knowledge and information networks in an italian wine cluster

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Knowledge and Information Networksin an Italian Wine ClusterAndrea Morrison a b c & Roberta Rabellotti ca Urban and Regional Research Centre Utrecht (URU), Faculty ofGeosciences, Utrecht University, Utrecht, The Netherlandsb KITeS—CESPRI, Universitá Bocconi, Milano, Italyc Department of Economics and Quantitative Methods, Universitàdel Piemonte Orientale, Novara, ItalyPublished online: 03 Jun 2009.

To cite this article: Andrea Morrison & Roberta Rabellotti (2009): Knowledge and InformationNetworks in an Italian Wine Cluster, European Planning Studies, 17:7, 983-1006

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Knowledge and Information Networksin an Italian Wine Cluster

ANDREA MORRISON�,��,† & ROBERTA RABELLOTTI†

�Urban and Regional Research Centre Utrecht (URU), Faculty of Geosciences, Utrecht University, Utrecht,

The Netherlands, ��KITeS—CESPRI, Universita Bocconi, Milano, Italy, †Department of Economics and

Quantitative Methods, Universita del Piemonte Orientale, Novara, Italy

(Received March 2008; accepted August 2008)

ABSTRACT The aim of this article is to analyse the nature and extent of knowledge and informationnetworks in an Italian wine cluster. Moreover, the relation between firms’ characteristics and theknowledge network structure is also explored. The empirical findings show that knowledge isunevenly distributed in clusters and that networks of knowledge and information differ a greatdeal in terms of their structure. In fact, knowledge flows are restricted to a tightly connectedcommunity of local producers, differing in terms of knowledge assets, innovation behaviour andoverall economic performance with respect to the rest of the firms in the cluster.

1. Introduction

Following Marshall’s (1920) seminal contribution, which introduced the concept of

“industrial atmosphere”, many scholars have stressed the public nature of knowledge in

geographically bounded areas such as industrial districts, clusters, milieux innovateurs,

local production and innovation systems.1 Many studies have underlined the role of per-

sonal, face-to-face interaction as an effective way to transfer knowledge locally (Asheim,

1994; Audretsch & Feldman, 1996; Becattini, 1990; Brusco, 1996; Feldman, 1999; Sax-

enian, 1994) related to some of the basic properties of knowledge; that it is idiosyncratic,

contextual, sticky and tacit. Since knowledge is incorporated in the skills of individuals,

learning mainly occurs through personal interaction, the necessary and, to some extent,

sufficient conditions of which are physical proximity and local embeddedness. Thus, it

is generally claimed that members of a geographical agglomeration benefit from local

knowledge spillovers (LKS), because they are spatially close and embedded in local

networks of informal contacts.

Correspondence Address: Roberta Rabellotti, Department of Economics and Quantitative Methods, Universita

del Piemonte Orientale, Via Perrone 18, 28100, Novara, Italy. Email: [email protected]; roberta.rabellot-

[email protected]

European Planning Studies Vol. 17, No. 7, July 2009

ISSN 0965-4313 Print=ISSN 1469-5944 Online=09=070983–24 # 2009 Taylor & FrancisDOI: 10.1080/09654310902949265

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Some scholars have recently challenged this traditional approach (Boschma & Frenken,

2006; Breschi & Lissoni, 2001a, 2001b; Capello & Faggian, 2005; Lissoni, 2001;

Malmberg & Maskell, 2002) by arguing that it overlooks the very different types of

knowledge flows in local agglomerations, and in turn it fails to distinguish between

those flows that are freely available (e.g. information) and those that are not (e.g. tacit

knowledge). They have argued that underlying the well-accepted belief that informal

contacts represent an easy conduit for transferring tacit knowledge in geographically

bounded areas there is a somewhat ambiguous definition of knowledge and information

as interchangeable concepts, sometimes referred to as rumours, ideas or know-how.

Thus, they suggest that the nature of knowledge flows and the mechanisms through

which they circulate need more detailed investigation.

In this view, physical distance is not the only and main factor explaining the spatial

diffusion of knowledge (Boschma, 2005; Staber, 2001). Several recent works have

shown that knowledge diffusion is influenced by other kinds of distance than geographical

distance, i.e. institutional distance (Rallet & Torre, 2005), cognitive distance (Nooteboom,

1999) and social distance (Breschi & Lissoni, 2009), and that, therefore, knowledge

spreads unevenly among members of a local agglomeration of economic activities

(Giuliani & Bell, 2005; Lissoni & Pagani, 2003). This unevenness also arises from the

inherent heterogeneity that characterizes firms’ learning trajectories and their mechanisms

for building capabilities (Dosi, 1988; Nelson & Winter, 1982), which, in turn, give rise to

the different absorptive capacities of firms (Cohen & Levinthal, 1990). Thus rather than

being an undifferentiated and homogeneous population of firms, these agglomerations

may embody different competing networks, characterized by structural differences (e.g.

dispersed, centralized, cohesive) (Giuliani, 2007; Lazerson & Lorenzoni, 1999; Rabellotti &

Schmitz, 1999).

This article aims to contribute to this field of studies by undertaking a detailed investi-

gation of the structure and constituent properties of knowledge and information networks

in a cluster, simply defined here as a geographical agglomeration of sectorally specialized

firms. Moreover, following a methodological framework proposed in Giuliani and Bell

(2005) and Giuliani (2007), we investigate the relationship between some characteristics

(e.g. size, export strategy, knowledge base) of the firms, their structural position in the

knowledge network as well as their extra-cluster linkages.

In doing so, we contribute to the debate in the literature in several ways. First, our study

goes beyond the often anecdotal evidence on the relevance and diffusion of informal chit-

chat in district-like areas by providing a detailed measure of knowledge and information

flows shared through informal relations. Such new evidence contributes to a growing

area of studies which use social network analysis to investigate linkages among firms

and the different actors in clusters or alike (Boschma & Ter Wal, 2007; Giuliani, 2007;

Graf, 2007; Kauffeld-Monz & Fritsch, 2007; Morrison, 2008; Samarra & Biggiero,

2008). Second, with respect to these network studies, it reaches some new and interesting

results on the role of intra-cluster linkages with respect to relationships with actors exter-

nal to the cluster. This contrasts with the prevailing view, which takes for granted the posi-

tive association between firms’ learning opportunities and network cohesiveness. Too

much clustering or network closure can indeed be detrimental to learning dynamic at

local level (Boschma, 2005). In our case study we show that the larger and more successful

producers in the local system are not interested in forging internal knowledge linkages

with the local small and very small firms; larger firms remain in the periphery of the

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knowledge network and strengthen their linkages with sources of knowledge external to

the cluster, whereas the smaller firms are highly interconnected and communicate with

external sources of knowledge only through a local broker (e.g. extension agency),

which plays the role of gatekeeper for the local system.

