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Clusters, structural embeddedness, and knowledge: A structural embeddedness

model of clusters

Shaowei He

Centre for Urban and Regional Studies

University of Birmingham, U.K.

Email:

[e-mail]

-Paper to be presented at the DRUID-DIME Winter PhD Conference, Skoerping

, Denmark, 26

th

– 28

th

January, 2006

Abstract: The paper is concerned with network structure and its relation to clusters

development with reference to knowledge creation and diffusion. Consciously trying

to avoid ‘sloppy thinking’, it revisits the concept of ‘structural embeddedness’ and

uses it as the analytical lens. The paper demonstrates that both cohesive internal

linkages or ‘closure’ and diverse external linkages or ‘range’ are important for

clusters development. While ‘closure’ is important for fine-grained information

transmission and action coordination, ‘range’ brings into the cluster novel information

and knowledge and therefore prevents the cluster being locked-in. As clusters and

firms face both uncertainties in knowledge access and in action coordination, the

network structure needs to strike an appropriate balance between ‘closure’ and ‘range’.

The specific network structure of a cluster depends on the specific uncertainties it

faces. When the prominent uncertainty is in accessing relevant knowledge, the

balance may need to lean towards ‘range’. When the greatest uncertainty is in

securing the coordination, the balance may need to lean towards ‘closure’. Finally, the

paper shows that the appropriate mix of ‘range’ and ‘closure’ is contingent on clusters

difference and cluster life cycles, which influence the uncertainties facing a cluster.

Key words: clusters, knowledge, structural embeddedness, internal linkages, external

linkages, range, closure,

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1. Introduction

Arguably cluster theory has evolved towards an embeddedness approach,

emphasizing the social context in knowledge creation and dissemination and

innovation. Consequently, the analysis of networking relationships has become the

core of various accounts of clusters. However, the arguments in current literature are

to a large extent biased toward local embeddedness or geographic proximity for

promoting and sustaining the kinds of relationship necessary for innovation. It has

been noticed that a good part of the existing literature looks at clusters as isolated and

self-contained entities (Breschi & Malerba, 2001). In particular, the growing emphasis

of tacit knowledge and collective learning process helps to legitimize the arguments

such as ‘localized capabilities’ (Maskell and Malmberg, 1999) and regional-specific

assets, for example ‘untraded interdependencies’ (Storper, 1995), which are based

upon intra-regional linkages. While there is recognition of the importance of external

relationships and linkages, especially where clusters are export-oriented, the nature of

extra-regional linkages is ‘characterized explicitly or implicitly as arm’s-length’ and

the external world is described at most ‘as a market presenting competitive challenges

that must be met through improved organization and effort within the cluster’

(Humphrey & Schmitz, 2002: 1019).

This bias towards intra-regional linkages might be explained by the interest in the

‘new regionalism’ (Lovering, 1999; MacLeod, 2001). In the ‘new regionalism’, it is

argued that most of the essential determinants of economic performance reside in

regions which are characterized as “containing the full range of activities required to

produce finished products for the world market, or at the very least retaining the core

functions’ (Humphrey & Schmitz, 2002: 1019). By presuming the importance of the

‘local’ or the ‘region’, innovation is viewed ‘as a result of social processes which

depend on close interaction and network linkages in localized production contexts’

(Bathelt, 2003: 769-770). Consequently, much of earlier work ends up with ‘find[ing]

indicators that conforming this’ (Wolfe & Gertler, 2004).

However, it has been pointed out how problematic this ‘new regionalism’ is and how

rare it is to find a self-referential region, as regions are strongly dependent on national

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institutions and other external influences and lack important political decision-making

competencies (Lovering, 1999; Hess, 2004; Bathelt, 2003;). In addition, numerous

empirical studies have demonstrated that external or global linkages are as much

important, if not more than, as internal linkages in the innovation processes (for

instance, Hendry et al, 2000; Saxienian & Hsu, 2001; Britton, 2003; Simmie, 2004;

Leibovitz, 2004).

Despite the well-documented importance of external linkages in innovation and

economic development (Grabher, 1993; Simmie, 2004) and a few recent attempts to

conceptualize them into cluster models (Wolfe & Gertler, 2004; Bathelt et al., 2004),

they are still ‘weakly theorized’ in the current cluster literature (Humphrey & Schmitz,

2002). There still lacks systematic and dynamic conceptualization of ‘embeddedness’

and ‘structural embeddedness’ in particular. If we accept that ‘structural

embeddedness’ is the structure of overall networking relations (Granovetter, 1992)

and define it as the structure of internal linkages and external linkages in the case of

‘clusters’, then the questions we need to ask are: What are the functions of internal

linkages and external linkages in knowledge creation respectively? What is the

relationship between these two kinds of linkages? What is the structure of the two

linkages in clusters and what are its impacts on knowledge flow and innovation

(Staber, 1996; Tracey & Clark, 2003).

It is the objective of this paper, based on the deficit in the literature, to develop a

systematic and dynamic analysis of clusters, using ‘structural embeddedness’ as the

theoretical analyzing lens. The arguments moves as follows: The next section revisits

the concepts of ‘embeddedness’ and ‘structural embeddedness’ in the economic

sociology literature (where they originated), with the aim to avoid ‘fuzzy’

conceptualization of ‘embeddedness’ in regional sciences (Hess, 2004). In section 3, a

structural embeddedness model of clusters is presented, discussing the functions of

both internal and external linkages and the relationship and structure of the two

linkages in regard to knowledge creation and diffusion. Section 4 moves on to discuss

the contingent factors that influence the ‘optimal’ structure of internal and external

linkages for clusters. The final section summarizes the argument of the paper and

presents its implications for policy.

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2. A brief introduction to embeddedness and structural embeddedness

The concept of ‘embeddedness’ has gained much prominence in regional

development studies including works in clusters. However, it is criticized that the

concept has been applied with ‘spatial fetishization’ (Lewis et al., 2002: 441) and

‘new regionalism’ is almost conceived as ‘the only spatial logic of embeddedness in

an era of globalization’ (Hess, 2004: 167). Particularly, as ‘many studies in the new

regionalism tradition pay attention almost exclusively to local and regional systems of

economic and social relations’ (Hess, 2004: 166) and overlooked the non-local

relations, the ‘structural embeddedness’ elements, which is emphasized much by

Granovetter (1992) is to a large extent neglected in regional studies until recently. It is

therefore worth pausing for a while to revisit the concepts of ‘embeddedness’ and

‘structural embeddedness’ and the basic ideas behind them.

2.1 embeddedness and structural embeddedness

According to Granovetter (1985, 1992), embeddedness refers to the fact that

‘economic action and outcomes, like all social action and outcomes, are affected by

actors’ dyadic (pairwise) relations and by the structure of the overall network of

relations’ which he refers to relational embeddedness and structural embeddedness

respectively (Granovetter 1992: 33). Therefore relational embeddedness reflects the

nature of dyadic (pairwise) relations (through strong ties or weak ties), which has

direct effects on individual’s economic action, while structural embeddedness goes

beyond the immediate ties and refers to the aggregated impact of dyadic relations.

