Knowledge and social networks: New dimensions of 

economic interaction between firms 
 

 

Michael Steiner * and Michael Ploder ** 
 

* University of Graz, Department of Economics, Universitätsstr. 15 F/4, 8010 Graz, Austria   

** Joanneum Research,  Institute of Technology and Regional Policy, Elisabethstr. 20, 8010 

Graz, Austria 

 

michael.steiner@uni-graz.at 

michael.ploder@joanneum.at 

 
Received 20 August 2011; received in revised form 20 November 2011; accepted 29 December 2011 

 
ABSTRACT: The paper explores the form and content of economic interaction of firms based on various 

concepts of agglomeration and social networks. It uses a case study of the machinery sector in the region of 

Styria as empirical background. Starting with types of clustering – the model of pure agglomeration, the 

industrial-complex model and the social-network model - the paper argues that certain geographical 

agglomerations allow different types of networks and different patterns of behaviour. Thus different forms of 

learning, knowledge sharing and knowledge creation. Some “stylized facts” in support of this perspective are 

derived from an analysis of a regional network. This network comprises individualistic open systems consisting 

of several areas which overlap. Physical linkages between these networks are weak, but intersections based on 

cooperative R&D and R&D infrastructure, qualification and informal exchanges are evident. From a regional 

perspective it can be seen to dominate. Despite evident sectoral concentrations direct links to the prevailing 

science base appear more significant as binding factors than long term supplier networks. These relationships are 

interpreted in terms of their need for proximity, their durability and above all their direction of knowledge 

dependency. 

 

Keywords: Agglomeration, Knowledge Transfer, Social Networks, co-evolutionary development 

 

Available for free online at https://ojs.hh.se/ 
Journal of Intelligence Studies in Business 1 (2011) 49-60 

mailto:michael.steiner@uni-graz.at
mailto:michael.ploder@joanneum.at
https://ojs.hh.se/


50 
 

 
 

1. Introduction 

 

While we are well aware that it is most likely 

impossible to provide one single theory of clusters 

and their networks there is nevertheless a certain 

consensus that several elements of specific theories 

may help us understand their forms and functions. 

They offer a certain unity of approach in identifying 

the important elements which are needed for 

explaining the changing character of the innovation 

process. 

Recent debate has begun to focus more on how 

far, and in which ways, clusters foster knowledge 

creation and organizational learning. It has also 

emphasized the organic-evolutionary dimension of 

cluster-based industrial agglomerations.  

Knowledge has been recognized as a major source 

of competitive advantage in an increasingly 

integrated world economy (Dosi and Malerba 1996, 

Grant 1996, Foss 1999, Nonaka et al. 2000). The 

most successful regions are perceived to be those 

whose firms display innovative capacity, i. e. those 

whose firms are able to adapt to a rapidly changing 

marketplace and stay one step ahead of competitors. 

The emphasis of cluster interpretation has changed 

from an analysis of forces of agglomeration to the 

various forms and contents of organizational 

learning and knowledge exchange. The original 

concentration on clusters as mere geographic 

concentrations of sectors and firms has been 

transformed into a search for institutions for 

knowledge management and organizational 

learning emphasizing the organic-evolutionary 

dimension. Growth of the knowledge base depends 

on intended and unintended individual processing 

of experiences. I.e. ‘learning’, while the 

interpretation, transfer and use of experiences is 

influenced by interaction between individuals and 

between organizations (Cohen and Levinthal 1989, 

Andersen 1995, Hartmann 2006). These insights 

have shifted the emphasis from material links to 

immaterial knowledge flows within clusters. They 

have also pointed to the need for connectivity 

between different agents concerning knowledge 

creation and diffusion. This has led to further 

questions concerning the degree to which clusters 

are to be regarded as non-market devices, by which 

firms may seek to coordinate their activities with 

other firms and knowledge-generating institutions. 

Ongoing learning processes between firms and 

within clusters stress the importance of institutional 

arrangements for the generation of knowledge and 

learning networks which are not available in 

markets (Maskell and Malmberg 1999). Since the 

necessary knowledge may lie outside a firm’s 

traditional core competence, interfirm alliances and 

networks are widely recognized as an important 

organization form of innovative activity (Gay and 

Dousset 2005). 

 

In this paper, we emphasize the ideas of 

agglomeration and knowledge exchange.We discuss 

to what extent this approach has specific regional or 

spatial dimensions while focussing on the necessity 

and forms of proximity, especially with respect to 

knowledge exchange.  

By means of network analysis we develop some 

“stylized facts” for the various dimensions of 

interaction within a given network of medium-tech 

firms in Styria, one of the nine provinces (regions) 

of Austria. The final section is used to interpret the 

findings. 

