltaics.

BMDH-diffusion also benefitted from cultural embedding and the development of positive discourses. Early BMDH-discourses focused on contributions to local economic development and revitalization of rural areas (Madlener and Bachhiesl, 2007). From the mid-1990s, positive discourses also began to emphasize environmental benefits. The notion of ‘energy regions’ gained salience, because it combined economic goals and environmental benefits, which enabled new alliances between businesses, NGOs and policymakers (Späth and Rohracher, 2010). By the mid-2000s, BMDH gained further discursive legitimacy

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through inclusion in national biomass strategies that emphasized energy autarky, sustainability, green growth, and export opportunities for Austria’s world-leading biomass energy systems. The Federal Minister of Agriculture, Forestry, Environment & Water Management supported this vision in the preface to a Bio-Energy Report: “My goal is Austria’s energy autarky. We can produce in Austria, on balance, as much energy from domestic, renewable sources as we consume by ourselves. This makes us independent from expensive, fossil energy imports such as oil and gas and brings about a boom in the economy as well as positive employment effects with new green jobs” (Austrian Energy Agency, 2012, p. 2).

BMDH also positively aligned with dynamics in the business environment. Declining wood prices in the early 1980s provided farmers with motivations to look for new markets for wood products, which they found in BMDH-systems (Rakos, 1998). Energy utilities became interested in BMDH in the late 1990s, because it provided an option to meet renewable electricity targets and offered attractive business opportunities, especially after the 2002 CHP feed-in-tariff.

Natural gas offered the same comfort as BMDH at slightly lower prices (Rakos, 1995). BMDH-diffusion was therefore more difficult in areas covered by natural gas grids. In many rural areas, however, the gas regime was under-developed, which provided windows of opportunity for BMDH (Späth, and Rohracher, 2010). In potential expansion areas, the gas industry actively hindered BMDH-diffusion, leading to “significant conflicts between agricultural lobbies and gas industry” (Rakos, 1995: 879). Business opposition also came from chimney cleaners and coal dealers who often lost their jobs in areas where BMDH-systems diffused (Interviewee 5).

Embedding in user environments was not a prominent issue, because BMDH-systems did not require many adjustments in skills, routines and interpretive categories. In fact, the shift towards rural BMDH-systems alleviated households of the need to handle and store coal or biomass. Operational characteristics of modern BMDH-systems have similarities to gas boilers, e.g. heat controls (that allow consumers to influence temperatures) and heat meters. Consumers did, however, experience some difficulties in understanding the bills for heat services, particularly the addition of service charges (for recovery of fixed costs, maintenance, metering) besides consumption-based charges (Metschina, 2014): “District heating is a service. They [the customer] aren’t used to the idea of a service. When they have gas heating they pay for the chimney cleaner, they have to pay for the annual service of the boiler, but they don’t see it on the bill of heating.... When you get a district heating bill you have hot water you have cold water (sometimes) and it is a little bit different” (Interviewee 2).

5. DiscussionThe BMDH-case confirms several general characteristics of system diffusion noted by Lyytinen and Damsgaard (2000): it was a long-term process (decades rather than years), during which BMDH-systems developed and differentiated into three configurations (village-heating, BMDH-CHP, micro-grids), which diffused into different application domains, leading to multiple diffusion curves (Figure 6). BMDH-diffusion was less about push and pull factors and more about co-evolutionary learning and multi-dimensional co-construction and embedding processes. These processes entailed non-linearities and setbacks, e.g. technical mistakes, local contestations, over-dimensioning. Non-linearities also occurred in the rationales for policy support, which successively linked BMDH to rural problems, climate change, and green growth. More generally, a specificity of the case was that BMDH-diffusion benefitted from policy support throughout the entire process (except for the first period).

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5.1. Temporal salience of different diffusion modelsThe case analysis in section 4 also demonstrates that system diffusion is a complex multi-facetted process, which is best understood with a modular approach that mobilizes causal mechanisms from multiple diffusion models. To go beyond that observation, we discuss how the salience of different models varied over time in the case.