By stressing structural embeddedness, Granovetter argues that not only do personal

relations matter, but also the structure of the overall network of relations. If the pair of

individual relations is abstracted out of the wider social context, the ‘atomization’ of

human behaviour ‘has not been eliminated, merely transferred to the dyadic level of

analysis’ (Granovetter, 1992: 34, emphasis original). The overall networks of

relations has subtle and indirect impact on human behaviour. Granovetter (1992)

explains this well:

“A worker can more easily maintain a good relationship with a supervisor who has good relations with

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most other workers as well. If the supervisor is at odds with the others, and especially if those others

are friendly with one another, they will be able to make life very difficult for the one worker who is

close to the supervisor; pressures will be strong to edge away from this closeness’. (Granovetter, 1992:

35)

There is a general agreement that social structure is a kind of capital that can create,

for certain individuals or groups, a competitive advantage in pursuing their ends (Burt,

2000). Two contrasting arguments emerge in the literature that illustrate functions of

different types of structural embeddedness. The first is Coleman’s (1988) closure

argument, to which many current embeddedness models to regional development

correspond. The other is Burt’s (1992) structure hole argument. Although it is highly

influential in organization sciences and business literature, it is often neglected in the

regional sciences.

2.2 Closure

Coleman’s (1988, 1990) basic conclusion is that actors are better off in networks with

‘closure ‘ - a dense network in which actors are tied to multiple actors, who are

connected to one another. The first advantage of ‘closure’ is that it affects access to

information and speeds up information transmission. As Coleman (1988: p104)

explains: ‘An important form of social capital is the potential for information inherent

in social relations….A social scientist who is interested in being up-to-date on

research in related fields can make use of everyday interactions with colleagues to do

so, but only in a university in which most colleagues keep up-to-date’.

The second advantage of ‘closure’ comes from the development of trust, norms and

culture, which govern actions in the network. In a closed community, trust develops in

the frequency of interaction, in ethnic and family ties, through religious affiliation and

so on. An example of the Jews-dominated wholesale diamond market in New York

City suffices to explain this (Coleman, 1988). There are close ties among these Jews

as they live in the same community in Brooklyn, have a high degree of intermarriage,

and go to the same synagogues. Precious stones could change hands for inspection

without formal insurance because of the presence of the closed ties within the

merchant community. Without these close ties, expensive insurance devices must be

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present to ensure the transaction to take place.

‘Closure’ governs actions within the community not only through trust, but also

through sanctions and monitoring. The news spread so quickly in the closed

community that bad behaviour and malfeasance accumulate to bad reputation in the

community which excludes the offender from the community. The economic and

social costs of developing a bad reputation is so high that one seldom dare to offend

(Granovetter, 1992).

In addition, the pressure against cheating not only arises from direct sanctions, but

also from the fact that ‘closure’ is more efficient than sparse relational networks at

generating norms, culture, rules. ‘Closure’, through its long history of mutual

interaction, has accumulated a set of ‘institutions’ such as conventions, rules (explicit

or implicit) and culture. Individual members have absorbed a set of standards from the

community because they have been in the community for so long, which means

cheating is ‘literally unthinkable’ (Granovetter, 1992).

Finally, we should also be reminded that the social capital derived from closure not

only facilitates certain actions, it also constrains others. The norms in the diamond

wholesale market for example may prevent members from transacting with dealers

outside of the community. Also the obligation to the community may ‘prevent

members from participating in broader social networks’ (Woolcock, 1998: 158).

2.3 structure hole

Rather than emphasizing the benefits of being within a cohesive network or ‘closure’,

Burt (1992, 1997, 2000) argues that social capital also emerges from another

particular type of network – network rich in structural holes. As ‘closure’ is often

associated with strong ties, structure holes are mainly concerned with weak ties or

nonexistent ties. Granovetter (1973) argues that weak ties are essential to the

information flow between otherwise disconnected groups. Burt (1992, 1997) extends

the argument. He claims that a structural hole exists wherever there is a gap between

the otherwise disconnected groups. For Burt, it is structural holes that breed potential

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benefits. Hence once they are bridged, whatever by strong ties or weak ties

1

, there are

advantages for the actors who bring the otherwise disconnected partners together.

The first advantage concerns information access. As discussed in the ‘closure’

argument, ties to multiple actors, who are connected to one another, provide

redundant information, as similar information circulate in the network (Granovetter,

1973; 1992). A structural hole however, indicates that the people on either side of the

hole circulate in different flows of information – nonredundant information, which is

more additive than overlapping (Burt, 1992). Therefore, an actor who spans the

structural hole has access to both information flows. This could be demonstrated in

the Figure 1(see the appendix):

In Figure 1, actor X in network A exists in a ‘closure’, which provide reliable,

fast-transmitted information. However, everyone in the network tends to know the

same. In network B, there are structural holes between groups K, L, M and N, while

actor X spans the structural holes. Therefore X get access to four distinct information

sources. The potential nonredundant information advantage that the structural holes

generate has been confirmed in one of Granovetter’s (1973) widely known study –

‘the strength of weak ties’, which demonstrates that white-collar workers find better

jobs faster because of the weak ties that link otherwise disconnected social groups.

In addition, actors spanning structural holes are awarded brokerage benefits, because

the otherwise disconnected partners could only communicate through them (Burt,

1992). Therefore they act as intermediaries or brokers who facilitate exchange flows

across the network and broker tensions between other actors, ‘which gives them

disproportionate say in whose interests are served when the contacts come together’

(Burt, 2000: 10). The brokerage role in turn presents opportunities for entrepreneurial

activities (Burt, 1992). The brokers may be able to extract superior terms of trade

because of possible control benefits (Gulati, 1998).

Despite the nonredundant information and brokerage benefits, there is downside of

structural holes: the sparse network in which structural holes exist cannot provide the

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Burt (1992) agrees that usually it is weak ties that span structural holes.

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governance mechanism to impede opportunism which is present in ‘closure’ (Rowley

et al., 2000).

3. A structural embeddedness model of clusters

If we view clusters as networks of firms and associated institutions, then there is

much that structural embeddedness can tell about how particular network structure

can lead to mutually adaptive learning processes, with consequences for innovation.

In this section, a structural embeddedness model of clusters is presented, examining

the role of internal linkages and external linkages as well as the impact of networking

structure on cluster development, particularly in regard to knowledge creation and

diffusion.

A cluster is defined here as a network of proximate firms and associated institutions,

linked by traded interdependence and untraded interdependence (Storper, 1995)

around a core activity. It has a densely connected critical mass in a geographical area

and is bridged through external linkages with the global production/innovation

network. Ongoing interactions between internal actors and/or external actors

determine the dynamics and evolution of the cluster (internal actors means actors

within the geographically critical mass while external actors refers to those outside the

critical mass).