2. Geographical agglomeration and local 

networks 

 
Since Marshall (1890), Weber (1929) and Hoover 

(1948), many authors have dealt with the 

phenomenon of geographical agglomeration. In the 

discussions of ‘clusters’, ‘networks’ and 

agglomerations, and particularly in those relating to 

‘industrial districts’ and agglomerations, there are 

certain common traits and frequently terms are only 

weakly differentiated. The basic idea of 

geographical agglomeration was presented by 

Marshall (1890) and the three sources of economies 

of agglomeration he mention is input sharing, labor 

market pooling, and knowledge spillovers, 

correspond with the core-elements of the current 

cluster-concept.  In this form it has been discussed 

since the early nineties in industrial countries. A 

more recent attempt to distinguish various cluster 

forms has been made by Belussi (2006) by 

contrasting geographical agglomeration and active 

clustering (as policy or firm-driven strategy).  

While implicitly focusing on geographical 

agglomeration and economies of agglomeration we 

stress a dimension of externalities beyond the 

tangible dimension of direct co-operation. On 

extending the basic idea of economies of 

agglomeration, we see that externalities are widely 

enforced by informal and non-economic 

dimensions. Amin and Thrift (1995) use the term 

“institutional thickness” to address the existence of 

a supporting environment beyond firms 

(institutionalized cooperations and networks). 

Geographic agglomeration (and concentrated versus 

dispersed location patterns) set a framework for 

economic interaction and material and immaterial 

linkages between economic actors. 

The existence of a cluster doesn’t necessarily 

imply the coexistence of all defining characteristics 

of a geographical agglomeration. On the other hand, 

a geographical agglomeration may also exist in the 

absence of a cluster or network.  

While the existence of a pure geographical 

agglomeration (e.g. a city) favours the development 

of clusters; growing networks and clusters can also 

cause the emergence of a geographical 



51 
 

 
 

agglomeration. This was the case perhaps in Silicon 

Valley in California. Myrdal’s (1957) idea of 

cumulative causation corresponds with a dynamic 

view of a co-evolutionary development of 

economies of agglomeration and growing clusters 

(without yet formalizing interdependency as was 

done by Kaldor (1972) and Dixon and Thirlwall 

(1975). In other words, additional local linkages 

and relations strengthen tendencies of concentration 

and agglomeration. Networks and clusters are 

possible means of overcoming constraints of 

exchange within and between geographical 

agglomerations and also facilitate the definition and 

defence of rules of exclusion, as already pointed out 

by Marshall (1890). Yet, what is still an open 

question is the micro-perspective. Economies of 

agglomeration and dimensions of interaction could 

be selective in respect to the actors since they 

regulate the extent to which the latter are able to 

participate or gain from externalities: e.g. with 

respect to exchange of physical goods versus R&D, 

or labour market pools for blue collar workers or 

engineers. 

In addition to direct physical exchange, input 

sharing and common labour market pools, 

systematic knowledge exchange and knowledge 

spillovers have gained considerably in importance 

as an argument for geographical concentrations of 

activities. A frequently used argument is that the 

collaborative nature of innovation processes has 

reinforced tendencies toward geographical 

clustering because of the advantages of locating in 

close proximity to other firms in specialized and 

related industries (Storper, 1995 and 1997). 

Transaction costs such as transportation costs and 

spatial communication costs in particular, reinforce 

the relationship between individual environment 

and the development of embedded social networks 

(Granovetter 1994).  

Firms establish a variety of types of interactions 

and relationships each of them having different 

impacts on the knowledge generation and diffusion 

process. Mariotti and Delbridge (2001) speak of the 

necessity for firms – in the face of knowledge 

ambiguity, knowledge related barriers, tacitness and 

complexity of knowledge to engage in the 

management of a portfolio of ties. Organizations 

are therefore likely to engage in inter-organizational 

relations that show a variety of types of ties.  

They can have quite different dimensions and 

can be defined according to the character of social 

relations between actors, the regulation of the 

relationship, frequency of use, length and duration 

of the relationship, and also in terms of the nature 

of the information exchange itself (Mariotti and 

Delbridge 2001). It is also important to distinguish 

between both content (i.e. the type of relation) and 

the form (i.e. the social structure of relations), as 

has been outlined by Powell and Smith-Doerr 

(1994).  

One additional question that needs to be 

addressed in this context concerns the legitimacy of 

a pure micro-level, individual firm approach in 

analysing the incentives for clustering.  

Individuals and firms alone are, from an 

economic point of view, not capable of delivering 

sufficient amounts and varieties of knowledge. We 

are confronted here with one of “the most 

troublesome issues in the social sciences …” (Felin 

and Foss 2006, 1). The question of the adequate 

level and unit of analysis. A question of whether the 

individual or social collectives (firms, networks, 

regions …) have explanatory primacy is of course 

part of an old debate in economics, sociology and 

the philosophy of science and is often now dealt 

with  under the heading of “methodological 

individualism” versus “methodological 

collectivism” (Hayek 1945, Popper 1957, Coleman 

1964, Douglas 1986).  