The article is organized as follows. Section 2 reviews the main issues in the literature

with a focus on the distinction between knowledge and information (Section 2.1) and

on the structure of knowledge networks (Section 2.2). Section 3 briefly describes the

local system under investigation. Section 4 describes the methodology and discusses the

sample and the survey design. Section 5 presents the findings of the network analysis,

focusing on the topology of information and knowledge networks. Section 6 concludes.

2. The Theoretical Framework

2.1 Information and Knowledge Diffusion in Clusters

A large body of empirical and theoretical literature in the fields of industrial and regional

studies concurs that in contexts such as industrial districts, clusters and local production

systems, informal relations are key channels for the diffusion of knowledge. The line of

reasoning in this literature is that local and personal (i.e. tacit) knowledge is primarily

exchanged by people who are involved in its creation or by those that are part of the

same local community.2

Following Marshall (1920), Becattini (1990) goes back to the concept of “industrial

atmosphere” and emphasizes the importance of localized knowledge externalities that

accrue from face-to-face contacts and the co-location of people and firms. In this

context, access to information and knowledge appears unintentional and facilitated by geo-

graphical proximity and by the fact that the different actors in the cluster (i.e. entrepre-

neurs, technicians, workers) have common cultural values, communication codes and

behavioural norms (Maskell, 2001). According to this view, informal contacts allow

knowledge to be shared by cluster members, while outsiders are excluded, since they

are not embedded in the local community.

Audretsch and Feldman (1996), in the context of US innovative activities, provide

robust empirical evidence of the existence of a positive relationship between spatial clus-

tering, localized knowledge spillovers and firms’ innovative output. The presence of LKS

explains why firms tend to co-locate, and informal relations (i.e. face-to-face contacts)

appear to be the relevant mechanisms for the transmission of tacit knowledge. The view

that knowledge spillovers are highly localized is also expressed in a number of other

econometric studies which show that physical proximity matters since it increases the

actors’ probability of contacts and, hence, the flow of information exchange among

them (Audretsch, 1998; Jaffe, 1989; Jaffe et al., 1993). In these studies, knowledge is con-

sidered to be a public good which spreads pervasively within a spatially bounded area.

Regional economists have criticized this interpretation of space because it focuses only

on geographical proximity; they stress the additional importance of institutional and cul-

tural proximity. A number of contributions have referred to the concept of innovative

milieu to account for the learning processes occurring at local and network levels

(Camagni, 1991; Capello, 1999; Keeble & Wilkinson, 1999; Rallet & Torre, 2005). In

this literature, learning is seen as a collective, social process involving people who

share strong social and cultural values. Informal relations within the milieu, along with

Knowledge and Information Networks in an Italian Wine Cluster 985

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other mechanisms (e.g. spin-offs; labour mobility; user–producer interactions), contribute

to sustaining the diffusion of knowledge at local level, which is considered a club good

within the boundaries of the cluster (Capello, 1999; Capello & Faggian, 2005). The

mileu approach clearly makes the point that it is not only firms’ geographical proximity

but also their embeddedness that influences the process of innovation in clusters.

However, in common with those contributions focusing mainly on the spatial dimension

of proximity, throughout most this literature there is the tacit assumption that clusters

are a homogeneous community of firms and entrepreneurs, which have the same

culture, origins and knowledge base.

A number of recent contributions in the field of geography of innovation have shifted

their attention to the learning dynamics in clusters at firm level (Boschma & Frenken,

2006; Breschi & Lissoni, 2001a, 2009; Giuliani & Bell, 2005; Malmberg & Maskell,

2002). These approaches point to the fundamental role of social interactions in the creation

and diffusion of knowledge and explore the extent to which geography mediate in this

process. Some of these scholars have also challenged the widespread belief that “industrial

districts, which by definition, rely upon long established and homogeneous social net-

works, are best placed to diffuse and produce tacit knowledge” (Lissoni, 2001,

p. 1480). This claim relies on the argument that tacit knowledge, being personal and

specific (Polanyi, 1962), cannot be communicated by word of mouth; therefore, knowl-

edge is not a public good, freely available to all clusters members, it is rather a club

good, circulating within a few small “epistemic communities”. These communities are

formed by small groups of people (e.g. technicians, professionals), who work in the

same technical fields—although in different firms, come up against the same technical pro-

blems, rely on similar heuristics and procedures on how to conduct research and own

common views about who is allowed to access their knowledge and which part of it can

be released (Cowan et al., 2000). In these more selective contexts as opposed to whole

clusters, informal contacts might function as channels along which knowledge is

exchanged, as shown in several empirical works on collective invention and knowledge

diffusion (Allen, 1983; Rogers, 1982; Schrader, 1991; von Hippel, 1987). These latter

contributions show that technicians feel themselves to be part of a cohesive professional

community, in which reputation and status play a key role in shaping interactions. They

also show that exchanges take the form of trading; technicians are willing to release

crucial information on the basis of reciprocity (Schrader, 1991; von Hippel, 1987). The

above implies that knowledge is not given for free, but is exchanged through barter, and

the preconditions for exchange are trust, mutual recognition and long-term relationships

(Carter, 1989).

However, in addition to exchange of technical advice, technicians use interpersonal

networks for a number of other different purposes: to share information about job oppor-

tunities, markets, use of machinery, inputs, new regulations, etc. (Cross et al., 2001;

Granovetter, 1973; Stuart & Sorenson, 2003). Therefore, contacts established between

peers (e.g. entrepreneurs, workers, researchers) do not necessarily entail transfer of tacit

knowledge; it is more likely that they serve to share information about who knows

whether a customer is reliable or not or whether a machinery is well functioning or not.

Some recent contributions using social network analysis seem to confirm this argument

(Boschma & Ter Wal, 2007; Giuliani, 2007; Morrison, 2008). They show that firms in

clusters or alike are connected through a variety of formal and informal networks (e.g.

business networks, advice networks, technological networks, managerial networks),


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but only a minor fraction of these contacts concern knowledge exchanges. Thus, much of

the evidence showing that people interact through informal means—the often cited cafe-

teria effect—does not support the claim that the content of these exchanges is knowledge,

which conversely is selectively appropriated by few members of the local community.