The definition describes a cluster as a network of internal actors and external actors.

Therefore the network relationships could be grouped into internal linkages (linkages

between internal actors) and external linkages (linkages between internal actors and

external actors). Compared to those between internal actors and/or external actors,

interactions between internal actors within the critical mass are usually very intensive

and frequent. Hence a cluster could be conceived as a dense network of internal actors

within the critical mass, linked through relatively sparse external linkages to other

clusters or organizations and therefore embedded in the global production networks or

global value chains (See figure 2). In this sense, a cluster is an ‘open system’ (Bathelt,

2003).

Figure 2 network structure of a cluster (sorry, I have to draw it manually)

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It is also worth noting that the definition talks about internal linkages and external

linkages rather than intra- and extra-regional linkages, as the critical mass of a cluster

rarely fits in well with the boundary of a region. External linkages may well include

linkages with actors outside the critical mass but within the same region. On the other

hand, some extra-regional actors may well be within the cluster critical mass.

As it is well acknowledged that economic actions are embedded in social relationships

and the ongoing network of social relationships, the following analysis adopts the

language of ‘structural embeddedness’ and put social relationships between actors and

networks of these social relationships at the center of the analysis. The focus is on the

relevance of these relationships/linkages and structure of these linkages to knowledge

creation, diffusion and exploitation. The analysis is not an attempt to explain the

whole story behind clustering. Rather, the aim is to emphasize the upsides and

downsides of both internal linkages and external linkages in knowledge generation

and innovation and therefore the need to strike an appropriate balance between them.

3.1 clusters, knowledge and network structure

internal linkages, closure, and knowledge

Firms and other organizations in a cluster are embedded in a dense critical mass of

internal linkages. The dense network of internal actors is in effect a ‘closure’ in

Coleman’s (1988) term. Each actor is not necessarily strongly connected to everyone

else. However, there are many strong ties and there are multiple links between actors

at multiple levels so that each members of the ‘closure’ is located ‘in a dense

communal web of overlapping affiliations and obligations’ (Owen-Smith & Powell,

2004).

Within the cluster critical mass, a network of communication and information

linkages develops among the densely connected internal actors as the result of the

history of prior interactions. Information and knowledge is transferred in the daily

interactions and face-to-face contacts. The rich history of social interaction within the

‘closure’ generates multi-level relationships between individuals and organizations,

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which link actors in multiple ways (as business partners, friends, agents, etc), offering

a great deal of channels through which information is transmitted from one end to

another (Uzzi, 1997). Over time, an ‘information and communication ecology’

(Bathelt et al., 2004), or ‘buzz’ to use Stoper & Venables’ (2002) terminology, is

formed in which information and knowledge is transferred with high velocity and is

continuously updated.

Within the ‘closure’, trust is developed when extra effort is voluntarily given and

reciprocated (Uzzi, 1997) and over time it is enhanced as misunderstanding and

suspicions are gradually eliminated through the ongoing interactions (Gulati, 1998;

Maskell & Lorenzen, 2004). In addition, ‘closure’ can also serve as a basis for

‘enforceable’ or ‘deterrence-based’ trust (Gulati, 1998). The potential sanctions

means that, even there is not real trust developed among actors in the ‘closure’, they

can still behave as if they trust each other (Granovetter, 1985; Maskell & Lorenzen,

2004).

The primary outcome of trust within the ‘closure’ is that it promotes greater exchange

of knowledge and access to privileged and fine-grained information, which is difficult

to obtain in other network structure (Uzzi, 1997). Trust not only affects information

access, it also helps to smooth interactions and develop flexible expectations among

partners (Gulati, 1998), all of which generate favorable conditions for the success of

collaboration, joint-problem solving and collective learning.

Apart from trust, ‘closure’ is conducive to the development of other institutions like

norms, conventions and routines, which are established and modified through

day-to-day interactions, regular meetings, collaborations and joint-problem solving.

They help firms to understand their partners, to develop trust and flexible expectations,

and to reduce uncertainty in knowledge transfer and economic transaction (North,

1990; Lawson & Lorenz, 1999; Bathelt, 2003). Therefore the institutional framework

serves as an enabler that facilitates the process of joint-problem solving and provides

the basis of collaborating and learning between cluster actors.

The previous discussion is a familiar story readers have seen in numerous studies in

regional sciences. However, there are further points that this paper would emphasize.

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The first concerns the mechanism by which ‘closure’ transmits information.

Owen-Smith & Powell’s (2004) ‘irrigation’ metaphor is very illustrative in this sense.

In a ‘closure’, fine-grained information is transferred between actors that are strongly

connected. Trust derived from previous interaction means one side is more inclined to

release information and the other side more willing to accept it (Reagans & McEvily,

2003). Apart from this point-to-point transmission, information could also be

transferred within the ‘closure’ through ‘irrigation’. Therefore within a ‘closure’,

actor A need not to be directly connected to actor C to get information. As long as

there is a co-partner B between them, B would pass by the information from C to A

and vice versa. By the same mechanism, other actors within the ‘closure’ could also

be ‘irrigated’ with the information originally from C. Hence the ‘closure’, which is

rich in relational linkages, breeds potential collective information benefits for all the

members within the community. As a result, firms not only benefits from greater and

finer information exchange from direct contacts, but also could tap into the

knowledge pool within the critical mass through ‘irrigation’.

The second point that deserves attention is the fine-grained nature and quality of the

information being transmitted in the ‘closure’ (Uzzi, 1997), particularly in the context

of learning and innovation. Actors within the ‘closure’ are likely to be familiar with

each other’s practices and behaviors. They may prescreen or sift information

according to their partners’ needs before passing over the information, because

through prior relation they know what their partners need. In addition, trust within the

community reduces the risk for the receivers to accept the information. In other words,

the receiver would accept the information because it believes the information is sent

by ‘good will’. Moreover, familiarity also makes it easier for the receivers to correctly

interpret the information that is passed over. Over time the ‘closure’ may even

develop its own language through which very detailed information could be

transferred. The information could be so subtle and implied than overtly expressed in

conversation that it is hard for outsiders to understand. This is illustrated in Uzzi &

Lancaster’s (2003) study on a local group of bank loan managers, which demonstrates

that knowledge circulated in ‘closure’ could be very ‘private’ but critical to the

learning process.

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the downside of ‘closure’

Despite the well-acknowledged and documented benefits of being in the ‘closure’, it

has been warned recently that there is a downside associated with densely networked

firms. Research in the business literature has demonstrated that the same processes by

which the ‘closure’ ‘creates a requisite fit with the current environment can

paradoxically reduce an organization’s ability to adapt’ (Uzzi, 1997: 57).