Further potential for conceptual differentiation 

relates to the forms, channels and mechanisms of 

knowledge exchange. As this exchange occurs 

through interaction, the structure of the interaction 

therefore influences the extent of knowledge 

diffusion (Gay and Dousset 2005). This coincides 

within the view that “spatialities and temporalities 

are not neutral frames, but constitutive elements of 

socioeconomic transformation” (Colletis-Wahl et 

al. 2008). 

The cross-sectoral dimension of knowledge 

spillovers is also a source of contention in the 

literature. Following Marshall (1890) and Arrow 

(1962) knowledge is predominantly industry-

specific. Knowledge spillovers may therefore arise 

between firms within the same industry. Jacobs 

(1969), on the other hand, mentioned the significant 

fact that knowledge may spill over between 

complementary rather than similar industries. 

The significance of geographical agglomeration 

and networking is strongly determined by the 

particular sector (industry) and the leading 

technology. There seems to be a clear agreement in 

the recent literature about cross-sectional 

differences in agglomeration forces: As has been 

emphasized by Botazzi et al. (2001 and 2002) and 

also Gordon and McCann (2000), huge intersectoral 

differences in spatial agglomeration outcomes can 

be identified.  

Following Gordon and McCann (2000) 

agglomeration economies appear particularly 

relevant in “scale-intensive sectors” hinting at the 

forms of hierarchical agglomeration discussed 

above - and in “supplier-dominated sectors”. 

Conversely, they appear the least relevant in 

“science-based” sectors. The importance of 

agglomeration depends on the prevailing sectoral 

and technological pattern.  

The following argumentation takes up two 

approaches to differentiating typologies and focuses 

on the different dimensions of agglomeration and 



52 
 

 
 

clustering viewed as helpful guidelines in the 

discussion of the network observed in Styria and in 

answering the key-questions of the empirical 

analysis. An attempt is also made to combine and 

differentiate the “agglomeration” approach with the 

additional insights mentioned above. 

Gordon and McCann (2000, 15) define and 

discuss three theoretical approaches for industrial 

clustering which reflect different (more or less 

idealized) perspectives on agglomeration: The 

model of pure agglomeration, the industrial-

complex model and the social-network model. 

 

 The phenomenon of economies of 
agglomeration as an intrinsic motive for 

clustering, in the sense of spatial 

concentration of economic activity, is 

attributed more or less exclusively to the 

traditional idea of Marshallian industrial 

districts. Following in the footsteps of 

Thünen in the field of locational 

economics, and Smith’s idea of division of 

labour, the model of pure agglomeration – 

which in the tradition of Marshall (1890) is 

based on a local pool of specialized labour, 

on the increased local provision of non-

traded input specific to an industry, and on 

technological spill-overs - may contribute 

to an “evolving localized environment of 

learning” (Gordon and McCann 2000, 

517). The Marshallian approach was 

quickly developed and extended by 

Hoover (1948) by distinguishing between 

localization economies and urbanization 

economies. Following Marshalls (1890) 

positive externalities of agglomerations are 

defined by regional non-traded inputs, 

knowledge and information spill-overs, 

and a local pool of skilled labour. From the 

perspective of knowledge flows and 

learning processes favoured by 

agglomeration, such externalities occur 

more or less unheard of and unseen. 

Knowledge exchange and learning occurs 

unconsciously via transfer of human or 

material resources. The most important 

point seems to be that the approach is not 

bound to the idea of direct supply-

relationships among the bulk of actors 

involved. Following the traditional idea of 

Marshallian industrial districts, interaction 

is primarily led by the needs of industrial 

production.  

 

 A second group of approaches pooled by 
Gordon and McCann (2000, 517) under 

the term of industrial complex models 

systematically tries to justify spatial 

concentration by the quantification and 

minimization of spatial transaction costs 

(reflecting of the origins of the approach, 

primarily transportation costs). The 

industrial complex model is associated 

with cumulative learning from sources 

inside the industry, non-transferable 

experience, the role of leading firms and 

power asymmetries (Iammarino and 

McCann 2005).  Although the implicit 

concealment of (unplanned) economies of 

agglomeration didn’t mean that they were 

not relevant. Attention shifted nevertheless 

to innovation as an interactive process 

involving the sharing and the exchange of 

different forms of knowledge between 

actors (Lawson and Lorenz 1999) – 

knowledge and competence as developed 

interactively and within subgroups of a 

(regional) economy (Freeman 1979, 

Lundvall 2002). The critique here has been 

concerned with the question of whether 

this interaction is an outcome of 

(neoclassical) rational behaviour or the 

result of a more ‘associative-relational’ 

mode of organization, or what has been 

termed ‘associative governance’, leading 

to the creation of clubs, forums, consortia 

and other institutional schemes of 

partnership (Cooke 1998; Cooke and 

Morgan 1998). There are elements of 

knowledge sharing in the sense that 

adopting the perspective of specific 

clusters represents a quasi-monopoly for 

the internalization of the benefits of 

innovation created within (more or less) 

“closed club”.  

