Why space matters in technological innovation systems ... · Paper for presentation at the 2012 Regional ... 1 2 Why space matters in technological innovation 3 systems – The

Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 1

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Why space matters in technological innovation 2

systems – The global knowledge dynamics of 3

membrane bioreactor technology 4

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Christian Binza, Bernhard Trufferb and Lars Coenenc 6

7 a Corresponding author. Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 8 Dübendorf, Switzerland. [email protected]. Tel: +41 (0)44 823 5674 9 10 b Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland. 11 [email protected] 12 c Circle, University of Lund, Sweden

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DRAFT VERSION – DO NOT CITE WITHOUT PERMISSION BY THE AUTHORS 16

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Keywords: technological innovation system, networked space, globalization, social 19

network analysis, membrane bioreactor 20

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Abstract 22

Studies on technological innovation systems (TIS) set the spatial analytical boundary at a 23

national level and treat supranational spatial levels as a geographic homogeneous and freely 24

accessible global technological opportunity set. In this article we criticize this simplistic 25

conceptualization of space and propose to understand relevant actors and processes in TIS 26

from a relational, networked perspective. An analytical framework is developed which allows 27

analyzing the spatial setup of a TIS through its knowledge creating network. Based on social 28

network analysis and a co-publication dataset from membrane bioreactor technology, we 29

illustrate that the spatial configuration of the knowledge creation function varies greatly over 30

short periods of time. This finding suggests that the delimitation of TIS and related policy 31

advice should be much more responsive to the geographic setup of TIS. 32

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1. Introduction 34

Global environmental problems urgently ask for an implementation of radical innovations in 35

infrastructure sectors to secure long-term sustainability. Innovation studies have therefore 36

recently developed concepts and theories on how environmental technologies might emerge 37

and replace established and locked-in sectors (Kemp et al., 1998; Markard and Truffer, 38

2008b). The narrow spatial focus of this literature on industrialized countries and processes at 39

a national scale is however increasingly criticized (Berkhout et al., 2009; Coenen et al., 40

accepted). Continuing globalization and the fast rise of environmental industries in emerging 41

economies adds considerable complexity to the analysis of relevant innovation processes. It is 42

now argued that newly industrializing countries could leapfrog over polluting technologies 43

and directly become key actors in the development and diffusion of environmental 44

technologies (Angel and Rock, 2009; Berkhout et al., 2009; Binz et al., 2012). It thus becomes 45

increasingly important for innovation scholars and policy makers to know how innovative 46

activity in environmental technologies is organized globally and how innovation processes 47

work at and between increasingly interrelated spatial scales. 48

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The technological innovation system (TIS) concept in principle allows for such an 50

international analysis. Conceptualizing innovation systems without a territorial preconception 51

can be seen as a distinctive feature of the TIS concept. In contrast to other innovation system 52

approaches that set a priori spatial boundaries, e.g. at the national (Lundvall, 1988) or regional 53

(Cooke et al., 1997) scale, it proposes to analyze emerging environmental innovation as a 54

system of actors, networks and institutions that contribute to innovation processes in a 55

specific technological field (Carlsson and Stankiewicz, 1991). By taking a specific technology 56

as a starting point, TIS proponents argue that the approach cuts through both geographical and 57

sectoral dimensions (Hekkert et al., 2007). In contrast to this vantage point, one can observe 58

that contemporary TIS research nonetheless delineates its empirical studies on the basis of 59

territorial (often national) boundaries. Moreover, international interaction is often 60

conceptualized as actors interfering with a geographically neutral and ubiquitously accessible 61

"global technological opportunity set" (Carlsson et al., 2002). This simplistic 62

conceptualization of space has recently been criticized by economic geographers. Coenen et al. 63

(accepted) propose a more careful treatment of space in TIS studies which is pronouncedly 64

relational and multi-scalar, avoiding a priori set scalar boundaries and hierarchies. In the 65

meantime, TIS proponents have started to acknowledge the need to better understand the 66


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relationships between technological and other types of innovation systems (regional, national) 67

to avoid a reified, decontextualized treatment of technological innovation systems (Jacobsson 68

and Bergek, 2011). 69

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In applying a relational conceptualization of space the paper aims to derive and test an 71

analytical framework for TIS that is explicitly spatial but at the same time avoids a territorial 72

lock-in. Instead of delimiting a TIS space a priori we propose to start from a networked 73

perspective and to use the relational properties of the TIS actors to identify relevant spaces in 74

different evolution phases of a TIS. This frame will be exemplified by analyzing the spatial 75

setup and identifying hubs of dense interaction in the knowledge creation function of the 76

membrane bioreactor (MBR) TIS. Following the critique of Coenen et al. (accepted) we 77

propose to "follow the network to wherever it leads, instead of setting system boundaries in an 78

arbitrary and closed-off way". By doing so we aim at providing answers to the following 79

research questions: What are the implications of the currently a-spatial practice in identifying 80

the system boundaries in TIS studies? How can meaningful linkages and relationships among 81

spatially separated TISs be analyzed empirically? 82

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These questions will be addressed in the following structure: In the next section, we will first 84

discuss the problems of mono-scalar TIS research and show the potential benefits of a 85

relational geographic perspective in more detail. Sections 3 introduces social network analysis 86

as a tool for spatial TIS analysis and develops and operationalizes a set of respective 87

indicators. Section 4 and 5 introduce the used co-publication dataset and apply our framework 88

to knowledge creation in the TIS of MBR technology. Our results suggest that knowledge 89

creation in MBR technology evolved from a globalized nurturing, to a Europe-based 90

expansion and finally to a multi-scalar, Europe- and Asia based consolidation phase. We 91

conclude by discussing the implications of the observed high spatial-temporal dynamics in 92

our case study for future TIS studies, discuss policy implications and identify promising fields 93

of future research. 94

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2. Conceptualizing space in TIS 96

The TIS concept emerged in the early nineties from a quickly expanding innovation system 97

literature, which is rooted in evolutionary economics and industrial dynamics (Freeman, 1987; 98


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Lundvall, 1992; Nelson, 1993; Cooke et al., 1997; Malerba, 2002). Technological (innovation) 99

system are defined as a "network of agents interacting in a specific economic/industrial area 100

under a particular institutional infrastructure or set of infrastructures and involved in the 101

generation, diffusion, and utilization of technology” (Carlsson and Stankiewicz, 1991: 111). 102

TIS research conceptualizes the innovation process as an interactive, recursive process, 103

embedded in a set of co-evolving actors, networks and institutions. It pronouncedly rejects the 104

idea of linear innovation paths and emphasizes instead the importance of systemic interplay of 105

complementary actors, interactive and recursive improvement processes and the institutional 106

embeddedness of innovation (Carlsson and Stankiewicz, 1991; Bergek et al., 2008a). 107

2.1. The need for a space concept in TIS 108

The first contributions of TIS literature criticized the narrow spatial focus of the national 109

(Freeman, 1987) and regional (Cooke et al., 1997) innovation system concepts and proposed a 110

territorially unbound perspective focusing on technologies (Carlsson and Stankiewicz, 1991). 111

This basic idea was later developed and refined conceptually and empirically in different 112

studies on renewable energy technologies such as biogas, wind power, photovoltaics or fuel 113

cells (Bergek and Jacobsson, 2003; Jacobsson et al., 2004; Bergek et al., 2008b; Markard and 114

Truffer, 2008a; Markard et al., 2009). The TIS definition above clearly does not define or 115

favor any spatial boundary or a single relevant scale1 for a TIS. In its strict interpretation, a 116

TIS thus incorporates all agents and institutions which are relevant for innovation in a field of 117

technology. 118

This notwithstanding, TIS studies have from the outset drawn on spatial boundaries for their 119

empirical investigations, typically delineating ex-ante the national level of one or a few 120

comparable industrialized countries as the appropriate level of analysis (see for example 121

Jacobsson, 2008; Negro and Hekkert, 2008; Negro et al., 2008; Markard et al., 2009). As such, 122

the ambition not to conflate system boundaries with national borders is not entirely realized. It 123

even seems that the desire to avoid a spatial fix has to some extent backfired. When outlining 124

the framework Carlsson (1997) on the one hand assumes that “the technological opportunities 125

facing any economic agent are virtually unlimited; the pool of global possibilities has 126

practically no boundaries”. At the same time, empirical findings on four technological fields 127

1 Scale refers to a vertical differentiation in which relations between actors are hierarchically organized among, for

example, global, supra-national, national, regional, metropolitan and/or local levels (Swyngedouw, 1997; Brenner,

2011).