When embedded in dense internal linkages in a ‘closure’, firms are connected with

one another through various relationships. While this network structure offers the

advantage of getting fine-grained information, it can also reduce the flow of new or

novel information into the ‘closure’. Because multiple links to the same network

partners means there are many redundant ties within the ‘closure’ but few or no links

to the outside members who can potentially contribute innovative ideas (Burt, 1992).

In other words, there is a danger for the ‘closure’ to be ossified without the inflow of

new or novel information.

In addition, as time goes on, trust, conventions, norms, and other institutions, which

are derived from the intensive interconnectedness, could become the very obstacles

for further growth or the seed of decline. Within the ‘closure’, there is a tendency for

homogeneity (Granovetter, 1992). Managers, engineers and workers tend to think and

behave in a similar way and to adopt similar cognitive frameworks or ‘mental models’

(Pouder & St. John, 1996) to solve problems, conforming to the institutions. These

‘mental models’ and institutions, once being formed, tend to persist, even in the face

of contrary evidence (Pouder & St. John, 1996). Therefore when faced with an

environmental jolt, firms within the ‘closure’ may not be able to recognize the need

for radical change. They are less likely to challenge institutional norms. Hence the

network structure of ‘closure’ may limit cluster firms’ ability to anticipate and react to

environmental shocks and therefore their innovative potential. In other words, there is

a real danger of homogeneity in relation to innovation (Tracey & Clark, 2003). This is

the process through which a cluster decline over time, from ‘hot spots’ to ‘blind spots’

(Pouder & St. John, 1996).

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external linkages, range, and knowledge

Firms and other actors within the cluster critical mass are connected with each other

by multiplayer relationships, which consist of the ‘closure’. At the same time, the

‘closure’ is linked to the outside world through some external linkages established by

some of its internal actors. They are the ones that bridge the ‘structure hole’ – the gap

between other internal actors and external actors. They are therefore hole spanners.

Without them, the critical mass would be insulated from the outside. In figure 2,

actors a, b and c are hole spanners. They link other internal actors with external actors.

At the aggregate level, they are also the channels of communication through which

the whole internal critical mass is connected with the external world. However, the

structure of these external linkages is likely to be different with that of the internal

linkages. The overlapping, multilevel and cohesive relationships within the ‘closure’

are less likely to be found in the network of external linkages. There could be some

strong ties involved in external linkages. However, it is rare to observe actors in the

external linkages to be connected with one another. In another word, external linkages

are more likely to be independent from each other. Reagans & McEvily (2003) use

‘range’ to describe the structure of the external links outside a cohesive group which

is rich in structural holes. Therefore, while ‘closure’ reflects the extent to which

internal actors are connected with one another, ‘range’ indicates the extent to which

the ‘closure’ spans structural holes between other clusters or groups. The different

network structure implies that ‘range’ may function differently from ‘closure’ in

terms of information and knowledge flow.

According to Burt (1992, 2000), hole spanners bridge different flows of information.

By building external relationships with actors outside the cluster critical mass, hole

spanners tap into nonredundant sources of information. This gives hole spanners huge

advantage. Through the internal mechanism of the ‘closure’, the nonredundant

information from structural holes will finally diffuse across the critical mass’: other

internal actors benefit the information through direct links with spanners and through

referral and irrigation. The nonredundant information then becomes redundant after

the process.

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The external linkages that span structural holes could be strategic alliances, R&D

collaboration projects, mobile labourers and so on. Hole spanners could be innovation

centers of international companies, subsidiaries of TNCs, international logistics firms,

and global component suppliers. They could also be key scientists and researchers

who travel and meet other people around the world. These hole spanners therefore

bring into the ‘closure’ new market information, new development of technologies in

other places and in other but related technological areas, new ways of organizing

production, new method of cost management, new production standards, etc.

It is this novel information that deserves our special attention. We have known

‘closure’ is good at circulating more reliable, more accurate but redundant

information. In contrast, information transmitted through structural holes is more

likely to be new and novel. Remember how important that new information is to

innovation as innovation is very much about breaking up old tradition. The novel

information coming from outside also provide stimulus to prevent the cluster from

being ‘locked-in’. We have known ‘closure’ generates homogeneity over time.

Cluster actors tend to conform to traditional rules and conventions formed through

previous experience and interaction, using old ‘mental models’ to solve problems.

Quite likely it is only through the injection of new ideas and knowledge through hole

spanners can cluster actors challenge institutional norms, discard old ‘mental models’,

and form new ones.

Burt (1992) based his structural hole at the individual person or firm level. However,

we could extend the argument to the aggregate group level or the cluster level. For

example, again in figure 2, there is a structural hole between cluster B and C. Only

through cluster A are they connected. Therefore cluster A is a hole spanner and it

enjoys novel and diverse information and knowledge coming from both cluster B and

C. According to Kaufmann & Tödtling (2001), the novelty of exchanged information

fades over time as ‘partners become locked into well-established routine interactions’

(p796). Therefore one of the propositions for clusters would be to build up a broad

range of diverse external linkages, widening the ‘range’. The wider the ‘range’, the

broader and more diverse information the cluster embraces. Also note that cluster A

also act as a broker between cluster B and C as otherwise B and C are disconnected.

The information advantage and brokerage role partly explain the success of Silicon

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Valley. According to Sturgeon’s (2003) analysis of the electronic industry, there has

been a trend towards spatially dispersed clusters, with Silicon Valley emerging as the

hub for the global network. The explanation from the ‘range’ perspective would be

that Silicon Valley enjoys diverse information sources as other clusters are all

connected with it (according to Bresnaham et al. (2001), the ICT clusters in Isarael,

India, Ireland and Taiwan all had significant ties with the Silicon Valley). It also

enjoys the benefits of its brokerage position, as it command and control the operation

of the whole global production network.

the downside of range

Certainly a network structure with many structural holes has its disadvantages. Firstly,

although ‘range’ may offer new and novel information which would not be circulated

in the ‘closure’ before, information transferred is less likely to be as subtle and

accurate as those diffused within a ‘closure’. Because many linkages that bridge

structural holes are weak ties, partners are less motivated and obliged to transfer

fine-grained information. The information is unlikely to be prescreened and

preprocessed according to the receivers’ needs. The receiver might have to pick up the

useful information from a lot of ‘noise’. Certainly overtime as more efforts are put in,

more interactions will take place and partners may become more familiar with each

other and therefore trust may develop with the relationship becoming more intimate.

However first that entails huge costs because of the distance and the gap between

different knowledge areas. Second, the novelty of exchanged information fades over

time as interactions become routine (Kaufmann & Tödtling, 2001).

The second disadvantage associated with ‘range’

is that it does not offer the same

kind of trust and institutions that present in ‘closure’. Trust between partners that are

distant and in different knowledge areas takes enormous time and efforts (Bathelt et.

al., 2004; Morgan, 2004). Even though trust could be built between individual

partners over time, there is no ‘public trust’ within ‘range’. As external partners are

unlikely to be familiar with each other, there is no sanction and group monitoring

mechanism in the ‘range’. Also as actors in the ‘range’ are less likely to be mutual

interdependent on one another, less likely to have day-to-day and fact-to-face actions

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and regular meetings, norms and conventions are not easy to develop. The uncertainty

derived from the distrust and unfamiliarity forms the barrier to fine-grained

information transfer and joint-problem solving.