The above considerations suggest that the literature hype about the importance of clus-

tering for knowledge diffusion has contributed to the creation of a myth (Dahl & Pedersen,

2004). In this article, we aim at investigating the nature of knowledge which is passed on

through informal contacts by distinguishing between those that convey know-how (i.e.

knowledge) and those that vehicle know what or declarative knowledge (i.e. information),

being the former mainly tacit and the latter mainly codified (Lissoni, 2001).3 Moreover,

given that informal contacts are established for different purposes and respond to different

motivations, we expect that the structure of information networks will differ from that of

knowledge networks.

To distinguish between knowledge and information is relevant since it enables to shed

some lights on the nature of knowledge that effectively circulates through informal

relations. The ultimate implication is that the co-location of firms in clusters or alike

may not be a sufficient condition for accessing knowledge, as often claimed by the litera-

ture, but on the contrary, the access to knowledge may be restricted only to a few actors.

It follows that it is also interesting to investigate which actors are able to participate in

those knowledge networks, and what characteristics do they have. Therefore, in order to

address this issue, we focus on the structure of the knowledge network and investigate

which are the firm-specific characteristics that ensure access to knowledge. These

points are further discussed in the next section.

2.2 Knowledge Networks Structure and Firm Characteristics

As to begin with and in line with the above, we can argue that knowledge networks,

differently from chit-chat conversations, are intentionally formed by their members.

Indeed, when technicians need specific technical advice, they purposefully search out

and select those colleagues who they believe are better endowed to provide effective sol-

utions to their problems (Schrader, 1991). Since nurturing these social and professional

relations is costly and time consuming—and also because “technical assistance comes

with the obligation to reciprocate later on” (Carter, 1989, p. 155)—technicians turn to

their smaller community of acquaintances (i.e. the epistemic or professional community),

within which they search for peers whose competencies and experience are not too far

removed from theirs, otherwise acquisition and reciprocation would be difficult and inter-

actions would not generate any benefits (Bathelt et al., 2004; Boschma, 2005). Therefore,

exchanges occur if a certain degree of similarity and complementarity in knowledge assets

holds. The above indicates that interactions aimed at obtaining specific problem-solving

knowledge do not emerge from unplanned and occasional contacts; rather they are struc-

tured and shaped according to the specific characteristics of the exchangers. The analyses,

both theoretical and empirical, of the specific structures and properties of networks affect-

ing knowledge diffusion and their relations with firm characteristics are the focus of a large

body of literature (Ahuja, 2000; Coleman, 1988; Cowan, 2004; Powell & Grodal, 2005;

Rauch & Casella, 2001).

Along these lines and drawing on the methodology used in a previous study that has

applied social network analysis to examine knowledge diffusion in Italian and Chilean


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wine clusters (see Giuliani & Bell, 2005; Giuliani, 2007), we examine the structure of the

knowledge network and relate it to the firm features in another Italian wine cluster.

From the seminal contributions by Nelson and Winter (1982) and Cohen and Levinthal

(1990), we know that firms are able to search, absorb or share knowledge flows according

to their technological capabilities (i.e. absorptive capacity) and inherited knowledge base.

These capabilities affect their position and role in the local knowledge network and con-

sequently shape its structure and, as it is shown in some recent studies, there is a positive

correlation between firms’ knowledge base and centrality in the cluster’s knowledge

network (Boschma & Ter Wal, 2007; Giuliani, 2007; Giuliani & Bell, 2005). In this

view, what really matters is the knowledge endowment of the individual firm, thus the

stronger the firm’s absorptive capacity, the deeper its embeddedness in the local web of

knowledge ties. In the above argument, there is an implicit assumption that firms are

willing to share, and also that they have knowledgeable counterparts in their neighbour-

hoods with which they can profitably interact. In fact, agents are keen to interact with part-

ners able to return valuable knowledge (Schrader, 1991). Thus, firms that are better

endowed with knowledge assets are at the core of the local knowledge network.

Conversely, if this assumption does not hold, we can instead expect that firms with

stronger knowledge bases may search for potential partners outside the local area, with

the ensuing effect of reducing their local embeddedness. This means that the actors at

the core of the local knowledge network are those with weaker knowledge bases, which

collaborate more intensively with other cognitively and spatially proximate actors, since

they are unable to reach distant knowledge sources.

Some recent studies on industrial districts (Belussi et al., 2003; Boschma & Lambooy,

2002) have shown the emergence of a similar scenario characterized by a few leading

firms, sometimes playing the role of knowledge gatekeepers, through which new ideas

enter and reinvigorate the local production system. In some cases, however, lead firms

can prevent knowledge leakage for strategic purposes, by sharing it with only a restricted

group of partners (e.g. collaborators, subcontracting firms) and keeping it marginal for

the others. In other cases, dominant firms, either because of their strategic power or

their stronger competencies, may feel themselves too cognitively distant from the local

system in which they are located and they therefore search for knowledge connections

with outsiders. In these cases, we would expect that firms better endowed with knowledge

assets are loosely connected or isolated with respect to the core of the local knowledge

network.

The above discussion suggests that firms in clusters are willing to invest in local knowl-

edge networks if they are interested in tapping into the local knowledge base; otherwise

they may choose to search for knowledge outside the cluster. This is of particular

relevance in a globalizing economy in which firms increasingly have many opportunities

to connect with distant actors.

3. The “Colline Novaresi” Wine Cluster

The study is based on the collection of primary firm level data in the wine cluster of

“Colline Novaresi”, located in Piedmont, a region producing many appellation wines,

which are among some of the best known Italian wines (e.g. Asti Spumante, Barolo and

Barbaresco).4 As far as exports are concerned, Piedmont is the second biggest exporting

region in Italy after Veneto, with a share of 23% of total Italian wine exports in 2007.


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Although its contribution to regional production is much less than the Southern part of

the region, the local system of “Colline Novaresi” is historically recognized as a wine

cluster, characterized by 300-odd micro- and small firms, most of them being grape

growers.5 Some of the wines produced in the area are classified as wines of quality

produced in identifiable regions Vin de qualite produit dans une region determinee

(VQPRD); these include four DOC wines and one DOCG. In general, local firms cultivate

autochthonous vines and produce local varieties of wine; therefore, the local terroir is one

of their key competitive assets.

During the last two decades, notwithstanding its old tradition, the area’s contribution to

total regional production (nowadays around 2%) has been decreasing, with a sharp reduction

in the area dedicated to vines (the total number of hectares decreased from 12,000 in the

1950s to 800 hectares today). The average size of the wineries in the area is very small

(on average 0.4 hectares), even smaller than the national average (0.8 hectares).6

Despite the average very small size of the firms, according to our findings (see Section

5.3) many local firms are not falling behind; on the contrary, they seem to have taken

measures designed to increase efficiency, improve the quality of wines, adopt new tech-

nologies and introduce innovations.