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in Sweden (sic) reveal the importance of both international linkages and geographic proximity 128

to understand the evolution of these fields. Interestingly, the results also reveal that the ability 129

to tap into an international system level is of crucial importance in all four sectors. 130

Internationalizing activities is identified as an efficient strategy to bypass domestic 131

bottlenecks in knowledge, resources or institutional settings. 132

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This notwithstanding, the emphasis on the national scale was later unintentionally further 134

amplified with a scheme of analysis by Bergek et al. (2008a), who proposed to focus TIS 135

studies on processes in a TIS2. In its empirical implementation, TIS literature thus so far 136

conceptualizes space as a national container which comprises all relevant processes of a 137

system. Even though it is often stated that TIS elements interfere with a “global technological 138

opportunity set” (Carlsson, 1997) or that TIS are “global by nature” (Bergek et al., 2008c), 139

there is little conceptual nor analytical guidance how to analyze TIS structure and functions 140

beyond national boundaries. Empirical applications of the concept thus contradict the original 141

conceptual notion of TIS scholars that TIS are in general global and that a respective analysis 142

“always needs to have a strong international component simply because a spatially limited 143

part of a global TIS can neither be understood, nor assessed, without a thorough 144

understanding of the global context” (Bergek et al., 2008a). A focus on national TIS was often 145

further justified by referring to a motive to inform policy-making (Jacobsson, 2011). Also 146

here, it is assumed that national institutions constitute the most relevant context for effective 147

policy intervention. As this contribution will show, giving primacy to the national scale 148

without satisfactory theoretical or empirical justification is problematic and might lead to 149

incomplete, or biased understanding of relevant processes and therefore also to flawed policy 150

advice. An explicit analysis of ‘TIS spaces’ is thus urgently needed to bring the concept closer 151

to its original conceptual ambitions and to improve policy advice. In this realm it is not only 152

important to analyze the location of TIS actors, but to scrutinize how actual innovation 153

processes (or functions) work in space. 154

2 They argue that seven basic processes or functions are important in TIS: Knowledge development, market

formation, legitimation, resource mobilization, influence on the direction of search, entrepreneurial

experimentation, development of external economies


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2.2. The spatiality of TIS functions 155

The functional approach to TIS studies was defined in two programmatic papers by Bergek 156

(2008a) and Hekkert (2007). In-depth reading of those sources reveals that the only function 157

which is explicitly assigned to a geographic level is “development of positive externalities”. 158

Bergek et al. (2008a: 418) argue by referring to Marshall (1920), that this function manifests 159

itself through dense localized interaction leading to knowledge spillovers and externalities 160

like pooled labor markets or specialized service providers. Economic geographers would 161

strongly agree with the importance of this geographic level for innovation processes (Porter, 162

1998). However, some of the relevant processes like tacit knowledge spillovers could 163

arguably also happen at other spatial scales. Examples comprise “temporal clusters” (2006) 164

forming in international trade fairs, symposia or conferences or activities in global 165

communities of practice (Wenger, 1998). 166

Knowledge development which is referred to in many TIS studies as “at the heart of 167

innovative activity” (Bergek et al., 2008a) is in turn assigned to a global level in Hekkert et 168

al.’s (2007) contribution: “the relevant knowledge base for most technologies originates from 169

various geographical areas all over the world” (Hekkert et al., 2007). At the same time, both 170

publications also hint that knowledge creation can have different qualities with corresponding 171

spatial outreach: Whereas scientific knowledge creation is an international process of codified 172

knowledge accumulation, Bergek et al. (2008a) argue that build-up of practical and tacit 173

knowledge is very likely restricted to dense interactive spaces and strongly localized 174

experimentation. As a consequence, one could assume to find very simple geographic patterns 175

in the networks of knowledge generation: either dense subnational clusters or global ties 176

connecting actors to the freely accessible global technological opportunity set. As we will 177

show in the empirical cases study, the actual geographic manifestation of this function is 178

much more complex than expected. 179

‘Entrepreneurial experimentation’, ‘market formation’, ‘guidance of the search’, ‘creation of 180

legitimacy’ and ‘resource mobilization’ have no spatial specifications in the two 181

programmatic papers, but are all discussed in the related case studies at a national to 182

subnational level. For market formation, indeed small localized initiatives at a regional level 183

can be of crucial importance (Dewald and Truffer, 2012). However, missing market formation 184

in a national TIS can also be compensated by tapping into foreign markets: Chinese PV 185

manufacturers as an example developed into a market leading position by nearly exclusively 186

tapping into downstream foreign markets in Europe and the US (de la Tour et al., 2011). 187


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Similarly, Binz et al. (Binz and Truffer, forthcoming) show that market formation in Asian 188

TIS can decisively depend on direct global-local linkages. 189

“Influence on the direction of search” is just another case where a limitation to a national 190

scale seems too simplistic: Both Bergek and Hekkert define it as a combination of long-term 191

policy goals and regulations by governments and the creation of vision and collective 192

expectations in an interactive process (Hekkert et al., 2007; Bergek et al., 2008a). Whereas 193

policy regulation can arguably be limited to a national scale (when ignoring the increasing 194

influence of supranational political institutions and treaties like the EU, UN or WTO), the 195

expectation dimension cannot. Even Bergek et al. (2008a) agree that expectations might be 196

influenced by growth occurring in TISs in other countries or by changes in the socio-technical 197

landscape (Geels, 2002), which lies outside the influence sphere even of specific national 198

agents. Cited examples of the German wind TIS (Bergek and Jacobsson, 2003) in fact show 199

that direction of the search in the German national TIS was strongly influenced by 200

developments in California and Denmark. Similar criticism applies to the ‘entrepreneurial 201

experimentation’, ‘resource mobilization’ and ‘creation of legitimacy’ functions which are 202

also introduced with an implicitly national focus. Also here, the actual spatial outreach of 203

processes is likely to transcend national borders more easily than could be assumed from 204

empirical TIS studies. 205

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In summary, tackling the spatial topology of a TIS is an important analytical problem yet to 207

solve. The interesting road forward is however not trying to assign functions to an appropriate 208

spatial level (see Coenen et al., accepted), but scrutinizing in more detail how processes in 209

different places and at different spatial levels interact and thereby define innovative outcomes 210

of the global TIS as a whole and of specific national subsystems. This implies that a 211

networked or relational perspective on TIS space might be a fruitful way forward. As 212

economic geography has been strongly involved in this kind of theorizing, the next section 213

scrutinizes on how to combine TIS with a networked perspective on space as derived from 214

economic geography. 215

2.3. Applying a networked perspective on TIS space 216

Economic geography has a long tradition in analyzing the influence of space and place on 217

innovation processes at various scales (Bunnell and Coe, 2001; Dicken and Malmberg, 2001; 218

Asheim and Coenen, 2006). On the one hand this literature strongly emphasizes the 219


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importance of territorial and institutional embedded learning and innovation processes in 220

densely localized “territorial innovation models” (TIM) such as regional innovation systems, 221

clusters, industrial districts or innovative milieus (Cooke et al., 1997; Porter, 1998; Boschma 222

and Lambooy, 2002; Camagni and Maillat, 2006). Dense and repeated cooperation, regular 223

face-to-face interaction, a build-up of specific supportive institutions and trustworthy 224

relationships are assumed to be central to the innovation process. This argument is not directly 225

transferable to TIS given its focus on very early innovation phases, where uncertainties are 226

high and a favorable institutional environment may still be under construction. Dense local 227

interaction might be important in this process, yet innovative actors and activities might also 228

spring up in different windows of locational opportunity at the same time (Boschma, 1997). 229