3.2 integration of ‘closure’ and ‘range’

mutual dependence between ‘closure’ and ‘range’

Knowledge flows through ‘closure’ and ‘range’ are indeed two distinct processes, but

they are not independent on each other. On the contrary, they rely on each other.

Firstly, ‘range’ provides opportunities to get access to novel knowledge, which is

critical to clusters development and help the ‘closure’ avoid being rigid and

‘locked-in’. There is similar thinking in Jessop’s (2001) “autopoietic system’ when he

argues that ‘an autopoietic system always co-exist with other systems that constitute

key elements in its environment and depends on them for essential conditions for its

own operation” (p218). It is argued that few clusters possess in the ‘local’ the

complete knowledge base they need to draw upon. On the contrary, the knowledge

flows that feed innovation in a cluster are increasingly being found somewhere else

(Wolfe & Gertler, 2004). Therefore clusters could only be successful if they can build

a variety of channels for accessing relevant information and knowledge from around

the world (Bathel et al., 2004), as demonstrated in the development of Taiwan’s

Hsinchu cluster (Saxenian & Hsu, 2001).

From a different perspective, Tracey & Clark (2003) explain how the ‘closure’

becomes rigid and ‘blinded’ if without external stimulus: individuals and firms are

often convinced of the effectiveness of their existing ways of thinking and operation.

They may continue to reply upon them and effectively make decisions within fixed

frames of reference, as the norms and routines have been taken for granted. Through

this negative ‘single-loop learning’, firms prison themselves in the existing patterns of

behaviour, which impairs their abilities to formulate the necessary strategy. It is

usually through the injection of external stimulus can the existing ‘mental model’ and

institutions be updated, as demonstrated in the development of the Chilean tomato

processing cluster where Japanese multinationals helped Chilean firms develop new

quality management practices and solve the long suffering problem of coordinating

17

between processors and suppliers (Perez-Aleman, 2005).

The other side of the mutual dependence between ‘closure’ and ‘range’ is that the

novel information grasped by ‘range’ needs ‘closure’ to be exploited. This is in fact

one of the central themes of Bathelt’s (2003) social systems model of geographies of

production as he proposes that ‘systems collect information from their (external)

environment, process and interpret this information and, subsequently, derive

operations from this’ (p766). How effective and efficient the ‘closure’ responds to the

new information and the change in its environment depends on its own ‘codes and

programmes’ (Jessop, 2001). Wolfe & Gertler (2004) step further by pointing out the

effectiveness of the ‘international pipelines’ depends on the quality of trust that exists

between the firms in the ‘local buzz’. ‘Closure’ helps to generate trust. According to

Krackhardt (1992), trust can reduce resistance and provide comfort in the face of

uncertainty. This partly explains how ‘closure’ can facilitate the change that ‘range’

brings.

Recent work in the business literature has corroborated the impact of ‘closure’ on the

effectiveness of ‘range’. Gargiulo & Rus (2002) for example, address the issue of top

management performance in terms of ‘access’ and ‘mobilization’: According to their

argument, while ‘range’ confers competitive advantages through maximizing access

to diverse information, ‘closure’ help to secure the mobilization necessary to reap the

benefits created by such access. Therefore, a successful team or group needs to

capture elements of both ‘range’ and ‘closure’ to achieve its goals. Similar

conclusions have been reached by other scholars (for instance, Woolcock, 1998; Burt,

2000; Rowley et al., 2000; Tracey & Clark, 2003; Reagans & McEvily, 2003;).

Perez-Aleman’s (2003, 2005) account of the development of Chilean tomato

processing cluster may suffice to explain this: New quality management practices was

introduced into the cluster through two Chilean firms’ connection with Japanese

companies. However, it was only through collective efforts of the local firms,

including group-based discussions and the established business association – the

Federation of Agro-industrial Food Processors that the novel knowledge spread and

the new practices diffused in the cluster. In this case, the established trust among local

players and the existing networking organizations helped the ‘internalization’ of new

18

knowledge, which in turn improved product quality and industrial productivity. For

Perez-Aleman (2005: 671), ‘local upgrading and growth, however, depend on

substantial indigenous efforts, rather than the classic story of multinationals

transferring technology to a passive recipient setting’.

Trade-off between ‘Closure’ and ‘Range’

While the mutual dependence between ‘closure’ and ‘range’ has attracted some

scholars in regional sciences in recent years (e.g., Sturgeon, 2003; Bathelt et al., 2004;

Wolfe & Gertler, 2004), the trade-off between the two has not been paid enough

attention. However, research in other disciplines suggests that ‘closure’ and ‘range’

could also compete against each other (Granovetter, 1992; Burt, 2000).

Firstly, too strong and too diverse external linkages may threaten the cohesion of

internal linkages and therefore the existence of the cluster (Bathelt et al., 2004). The

introduction of new ways of thinking from outside is usually different from what

exists in the ‘closure’. In addition, they are first brought in by the hole-spanners and

introduced to their direct contacts. Therefore, when the new and alien knowledge,

norms and practices are introduced into ‘closure’ by the hole spanners to their direct

contacts, there is a possibility that these new things would reduce the homogeneity of

bahaviour (Granovetter, 1992) within the ‘closure’ and hence its cohesion, as other

internal actors still stick to existing ‘frames of reference’. The impact of the ‘range’

could be so strong that the foundation of ‘closure’ could be wiped out. Bathelt et al.

(2004) are aware of the danger to clusters when they argue that:

“when actors focus primarily on external linkages, global pipelines begin to dominate the local milieu.

Therefore, less attention is being paid to local communication and information flows and people are

less interested to participate in local broadcasting. As a consequence, the local buzz becomes quieter

and the reasons for firms to locate and remain in the cluster evaporate. Such ‘hollow clusters’ might not

survive in the long-term as firms eventually shift to other locations” (page 48)

Secondly, when connections between internal actors are too dense, ‘closure’ may

become blinded to external knowledge and changes and therefore detrimental to the

effectiveness of ‘range’. This has been explained before and has been shown in many

other studies of the phenomena of ‘lock-in’, ‘blind-confidence’, and

19

‘overembeddedness’ (Uzzi, 1996; 1997; Grabher, 1993b).

Thirdly, internal linkages and external linkages compete for investment of time and

resources. Bathelt et al. (2004) are right to point out that the establishment and

maintenance of global pipelines is not free. The process entails enormous amount of

investment and resources, as the partners on both ends need to develop special

schemes to understand each other’s institutional context and a certain degree of trust

in order to engage interaction (Owen-Smith & Powell, 2002). However, it may be

arbitrary to allege that ‘participating in the buzz does not require particular

investment’ (Bathelt et al., 2004: 38). The nature of buzz or ‘closure’ is indeed

spontaneous and fluid. However, this is the result of a long history of interaction,

which takes a good deal of time and effort. To enjoy the fine-grained information

flowed in the ‘closure’, firms need the ‘insider’ identity, which is not free-given.