The local branch of Vignaioli Piemontesi plays a leading role in encouraging modern-

ization. Vignaioli Piemontesi is a regional association, the largest in Italy, which provides

technical assistance to producers in viticulture and related fields. As discussed in Morrison

and Rabellotti (2007), Vignaioli Piemontesi is a key player in the Piedmont Wine Regional

Research System, connecting small and marginal producers to several sources of knowl-

edge, such as the university in Turin and other regional and national research institutions.

The extension agency contributes to diffusing information on the newest technological

advancements and best practice which would otherwise not be accessible to small produ-

cers, through its magazine, through demonstrations to farmers or through a direct consul-

tancy activity.

This process of modernization is a key strategy to survive in the wine industry, an indus-

try known for being traditional but recently having experienced intensive technical

change. In a global market characterized by a shift in demand from bulk to quality

wines, and by an increasing number of competitors from the “new world”, access to

knowledge is a key competitive asset. From this follows the relevance to investigate

how knowledge circulates among firms, through intra-cluster linkages with respect to lin-

kages with actors external to the cluster in a wine local system, such as “Colline Novaresi”

under analysis in this article.

4. Methodology

4.1 The Population and Data Collection

Primary firm level data have been collected via a structured questionnaire enquiring about

intra- and extra-cluster flows of information and knowledge. With regard to selection cri-

teria, the firms interviewed were identified according to their activity and location: from

the total population in the Novara Chamber of Commerce database, we extracted the popu-

lation of wine makers (i.e. wineries that produce wine on their own), thus excluding grape

growers and wine traders.7 Table 1 summarizes some of the structural characteristics of the

26 wine makers interviewed.


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Given that in the “Colline Novaresi” wine cluster, as it is very common in Italy, wineries

are generally family-run and mostly individual businesses, firm’s technician very often

coincides with the owner. Consequently, our interviews were directed to the owner,

being also the oenologist and/or the viticulturist of the firm and therefore the best

informed person in the firm about its broad range of activities and external relations, in

particular those related to technical issues.8

In addition to some general background information, the questionnaire includes (a) a

section on firms’ economic performance (e.g. total sales, exports, main destination

markets), (b) a section on innovation activities (e.g. amount of investments in new tech-

nologies, experimentation activities carried out), (c) a section on firms’ endowment of

human capital (e.g. level of education and training of qualified personnel, external consult-

ants) and (d) two relational sections on intra- and extra-cluster informal linkages aimed at

exchanging information and knowledge.

Relational data were collected through a “roster recall” method: interviewees were pre-

sented with a complete list of the other firms in the cluster and were asked to name all the

firms with which they exchanged knowledge and information. Similarly, a roster including

the main supporting organizations located either within or outside the local area and wine-

ries outside the cluster was shown to the firms.9

4.2 Some Definitions

In this study, we focus on flows of knowledge and information taking place through infor-

mal networks defined as linkages among individuals via face-to-face contacts, which are

not mediated by marked-based mechanisms. Therefore, winery technicians (often

coinciding with the owner) are depicted as nodes in the network and their linkages as

flows of knowledge and information connecting them. Both the individuals connected

through direct ties and those linked to them can in principle accrue benefit from the

sharing of knowledge and information circulating in the network.

A key issue is how to make the distinction between information and knowledge operational

(Lundvall & Johnson, 1994). Information is considered generic information (i.e. declarative

knowledge or know what). Information exchanges can involve a wide range of business

issues. The question is:

Do you have any informal contact with employees—or the owner—of the following

firms (see the list) aimed at exchanging information (about business opportunities,

markets, providers, inputs, machineries or technologies)?

Table 1. The population of wine-making firms (n ¼ 26)

N %

Number of employees1–3 10 38.53–10 11 42.3�10 5 19.2

Year of foundation1990 to today 9 34.61950–1990 7 26.9Before 1950 10 38.5


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(Please indicate the frequency of such interaction: none ¼ 0; occasional ¼ 1; frequent

(every month) ¼ 2; highly frequent (weekly) ¼ 3).

Knowledge exchanges are defined as technical advice to solve problems and the ques-

tion is:

If you experience a technical problem in your routine activity, to which of the following

firms (see the list) do you turn for technical support?

(Please rate the importance you attach to the advice provided for solving your problem

on the basis of the following scale: none ¼ 0; low ¼ 1; medium ¼ 2; high ¼ 3).

As a control, respondents were also asked to indicate the firms that had benefited from

their technical support. These questions allow identifying the sub-networks that arise from

informal conversations among peers—for example, those that oenologists conduce with

colleagues to be informed about market opportunities—from those that imply a learning

process. The former are likely to be highly frequent and based on weak ties. The latter

entails higher stability, need of reciprocation and purposiveness. In other words, they

are based on strong ties and, according to the literature, are more effective in transferring

complex and tacit knowledge (Sorenson et al., 2006)

Based on the relational data, we built a directional database in which every node can be

a source (outward arrow) and/or a receiver (inward arrow) of information/knowledge.

Furthermore, respondents were also asked to indicate and qualify their relationships

with distant actors, in terms of both geographical and social/cognitive distance. We

included in this group local and national business associations, research institutions

such as universities, technical schools and laboratories, extension agencies and wineries

located outside the local cluster.

In the next section, we use socio-metric techniques and graph theory (Wasserman &

Faust, 1994) to analyse the structural characteristics of the networks established in the

local area and to study how knowledge circulates among actors.10

5. The Main Empirical Findings

5.1 Structural Properties of the Networks

A visual examination of the information and knowledge networks (Figure 1) shows that,

as expected, they are different: the former being quite dense and highly connected and the

latter less dense with nodes connected by relatively fewer linkages and with some isolated

nodes disconnected from the local network. The differences are confirmed by some of the

measures presented in Table 2: overall density measures are consistently higher for the

information network than for the knowledge network, these differences being always

statistically significant. As far as non-directional ties are concerned, on average, each

firm is in touch with half (50%) of the actors in the information network; conversely,

the number of contacts drops when knowledge flows are considered (18%). It should

be noted that such a gap is stronger for non-directional density than for mutual

density. This result suggests that contacts through which knowledge circulates, although

undoubtedly fewer than those in the information network, are possibly based on mutual

and stronger relationships. These findings are confirmed by the average number of

contacts established by each winery.