On the other hand, economic geography also increasingly argues that relevant structures and 230

agents for the innovation process are in most cases not restricted to a specific cluster or region, 231

but that linkages with actors and networks elsewhere and relationships with institutions at 232

different scales can be, at least, of equal importance (Bunnell and Coe, 2001; Amin, 2002; 233

Coe and Bunnell, 2003; Coenen et al., accepted). This shift in perspective is one of the 234

hallmarks in the so-called relational turn in economic geography (Bathelt and Gluckler, 2003; 235

Boggs and Rantisi, 2003). "Spaces and places are shaped not only by processes and relations 236

internal to their demarcated spatial boundaries but also through processes which function 237

through wider sets of relations and network connections" (Bathelt and Gluckler, 2003; Yeung, 238

2005). These processes and relations are fluent and constantly reorganizing at all scales 239

(2002). The basis of a relational approach is that individual actors have significant 240

relationships (through which they seek to access resources to achieve their individual goals) 241

that influence their behaviour simultaneously at a number of different scales. As a 242

consequence, relational economic geography has put a premium on networks as a conceptual 243

and methodological underpinning to analyze (uneven) spatial development (Glückler, 2007; 244

Ter Wal and Boschma, 2009). So opposed to fixed territorial surfaces and boundaries, 245

networks span space by establishing transversal and topological interlinkages among 246

geographically dispersed locations or organizational units (Whatmore and Thorne, 1997; 247

Leitner, 2004;(Brenner et al., 2011)). This does not mean that a networked perspective by 248

default presupposes distanced, global relations. Network spaces may very well be 249

concentrated in a particular locality through dense local relations. It is in fact often a 250

combination of dense local ties (‘local buzz’) and extended extra-regional connections 251

(‘global pipelines’) that create successful long-term innovativeness of TIMs {{10317 Bathelt, 252

Harald 2004}}. How this combination plays out in reality is however contingent on a number 253


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of factors such as the type of industry and its knowledge base (Asheim and Coenen, 2006) and 254

the institutional conditions of a region (Tödtling and Trippl, 2005). 255

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A networked perspective on space thus suggests that relying only on interaction at one scale 257

(e.g. the regional scale in regional innovation systems or, the national scale in technological 258

innovation systems) curtails the significance of processes, relations and institutions elsewhere 259

or treats these as merely exogenous factors. This reveals a key challenge for TIS research. 260

While indeed the development of a technology or technological fields does not stop short of 261

territorial borders, they do articulate particular forms of uneven spatial development. 262

To give a hypothetical example, a TIS for water recycling technology could in theory develop 263

in a region in Italy based on local environmental movements, coherent EU regulation and 264

competing Chinese and German companies which develop specific market solutions in 265

cooperation with a Spanish university and an industry association from neighboring regions in 266

Italy. Obviously, such a TIS would not be anchored at any specific territorial unit or delimited 267

to any particular regions or nation. It would rather be integrated in a network spanning 268

between all involved actors and connecting different territorial units. 269

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Similarly, TIS actors are usually not just defined by specific actors’ individual resource 271

endowments or capabilities, but by their networks with other complementary actors in the 272

same or related fields. Also important processes for system buildup like standard setting, 273

market formation or lobbying for regulative changes in TIS are assumed to be enacted 274

through specific relational networks (Musiolik and Markard, in press). However, despite the 275

prominent position of networks in TIS terminology (TIS are defined a set of networks of 276

actors and institutions), the actual use of the term has been restricted to a mostly qualitative 277

and metaphorical level (Kastelle and Steen, 2010). This is not very surprising as getting grasp 278

of the plentiful and very diverse types of networks that define a TIS is a very delicate task. 279

Networks in a TIS can be formal, informal, short-run, long-lasting, transdisciplinary, 280

exclusive, open or strategic and spanning between diverse actor types (Musiolik and Markard, 281

in press). Thus, also when speaking about networks, TIS research so far mostly referred to a 282

national boundary. As a result, it would probably be more correct to speak of a national TIS 283

rather than a TIS. International actors, linkages and foreign or supra-national institutions are 284

ultimately treated as ‘foreign’ and exogenous. That is, the systemicness of the TIS remains 285

largely grounded in the national context of study. Therefore the paradoxical outcome of TIS 286


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initial aspatial treatment of innovation system has resulted in a territorial fixation of TIS 287

dynamics. 288

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Shifting to a networked perspective on space is however not only referring to the structural 290

elements of a TIS, but also to the processes working in a system. As discussed before, a 291

networked perspective on space might ultimately also lead to a better understanding of what is 292

going on in the functional pattern of a TIS and of how processes in localized or very distant 293

places might influence the functioning of a national system in focus. We suggest that a main 294

reason why unpacking these spatial complexities of TIS was avoided for a long time was due 295

to problems of data availability in particular if the focus is extended beyond the borders of 296

small European countries. Clearly, international TIS studies cannot follow the protocol of TIS 297

assessment as proposed by Bergek et al. (2008c) and Hekkert (2007). In addition to the 298

conceptual challenges, a whole new set of methodologies and indicators is thus needed to 299

enable the proposed networked TIS analysis. 300

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In the following sections we will thus propose a first step in this direction by developing a set 302

of indicators based on social network analysis that allow for a networked, spatial analysis of 303

TIS functions. To reduce complexity, but still allow for an in-depth study of spatial dynamics, 304

the analysis in this paper will be limited to the ‘core’ function of TIS, knowledge creation. 305

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3. Measuring international network topologies of TIS 308

In the proposed perspective on TIS, the used methodological approach has to be able to 309

scrutinize actor network evolution in global space. Here, social network analysis (SNA) enters 310

the stage as a tool which already developed a set of heuristic routines for this purpose and 311

which is able to handle large global databases (Wasserman and Faust, 1994). In this paper, a 312

three step procedure of analysis is proposed. First, the network as a whole and its core actors 313

can be characterized with a set of descriptive SNA indicators. As these are standard measures, 314

they will be introduced directly in the results section, detailed descriptions can be found in 315

Appendix 2. Secondly, a nationalization index is developed, which gives a direct measure for 316

how much of the cooperation in knowledge creation of a TIS is actually happening inside 317

national borders. Thirdly, the concept of “hubs” will be introduced. Hubs are defined as 318

cohesive subgroups in a network which show particularly tight interaction and which integrate 319

different types of actors into a transdisciplinary knowledge creation process. As TIS 320

emphasizes the importance of such transdisciplinary cooperation, one can assume that hubs 321

indicate the core of innovative activities in a network, say the most vibrant and innovative 322

spaces in a TIS. Hubs can be strongly localized, develop in a regional cluster, form between 323

actors at a national level or even be international in their outreach. After introducing in more 324

detail the latter two indicators, they allow formulating a typology of spatial setups of 325

knowledge creation (or ultimately the TIS as a whole, if the other functions are equally 326

covered with similar approaches) and speculate on the spatial errors which nationally 327

delimited cases are likely to commit. 328

329

The nationalization index is defined as the average ratio of links among actors inside one 330

country versus the links with actors outside a country. Its definition is based on the E-I index 331

by Krackhardt and Stern (1988), but combined with the spatial attributes ‘national’ and 332

‘international’. As indicated before, this index gives a direct measure for the average 333

importance of nationally delimited interaction in knowledge creation. It can thus designate 334

how much information on external linkages is lost in nationally delimited case studies and 335

thus how strongly one might be including spatial biases in such cases. The following formula 336

can be used to analyze this ratio: 337

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340

1) 341

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Nc:= "nationalization index" of all actors in a specific country in the TIS, Li:= internal link, Le:= external link 343

344

2) 345

346

347

:= nationalization index of the TIS as a whole, c:= country 348

349

This index can be used to both assess the nationalization of one single country in the TIS 350

(equation 1) or also to get a cumulated measure for the overall nationalization of the TIS 351