Reciprocity is present in the ‘closure’ because ‘we have been close for so long that we

expect this of one another’ (Granovetter, 1992: 42). In addition, the establishment of

norms, conventions and common rules should be the result of a long process of

meeting and negotiation. Furthermore, relationships between the internal actors also

need time and resources to maintain; otherwise they may fade over time. To

summarize, the central theme is that clusters and firms may need to choose between

internal linkages and external linkages as time, investment and other resources are

limited.

Integrating ‘closure’ and ‘range’

There has been dispute in the literature in what is the right network structure for

individuals or groups to be in. More sophisticated arguments suggest though, that an

optimal network structure is a combination of ‘range’ and ‘closure’ as both have

advantages and disadvantages (Woolcock, 1998; Burt, 2000; Reagans & McEvily,

2003). It is argued that the ‘closure’ perspective emphasizes ‘dense patterns of local

interaction as the basis for coordination and collective action’, while the ‘range’

perspective focuses on ‘bridge across global divisions as the basis for information

transfer and learning’ (Reagans & Zuckerman, 2001: 512). Similar but more

embryonic thinking could also be found in economic geography, for example ideas of

20

‘neo-Marshallian nodes in global networks’ (Amin & Thrift, 1992; Coenen et al.,

2004) and ‘local buzz, global pipeline’ (Bathelt et al., 2004).

For Gargiulo & Rus (2002), the controversy on the right type of network structure

with which actors are better off conceals a fundamental difference in the assumption

about the type of uncertainty to which ‘closure’ or ‘range’ is a solution

2

. The ‘closure’

perspective implicitly assumes the uncertainty facing actors is how to secure

coordination between cluster members. The uncertainty is reduced if actors are

embedded in a densely connected network that facilitates information flow and trust

generation. The ‘range’ perspective assumes that the greatest uncertainty is how to

economically secure privileged access to relevant knowledge and information. Thus

actors that can bridge structural holes have a competitive advantage in pursing their

interests.

Yet clusters and firms may face both uncertainty in having access to the right

knowledge and information and uncertainty in their ability to secure coordination and

collective actions to exploit the knowledge. An integration model of clusters therefore

needs to incorporate these two uncertainties into the equation. While the ‘range’ of

external linkages ensures the cluster’s access to diverse knowledge and information,

‘closure’ of internal linkages secures the necessary coordination and cooperation to

pursue the opportunities created by such access. For a given cluster, the appropriate

network structure would then be a function of the criticality of these two distinct

uncertainties. When there is no difficulty to get access to the right knowledge and

information but the coordination is crucial, ‘closure’ of internal linkages will provide

the mechanism to overcome the problem. On the contrary, when accessing to the

necessary information and knowledge rather than the need for coordination appears to

be the critical, ‘range’ of external linkages will be the solution.

4. Contingent factors of structural embeddedness

As time and resources devoted to their networks are limited, clusters and firms need

2 Gargiulo & Rus (2002) discuss the uncertainty that ‘network closure’ and structural hole theory implicitly

assume in the case of top managers’ performance rather than clusters.

21

to strike a balance between ‘range’ and ‘closure’. However, because of the complex

relationship between ‘closure’ and ‘range’, the proper match between the type of

uncertainty and the type of network structure is not easy. Recent work indicates that

the optimal mix of ‘range’ and ‘closure’ is contingent on a number of factors

3

, among

which are type of region, type of industry, type of activity, type of knowledge, type of

learning, etc (Ahuja, 1997; Oinas, 1999; Rowley et al., 2000; Breschi & Malerba,

2001). Obviously this paper cannot discuss all these contingent factors. It hence

chooses to address two important factors: cluster difference and cluster life cycle. The

proposition is that, the criticality of uncertainties in knowledge access and action

coordination is different across different clusters and various cluster life stages, which

requires different structures of ‘closure’ and ‘range’ to facilitate cluster development.

4.1 cluster difference

Clusters vary with the core sectors they are around

4

. To a large extent, the difference

between clusters is determined by the difference between the core sectors they are

based upon. Therefore the cluster difference addressed here is very much similar to

sectoral difference, with particular attention being paid to the fact that clusters are

usually broader than sectors.

Previous empirical studies suggest that the degree of importance of ‘range’ and

‘closure’ is different for different clusters or sectors. Freel (2003), for example,

indicates that, novel innovators have greater geographical reach of their innovation

links, while incremental product innovators appear to be more locally embedded. This

seems to suggest that the ‘range’ of non-local linkages is more important for

research-intensive sectors and local linkages are more important for other sectors.

This is echoed in a study of a biotechnology cluster in Scotland (Leibovitz, 2004)

3 According to Owen-Smith & Powell (2004), this is a proposition that has not been well attended

to in literatures.

There are some embryonic ideas though. For example, Bathelt et al. (2004) agree that a mix of ‘local buzz’ and

‘global pipeline’ is necessary to ensure continued growth and innovation. they also argue that the particular mix

can vary across value chains, technologies and markets segments. they suspect some industries require more buzz

while others need more pipelines, but they do not provide any answer to why that should be the case.

4

Clusters vary along at least three dimensions: the core sectors they are based, the space they are concentrated in,

22

which finds that local interactions are surprisingly weak and that many of the key

relationships operate at an international scale. Hendry et al., (2000), in their study on

opto-elctrinics clusters in six locations also demonstrate stronger global relations vs.

local relations. However, opposite arguments and observations exist. Some studies

have shown that the local environment, and the relative propinquity of innovation

partners, is likely to be more important for novel innovators than for incremental

innovators (Baptista and Swann, 1998; Nooteboom, 1999).

It seems that without clear theoretical guidance, it is very easy for empirical studies to

be biased towards either the ‘range’ argument or the ‘closure’ argument because of

the specific nature of the clusters being investigated and the feature of the locality.

The existing literature can hardly answer the question of ‘why there is cluster

difference in terms of relative importance of closure or range’. Probably the best we

could found is the embryonic idea that ‘some industries require more buzz while

others need more pipelines’ (Bathelt et al., 2004). To find the answer of why, one of

the key questions we need to ask first is, from the knowledge and learning perspective,

what differentiates clusters from each other? In this respect, recent research on

Sectoral Systems of Innovation (SSI) provides important food for thoughts (for SSI,

see Breschi & Malerba, 1997; Malerba, 2001, 2002).

Based on the empirical evidence of sectoral patterns of innovative activities and the

notion of ‘technological regime’ (Nelson & Winter, 1982; Winter, 1984), the concept

of SSI lays out five aspects in which sectoral systems of innovation and production

are different from each other: knowledge base and learning processes; basic

technologies, inputs and demand, with key links and dynamic complementaries; type

and structure of interactions among firms and non-firm organizations; institutions;

processes of generation of variety and selection (Malerba, 2002).