Table 2 also shows that in both networks, mutual density, measured on only recipro-

cated contacts, is lower than non-directional density, which also includes unreciprocated


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contacts. Moreover, the degree of reciprocity shows that knowledge exchanges are almost

exclusively restricted to where ties are reciprocated; for information exchanges this is

always lower. In fact, reciprocity entails high stability and trustworthiness, and is com-

monly associated with knowledge sharing (Schrader, 1991). This finding is in contrast

to the widespread belief, usually supported only by anecdotal evidence, that local

Figure 1. (a) Information and (b) knowledge network

Table 2. Average density measures in the information and knowledge networks

Type of relationInformation

networkKnowledge

networkBootstrap paired

sample t-test

Average density Mutuala 0.35 (0.47) 0.14 (0.35) 4.97�

Non-directionalb 0.50 (0.49) 0.18 (0.39) 6.84�

Valuedc 0.60 (0.94) 0.34 (0.87) 3.47�

Average numberof contactsd

Mutual 8.88 (6.65) 3.69 (2.77)Non-directional 12.62 (5.46) 4.69 (2.71)

Minimum numberof contacts

Mutual 2 0Non-directional 4 0

Maximum numberof contacts

Mutual 25 11Non-directional 25 12

Isolated nodese Mutual 2 5Non-directional 0 1

Degree ofreciprocity (%)f

57.23 89.23

Note: Standard deviation in brackets.aIn a mutual network, a tie between two nodes exists only if reciprocated. This measure is based on an asymmetric

binary matrix.bThe density of a binary network is the total number of ties divided by the total number of possible ties. The

measure is based on the symmetry of the original matrix.cFor a valued network, the indicator is the total of all values divided by the number of possible ties. In this case,

the density gives the average value.dThe average number of other nodes to which a node is directly connected.eIn a directed graph, two vertices are in the same weak component if there is a semi-path connecting them. Two

vertices, x and y, are in the same strong component if there is a path connecting x to y and a path connecting y to x.fWeak reciprocity: percentage of symmetric pairs, no value data.�Significant at 5% level.


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production systems are communities characterized by dense, social interactions based on

reciprocity among local actors.

The heterogeneity in the relational capability of both networks, as measured by the

number of contacts, is another finding that contradicts the traditional wisdom about

local production systems. In the cluster under analysis, there are actors that maintain a

much higher number of contacts than others, indicating a very heterogeneous access to

informational resources in the local knowledge system.

Overall, the evidence presented above confirms that the structure of the information

network is different from that of the knowledge network in terms of connectivity. The

results also provide preliminary evidence supporting the argument that knowledge is

shared within relatively smaller groups of actors with respect to information. They

suggest that dense interactions (i.e. high relational capital) at local level can be observed

only by assuming non-reciprocity among interacting agents.

The next step in the analysis is to investigate how the networks are structured (e.g. frag-

mented, polarized). This is an important issue since the partition of networks in either few

or many communities (e.g. cliques, sub-groups, partitions, core–periphery structure, etc.)

may affect the extent to which knowledge and information circulate within the cluster.

5.2 The Structure of the Knowledge Network

To explore how knowledge and information are shared among the local community and

whether there is a dominant actor, or group of actors, we compute the degree of variability

(i.e. heterogeneity) of the power and betweenness indices (see the appendix for definitions)

and their degree of centralization (i.e. concentration). The degree of variability measures

to what extent actors differ in terms of their abilities to produce, acquire and share

knowledge, whereas the degree of centralization detects the emergence of leaders in the

network. Table 3 shows that both networks have a moderate degree of heterogeneity

(the mean value is higher than the standard deviation), with higher variability in the

knowledge network (57.5) than in the information network (33.6). Similarly, the knowl-

edge network is slightly more concentrated (15.7 vs. 10.35), although in both networks

the degree of centralization is moderate. As far as the betweenness is concerned, we

find much higher variation (the mean value is lower than the standard deviation), and a

slightly, bust still moderate, concentration. Results are reversed for the two networks,

pointing to the presence of more actors acting as bridges in the information network.

Table 3. Concentration and heterogeneity in the information and knowledge networks

Powera Betweennessb

Informationnetwork

Knowledgenetwork

Informationnetwork

Knowledgenetwork

Degree of heterogeneityc 33.6 57.5 163 121Concentration (%) 10.35 15.78 20.69 15.74

aBeta parameter: 0.100000, matrix symmetrized and dichotomized.bNormalized betweenness; matrix dichotomized not standardized.cStandard deviation divided by mean times 100.


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The above results suggest that knowledge and information are not appropriated and

controlled by one single actor. This means that the two networks do not resemble a

typical star configuration in which a central actor controls all the communication flows.

In principle, this is good news since diversity engenders learning. The moderate degree

of heterogeneity, along with the low degree of concentration, suggests that some firms

may be more influential in informal exchanges. This is supported by the in/out-degree

and betweenness centrality indices presented in Table 4, which shows that some actors

(e.g. Rovellotti and Brigatti) have achieved a position over the mean.

Given the heterogeneity detected and the emergence of some leaders, it is interesting to

assess whether a core–periphery configuration emerges out of this group of more central

actors. A core–periphery structure intuitively consists of two groups of actors: on the one

side, those that are tightly connected to each other (i.e. the core) and on the other side,

those that are loosely connected to the core and also are not connected to each other

(the periphery).11 This specific structure of network is particularly relevant because it rep-

resents the degree of cognitive polarization of the local area relating it to specific actors’

features.

Testing this hypothesis implies verifying to what extent the two networks (i.e. infor-

mation and knowledge) reproduce an ideal core–periphery configuration (Borgatti &

Everett, 1999; Everett & Borgatti, 1999). Taking into account the densities within

groups, we focus the analysis on the knowledge network, which is characterized by

such a configuration, while information flows appear rather dispersed over both groups

(Table 5). In the knowledge network, we observe intense core-to-core interactions (the

valued density is 2.35) compared with periphery-to-periphery connections (0.22). The

core-to-periphery (0.37) and periphery-to-core (0.42) linkages, as expected, are rather

sparse, although it is worth noting that a certain amount of knowledge does circulate

between the two groups, especially from the periphery to the core. It is also significant

that the density within the core community is several times higher than the network

average density (0.34, see Table 2), confirming the existence of a cohesive core. Conse-

quently, the remaining part of the network is close to a periphery structure with a lower

than average density (0.22).