(equation 2). If most actors are cooperating in a national or subnational context, this ratio will 352

show values above 0 and tend towards 1. If internal and external links are equally important, 353

the value will be close to zero. Consequently, in a more internationalized TIS, it will take on 354

negative values and tend towards -1. 355

356

Hubs will be assessed by identifying and characterizing network components and cohesive 357

subgroups. Whereas components depict completely isolated fractions of a network, cohesive 358

subgroups are defined in SNA terminology as a subset of a network that displays some 359

minimal level of cohesion. Subgroup identification will be based on geographically sensitive 360

n-clan analysis. An n-Clan is defined as a subgraph in which the (geodesic3) distance between 361

all actors is not greater than n (Wasserman and Faust, 1994). An n-value of 2 was chosen here, 362

meaning that every actor in each n-Clan is divided from all other actors by no more than one 363

intermediary. In addition, n-Clans allow for the definition of a minimum value of participants. 364

The minimum size of n-Clans was set at 9 actors4. 365

366

Summarizing, the proposed networked analysis of the knowledge creation function can be 367

operationalized based on scrutinizing the overall knowledge network structure, the grade of 368

3 Godesic distance: The shortest possible path between two connected actors in a social network.

4 This value was chosen based on the used co-publication data. A few publications in the dataset contain up to 8

authors. The threshold level was therefore set at nine actors in order to avoid that a single publications with many

involved actors would be interpreted as one distinct hub in the TIS

c

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the TIS in focus and shortly describe the sampling methods, portray the retrieved network 391

data and distinguish between three development phases of the TIS. 392

4.1. Membrane bioreactor technology 393

MBR technology is a booming wastewater treatment process with a high future development 394

potential (Gleick, 1998). It is based on conventional biological wastewater treatment, 395

combined with a micro-porous membrane which serves as a barrier for almost all germs and 396

solid matters larger than a water molecule. It produces a directly reusable, reliably clean 397

effluent and significantly diminishes the footprint of wastewater treatment facilities, which 398

makes it particularly attractive for application in industrial, municipal and innovative small-399

scale wastewater recycling plants (Fane and Fane, 2005). The basic process was first invented 400

in 1966 in a lab of Dorr-Oliver Inc. in the USA (Wang et al., 2008). In the following 20 years, 401

innovation in this field however remained dormant. Activities gained considerable momentum 402

only after a decisive innovation by a Japanese professor in 1989 (Judd and Judd, 2006) and 403

especially in the past ten years (Lesjean and Huisjes, 2008). The TIS surrounding the 404

technology is thus in a late formative phase. Commercial applications are booming recently 405

(Lesjean and Huisjes, 2008; Lesjean and Huisjes, 2008; Zheng et al., 2010), but the 406

technology is still subject to major uncertainties, not yet fully standardized and developed by 407

a heterogeneous set of small start-ups, large transnational companies and many research 408

institutes and universities worldwide. 409

MBR technology is embedded into a set of supportive institutional structures. First of all it is 410

one of the key topics in many events of the international water association (IWA), a very 411

powerful international organization which engages in pushing the diffusion of the MBR 412

concept. Secondly, it was supported by different national and EU-based R&D support 413

programs. The most well-known program is an initiative set up by the EU which allocated 16 414

million Euros to four large scale research initiatives across Europe. Also South Korea, Japan 415

and China supported basic research on the technology in strategic national support programs 416

(Wang et al., 2008). 417

Innovation in the field is strongly engineering-driven and tightly intertwined with scientific 418

research and government agencies or utilities that foster pilot plant applications. Dense 419

interaction between researchers, companies and government agencies is thus crucial for the 420

development and diffusion of the technology. The results of experimentation are widely 421

published in international academic journals or at relevant conferences. The most important 422


15

companies in the field run own research institutes and R&D departments and are regularly 423

involved in basic research, scientific publications and in organizing and supporting 424

conferences and trade fairs. Relatively abundant data about multidisciplinary and 425

transdisciplinary cooperation in the innovation process is thus included in the publication 426

record of this field of technology. As a consequence, co-publication data was chosen as a 427

source of network data. This data (ideally) stands for a long-term cooperation activity in a 428

complex process, necessitating intensive knowledge exchange among participating authors, 429

which by definition leads to a small piece of innovation. In the specific case of MBR 430

technology publication data additionally appears to be more informative than patent data. 431

MBR companies are rather reluctant to patent their innovations which hindered us from 432

complementing co-publication with patent data. As the co-publication dataset includes a 433

balanced set of actor types (only 53% of actors originate from universities, the rest includes 434

companies, research institutes, government agencies and associations, see Figure 2B), we 435

maintain that a sufficiently valid part of the innovation network structure of this function is 436

covered with this dataset. Despite well documented problems with such data (Katz and Martin, 437

1997), co-publication data thus forms a suitable proxy for the actor structure and relational 438

pattern of the knowledge creation function in the MBR TIS. 439

4.2. Sampling method 440

Data collection was based on an extensive query in the publication database of ISI web of 441

knowledge5. A dataset of 1'102 publications covering a timeframe from 1992-2009 was 442

obtained by searching for TS=(membrane bioreactor” AND water) and subsequently filtering 443

unrelated topic areas6. For the scope of this study, author affiliation information in each 444

publication was the key data source. Because of errors in ISI affiliation data (false or missing 445

affiliation information, several names for the same organization, typing errors, redundancies 446

etc.) the raw data was converted into a network matrix manually. During this procedure, about 447

5 Thomson Reuters Web of Knowledge, http://apps.isiknowledge.com/

6 Search string: TS=("membrane bioreactor" AND water). All subject areas filtered, except for: water resources; engineering,

chemical; environmental sciences; engineering, environmental; biotechnology & applied microbiology; polymer science;

chemistry, multidisciplinary; biochemistry & molecular biology; engineering, civil; energy & fuels; agricultural engineering;

food science & technology; microbiology; chemistry, analytical; chemistry, applied; materials science, textiles;

multidisciplinary sciences; ecology; engineering, aerospace; engineering, biomedical; engineering, electrical & electronic;

engineering, multidisciplinary; environmental studies

448

449

450

451

452

453

454

455

456

457

458 459

460

461

462

463

464

465

466

467

468

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17

co-publication process. Actor attributes (such as type of organization, location) were derived 469

from the affiliation information provided in the database of ISI web of knowledge. 470

471

Table 1: Actors in the MBR TIS 472 Actor type Number %

University 273 53.2

Company 109 21.2

Research Institute 84 16.4

Government Agency 39 7.6

Research Institute of Company 5 1.0

Association 2 0.4

Government Research Institute 1 0.2

Total 513 100

473

Table 1 reveals that the dataset contains a mixed set of actors dominated by universities, but 474

also containing a fair number of non-academic commercial and public actors. Actors from 46 475

different countries are involved in the network. Publications on MBR technology thus 476

developed into a complex spatial structure relative quickly. The location of TIS actors 477

furthermore corresponds with the origin of publications in Figure 2B. Seen from this 478

aggregated perspective, knowledge creation is forming around three key blocks of innovative 479

activity in Europe, Asia and – to a lesser extent – North America (see Appendix 1). 480

Distinguishing development phases of the TIS 481

In order to be able to discuss temporal dynamics, we will divide the evolution of the MBR 482

TIS into three development phases which will then be separately analyzed. The time period 483

from 1993 to 2001 shows only limited publication activity. In order to get enough data for a 484

meaningful SNA analysis, the aggregated network pattern between 1993 and 2001 is thus 485

aggregated and taken as the starting point for a more detailed analysis between 2001 and 2009. 486

487

From 2001 to 2009, the network oscillates from a dense to a dispersed and back to a more 488

dense cooperation pattern. Initially, relatively short paths span between most actors. After 489

2003, the network starts expanding quickly, many new actors enter the field and mean 490

distance between actors grows longer (see Table 2). This trend reverses only after 2007, when 491

average connecting paths get shorter again. In parallel, the network oscillates from a 492

centralized to a more equally connected and back to a more centralized setup. 493

494


18

Table 2: Three phases of network evolution (non-cummulative except for ‘number of actors’) 495