From the knowledge and learning perspective, the discussion on knowledge and

learning processes in the SSI literature is of particular interest as it sheds light on why

and how different clusters may have different emphasis on ‘range’ or ‘closure’.

Following Nelson & Winter (1982)’s idea of technological regime, Malerba (2001,

and the time dimension, i.e., the dynamic evolution process.

23

2002) maintains that, accessibility, appropriability and cumulativeness are key

dimensions of knowledge, which differ across sectors. Technology accessibility

reflects the likelihood of gaining knowledge that is external to firms. Accessible

knowledge could be internal and external to the sector. Cumulativeness refers to the

degree by which new knowledge builds upon current knowledge or the possibility of

innovating along specific trajectories. Appropriability of innovation reflects the

possibility of profiting from innovative activities by constraining imitation and other

opportunitistic behaviour. It is argued that these specificities of knowledge and

technological regimes “provide a powerful restriction on the patterns of firms’

learning, competencies, behaviours and organization of innovative and production

activities in a sectoral system” (Malerba, 2002: 254). Support for the argument could

be found in Pavitt’s sector taxonomy, which demonstrates the contrasting sectoral

patterns in terms of sources of technology, cumulativeness in development and

possibilities for appropriation (Pavitt, 1984).

Extending the argument to cluster difference and exploring its implication to the

structure of ‘range’ and ‘closure’, one could propose that the specificities of

technological regimes and the knowledge base condition the environment within

which clusters and firms operate. A given technological regime and knowledge base

sets the parameters for specific uncertainties in knowledge access and action

coordination. Therefore clusters may be better off with some distinct mix of ‘range’

and ‘closure’ but not with others.

For those clusters characterized by high levels of technological accessibilities,

knowledge is relatively easy to obtain. Clusters and firms hence should endeavor to

build up the necessary coordination mechanism to exploit the knowledge, which

requires frequent and intensive interaction and therefore ‘closure’ of internal linkages.

For those clusters characterized by low level of appropriability of innovation and

opportunistic behaviour, ‘closure’ will help to establish norms, common rules,

regulations, and other institutions, which provide the necessary protection. ‘Closure’

also helps to ease information flows, which is another barrier to appropriability

(Robertson & Langlois, 1995). For those clusters with low level of cumulativeness at

the cluster level, the necessary information and knowledge that is critical to cluster

development is more likely to be found outside the cluster critical mass. Therefore

24

these clusters would benefit more from ‘range’ of external linkages, which provide

the critical new and novel information from many different alternatives.

4.2 cluster life cycle

Obviously the uncertainty problem facing clusters is not static. The structural

embeddedness model therefore needs to consider clusters development over time and

the evolving match between the network structures and the uncertainties they are

aligned to. It is not the case that previous studies paid no attention to the time

dimension of cluster development. Audertsch & Feldman (1996) for example, have

found empirical support for their argument that innovative activity tends to cluster

more during the early stages of the industry life cycle, but has the propensity to be

more dispersed during the mature and declining stages. In the OECD proceedings on

innovative clusters, Peneder (2001) presents a stylized industry life-cycle model to

illustrate the development of culture industry-based clusters. Although these studies

are highly illustrative, it is maintained here that the more direct and relevant subject of

inquiry is cluster life cycle rather than general industry life cycle.

Some cluster life cycle models have emerged in the literature in recent years (for

example, Pouder & St. John, 1996; Klink & Langen, 2001). It is usually proposed that

clusters evolve through stages of emergence, expansion, mature and transition or

decline. While a stage approach has clear limitations

5

, this perspective is useful in

framing the general processes of cluster evolution and the characteristics of each

process. The key point here is that, from a knowledge and learning point of view, each

stage represents a unique, strategic context with particular uncertainties in knowledge

and coordination, which needs appropriate network structure for a given cluster to

successfully survive and grow.

A stylized three-stage model is proposed here: origination, convergence, and

reorientation or decline. The stages are named following Pounder & St. John (1996),

while they are assigned to different characteristics here. The origination stage

concerns the emergence of the core or anchor firms as the result of successful initial

5

For example, how to define the boundaries between stages

25

entrepreneurial efforts and the follow up of a few similar firms and suppliers and so

on in the same location. As the critical mass has not been achieved, the ‘cluster’ at

this stage is not a cluster in the real sense. Although much has been written about the

fully-fledged clusters, how clusters emerge or originated has been less theorized.

Some case studies show that agglomeration economics and external effects that are

often associated with clusters play only a small role in the origination stage. Drawing

on case studies on the emergence of various new clusters, the AIM research (Andriani

et al., 2005) summarizes that different conditions may underpin the emergence of

clusters such as a lead or anchor firm, public sector investment and activities, shocks

and precipitating events, and local demand and market patterns. The main theme of

these case studies is that the engines that foster the rise of a cluster can be very

different from those that keep it going. Indeed, it is argued that the critical factor

common in emerging clusters is the efforts of pioneering entrepreneurs to take

advantage of new technological and market opportunity that had not already been

exploited (Bresnahan et al, 2001; Feldman, 2001). Certainly, both uncertainties in

knowledge accessibility and in coordination are high at this stage. In addition, the

cluster critical mass has not being formed. Therefore it is difficult to discuss the

structure of ‘closure’ and ‘range’ at the origination stage.

At the convergence stage, an agglomeration of firms has begun to from. A critical

mass has come into being and is continuing growing. In addition, the knowledge base

of innovative activities is evolving towards a dominant design (Malerba, 2002). In

other words, knowledge is rather cumulative at the cluster level. Based on past

successful experience, ‘current innovative firms are more likely to innovate in the

future in specific technologies and along specific trajectories’ (Malerba & Orsennigo,

2000:302). Moreover, a mobile labour force, cooperative alliances, personal

relationships, direct observation and monitoring, and also local media lead to a high

level of information exchange among the cluster internal actors (Pouder & St. John,

1996; Maskell, 2001; Bathelt et al., 2004). Face-to-face interaction, the intimacy

involved in the interaction, and the similar ‘mental models’ (Pouder & St. John, 1996)

make the information being exchanged highly interpretable. Therefore knowledge

circulated in the critical mass is abundant and highly accessible. In summary, the

convergence stage could be characterized by high accessibility and cumulativeness of

knowledge. The prominent uncertainty facing the cluster at this stage is how to secure

26

the coordination among members to exploit the opportunities derived from the

available knowledge. As has been discussed before, the cluster will be better off if the

network structure is leaned towards ‘closure’ to establish the necessary institution.