Table 4. Out-degree and betweenness centrality indexes

Nodes

Information network Knowledge network

Out-degree Betweenness Out-degree Betweenness

Nodes with the highestcentrality

Rovellotti 52 12.542 22 20.585Brigatti 48 22.93 29 19.812Prolo 43 1.789 10 5.017Mazzoni 29 6.651 7 0.067Dessilani 25 8.589 7 1.684Miru 24 5.689 19 17.963

Nodes with the lowestcentrality

Le Piane 4 0.114 7 3.363Zanetta 4 0 3 0Turetta 4 0 2 0Bianchi 4 0.393 0 0Barbaglia 2 0 9 0.525Ca Nova 2 0.077 3 0


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To better understand the relative importance of linkages among the actors in the core

and in the periphery, we examine the sub-group of the knowledge network consisting of

only strong ties, providing key advice to solve technical problems (see the appendix for

details). Figure 2 shows that the core of strong ties, with the exception of one, coincides

with the original core; also, core nodes are among those with the highest out-degree index

(measured by the size of the node). Evidence of cohesion in the core is further supported

by comparing densities within the two groups (i.e. core vs. periphery). Table 5 shows that

density of core-to-core interactions is equal to 1, while that of periphery-to-periphery

interactions is close to 0 (0.03). Intra-periphery knowledge flows almost disappear

when only strong ties are examined, indicating that they are mainly based on weak and

unstable linkages. Consequently, periphery actors cannot be regarded as an interlinked

knowledge community, although they maintain a few strong ties with the core. This

confirms that the core is a cohesive group of highly interconnected actors who share

knowledge among themselves rather than with peripheral actors.

Given the emergence of a core–periphery configuration in the knowledge network, it

is interesting to link the actors’ different position in the network to their structural and

cognitive characteristics. This is the aim of the next section.

5.3 The Core and the Periphery of the Knowledge Network

In this section, we investigate the existence of a relation between an actor’s position in the

knowledge network and its connections with other actors internal and external to the

cluster, its structural features, its performance indicators and its knowledge bases. To inter-

pret the findings presented in Table 6, we also draw on qualitative evidence collected

through in-depth interviews with technicians, entrepreneurs and key informants from the

locally operating technical and research institutions (e.g. extension services, universities).

In these interviews, we have explored both the motives driving the connections between

firms and the different actors and the participation of the different cognitive groups of

firms (i.e. core, periphery and isolated periphery) in the learning activity in the wine cluster.

Table 5. Core–periphery structure: densities within groupsa

Information network(all ties)b

Knowledge network

All tiesb Strong ties onlyc

Core (11)d Periphery (15) Core (5) Periphery (21) Core (4) Periphery (22)Core 1.41 0.84 2.35 0.37 1 0.17Periphery 0.37 0.18 0.42 0.22 0.15 0.03

aThe core–periphery structure has been obtained using a genetic algorithm implemented by UCINET VI. In order

to test the robustness of this structure we have run the Tabu search algorithm, implemented through the same

software, which provides similar results. The initial and final fitness coefficients are, respectively, 0.247 and 0.593

for the information network and 0.4 and 0.529 for the knowledge network.bThe density of a valued network is given by the total of all tie values divided by the number of all possible ties. In

this case the density gives the average value.cComputed on a dichotomous matrix of the original data set. The maximum value of the density indicator in this

case is equal to 1. Strong ties identify relations that provide the key contribution to solve technical problems (see

the appendix for details).dNumber of firms in parenthesis.


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Table 6 presents information about the structural characteristics, the performance and

the knowledge base of the different groups of firms, identified on the basis of their position

in the local knowledge network.12

Taking into account firms belonging to the overall periphery of the network, they are

larger both in terms of volume and value of production; besides on average, they sell

more to supermarkets13 than firms in the core, and they produce higher-priced wines.14

In terms of performance, peripheral firms have better indicators with respect to both the

rate of growth and the export performance, indicating that they are more internationally

connected than the core firms. Moreover, their performance is also better in terms of

innovation.

In terms of knowledge base, we find that firms in the overall periphery are better

endowed, in terms of knowledge assets than those at the core. In the cluster under inves-

tigation, therefore, peripheral firms are cognitively distant from the firms in the core,

which build a large part of their technological and informational assets on local

sources. These peripheral firms maintain stronger connections than the core with external

sources of knowledge and contribute very little to the intra-cluster learning system. The

relational indicators confirm that the overall periphery is the best connected to external

sources of knowledge. When this finding was discussed with some key informants, they

agreed that for problem-solving activities, peripheral firms rely either on their internal

human resources or on direct contacts with external oenologists,15 rather than on local con-

tacts with other firms or the extension services. Among peripheral firms, most external

contacts are with firms located in the South of Piedmont, which is far more advanced

and export-oriented than the cluster under investigation (see Section 3).

Figure 2. Core–periphery structure of the knowledge network (strong ties only).Note: Node size is proportional to actors out-degree centrality. Triangle nodes stand for core actors,

square ones for periphery actors.


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Table 6. Are firms in the core knowledge network different from firms in the periphery?

Knowledge network position(no. of firms)

Firms structural characteristics (average value)

Wine production (l) Sales (000E)Sales to large retailers (as % of all

distribution channels)

Premium and super-premium wine (as % of the

total production)

Core (4) 27,680 112.4 0 19.7Periphery (8) 37,935 718.6 1.1 20.8Isolated periphery (14) 38,500 261.5 6.6 30

Performance indicators (average value)

Annual growth rate (%) Export performance(export/sales) (%)

Innovative performancea

Core (4) 8.9 7.0 60.0Periphery (8) 16.0 13.5 64.7Isolated periphery (14) 17.5 30.0 75.0

Knowledge base indicators (average value)

% of graduates in technical fields External oenologists(%)

Experiments(%)

Investment in newtechnologies (000E per ha)

Core (4) 40.0 20.0 60.0 2613.2Periphery (8) 64.7 60.0 58.8 4431.7Isolated periphery (14) 50.0 75.0 75.0 12301.5

Openness Local connectivity

% of firms withlinkages outside the

local system

% of firms with linkageswith local business

associations

% of firms withlinkages with

extension services Betweenness Out-degree centrality

Core (4) 40.0 20.0 80.0 10.8 17.2Periphery (8) 52.9 35.2 76.0 5.7 8.1Isolated periphery (14) 50.0 50.0 25.0 0.05 0.5

a% of firms introducing process innovation.