Number of actors Number of Links Average Degree Mean distance Inclusiveness

93-03 104 201 1.481 5.6 0.673

03-07 291 553 1.608 11.1 0.674

07-10 513 945 1.626 12.2 0.684

Explanations of the indicators used in this table are summarized in Appendix 2 496

497

This co-variation of network measures allows us to distinguish three development phases: 498

First, a nurturing phase between 1993 and 2003 in which research activity is growing and first 499

cooperation forms around a few central actors in a relatively dense and centralized network. 500

Subsequently a rapid expansion phase (2003-2007) in which knowledge development grows 501

exponentially and many new actors enter the TIS in an increasingly broad and decentralized 502

network pattern. Third and finally a consolidation phase (2007-2009) in which the growth in 503

research and the entrance of new actors to the TIS slows down and the network gets more 504

complex and centralized (and therefore unequally connected) again. 505

506

Sumamrizing, this reconstruction of the network structure without any spatial connotation 507

shows an international fast growing cooperation structure, which corresponds to what could 508

be expected from the notion of a “global technological opportunity set”. However, Figure 2B 509

already indicates that activities are dispersed unequally in space. Relating the publication data 510

to the development history of MBR technology as reported in secondary sources, we see that 511

the first 20 years of very low innovative activity are not covered by publication records. 512

Publication data only starts after the decisive invention in Japan and therefore corresponds 513

with the time when systemic structures in the sense of an MBR TIS were first built up in 514

different places of the world. The first phase in our data then nicely corresponds with the 515

nurturing phase in the TIS as whole when activities grow and first commercial experiments 516

with the technology are realized (Lesjean and Huisjes, 2008; Wang et al., 2008). The 517

subsequent phase matches the expansive phase in the TIS when commercial applications start 518

booming and many new actors enter the TIS also drawn by large research and development 519

programs in different parts of the world. The last phase finally corresponds with a 520

consolidation in the MBR industry where dominant designs get increasingly visible and some 521

failing companies leave the field or others get bought by large transnational companies. 522

523

524

525

526

527

528

529

530

531

532

533

534 535

536

537

538

539

540

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20

and developed into a more differentiated multi-scalar spatial setup only in the later expansion 544

and consolidation phases. 545

546

Also nationalization index values for single countries show strong temporal variation. In the 547

first years of analysis this dynamism can still partly be attributed to low data volumes and 548

corresponding high impact of a few publications on the overall index value. Nevertheless, US 549

actors show that the spatial outreach of cooperation can change greatly and repeatedly also in 550

later phases of TIS development. Chinese actors’ index values are exclusively international in 551

the first four years and then suddenly switch to nationalized index values in 2005. This shift 552

happens in a time when many new Chinese actors enter the TIS and MBR technology gets 553

increasingly integrated into heavily funded strategic national R&D programmes (Wang et al., 554

2008; Zheng et al., 2010). This pattern thus reveals a catching-up process in which the 555

Chinese TIS actors first tapped into global knowledge sources before domestic research 556

capabilities were built up in the wake of the formulation of basic research programs and 557

industrial policies. South Korean actors, finally exemplify a publication strategy which is in 558

most time periods focused on cooperation in national boundaries. 559

560

Hub analysis 561

The so far highly aggregated view on the network can now be complemented with a hub 562

analysis. As mentioned in Section 3, network components and n-Clans are used to identify 563

hubs of innovative activity. 564

Table 3: Relevant scales of n-clans in the network of MBR technology 565

Type of n-clan Nurturing 1993-2003 Expansion 2003-2007 Consolidation 2007-2009

National n-clan 0 0 9

Continental n-clan 0 6 45

Global n-clan 3 0 3

Source: ISI web of knowledge. National n-clans: n-clans more than ½ of the actors from one specific country; Continental: n-clans with more than ½ of the actors from one continent; Global: n-clans containing actors from at least three different continents, without a dominant region

566

The results of the n-Clan analysis in Table 3 also reflect the previous insights on the spatiality 567

of knowledge creation. In the nurturing phase, the three identified n-Clans are exclusively 568

global. The second phase is mainly dominated by n-Clans at a continental level. In the last 569

phase, the clustering pattern gets more differentiated. Continental n-clans are the dominant 570


21

level of interaction, but global and national hubs play a role, too. The affiliation of actors to n-571

Clans will be discussed in more detail in the next sections together with a more detailed 572

discussion of the observed spatial pattern at each development stage. 573

5.1. 1993-2003: Globalized TIS dominated by French 574

transnational water companies 575

The nurturing phase is a relatively dense global network that spans around a few central actors. 576

Network measures in Table 2, the nationalization index and n-clan analysis affirm that the 577

global scale is the most relevant in this development phase (also see network visualization in 578

Appendix 3). The network is centered on CIRSEE (Centre International de Recherche Sur 579

l'Eau et l'Environnement), a French company research institute, and its subsidiaries in 580

Malaysia (ASTRAN Malaysia) and Australia (ASTRAN Sydney). This setup can be 581

explained with a global research network that was setup by a large French transnational water 582

company in this early phase of TIS development. Asian and European actors are strongly 583

linked to this network from the outset: The Chinese Qinghua University or the institute of the 584

Asian Technology and Research Network in Malaysia occupy a relatively central position in 585

the network already in 2003 (Binz and Truffer, 2012). 586

587

Figure 4 further illustrates that the network disintegrates into two main components and three 588

strongly overlapping n-clans (inside the red circle) in the nurturing phase. These n-Clans are 589

forming around French company research institutes and universities. This group of actors thus 590

forms the core hub of the TIS in this phase. It comprises actors from France, Malaysia, 591

Singapore, Macao, China, Australia, Canada and the USA. Knowledge creation in the MBR 592

TIS in the nurturing phase was thus strongly based on a globalized hub of actors spanning 593

three continents around a network developed by French transnational corporations. Besides 594

this core of the TIS, a second hub is forming around another isolated component comprising 595

Cranfield University, the National University of Seoul and connected institutes in South 596

Korea, the USA and Malaysia. However, cooperation in this component is less tight than in 597

the main component around CIRSEE and the involved actors are less diverse. 598

599

600

601

602

603

604 605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

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24

until 2003. Between 2003 and 2007, they multiply their activity and especially German actors 641

move to the central position, taking it over from the French company research network. This 642

shows that the spatial configuration of knowledge creation can change quite dramatically in a 643

short period of time, with possibly relevant implications also on other functions of the TIS. 644

Both the central scale and set of actors considerably differ between the first and second phase, 645

meaning that the spatial focus of TIS studies would have to be adapted accordingly. Secondly, 646

the core knowledge creating hub of the TIS also changed qualitatively from a company-647

dominated mode to a more transdisciplinary mode, connecting 7 universities, 5 companies, 5 648

research institutes, 3 government organizations and one company research institute. Finally, 649

the spatial setup of the TIS switched from a globalized to a slightly more multi-scalar setup. A 650

new important scale of interaction is being constructed by support programs of the European 651

Union, whereas European actors are at the same time still involved in global relations which 652

were established in the previous phase. 653

5.3. 2007-2009: Multi-scalar TIS connecting European and 654

Asian actors 655

In the last development phase interaction in the network gets very strong and a consolidation 656

happens with existing actors intensifying their ties in the main network component and fewer 657

new actors entering the field. Even though the number of very small components still 658

increases, most actors are now included in a very extensive central network component, 659

connecting 340 actors. 660

661

The results from section 4.3 describe the consolidation phase as a multi-scalar setup with 57 662

n-clans. The high number of (frequently overlapping) n-Clans in Table 4 makes interpretation 663

of the data more challenging. 664

665

Table 4: n-Clans 2007-2009 666

number of n-clans dominant region 35 EU 7 Asia 4 Germany 4 South Korea 3 EU and Asia 2 Global 1 China 1 Middle East

667

668

669

670

671

672

673

674

675

676

677

678

679

680

681

682

683

684

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26

the national scale in Germany contains some relevant innovative activity, but ultimately 686