 

 The social-network model as the third type 
– relying on trust and social embeddedness 

as the dominant link between the cluster 

firms (and therefore not on deliberate 

economic decisions based on the 

minimization of different transaction 

costs) – also favours the exchange of 

knowledge. However, such exchange is 

here based on strong interpersonal 

relationships that transcend firm 

boundaries and allow for diverse forms of 

knowledge sharing. Following Iammarino 

and McCann (2005) traditional and recent 

approaches of social networks may be 

differentiated. The traditional approach 

corresponds to the ‘Marshall-stimulated’ 

industrial districts where knowledge is 

mainly codified and oriented to process 

innovation transferred by personal contacts 

and social and political lobbying. While in 

the traditional approach the network seems 

to be based on geographical proximity 

rooted in historical experience, the new 

approach of social networks seems to be 



53 
 

 
 

based on relational and organizational 

proximity. The links between actors are 

then all the stronger the more they are 

based on elements of social 

embeddedness: norms, sets of common 

assumptions, habits formed by culture, 

history, and of course (but not necessarily) 

spatial proximity. They form social capital 

that favours the explicit and implicit 

sharing of knowledge. New physical 

technologies are not just there, innovations 

do not just happen, but need social 

technologies as pathways to coordinate 

human action.  

 

As Iammarino and McCann (2005) mention, much 

of the discussion in the literature is based on ideal 

types, whereas in reality all spatial clusters and 

industrial agglomerations will contain more or 

fewer of the above characteristics. Furthermore, 

clusters may mutate from one typology to another. 

From another perspective this is also outlined by 

Rychen and Zimmermann (2008, 768): The concept 

of cluster “usually considered as a spatial 

concentration of industrial and technological 

activities” has to be enriched. “It is more important 

to understand how and why firms build links and 

how the structure of links will give sense to the co-

location of actors.” It is therefore important to 

incorporate the dimension of collaboration, the 

basic conception of firms is a “network-driven 

economic strategy built on collaboration among the 

participants” (Reid et al. 2008, 2). 

The following section is dedicated to 

interpreting the case network investigated in the 

light of the approaches discussed above. The 

suppositions that context, typology and significance 

of geographical agglomerations and embedded 

networks change, seems to be reflected in the case 

of the machinery sector in the region of Styria.  

3. The empirical analysis – a qualitative 

context oriented approach based on social 

network analysis 

 

The empirical analysis starts with an analysis of 

relevant regional data and expert interviews and 

then continues with a case study analysis of the 

relations of engineering firms in Styria. 

3.1 Interaction in the observed network 

 

Network analysis is a well established method in 

the social sciences. Recently, the method has also 

been applied to the analysis of production clusters 

(Krätke 2002), innovative activity and knowledge 

exchange (Giuliani 2005), and alliance networks 

(Gay and Dousset 2005) or R&D networks. 

Social network analysis is a helpful tool for 

discussing the structure of networks since it allows 

the mapping and measuring of the relationships 

(communication and transaction) between different 

actors, i.e., the existence, context and portfolio of 

relations between actors in a regional network. It is 

a method for revealing relations between different 

actors. Such relations are phenomena that cannot be 

reduced to the properties of individual actors or 

firms themselves and thus need to be interpreted as 

properties of systems than of individual actors.  

3.2  The empirical database 

 

The present network analysis is based on an 

empirical sample of firms identified by a 

snowballing method of sampling in cluster and 

network investigation. This corresponds with the 

relational approach and is developed by means of 

the references to actors as revealed by previous 

respondents (Frank 1979, Scott 2000). 

Our starting point was a large system supplier in 

the automobile sector located in the region of 

Styria/Austria. The snowball method produced 

firms belonging to different sub-sectors of the 

manufacturing sector and related supply-chain and 

innovation-strategies. Starting with the initial firm, 

a sample was developed. Following a citation path 

of regional suppliers (production or 

commercialization of goods and services) and of 

regional partners in the field of research and 

development (cooperative R&D and related 

activities and exchange). In this way the database 

for the subsequent network analysis was extended 

to 23 firms, of which 18 are producers (with 

different positions in the supply-chain such as 

system-suppliers, component suppliers and toll-

manufacturers). The remaining 5 are technical 

business services. Additionally nine R&D 

institutions (universities, co-operative R&D 

institutions) are included. The information and data 

collected are based on extensive qualitative 

interviews and supported by a quantitative survey 

concerning specific data. 

3.3 Indicators of interaction  

 

Qualitative indicators revealing individual 

strategies of innovation are helpful discussing 

individual strategies and their aggregation at the 

level of networks. They are selectively used here to 

find – via network analysis - the structural features 

of the network of 32 actors. The selected indicators 

of the relations cultivated by the organizations 

cover three dimensions of interaction: direct 

delivery relations, R&D, and technological 

innovation in a competitive and a pre-competitive 

context. 

