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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DRAFT VERSION – DO NOT CITE WITHOUT PERMISSION BY THE AUTHORS 16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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Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 2
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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
49
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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 3
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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
70
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
83
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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 4
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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
133
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
206
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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 8
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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
256
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
270
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
289
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
301
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
306
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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
338
339
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340
1) 341
342
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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 15
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
Paper fo
200 them
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and 201
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MBR te
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Actor
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growth and
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h institutes o
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2A). This inc
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A
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2008).
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Wang et al
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he datas
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l., 2008).
MBR technolog
spatia
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only after th
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he overall T
izations suc
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sis. Publicat
be complete
Miner 3 softw
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ponentially o
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gy 1993-2010
B
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he year 2000
e data betwe
TIS reported
h as compa
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ications cov
tions from 2
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tware.
over the las
nds with rap
ystems repor
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time and th
0. Our analy
een the year
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16
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Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 17
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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 18
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
541
542
543
Paper fo
5. Sp
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The spa
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3 depicts an
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: Nationaliza
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and the natio
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In the conse
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Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 20
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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 21
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
620
Paper fo
Figure 4:
Source: O
5.2
The sub
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expands
Table 2)
Append
program
internat
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621
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Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 24
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
685
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35 overl
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Clans co
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Figure 6:
This hub
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12 Regional S
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a multi-scala
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on space: O
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25
25
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st sight,
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 26
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
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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
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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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 29
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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 30
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
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 31
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
ndix
x 1: Worldwide
refer to the num
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ata from ISI web
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917
918
Paper fo
Appendix
Indicator
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Average D
Mean Dist
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Number of
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a TIS wou
disintegrat
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f components
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or split into dif
uld be an inclusi
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33
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919
920
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922
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927
928
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935
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Paper fo
Appendi
or presentatio
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on at the 201
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12 Regional S
of MBR techn
Studies Asso
nology 2003-
ociation Euro
2007
opean Confeerence, Delft 34
34
Paper for presentation at the 2012 Regional Studies Association European Conference, Delft 35
35
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