German actors like the TU Berlin generate knowledge more strongly in an international 687

network. 688

689

In addition to this central hub, the n-Clan analysis now also reveals an extensive set of small 690

hubs at other scales. These clans show some internal cohesion, but at the same time all 691

contain actors which are connected to the central hub around the Technical University Berlin. 692

Only very few nearly isolated hubs exist. Most striking here is a small cohesive subgroup 693

formed by research institutes and companies from Israel, Spain, Turkey, China and Japan. 694

45 out of the 57 n-Clans contain cooperation at a “continental” scale, connecting actors from 695

closely neighboring countries. Most of them form in the European Union, but also in Asia or 696

the Middle East. 2 n-clans are still fully globalized, comprising equal amounts of actors from 697

at least 4 continents. In addition, 9 clans are now identifiable at a national scale. This 698

distribution on the one hand reflects the influence of the research programs of the European 699

Union on the spatial setup of knowledge creation. On the other hand it also shows the 700

increasing importance of Asian R&D support in the field, especially in South Korea and 701

China (Zheng et al., 2010). Interestingly no n-clan could be identified that is dominated by 702

North American actors. This finding interestingly corresponds with empirical studies which 703

claim that North American actors are indeed partly decoupled from mainstream research 704

activities and following a distinct technology development path focussing on side-stream 705

MBR systems (Wang et al., 2008). Summarizing, the last phase of development again differs 706

considerably from the previous phases. International, continental and national scales all 707

contain relevant hubs of interaction and the center of the core hub still stays in Europe, but 708

increasingly shifts towards Asia. This pattern thus perfectly exemplifies a multi-scalar TIS 709

setup. 710

5.4. Discussion 711

In the light of existing innovation system studies, the presented findings are remarkable for 712

two main reasons: First of all, international (or better multi-scalar) interaction is surprisingly 713

important in most evolution phases of the MBR TIS. This finding sustains our core argument 714

that innovation system research should explore multi-scalar processes and especially the 715

international scale more prominently. Secondly, our findings contradicts a linear spatial 716

diffusion model which would expect a TIS to develop in a specific place and then to trickle 717


27

down and expand to other spatial levels (local-regional-national-international) in consecutive 718

steps. Data on the MBR TIS suggest the exact opposite with knowledge creation forming 719

around several central actors around the world in the first stage. Only in later phases does the 720

knowledge creation network get geographically more differentiated and also reportedly spans 721

to other spatial scales, including first a continental and then a national scale. This pattern 722

matches better with ideas of Oinas (2000) and Rutten (2003), who supposed that the relevant 723

scale changes with phases of the innovation process; basic research being global, engineering 724

being territorially embedded and subsequent production getting global again. 725

726

As discussed before, the used dataset limited the empirical assessment to the knowledge 727

creation function. A full TIS however also comprises six other functions and a plethora of 728

interaction and exchange processes, which all feed back to system evolution. Despite clear 729

limitations of our data, it is nevertheless very likely giving hints on the relevant scales of 730

interaction in other functions at different points in time. As an example, the dense hub 731

spanning between German universities, companies and authorities in the last phase increases 732

the likelihood that other functions of the MBR TIS (like entrepreneurial experimentation, 733

guidance of the search or creation of positive externalities) are developing in the respective 734

space, too. In contrast, the strongly globalized knowledge creation hub in the first phase could 735

indicates that the entrepreneurial experimentation and other functions were likely restricted to 736

that international TNC scale, too. Anecdotal evidence from visits at MBR trade fairs and 737

conferences supports this point. In other cases, our data however also missed on important 738

functional patterns. Entrepreneurial experimentation and market formation as an example 739

were likely not exclusively based on French actors in the first phase, but also on companies 740

from North America and especially Japan. As these actors did not participate strongly in 741

scientific publications, their influence on knowledge creation appears to be underevaluated in 742

our publication data. Also market formation as an example happened in Japan, the US, South 743

Korea and the EU in a very diversified spatial pattern which is not derivable from the 744

presented data. Such processes defining other TIS functions would thus have to be analyzed in 745

more detail and with other methodologies. The presented framework thus allows getting a 746

rough feeling of spatial system boundaries, relevant actors and scales in the innovation 747

process and different actors’ position in knowledge creation of an emerging TIS. For a 748

comprehensive understanding of the global TIS as a whole, a more encompassing and mixed 749

method approach would have to be applied. 750

751


28

Our analysis did also underemphasize the institutional dimension of TIS. From a co-752

publication network we cannot directly derive implications for the institutional dimension. 753

However, the results section presented some evidence on how national and European 754

institutional contexts (like the implementation of research programs and related industrial 755

policy in the EU and Asia) created relevant scales of knowledge creation. Our results 756

furthermore revealed that the influence of such institutional interventions shape the innovation 757

processes also in more distant places of the TIS. National policy making is thus much more 758

intertwined with institutional contexts in other places than what could be expected from 759

existing TIS studies. We also sustain that a global mapping of institutional contexts of a TIS 760

similar to what was done in this study for knowledge creation could be a very interesting way 761

forward for theory development in the TIS field. 762

763

Thirdly, the results might be subject to some biases originating from the used dataset and 764

methodology. Firstly, the high importance of international linkage in all development phases 765

of the MBR TIS might be partially attributable to the bias of publications from ISI web of 766

knowledge towards research in international projects and published in international journals 767

(Andrew J., 2009). For a more balanced view one would have to integrate other data types 768

like patents or licenses and possibly publications from non-ISI journals into the analysis. This 769

could create a more comprehensive actor set and add important insights into spatial dynamics 770

in the knowledge development at a sub-national scale. Besides patent or license data, one 771

could try to extract relational data also conference participation lists, affiliation information 772

from industry associations, participation in websites, blogs and social networks or 773

participation of different TIS actors in research, industry, or informal exchange networks. 774

Even in the light of these data issues, we maintain that in the presented case co-publication 775

data is still indicative as scientific publication represents the core of the knowledge creating 776

network of the MBR TIS. This might be different in other technology fields, so the data 777

sources would have to be adapted accordingly. Furthermore, data limitations do not challenge 778

the usefulness of the developed conceptual and analytical framework. Once more data on 779

other functions gets available, the same frame could be applied and be used for assessing the 780

spatial dynamics in other TIS functions. 781

782

Finally, this study far from exploited the vast potential that lies in the toolset of social network 783

analysis. We only scratched the surface of possibilities. One could add a position and role 784

analysis or try to correlate network properties to innovative performance of TIS actors. Also 785


29

longitudinal data analysis and modeling of network dynamics could potentially be used to 786

forecast future spatial setups of the system. 787

6. Conclusions 788

This paper aimed at discussing the implications of the currently a-spatial practice in 789

identifying the system boundaries in TIS studies and to illustrate how a a spatialized TIS 790

framework could contribute to identifying meaningful linkages and relationships with other 791

(territorial) types of systems of innovation. As showed in the literature discussion, adding 792

space to the TIS concept could be a crucial step in avoiding an increasing reification of the 793

concept and its empirical application. The developed analytical framework is an answer to 794

second research question. It showed that setting spatial system boundaries a posteriori and 795

with a relational, multi-scalar conception of space allows for a more differentiated 796

interpretation of relevant processes and scales of innovation than conventional TIS analysis. 797

The empirical case study revealed that knowledge creation in the MBR technology TIS 798

changed substantially in a short period of time from a transnational company-based nurturing, 799

to a Europe-based expansion and finally to a multi-scalar, Europe- and Asia based 800

consolidation phase. This implies that TIS space is dynamic and innovation processes can 801

change quickly both in their spatial outreach and quality. A more reflected spatial perspective 802

in TIS research could therefore be used for new explanations on where and when specific 803

innovations develop, for the diffusion of innovative activity and for the relevance of 804

interrelated processes at places which seem to be unrelated at first sight. Our results contradict 805

the existing assumption in existing studies that all relevant processes in a TIS actually work at 806

a national scale. Developments in distant parts of the world might be more closely connected 807

than expected by mono-scalar TIS studies. If as an example knowledge creation in the MBR 808

TIS in South Korea is supported by a government program, then knowledge creation in 809