(DELIV): The firms were questioned 

concerning direct delivery relations (goods or 

services) to clients, suppliers or partners (in the case 

of synergetic product bundles)..The direct delivery 



54 
 

 
 

of goods and services is not reduced to the material 

dimension since it also covers questions of 

innovation in information management or capacity-

extending investments. 

(PRE-COMP): The dimension of interaction in 

the context of pre-competitive R&D was also 

analysed. Pre-competitive Research and 

Development aim to extend the product spectrum, 

as well as introduce new processes and alternative 

materials. Pre-competitive research includes 

fundamental research, which is an activity designed 

to broaden scientific and technical knowledge not 

necessarily linked to industrial or commercial 

objectives, as well as industrial research, i.e. 

research aimed at developing or improving new or 

existing products, processes or services in so far as 

it is also not directly connected with a client tender, 

offer or an existing business relation. 

 (COMP): Competitive Research and 

Development and innovation processes are short 

and medium term oriented and mostly associated 

with direct expectations of return or with a direct 

tender or offer etc. in contrast to pre-competitive 

R&D which is long-term oriented. 

3.4 Structure of the network and network density 

 

Following the socio-centric approach, the density of 

a network is given by the ratio of relations realized 

to the total number of potentially maximum 

possible relations. We dichotomized the relations in 

that we only differentiated between existence and 

non-existence of a relation between two actors [0; 

1], and therefore disregarded the intensity of the 

relations (in our case the frequency of interaction) 

surveyed. This enabled us to avoid the problems 

typically associated with the measurement of the 

intensity of evaluated graphs (Scott 2000). Network 

density yields information on the general structure 

of the network as a whole.  

One of the core features of an actor identified in 

network analysis is its centrality. Using the concept 

of centrality (in different forms) we gain insights 

into the specific features of the interaction of the 

actors in the network and their specific position 

and/or embeddedness in the network. While density 

focuses on the properties and general structure of 

the network as a whole, centrality tries to capture 

the position of individual actors or groups of actors 

within the network. This is again based on the 

relations revealed by the actors, where the relations 

are valued ordinally in terms of frequency of 

interaction. The potential centrality of an actor is 

determined by a broad range of industry or sector-

specific factors (Cohen et al. 2000), by capacity and 

individual motivation (Bayona et al. 2001, Theter 

2002). A high centrality is positively associated 

with multiple possibilities for receiving and 

generating knowledge. 

Keeping in mind that interregional and international 

relations exist and may be of major priority, e.g. 

direct delivery relations the analysis below focuses 

on regional interaction. Table 1 presents the density 

measure for the three dimensions of relations 

between the actors.  

 

Relational dimensions    Density 

(DELIV)  direct delivery 

relations 

0.068 

(PRE-

COMP) 

interaction in 

the context of 

pre-

competitive 

R&D 

0.143 

(COMP) interaction in 

the context of 

competitive 

R&D and  

0.074 

 

Table 1: Density of the observed dimensions of 

networking 

Direct delivery relations have the weakest density. 

Although the datasets have been dichotomized and 

therefore relations with a very low frequency of 

interaction have been “up-graded” the density of the 

network of direct delivery relations is lower than 

the density of knowledge intensive innovation-

related interaction. Regional input-output relations 

were reduced in order to focus attention more on 

international markets.  

While competitive R&D and innovation 

processes, especially in the case of domestic system 

suppliers, are partially similar in density to direct 

delivery relations, the regional density of the 

network in pre-competitive R&D is much higher. 

R&D institutions are of negligible significance in 

respect of direct delivery relations, the network is 

based to a considerable degree on relations with 

cooperative R&D institutions. 

The relational data can be used to provide a 

graphical representation of the transaction network 

for the organizations observed. While network 

diagrams offer a traditional and basic methodology 

for formalizing network analysis, and are a very 

helpful mean of interpretation and discussion, 

clarity suffers greatly as the number of actors 

observed increases.
 

A quite useful method of graphical 

representation which is implemented in most 

software packages follows the approach of the 

Kamada and Kawai (1989) spring embedding 

algorithm. This is employed below. 



55 
 

 
 

 

 gives an overview of all relations recorded and 

combines the three dimensions discussed above. It 

also takes into account the valuation of the relations 

in terms of frequency of interaction. The shape of 

actors (nodes) corresponds with the different types 

of organizations. The size of the nodes corresponds 

with the size of the organisation, and the length of 

lines corresponds with the distance between the 

actors observed. 