Kn

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Concerning the small firms in the core, they are mainly connected to external sources of

knowledge through the main local extension agency, Vignaioli Piemontesi, a key player in

the wine system at regional level, which acts as gatekeeper in the cluster (Morrison &

Rabellotti, 2007; Section 3). As shown in Table 6 and widely confirmed by our in-

depth interviews, the core firms interact intensively with the local extension agency in

many different ways. Many experiments and innovations undertaken by small local

firms, particularly those in the core, are encouraged and often implemented with the assist-

ance of the association’s technicians. Vignaioli Piemontesi is a source of up-to-date tech-

nical knowledge because it participates directly in many research projects financed by

Regione Piemonte in collaboration with scientific institutions such as universities and

other research centres.

To sum up, the above evidence is indicative of a knowledge system that is centred on a

weak core in terms of knowledge endowments, surrounded by a periphery of more knowl-

edgeable actors, connected less strongly with the local area than with external knowledge

sources. The emergence of this peculiar structure suggests that the process of knowledge

socialization, which consists of exchanging ideas, technical advice and problem-solving

activities, does not take place at level of the whole cluster, but it is restricted to few

bounded communities. In the wine local system under investigation, the strongest commu-

nity in terms of density of interactions is the weakest as far as knowledge resources are

concerned. This implies that relying on the local knowledge basis, this community may

be locked into a declining learning path. However, the local extension agency acting as

a technological gatekeeper plays a key role in connecting this weak core to external

sources of knowledge.

6. Conclusions

In recent years, scholars have suggested that informal contacts are relevant and effective

channels for sharing information and knowledge in districts and clusters but few empirical

studies have provided detailed and convincing evidence on this issue. This article contrib-

utes at filling this gap by providing empirical evidence at firm level on informal contacts in

a small Italian wine cluster in order to understand the structural differences of information

and knowledge networks and how firms’ specific characteristics (e.g. size, performance,

knowledge base) may affect their structural position in the knowledge network as well

as their extra-cluster linkages.

Our empirical findings suggest that the conceptual distinction between information and

knowledge is indeed relevant in the analysis of informal networks in local production

systems. In the cluster of “Colline Novaresi”, the investigated networks of informal con-

tacts show different structures depending on what they convey: on the one hand, there is a

dense information network in which actors are linked by non-reciprocal ties; on the other

hand, the knowledge network is rather sparse, with a core of actors connected through

mutual ties. Thus, this study provides new insights into some controversial issues. The

local system analysed is not populated by a homogeneous community of entrepreneurs

and technicians, sharing technical advice and generic information. On the contrary, knowl-

edge flows, defined as technical advice, are restricted to a tightly connected community of

local producers, while information is easily accessible by almost everyone. This implies

that face-to-face contacts are limited in their scope and mainly serve to know what is

produced and who sells it. Knowledge, however, far from being a local public good, is


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actually a club good, whose membership is restricted and not simply regulated by

geographical proximity.

Our finding is in line with a recently increasing body of literature in which it has been

shown that knowledge is unevenly distributed in clusters and that networks (of knowledge,

information, business relationships, input exchanges, management ties, etc.) in clusters

differ a great deal in terms of their production and diffusion mechanisms (Bell, 2005;

Boschma & Ter Wal, 2007; Dahl & Pedersen, 2004; Giuliani, 2007; Giuliani & Bell,

2005; Kauffeld-Monz & Fritsch, 2007; Lissoni & Pagani, 2003; Morrison, 2008). Over-

looking these differences may lead to a biased analysis and misleading implications for

policy. In particular, cluster policy initiatives aimed at facilitating the access to local

know-how should take into account the existence of specific communities of firms/tech-

nicians and eventually select them as policy target rather than the geographical cluster as a

whole. Moreover, knowing the nature, size, cohesiveness and relations among the different

networks populating a cluster may provide useful insights on how to design better services

for potential users.

Furthermore, this study provides interesting findings on who participate in the local

knowledge network. We find that the core of the network, sharing a great amount of

local knowledge, is populated by a group of wineries that are on average smaller in

size, less innovative and less open to external knowledge than the periphery. We

suggest that these features (i.e. size, innovative behaviour) explain the cognitive closeness

of these firms. Their gateway to external sources of knowledge is represented by their

intensive connections with a local extension agency, which is a key player in the regional

system of innovation for the wine industry (Morrison & Rabellotti, 2007).

Conversely, the remaining firms (the periphery), which are on average larger, more

innovative and open to external sources of knowledge, can access knowledge inputs

either through their outside linkages, mainly to the dynamic wine system of the Southern

Piedmont, or by their better-developed internal resources. These peripheral firms are not

interested in tapping into local knowledge and therefore they contribute very little to

intra-cluster learning. These latter findings contrast with the cases discussed in some

recent studies on networks in clusters (Boschma & Ter Wal, 2007), but are in line with

other cases, for instance the Colline Pisane wine cluster discussed in Giuliani (2007),

which appears to be a typical example of declining wine cluster. In other words, our

results, along the lines of some existing evidence, suggests that intra-cluster knowledge

connectivity is not per se associated with positive knowledge endowments or with

better economic and technological performances, rather it depends on the context-specific

characteristics of the cluster under analysis.16

From our results it further descends that larger and more knowledgeable firms (relative

to other cluster’s members) do not necessarily act as knowledge gatekeepers (Graf, 2007;

Morrison, 2008). These firms in fact look for knowledge assets, which, if not available

inside the cluster, are searched elsewhere. Overall, this latter evidence provides insights

into the theoretical argument claiming that global pipelines “when they are too strong

they could threaten the long term existence of a cluster. Strong external linkages

could then provoke segmentation among the members of a cluster, reducing its coherence

and threatening its long-term future” (Bathelt et al., 2004, p. 48).

This study has a number of limitations that we hope will encourage further research on

this topic. The empirical analysis is based on a small number of firms in a peculiar wine

cluster, so concerns may be raised about the universality of the results. As partial answers


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to these potential drawbacks, we stress that the firms included in the research are not drawn

from a sample but they do constitute the population of the wineries in the cluster and

besides, through the survey, we have collected unique relational data, which can be

barely found in already existing larger surveys or secondary databases. Nonetheless, in

order to strengthen the robustness of our conclusions, more empirical evidence from

case studies, covering different sectors and geographical areas, is highly needed.

Moreover on a theoretical ground, this study relies on a definition of knowledge and

information that can be regarded as rough, and partly blurring. We acknowledge this limit-

ation and we believe that a richer conceptualization (on this point see, among others,

Asheim and Gertler (2005)) would be helpful for a deeper understanding of the process

of interactive learning and knowledge diffusion. However, on an empirical ground the

operationalization of somehow vague, abstract and complex concepts into observable

and measurable indicators necessarily requires for simplification.