The analytical traction of different adoption models varied substantially over time. The epidemic model had some traction in the first period (local visits, word-of-mouth information exchange), but was not crucial, since information was a necessary but not sufficient criterion for BMDH-adoption decisions. Social-psychological models offered some analytical traction for village-BMDH adoption in early periods, when household adoption decisions were sometimes motivated by non-economic considerations (supporting farmers, local pride, peer pressure). But early adoption decisions also had a rational choice component, since village-BMDH offered benefits (convenience, comfort) compared to existing heating modes, although at somewhat higher prices. The rational choice model offered greater analytical traction for later periods, when cost-benefit calculations informed choices by adopters of BMDH-CHP and micro-nets. These calculations were possible, because rules and institutionalized had stabilised by then. The increasing-return-to-adoption model was mainly relevant in the third period when BMDH-diffusion experienced network externalities (it became easier for households to connect once a basic heat infrastructure was created), technological interrelatedness (increased availability of better pipes, boilers, biomass logistics), and economies of scale in component production (but also case-specific negative scale economies).

The emphasis in Large Technical System theory on tangible elements and system building was relevant in all periods. But the identity of system builders changed substantially over time: sawmill owners in first period, farmers from the second period onwards, energy utilities and National Forestry Agency (with regard to BMDH-CHP), engineering, consultancy firms and project developers in the third period (with regard to BMDH micro-grids). This variation, which is larger than LTS-theory predicts, relates to the diversification of BMDH-configurations and application environments. It also shows an interesting pattern, in which initiatives by new entrants were followed by professionalization and the involvement of larger, incumbent actors. The LTS-notion of ‘momentum’ was mainly relevant for the third period, when more organizations became involved, when supply chains and technical skills accumulated, and when federal policymakers (from electricity and climate domains) enhanced support. The case did not show up-scaling of local systems into larger ones, which LTS-theory predicts for later periods. Instead, BMDH-systems remained localized, because of two diseconomies of scale (related to biomass supply and long-distance heat distribution).

The four phases of the socio-cognitive model were clearly visible in the case: 1) local initiatives, with some ad-hoc circulation and visits, in the first period, 2) community emergence, creation of intermediary organizations (Austrian Biomass Association, provincial energy agencies), and early aggregation efforts (benchmarking) from the mid-1980s to the mid-1990s, 3) stabilization, standardization, and dissemination activities by intermediary actors from the mid-1990s to the early 2000s, and 4) codification, formalization, and institutionalization after the early 2000s. The case study also confirmed the notion that local infrastructure diffusion unfolds at two analytical levels simultaneously: the building (and use) of infrastructure systems in specific localities and the gradual development of an organizational field and (cognitive) institutions. However, the socio-cognitive model did not explain the accelerated diffusion in the last period. The model’s analytical focus basically stops once field-level knowledge institutions have stabilized. While this is useful to

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understand socio-cognitive lock-in, other theories are needed to explain the accelerated building of material infrastructures and market diffusion in the last period.

The analytical traction of theories of societal embedding also varied over time. Domestication theories had limited relevance, because embedding in user environments did not entail many changes. Regulatory embedding was highly important throughout, because policymakers supported BMDH in all periods (except the first). Policy support increased over time because of two mechanisms. First, BMDH-systems became successively linked to broader policy concerns (‘issue linkage’): initially, agricultural policymakers saw BMDH as an opportunity to alleviate rural decline; subsequently, BMDH became linked to climate change and renewable electricity policy; finally, BMDH became linked to exports, green jobs, and energy independence. The second mechanism was up-scaling within multi-level governance: support started in two provincial states (Lower Austria, Styria), then moved to the federal level, and in the last period even gained Ministerial support. Both mechanisms facilitated the enrolment of larger, more powerful policy actors. Cultural discourses, which legitimated increasing policy support, also addressed larger issues over successive periods. Early discourses aimed to purge negative perceptions of biomass and establish BMDH as modern, convenient system offering a lifeline for local farmers. Subsequent discourses addressed climate change (end of second period) and green growth and energy autarky (third period). Embedding in the business environment occurred via three processes with varying temporal salience. First, there was an endogenous process of carving out a new business space, which started with new entrants importing components from abroad (first period), was followed by the build-up of endogenous skills and supply chains (second period), culminating in successful export-oriented clusters and ‘energy regions’ (third period). The second process was competition between BMDH and existing heating systems, which disrupted local chimney cleaners and coal dealers in the second and third period. The third process was reorientation of incumbent actors (energy utilities, National Forestry Agency, engineering/consulting firms) who became involved in the third period once BMDH had emerged as an attractive opportunity.