 

             

 

Figure 1: Network of firms and knowledge generating institutions 

Figure 2: Legend 

 

 

A further interesting dimension of network analysis 

is ‘coreness’, which follows basically the idea of 

core and periphery. Here we use the concept of the 

k-core (Seidman 1983, Scott 2000). A k-core is a 

sub-graph in which each actor is adjacent to at least 

k other actors in the sub-graph. That is, for all 

nodes in the sub-graph minimum the number of the 

actors’ direct relations within the sub-graph is k (in 

our case eight). K-core analysis complements the 

measurement of density, since the latter is not able 

to reflect structural features of the network. The k-

core is an area of relatively high cohesion. As can 

be seen at first glance, we can differentiate between 

those actors in the core of the network (coloured 

black) and those actors more or less on the 

periphery of the network (coloured white). The 

diagram reveals the high density of the realized 

relations calculated in the previous paragraphs. In 

the k-core of the diagram we find a group of 

institutions that seem to interact multilaterally. In 

the “core” of the network we find R&D institutions, 

large system suppliers and toll manufacturers 

(surface-treatment, heating etc.) which maintain 

multiple but weak relations with a broad range of 

regional clients. 

3.5 Spotting a leading firm in the network 

Here we focus on a specific firm, ss 20 in the total 

network. This is a highly specialized manufacturer 

who measures equipment for science and industry. 

Their success is based on the direct application and 

transfer of scientific knowledge gained in the 

measurement of physical or chemical phenomena. 

The firm is highly vertically integrated and is 

embedded in smaller network following niche 

strategies. The partners of the firm in direct delivery 

(component and toll-manufacturers) and its partners 

in competitive and pre-competitive research and 

development (key clients, highly specialized 

business services, universities) are not identical. 

On the delivery side, the observed firm interacts 

with component suppliers in the field of die casting, 

spray casting, plastics processing, electronics, sheet 

metal forming, and manufacturing of high 

performance glasses.  

Originally, the firm was a pure converter, 

producer and specialist in marketing. This division 

of labour has changed since the 1980s. A well- 

established cooperative base allows access to 

university partners and to an independent research 

laboratory which provides exclusive science driven 

R&D. The firm enjoys a relatively high in-degree 

systems supplier

component supplier

toll manufacturer

technical business services

R&D- institutions

systems supplier

component supplier

toll manufacturer

technical business services

R&D- institutions2 1 



56 
 

 
 

centrality in respect to direct deliveries. The out-

degree centrality of the firm in the region, with 

respect to deliveries, is low owing to the high 

export intensity. 

A high share of the turnover is reinvested in 

R&D activities, 10% for intramural R&D and an 

additional 10% of the turnover for external R&D. 

The degree centralities in respect to R&D (pre-

competitive and competitive) are higher than for the 

average of the leading firms in the network. The 

core competences of the firm are based on 

combining and applying findings from basic 

research, in precision engineering and electronics.  

While radical innovations and market novelties 

mostly emanated from R&D or client-partners, 

incremental improvements are promoted by internal 

R&D. R&D and production and marketing of new 

products are concentrated within the region. 

As the firm is not located in the core of vehicle 

manufacturing but in the interface with other 

sectors such as manufacturing of plastic products or 

measurement techniques it has a relatively high 

value for betweenness centrality. The findings for 

this specific firm serve to strengthen the thesis that 

firms acting in market niches demanding highly 

specialized cooperation tend to work for long-term 

cooperation. 

3.6 The historical background and changing 
role of geographic agglomeration in the 

medium-technology sector in Styria 

 

The majority of the observed firms have been in the 

region for more than 10 years. The current situation 

and recent developments cannot therefore be 

adequately analysed without considering the 

historical background and structural change of the 

regional industry over the last few decades. 

In the late 1960s and 1970s the networks in the 

medium-technology sector were dominated by large 

state-owned firms that were highly vertically 

integrated and had lost their headquarter functions 

to the city of Vienna. While supply-side linkages to 

the region still existed agglomeration took a very 

limited traditional form. In most cases, planning, 

R&D and marketing/distribution functions, i.e. 

those functions responsible for the monitoring of 

markets and technology, had been lost. Clearly 

observable lock-in effects had let to agglomeration 

becoming a mere by-product of path-dependence 

with none of the advantages of agglomeration 

mentioned by Botazzi et al. (2001).  

Against the background of a history of outward 

dependence and nationalized standardized mass 

production the traditional indicators used to 

measure the strength of social networks had become 

weak. According to Iammarino and McCann (2005) 

social networks exhibit the following 

characteristics: Knowledge is largely codified and 

mature and mainly oriented to process innovation, 

transmitted essentially by way of personal contacts; 

there is extensive  social and political lobbying, 

backward and forward linkages. As far as social 

networks still existed (e.g. in the machinery and the 

automobile sector) they became a fruitful base for 

the restructuring in the 1990s.  

During the developments of the last few decades 

the typology of agglomeration and the role of 

networks have changed considerably. Many large 

firms were re-privatized and downsized at the end 

of the 1980s. Firms thus needed to learn to 

collaborate and develop their potential for 

innovation as a strategic resource. This entailed 

abrupt and long overdue changes from a Fordist to 

a more flexible mode of production.  

A massive structural change took place, 

beginning in the 1990s, especially in sectors related 

to steel production such as mechanical engineering 

and the automotive industry. High degrees of 

diversification and broad unspecified clients were 

replaced by a focus on market niches and 

technological specialization, while higher lot sizes 

enabled higher cross-functional integration and 

raised flexibility by leaving a scope for automation. 