Berlin’s universities and companies will likely profit directly through its dense links with 810

South Korean actors. 811

812

Our results furthermore provide evidence that the international scale might be more relevant 813

for innovation processes in TIS than can be expected from existing literature. We thus argue 814

in line with Carlsson (Carlsson, 2006) that this scale needs more attention in future conceptual 815

and empirical work. Our results also indicate that the “global technological opportunity set” 816

should not be understood as a geographic neutral and ubiquitous resource for TIS actors. It is 817


30

rather has to be characterized as a dynamically evolving network structure to which actors 818

with different relational positions have different access at different points in time (e.g. in a 819

first phase French companies were central, later German universities, which strongly changed 820

the set of actors which could access the produced knowledge). 821

822

Reconsidering the conceptualization of space and spatial boundary setting in TIS studies 823

could finally help to avoid errors in boundary setting. One can argue that national boundaries 824

are only legitimate in 1) mono-scalar, national TIS setups or 2) in multi-scalar TISs where 825

actors from the country in focus are both interacting at a national scale and also integrated 826

into the central innovative hub of the TIS. In our example, only Germany, South Korea and 827

China fulfil these conditions and not in every point in time. Focussing national studies of the 828

MBR technology TIS on Germany, South Korea or China in the time period between 2003 829

and 2009 is arguably legitimate (though potentially affected by errors of omitted context), as 830

their actors are present in the core hub of innovative activity and combine the requested kinds 831

of internal (national) cohesion and strong link to the central knowledge creating hub. 832

Focussing on the US on the other hand would rather have produced an isolation error, as US 833

actors follow a distinct spatial networking pattern and technological trajectory. Doing 834

nationally delimited TIS studies in the nurturing phase, finally, would in all countries have 835

produced system misinterpretation errors: Both the overall TIS structure and the central 836

knowledge creation hub were dominated by globally operating companies at that time, so 837

national delimitation would not have identified the core actor and spatial level of the system. 838

839

This also implies a central lesson for policy making: innovation and industrial policy and 840

especially subsidies for specific technologies should be responsive to the spatial setup of TIS 841

structures and functioning. National policy intervention in a globalized (and in some specific 842

cases multi-scalar) TIS setup bears the risk of becoming effective at another, unintended 843

spatial scale. Interventions should therefore be designed in a way that avoids supporting 844

effects from flowing off to other spatial scales or which consciously profits from the 845

opportunities stemming from multi-scalar interaction: As an example, tapping into innovation 846

networks at a global scale and a proactive promotion and management of strategic 847

international couplings might have been considerably underrated as a policy option, especially 848

in newly industrializing countries (Binz et al., 2012). 849

850


31

Future TIS research could be inspired by this contribution in three ways: Firstly, one could 851

extend this analytical framework more strongly to the sub-national level and use it as a 852

spatially fully differentiated analytical tool for the “knowledge development and diffusion” 853

function in TIS. Secondly, spatially sensitive studies of other TIS functions would be possible 854

with similar frameworks. We argue that a better spatial conceptualization of TIS functions 855

could open fruitful ways forward for theory development. Thirdly, our results could feed into 856

a spatialized TIS lifecycle theory. Understanding which scales are relevant in what fields of 857

technology and at what phase of system development could generate important input for 858

improving theory development and policy advice here. Finally, our study just covers one 859

narrow field of water recycling technology. Similar studies in other technological fields (and 860

especially in environmental technologies already covered by TIS research) would help to get a 861

better understanding of the spatial errors incorporated in existing TIS studies and to formulate 862

strategies on how to avoid them in future studies. We therefore strongly encourage more work 863

in this exciting field of inquiry. 864

865

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Paper fo

Appen

Appendix

Numbers r

A zoomab

source: da

or presentatio

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Paper fo

Appendix

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Average D

Mean Dist

Inclusiven

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Number of

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ation index

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icators allow ge

or split into dif

uld be an inclusi

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Ratio of the nconnections i

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of MBR techn

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ociation Euro

2007

opean Confeerence, Delft 34

34


35

References 937

Amin, A., 2002. Spatialities of globalisation. Environ.Plann.A 34, 385-399. 938

Andrew J., N., 2009. Measuring knowledge spillovers: What patents, licenses and 939 publications reveal about innovation diffusion. Research Policy 38 (6), 994-1005. 940

Angel, D., Rock, M.T., 2009. Environmental rationalities and the development state in East 941 Asia: Prospects for a sustainability transition. Technological Forecasting & Social Change, 76 942 (2), 229-240. 943

Asheim, B., Coenen, L., 2006. Contextualising Regional Innovation Systems in a Globalising 944 Learning Economy: On Knowledge Bases and Institutional Frameworks. Journal of 945 Technology Transfer 31, 163-173. 946

Bathelt, H., Gluckler, J., 2003. Toward a relational economic geography. Journal of Economic 947 Geography 3 (2), 117-144. 948

Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., Rickne, A., 2008a. Analyzing the 949 functional dynamics of technological innovation systems: A scheme of analysis. Research 950 Policy 37 (3), 407-429. 951

Bergek, A., Jacobsson, S., Sandén, B.A., 2008b. 'Legitimation' and 'development of positive 952 externalities': Two key processes in the formation phase of technological innovation systems. 953 Technology Analysis and Strategic Management 20 (5), 575-592. 954

Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., Rickne, A., 2008c. Analyzing the 955 functional dynamics of technological innovation systems: A scheme of analysis. Research 956 Policy 37 (3), 407-429. 957

Bergek, A., Jacobsson, S., 2003. The Emergence of a Growth Industry: A Comparative 958 Analysis of the German, Dutch and Swedish Wind Turbine Industries. In: Metcalfe, J.S., 959 Cantner, U. (Eds.), Change, Transformation and Development. Physica-Verlag (Springer), 960 Heidelberg, pp. 197-228. 961

Berkhout, F., Angel, D., Wieczorek, A., 2009. Sustainability transitions in developing Asia: 962 Are alternative development pathways likely? Technological Forecasting & Social Change 76 963 (2), 215-217. 964

Binz, C., Truffer, B., forthcoming. 965 Reconceptualizing niche upscaling in emerging Asia with multiscalar technological 966 innovation systems – the case of on-site water recycling in Beijing. 967

Binz, C., Truffer, B., 2012. 968 Technological innovation systems in multiscalar space. Analyzing an emerging water 969 recycling industry with social network analysis. Geographica Helvetica 66 (4), 260. 970

Binz, C., Truffer, B., Li, L., Shi, Y., lu, Y., 2012. Prospective analysis of leapfrogging 971 trajectories – An analytical framework and a case study in the Chinese wastewater sector. 972 Technological Forecasting and Social Change 79 (1), 155-171. 973


36

Boggs, J.S., Rantisi, N.M., 2003. The relational turn in economic geography. Journal of 974 Economic Geography 3, 109-116. 975

Boschma, R.A., Lambooy, J.G., 2002. Knowledge, market structure, and economic 976 coordination: Dynamics of industrial districts. Growth and Change 33 (3), 291-311. 977

Boschma, R.A., 1997. New industries and windows of locational opportunity: a long-term 978 analysis of Belgium. Erdkunde 51 (1), 12-22. 979

Brenner, T., Cantner, U., Fornahl, D., Fromhold-Eisebith, M., Werker, C., 2011. Regional 980 innovation systems, clusters, and knowledge networking. Papers in Regional Science 90 (2), 981 243-249. 982

Bunnell, T.G., Coe, N.M., 2001. Spaces and scales of innovation. Progress in Human 983 Geography 25 (4), 569-589. 984

Camagni, R., Maillat, D., 2006. Milieux innovateurs. Economica, Paris. 985

Carlsson, B., 1997. Technological systems and industrial dynamics. Springer, Boston and 986 Dordrecht. 987

Carlsson, B., 2006. Internationalization of innovation systems: A survey of the literature. 988 Research Policy 35 (1), 56-67. 989

Carlsson, B., Jacobsson, S., Holmen, M., Rickne, A., 2002. Innovation systems: Analytical 990 and methodological issues. Research Policy 31 (2), 233-245. 991