5.2. Temporal positioning in the MLPBuilding on the above discussion, we further develop the modular approach by tentatively positioning the various diffusion models in the Multi-Level Perspective (Geels, 2004; 2011).11 As noted in the introduction, the MLP accommodates both endogenous explanations and contextual influences. Adoption models, Large Technical Systems (LTS) theory and the socio-cognitive models identify various causal mechanisms that drive niche-development, whereas the societal embedding model refers to interactions between niche and regime levels. The relative importance of these niche-regime interactions changed over time: in early phases, contextual ‘regime-to-niche’ influences were important (e.g. pre-existing political concerns about the country-side lead to policy support for BMDH-systems; wider discourses shape adopter attitudes); in later phases, ‘niche-to-regime’ influences gained importance, as BMDH-diffusion led to structural adjustments (because of stronger lobbying efforts and increasing involvement of incumbent actors).

The other two socio-technical models also make important distinctions between early and later phases, which are demarcated by increasing stabilization (of technical designs, rules and social networks). LTS-theory talks about a reversal from flexibility and fluidity in early

11 We thus distinguish between specific theories/models and broader frameworks. Positioning theories in the MLP does not mean that the latter is a ‘theory of everything’. Rather, the MLP is a middle-range framework (developed to understand socio-technical transitions), which can “benefit from including insights from auxiliary theories” (Geels, 2011:30). There are obviously limits to this. Incommensurable theories, for instance those with flat ontologies, should not be included in an MLP.

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phases (when system builders attempt to weave heterogeneous elements into seamless webs) to ‘dynamic rigidity’ and ‘momentum’ in later phases (represented by greater organizational involvement, investments, technical components, and bureaucracy). And the socio-cognitive model also talks about reversal between early phases (when circulation of experiences and aggregation of rules requires dedicated ‘work’) and later phases (when institutionalized rules become more robust and constraining).

We therefore divide the well-known MLP-figure (without the normal text) into two phases that represent ‘before’ and ‘after’ stabilisation (schematically indicated with the vertical dotted line). Drawing on the case analysis, we position the epidemic and socio-psychological adoption models in the fluid phase. We also position parts of LTS (system building), societal embedding (contextual influences) and socio-cognitive models (circulation, replication, aggregation) in that phase.

We further suggest that the stabilization of a dominant design, networks of dedicated organizations (including intermediary actors) and field-level institutions enable the shift to the later phase, where broad diffusion may occur. The BMDH-case suggests that rapid acceleration in the third period occurred because of the following mechanisms (which are linked to different models):- The stabilisation of rules and standards (per socio-cognitive model) enabled more reliable cost-benefit calculations (per rational choice model).- Price/performance improvements (per increasing-returns-to-adoption model) enhanced BMDH-attractiveness which stimulated market-mediated adoption (per rational choice model).- Growing markets and stable rules stimulated the involvement of financially powerful actors such as utilities and National Forestry Agency (per business embedding model), which introduced more resources (finance, capabilities, organisational skills) and increased socio-organizational momentum (per LTS-model).- The inclusion of BMDH into wider cultural discourses about green growth, jobs, energy independence, and export opportunities (per cultural embedding model) stimulated involvement by incumbent policymakers (including advocacy by a Minister) and legitimated further policy support (regulatory adjustments).