Technological upgrading (including the 

introduction of quality and measuring standards) 

opened doors to new clients. This was accompanied 

by extending their responsibilities for tool making 

and sourcing capabilities and also by shifting the 

responsibility for quality and price from clients to 

suppliers. Innovations in these sectors were 

influenced by applications of specialized 

knowledge in the field of materials, tooling and 

processing techniques, or by the need to solve very 

specific problems in the machinery sector. On the 

supply side, hierarchical, spatially localized 

relations were developed. These have been formed 

around elite R&D-intensive export-oriented large 

firms. 

3.7 Human resources and the regional labour 
market 

 

A typical characteristic of agglomerations, in the 

sense of the model of pure agglomeration 

mentioned by Gordon and McCann (2005), 

following Botazzi et al. (2001) - namely, a more or 

less common labour market pool – was not 

observed. For the investigated component supplier 

firms (here in more or less rural and isolated areas) 

it is still the case that they operate with reference to 

very local labour markets, binding traditions and a 

low mobility of employees. Small and medium 

sized supplier firms exhibit family-based traditional 

structures, sometimes over generations. Concerning 

the qualification structure, there are deep 

differences between Original Equipment 

Manufacturers or system-suppliers with R&D units 

on the one hand, and basic technology providers, 



57 
 

 
 

extended work-benches or third party 

subcontractors on the other hand.  

This is true for both lower as well as high 

skilled workers, where employee turnover is 

normally a more or less excepted instrument of 

knowledge transfer and networking among firms. 

Because of the immobility of the local labour force 

and the restricted capacity of the regional labour 

market most of these firms were able to retain key-

personnel and competences and the regionally 

integrative potential of their personnel. Yet, by the 

same token, this implies only little mobility of 

qualified personnel coming from Europe (for 

language reasons, predominately from Germany). 

Also due to official restrictions labour inflow from 

the new EU member countries remains limited. 

3.8 Discriminative capabilities and 
heterogeneous strategies in the case of R&D 

and Innovation 

 

As already mentioned, leading firms do not play a 

dominant role on the demand side. The broad range 

of material input-output linkages is directed 

outward and direct material linkages on the regional 

level areweak. In fact the opposite seems to be true. 

In the case of large firms, agglomeration 

phenomena based on knowledge complementarities 

(Botazzi et al. 2001) seem to be clearly evident.  

The R&D capacities of the observed firms were 

highly varied. Nearly half of the investigated firms 

(mostly SMEs) do not employ permanent R&D 

staff. The leading firms have intensified R&D 

activities and formal co-operation with knowledge-

generating institutions since the mid of the 1990s. 

Especially in the case of pre-competitive R&D co-

operative publicly supported projects or 

participation in cooperative R&D institutions has 

gained an increasing role as a policy measure 

during the last decade.  

In respect to knowledge-driven activities, 

elements of agglomeration phenomena based on 

knowledge complementarities were observed 

(following Botazzi et al. 2001). These were also in 

line with the exclusivity characteristics suggested 

by the industrial complex model (Gordon and 

McCann 2005). The large R&D intensive firms 

observed here, constantly seek forms of regional 

pre-competitive R&D cooperation. This may result 

in the formation of “Clubs” (Gordon and McCann 

2005, Cooke 2000) of closer interaction especially 

in respect of R&D, or in some cases cooperative 

R&D institutions. While material input-output 

linkages are spreading widely and are outward 

oriented the R&D-oriented sphere is concentrated 

on the local context. This to a large extent 

supported by intensive direct and indirect social 

interaction (informal exchange, contacts in the local 

technical community). During the past few years, in 

terms of innovation firms already active in R&D 

have undergone a shift from being demand pull 

driven (responding to market demands) to 

technology push driven (firms have become 

proactive in their search for new technologies and 

USPs). This has increased the motivation to be 

integrated in the regional (technical) science 

community. The main spheres of economies of 

agglomeration have shifted considerably during the 

last few decades. The newly identified research 

‘Clubs’ in publicly supported R&D-projects are 

able to utilize economies of agglomeration 

primarily concentrated in the field of R&D and 

science.  

As long as natural spill-overs are high and 

competitive conflicts are manageable (e.g. in the 

case of material sciences) larger firms accept 

weaker partners and smaller firms and are willing to 

integrate them into their activities. Low spill-overs 

and a higher market orientation favour more 

restrained, sometimes exclusive behaviour from the 

stronger party.  This corresponds with the findings 

in the literature for partner selection in R&D-

cooperations (Atallah 2005).  

This form of agglomeration, partially taking 

place beyond formal networks, also corresponds 

with the idea of a new type of social network 

mentioned by Iammarino and McCann (2005). 

Firms engaging in cooperative pre-competitive 

R&D and knowledge generation appeared to seek 

suitable equal partners.  