Carlsson, B., Stankiewicz, R., 1991. On the nature, function and composition of technological 992 systems. Journal of Evolutionary Economics 1 (2), 93-118. 993

Coe, N.M., Bunnell, T.G., 2003. 'Spatializing' knowledge communities: towards a 994 conceptualization of transnational innovation networks. Global Networks 3 (4), 437-456. 995

Coenen, L., Benneworth, P., Truffer, B., accepted. Towards a spatial perspective on 996 sustainability transitions. Research Policy. 997

Cooke, P., Gomez Uranga, M., Etxebarria, G., 1997. Regional innovation systems: 998 Institutional and organisational dimensions. Research Policy 26 (4-5), 475-491. 999

de la Tour, A., Glachant, M., Ménière, Y., 2011. Innovation and international technology 1000 transfer: The case of the Chinese photovoltaic industry. Energy Policy 39 (2), 761-770. 1001

Dewald, U., Truffer, B., 2012. The Local Sources of Market Formation: explaining regional 1002 growth differentials in German photovoltaic markets. European Planning Studies, 420. 1003

Dicken, P., Malmberg, A., 2001. Firms in Territories: A Relational Perspective. Economic 1004 Geography 77 (4), 345-363. 1005

Fane, A.G., Fane, S.A., 2005. The role of membrane technology in sustainable decentralized 1006 wastewater systems. Water Science and Technology 51 (10), 317-325. 1007


37

Freeman, C., 1987. Technology and economic performance: lessons from Japan. Pinter, 1008 London. 1009

Geels, F.W., 2002. Technological transitions as evolutionary reconfiguration processes: a 1010 multi-level perspective and a case-study. Research Policy 31, 1257-1274. 1011

Gleick, P.H., 1998. Water in crisis: Paths to sustainable water use. Ecological Applications 8 1012 (3), 571-579. 1013

Glückler, J., 2007. Economic geography and the evolution of networks. Journal of Economic 1014 Geography 7 (5), 619-634. 1015

Hekkert, M., Suurs, R., Negro, S., Kuhlmann, S., Smits, R., 2007. Functions of innovation 1016 systems: A new approach for analysing technological change. Technological Forecasting and 1017 Social Change 74 (4), 413-432. 1018

Jacobsson, S., Bergek, A., 2011. Innovation system analyses and sustainability transitions: 1019 Contributions and suggestions for research. Environmental Innovation and Societal 1020 Transitions 1 (1), 41-57. 1021

Jacobsson, S., 2008. The emergence and troubled growth of a 'biopower' innovation system in 1022 Sweden. Energy Policy 36 (4), 1491-1508. 1023

Jacobsson, S., Sandén, B.A., Bångens, L., 2004. Transforming the energy system-the 1024 evolution of the German technological system for solar cells. Technology Analysis and 1025 Strategic Management 16 (1), 3-30. 1026

Judd, S., Judd, C., 2006. The MBR book. Elsevier, Oxford. 1027

Kastelle, T., Steen, J., 2010. Using network analysis to understand innovation. Innovation: 1028 Management, Policy and Practice 12, 2-4. 1029

Katz, J.S., Martin, B.R., 1997. What is research collaboration? Research Policy 26 (1), 1-18. 1030

Kemp, R., Schot, J., Hoogma, R., 1998. Regime Shifts to Sustainability Through Processes of 1031 Niche Formation: The Approach of Strategic Niche Management. Technology Analysis & 1032 Strategic Management 10 (2), 175-195. 1033

Krackhardt, D., Stern, R.N., 1988. Informal Networks and Organizational Crises: An 1034 experimental simulation. Social Psychology Quarterly 51 (2), 123-140. 1035

Lesjean, B., Huisjes, E.H., 2008. Survey of the European MBR market: trends and 1036 perspectives. Desalination 231 (1-3), 71-81. 1037

Lundvall, B., 1992. National Systems of Innovation - toward a Theory of Innovation and 1038 Interactive Learning. Pinter, London. 1039

Lundvall, B., 1988. Innovation as an interactive process: from user-producer interaction to the 1040 national system of innovation. In: Dosi, G., Freeman, C., Nelson, R., Silverberg, G., Soete, L. 1041 (Eds.), Technical Change and Economic Theory. Pinter, London, pp. 349-369. 1042


38

Malerba, F., 2002. Sectoral systems of innovation and production. Research Policy 31 (2), 1043 247-264. 1044

Markard, J., Stadelmann, M., Truffer, B., 2009. Prospective analysis of technological 1045 innovation systems: Identifying technological and organizational development options for 1046 biogas in Switzerland. Research Policy 38, 655-667. 1047

Markard, J., Truffer, B., 2008a. Actor oriented analysis of innovation systems: Exploring 1048 micro-meso level linkages in the case of stationary fuel cells. Technological Analysis and 1049 Strategic Management 20 (4), 443-464. 1050

Markard, J., Truffer, B., 2008b. Technological innovation systems and the multi-level 1051 perspective: Towards an integrated framework. Research Policy 37, 596-615. 1052

Marshall, A., 1920. Principles of Economics. 8th editionMacmillan and Company Ltd., 1053 London. 1054

Maskell, P., Bathelt, H., Malmberg, A., 2006. Building Global Knowledge Pipelines: The 1055 Role of Temporary Clusters. European Planning Studies 14, 997-1013. 1056

Musiolik, J., Markard, J., in press. Creating and shaping innovation systems: Formal networks 1057 in the innovation system for stationary fuel cells in Germany. Energy Policy. 1058

Negro, S.O., Hekkert, M.P., 2008. Explaining the success of emerging technologies by 1059 innovation system functioning: The case of biomass digestion in Germany. Technology 1060 Analysis and Strategic Management 20 (4), 465-482. 1061

Negro, S.O., Suurs, R.A.A., Hekkert, M.P., 2008. The bumpy road of biomass gasification in 1062 the Netherlands: Explaining the rise and fall of an emerging innovation system. Technological 1063 Forecasting and Social Change 75 (1), 57-77. 1064

Nelson, R., 1993. National Innovation Systems: A Comparative Analysis. Oxford University 1065 Press, New York. 1066

Oinas, P., 2000. Distance and learning: does proximity matter? In: Boekema, F., Morgan, K., 1067 Bakkers, S., Rutten, R. (Eds.), Knowledge, innovation and economic growth the theory and 1068 practice of learning regions. Edward Elgar, Cheltenham. 1069

Porter, M.E., 1998. Clusters and the new economics of competition. Harvard Business 1070 Review 76 (November-December 1998), 77-90. 1071

Rutten, R., 2003. Knowledge and Innovation in Regional Industry: an entrepreneurial 1072 coalition. Routledge, London. 1073

Ter Wal, A.L.J., Boschma, R.A., 2009. Applying social network analysis in economic 1074 geography: Framing some key analytic issues. Annals of Regional Science 43 (3 SPEC. ISS.), 1075 739-756. 1076

Tödtling, F., Trippl, M., 2005. One size fits all? Towards a differentiated regional innovation 1077 policy approach. Research Policy 34 (8), 1203-1219. 1078


39

Wang, Z., Wu, Z., Mai, S., Yang, C., Wang, X., An, Y., Zhou, Z., 2008. Research and 1079 applications of membrane bioreactors in China: Progress and prospect. Separation and 1080 Purification Technology 62 (2), 249-263. 1081

Wasserman, S., Faust, K., 1994. Social Network Analysis: Methods and Applications. 1082 Cambridge University Press, Cambridge. 1083

Wenger, E., 1998. Communities of practice Learning, meaning, and identity. Cambridge 1084 University Press, Cambridge, UK. 1085

Yeung, H.W., 2005. Rethinking relational economic geography. Transactions of the Institute 1086 of British Geographers 30 (1), 37-51. 1087

Zheng, X., Zhou, Y., Chen, S., Zheng, H., Zhou, C., 2010. Survey of MBR market: Trends 1088 and perspectives in China. Desalination 250, 609-612. 1089

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