Based on these considerations, Figure 10 tentatively suggests how various diffusion models and theories can be positioned in the MLP. This positioning in a broader framework serves two purposes. First, it provides a map that captures the complexity of system diffusion and indicates how different theories address parts of it. Second, it suggests how the MLP’s understanding of diffusion can be improved by more systematically using causal mechanisms from multiple diffusion theories, including rational choice adoption models (which socio-technical scholars often tend to downplay).

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Figure 10: Positioning different diffusion theories in the multi-level perspective (adapted and expanded from Geels, 2004:40)

6. ConclusionThe paper has investigated an under-studied topic: the diffusion of socio-technical systems. Drawing on conceptual arguments and case study analysis, we conclude that understanding system diffusion requires a modular approach, which mobilizes insights from multiple diffusion theories. We discussed the phenomenological characteristics and causal mechanisms of four adoption models and three socio-technical models and empirically demonstrated how their combination contributed to a more comprehensive understanding of the diffusion of Austrian biomass district heating systems.

Drawing on different theoretical and ontological traditions, adoption models and socio-technical models can usefully complement each other in a modular approach. Socio-technical models highlight several aspects that receive less emphasis in adoption models: 1) the importance of supply-side dynamics in system building, 2) the socio-cognitive activities that make knowledge and experiences ‘flow’ between local practices, 3) the adjustments in wider structures and ‘regimes’ that can create more favourable conditions for diffusing entities. Vice versa, adoption models offer useful correctives to socio-technical models which emphasize processes (e.g. alignment, co-construction, circulation, interaction), but often terminate their analysis when entities have stabilized and economic (adoption) calculations

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become possible. The adoption model’s focus on buyers and their decisions and motivations can thus fruitfully complement socio-technical models.

We further conclude that that the temporal salience of the theories varied for the BMDH-case. The socio-cognitive model operated throughout, but did not explain rapid diffusion in the fourth period. LTS-theory also operated throughout, with (various kinds of) ‘system building’ activities more important in early periods, and ‘momentum’ in later periods. Epidemic and social-psychological adoption models were more salient in early periods, while rational choice and increasing-returns-to-adoption models offered greater analytical traction in later periods. Cultural and regulatory embedding increased in importance throughout the diffusion process, as BMDH-systems became discursively linked to broader goals and benefitted from more policy support. Embedding in the business environment gained salience in the third period, when incumbent firms became involved and successful export clusters formed.

The paper has several limitations, which offer directions for future research. First, the temporal relevance findings should be treated with caution, because they are based on a single case with particular specificities (limited transportability of heat, diseconomies of scale in biomass collection, Austria being a small, federalist country with cooperative traditions and high degrees of trust in policymakers). Future research could test our finding in other cases. A second limitation is that our interviewees (Table 2) did not include user representatives. This may have influenced our interpretation of qualitative changes in the domestication of BMDH in user environments (which we found to be limited). Future research could investigate this further using in-depth user interviews. Third, our plea for ‘modular explanations’ of system diffusion implies that we privilege two epistemological criteria: comprehensiveness and specificity/accuracy. As a trade-off, it performs less on the criterion of simplicity/parsimony (Weick, 1999), which can thus be seen as a limitation. A fourth limitation is that our discussion and investigation of space has remained somewhat limited, treating it as a reified category. To address how ‘space’ is co-constructed with diffusing technologies, future research could consider mobilizing further ideas from actor-network theory (which were only briefly discussed in section 2.3). Fifth, our exploratory research of system diffusion was based on a single qualitative case study. Future research could complement this in many ways, e.g. empirical studies of other systems (e.g. smart grids, tram systems, integrated transport systems), country comparative research designs, mixed-method designs, and accelerated system diffusion (which is especially relevant for addressing climate change).

AcknowledgementsThis work has been supported by the Centre on Innovation and Energy Demand, funded by the EPSRC/ESRC (grant number EP/K011790/1). We want to thank three reviewers, Mike Hodson, and Andy McMeekin for their thoughtful comments on an earlier paper.

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