The qualitative interviews strengthened the 

notion that firms attempt to generate a portfolio of 

cooperative partners which consciously combines 

specialization and flexibility. In terms of 

knowledge generation and exchange, the 

geographic dimension is relevant as long as the 

actors are able to utilize knowledge potential. While 

larger firms with noticeable R&D-capacities are 

able to utilize international contacts in research and 

development activities, smaller low- or medium-

tech firms stick to the region and to their regional 

partners. Smaller firms are confronted with a self-

reinforcing combination of low R&D capability on 

the one hand, and limited market demand on the 

other. 

In agreement with the concept of absorptive 

capacity, it was found that firms with low R&D and 

innovation potential (mainly component suppliers, 

where innovation is predominately directed by 

investment) found it difficult to build up and retain 

adequate relations with knowledge generating 

organizations. The medium-tech component 

suppliers observed here, proved to be unable to 

maintain continuous relationships with knowledge 

generating institutions. They were not capable of 

defining, setting up and managing relevant projects. 

These low and medium-tech firms did, however, 

partially utilize opportunities to establish long-term 

contacts with individual public or semi-public 

R&D-institutions (dealing with basic technologies 



58 
 

 
 

such as material sciences). They also tried to gain 

from possible spill-overs from appropriate events or 

informal inquiries. Here we found that, in direct 

delivery and in competitive and pre-competitive 

research and development (as far as existent) is not 

identical.  

In addition, two interesting long-term 

partnerships between small knowledge intensive 

technical business services and large systems and 

component suppliers were observed, in the network 

analysed here. They were based on long-term trust 

and informal exchange.  

4 Final remarks 

 
The roles of clusters, networks and geographical 

agglomeration are subject to considerable co-

evolution. Different approaches concerning forms, 

channels and mechanisms of knowledge exchange 

offer different conclusions with respect to the 

significance of geographical agglomeration in 

knowledge exchange. 

In the case study analysis different dimensions 

of interaction can be observed. There are 

networking-dimensions of material, supply-oriented 

transactions and networking-dimensions of 

knowledge sharing. The first belongs to the process 

of division of labour dealing with the exchange of 

goods and services, the second with knowledge. 

The main differences reside in the form of 

interaction and in the impact of interaction. The 

spheres of physical interaction (subcontracting 

relations) differ considerably from the spheres of 

knowledge intensive and R&D-driven interactions. 

They are different in respect to actors involved, 

spatial extension, and significance of geographic 

agglomeration. 

The observed network is, in its regional 

dimension, dominated by knowledge intensive 

relations. The qualitative evidence gathered by 

numerous in-depth interviews reveals that the 

highest number of interactions was reached in pre-

competitive R&D knowledge exchange and that 

immaterial dimensions dominate the material ones. 

The (industrial) firms do have extensive supplier 

relations but only to a very limited extent within the 

region and within the network. There is no 

automatic parallelism of interactions. This does not 

necessarily exclude automatic spill-over of 

knowledge connected with supplier relations, but it 

does emphasize that higher intensities of knowledge 

exchange, as indicated by the revealed forms of 

interaction are actively chosen and not a mere by-

product. Knowledge oriented relations within the 

network are to a large degree regionally 

concentrated. Proximity per se is not sufficient to 

generate knowledge between firms. The diffusion 

of knowledge within clusters is highly selective and 

depends strongly on the position of firms within 

networks and their absorptive capacity. Especially 

in pre-competitive research, local universities and 

cooperative R&D institutions play a dominating 

role and assume gatekeeper functions. Firms with a 

relatively high R&D capacity also take up such a 

role, thus indicating the necessity of a well-

developed internal knowledge base. The 

dominating role of the newly founded cooperative 

R&D institutions (competence centres) can be taken 

as an indication that this kind of network relation is 

rather new and that the pattern of interaction has a 

temporary character and depends of the existence of 

specific kinds of knowledge generating institutions. 

In the Styrian case, the main dimensions of 

economies of agglomeration have changed 

considerably during the last few decades. The 

portfolio of interactions and the meaning of 

agglomeration for the observed firms cannot be 

reduced to specific dimensions taken as a given or 

not. To this extent that they are determined by firm 

capabilities and firm behaviour, not all dimensions 

of agglomeration and thus economies of 

agglomeration are accessible for all agents. While 

small and medium sized firms partially gain from 

economies of agglomeration in the field of basic 

technologies such as material sciences or tool 

making, large firms concentrated pre-competitive 

research in the region in order to gain from 

economies of agglomeration in the field of science 

and R&D.  

These agglomeration effects still seem to be 

concentrated around certain insider ‘clubs’. A 

considerable share of the firms investigated is not 

able to participate and gain from economies of 

agglomeration. While there is a long tradition of 

pro-active cluster and network promotion in Styria 

sectorial diversity (i.e. a low critical mass of actors) 

and relatively low absorption capacity serve to 

hamper the potential gains from economies of 

agglomeration for a considerable number of SMEs. 

 

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