After a ‘long time spans of incremental change and adaptation which elaborate

structure, systems, controls, and resources toward increased coalignment’ (Tushman

& Romanelli, 1985: 215, quoted from Pouder & St. John, 1996: 1205), clusters enter

into the reorientation or decline stage. The availability and richness of information

about internal actors is still high and may still increase over time. However, ‘as

innovation process changes to involve the development of more complex technologies,

the production of these technologies requires the support of sophisticated

organizational networks that provide key elements or components of the overall

technology … increasingly the components of these networks are situated across a

wide array of locations’ (Wolfe & Gertler, 2004: 1077). At this stage, the ‘closure’ is

neither large enough nor heterogeneous enough to provide the necessary knowledge

and resources for more complex innovation and production. Information within the

dense internal network is ‘high in redundancy and low in diversity’ (Hite & Hesterly,

2001). In other words, the self-sufficiency or cumulativeness of knowledge is

declining at the cluster level

6

. Moreover, as internal actors tend to stick to the

established norms, institutions and the ‘mental models’ (Pouder & St. John, 1996),

which are primarily based on what happened in the critical mass, they become more

homogeneous and less sensitive to external stimulus. Therefore information and

knowledge from the outside will be subject to less rigorous scrutiny (Pouder & St.

John, 1996), which effectively reduces the accessibility of external knowledge. In

summary, at the reorientation/decline stage, knowledge has begun to be less

cumulative and the accessibility of knowledge becomes lower. The main uncertainty

facing the cluster is therefore shifting to how to secure the necessary information and

knowledge. As has been discussed before, the only chance for the cluster to

restructure or to rise ‘like phoenix from the ashes’ (Tödtling & Trippl, 2004), is to

have a network structure leaning towards ‘range’.

6

of course, as the cluster grows, there are more external actors being involved, but the necessary horizon could

still beyond the reach of the cluster if no particular efforts is made. and the decline of the self-sufficiency and

27

The above discussion gives a rudimentary answer to Malerba’s (2002:259) question:

“do relationships among agents and networks show a great stability or do they change

over time, and if so, in which direction?” The analysis demonstrates that clusters’

network structure co-evolve with their development and the changing uncertainties in

knowledge and in coordination. As clusters move from origination stage to

convergence stage, they need to build up ‘closure’ to reduce the uncertainty in

coordination and exploit the rich information circulated within the network. As the

cluster become matured and shows signs of decline, it needs special efforts to

diversify the ‘range’ of external linkages, in order to get access to the relevant

knowledge to restructure itself. This is captured in Granovetter’s ‘coupling and

decoupling’ solution to the network structure problem facing ethnic entrepreneurs, “in

which members of economic groups draw initially upon the resources of family and

peers but then attempt to forge broader and more autonomous ties beyond the group

as their need for lager markets and more sophisticated inputs expands” (Woolcock,

1998: 175).

5. Conclusion and policy implications

Starting from the debate around internal and external linkages in clustering, this paper

addresses the relationship between network structure and cluster development. With a

theoretical core based upon ‘structural embeddedness’, a knowledge-based model of

clusters is presented, aiming at clarifying functions and structure of internal and

external linkages in knowledge creation and the learning process.

The model demonstrates that both cohesive internal linkages and diverse external

linkages are important for cluster development. Cohesive internal linkages, or

‘closure’ are important for fine-grained information transmission and generating trust,

norms and other institutions and therefore facilitating coordination and collective

actions. In contrast, ‘range’, or network of diverse external linkages rich in structural

holes, brings into the cluster critical mass novel information, new ways of doing and

thinking, and therefore prevents the critical mass being locked-in.

cumulativeness of knowledge is an inevitable trend as demonstrated by the Silicon Valley case.

28

The relationship between ‘closure’ and ‘range’ is very complicated. On one hand,

‘closure’ and ‘range’ are mutual dependent on each other. ‘Range’ could bring in

novel knowledge, which is crucial to the critical mass. The information through

‘range’ however, needs ‘closure’ to coordinate cluster members in order to develop

action and exploit the novel information. On the other hand, there is trade-off between

‘closure’ and ‘range’. Too diverse external linkages may threaten the cohesion of

‘closure’ and therefore the existence of the cluster. When the internal connections

become too dense, ‘closure’ may become blinded to external knowledge and change

and therefore detrimental to the effectiveness of ‘range’. In addition, as relationships

need time and efforts to build up, maintain and improve, ‘range’ and ‘closure’

compete for the limited resources.

The advantages and disadvantages associated with ‘closure’ and ‘range’ and the

complex relationship between them imply that clusters need to integrate both of them.

According to the integration model, the network structure of a cluster should

facilitates access to a diverse range of contacts outside the critical mass who might

provide the necessary information on one hand, and on the other hand provide

mechanisms to coordinate firms and other organizations within the critical mass to

secure the execution of the intended strategies.

Finding an appropriate mix of ‘range’ and ‘closure’ though is problematic as it

depends on the specific uncertainties facing the cluster. When the prominent

uncertainty is in accessing relevant knowledge, the balance may need to lean towards

‘range’. When the greatest uncertainty is in securing the coordination, the balance

may need to lean towards ‘closure’.

In addition, an appropriate balance is contingent on a number of factors, among which

are cluster difference and cluster life cycle. Different clusters belong to different

technological regimes and have a different knowledge base, and are therefore facing

different degree of uncertainty in knowledge access and in action coordination.

Consequently, the ‘ideal’ mixes of ‘range’ and ‘closure’ are different. Similarly the

uncertainties facing clusters also vary along cluster life stages. Hence the appropriate

network structure should co-evolve with the development of the cluster.

29

The model has important implications for cluster policies. It calls for attention to be

paid to the network structure of internal and external linkages when designing cluster

policies. It proposes the inclusion of external linkages into policy consideration. The

model demonstrates both internal linkages and external linkages could be beneficial

as well as detrimental to cluster development. Therefore it suggests the importance of

striking the right balance between ‘closure’ and ‘range’ in regard to knowledge

creation and interactive learning.

The inclusion of external linkages is worth particular attention of policy makers, as

the majority of current policies are still focusing on local networking and overlooking

the importance of external communication channels (Bathelt et al., 2004). In addition,

the model emphasizes the importance of building up diverse or range of external

linkages. The implication is that it may not be a good idea for clusters to be linked

with one or several dominant external actors.

Finally, the model suggests that the appropriate mix of ‘range’ of external linkages

and ‘closure’ of internal linkages is a subtle balancing act and contingent on a number

of factors. Therefore standard ‘one-fits-all’ (Martin & Sunley, 2003) cluster policy

may not be successful. In particular, the model reminds policy makers that the balance

between ‘range’ and ‘closure’ may be different for clusters based on different sectors

and even for the same cluster but at different cluster life stages.

Acknowledgement:

I would like to thank Stewart MacNeill and Alex Burfitt for their useful comments.

The usual disclaimer applies. Sponsorship from the School of Public Policy

Studentship and the Pat Cam Award is gratefully acknowledged.

30

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Appendix:

Figure 1 closure and structural hole

X

X

L

K

M

N

Network A

Network B