To conclude, we suggest that some issues, only marginally touched in this study, will

deserve more attention in the literature. Further research should explore themes like the

role of external relations, given that network analyses in clusters have so far been

focused mainly on local linkages, while little is still known on the structural characteristics

of external networks. Another interesting question to address is related to external actors in

clusters: are they likely to have different ability to access to local knowledge networks?

Finally, the dynamics of the networks is still little explored; hence, an effort in collecting

longitudinal data on inter-firm collaboration within clusters is highly welcome.

Acknowledgements

We wish to thank Elisa Giuliani for letting us access the questionnaire elaborated for her

PhD thesis at SPRU, University of Sussex, and for her comments. We acknowledge the

collaboration in the interviews of Ombretta Cabrio and Marco Vuturo. Thanks go to

two anonymous referees, Mauro Lombardi, Mari Sako, Michael Storper and participants

at seminars held at Cespri, Universita Bocconi, and DRUID, Copenhagen Business School

for their comments. Finally, a special thanks goes to the people interviewed, who gave us

their time and knowledge besides frequent opportunities for wine tasting. Financing

from PRIN “Capabilities dinamiche tra organizzazione di impresa e sistemi locali di

produzione”, IRES Piemonte and Fondazione CRT—Progetto Alfieri is gratefully

acknowledged.

Notes

1. The literature has proposed many different, sometimes overlapping, terms and definitions of geographi-

cal agglomeration of economic activities. While acknowledging the relevance of this debate (see Panic-

cia (2002) for a review), the analysis of the peculiarity of these different forms of agglomerations goes

beyond the aim of this work.

2. Most of these approaches refer either explicitly or implicitly to Polanyi’s (1962) conceptualization of

knowledge.

3. We acknowledge that there are many types of knowledge which may be exchanged in informal networks

as it is suggested in Asheim and Gertler (2005), who distinguish among synthetic, analytical and sym-

bolic knowledge. Nevertheless, in the empirical exercise undertaken in this article the definition of

knowledge is simplified for making it operational.

4. The attribution of these appellations depends on strict regulations that establish the production area, the

grape varieties that can be used in a particular regional blend, the vine yield, the wine/grape yield, the


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alcoholic content, production and ageing methods, together with a specification of which kind of

information can be put on the wine label (Odorici & Corrado, 2004). Piedmont produces 11 DOCG

(Denominazione di Origine Controllata e Garantita) wines (over 32 in all Italy) and 45 DOC (Denomi-

nazione di Origine Controllata) (over 311 in all Italy), which account for almost 80% of the overall

regional production and 15% of Italian production of appellation wines. This makes Piedmont the

second biggest Italian producer of DOC and DOCG wines after Tuscany.

5. This area, known as the “wine route”, has its main nodes in four villages: Sizzano, Ghemme, Fara and

Boca located in the province of Novara. The area includes 25 municipalities.

6. Assoenologi (1998) and http://www.provincia.novara.it/Agricoltura/TerritorioAgricolo/vite.php

7. The Chamber of Commerce database includes 71 firms. We have excluded grape growers, farmers pro-

ducing only for self-consumption and farmers for whom wine production is not the core business. The

selection has been refined, along with the criteria indicated above, with the assistance of the representa-

tives of the two largest local associations of farmers (Coldiretti and Vignaioli Piemontesi). The total

number of firms satisfying our criteria is 32, from which four producers are excluded because they

had ceased their activity and two because they were not keen to answer the questionnaire.

8. For those few cases in which the two figures were different, we interviewed both the owner and the

technician.

9. Since an exhaustive list of organizations and firms located outside the local area was not available, we

gave respondents an incomplete list (open roster), asking them to add any other organization they have

contacted. For the firms, we asked them to indicate the number of firms they had been in touch with, their

location and the type of contact.

10. For a brief introduction to the measures and indicators used, see the appendix.

11. Borgatti and Everett (1999, p. 377) define the idea of core–periphery in a discrete model as follows: “the

core periphery model consists of two classes of nodes, namely a cohesive sub-graph (the core) in which

actors are connected to each other in some maximal sense, and a class of actors that are more loosely

connected to the cohesive sub-graph but lack of any maximal cohesion with the core”.

12. Due to the scarceness of more fine-grained data, we acknowledge that some of the indicators used in the

analysis may be considered weak from a statistical point of view. Nevertheless as far as the firm-specific

indicators are concerned, the questions in our survey are in line with the standard surveys carried out at

the more aggregated level (e.g. national census, CIS).

13. In general, only larger producers are able to sell to large retailers such as supermarkets because the

requirements in terms of quality standards, size and continuity of orders are difficult to satisfy for

smaller firms.

14. Bottles sold at an ex-producer price higher than E8.

15. This is confirmed by evidence reported in the last section of Table 6. For example, 60% of firms in the

periphery and 75% of those in the isolated periphery employ an external oenologist as a consultant.

16. This is in line with Ahuja (2000, p. 251, emphasis added) who claimed that “the impact of different

network attributes and positions can only be understood relative to a particular context”.

17. See Wasserman and Faust (1994) for a comprehensive review of measures. Indicators have been com-

puted using UCINET VI and PAJECK.

18. For details, see Bonacich (1987).

References

Ahuja, G. (2000) Collaboration networks, structural holes and innovation: A longitudinal study, Administrative

Science Quarterly, 45(3), pp. 425–455.

Allen, R. (1983) Collective invention, Journal of Economic Behavior and Organization, 4(1), pp. 1–24.

Asheim, B. (1994) Industrial districts, inter-firm co-operation and endogenous technological development: The

experience of developed countries, in: UNCTAD, Technological Dynamism in Industrial Districts: An

Alternative Approach to Industrialization in Developing Countries? pp. 91–142 (New York: UNCTAD).

Asheim, B. & Gertler, M. S. (2005) Regional innovation systems and the geographical foundations of innovation,

in: J. Fagerberg, D. Mowery & R. Nelson (Eds) The Oxford Handbook of Innovation, pp. 291–317 (Oxford:

Oxford University Press).

Assoenologi (1998) Cinquant’anni di storia della vitienologia novarese (Novara: mimeo).


Dow

nloa

ded

by [

Uni

vers

ite D

e Pa

ris

1] a

t 03:

51 0

8 M

ay 2

013

http://www.provincia.novara.it/Agricoltura/TerritorioAgricolo/vite.php

http://www.provincia.novara.it/Agricoltura/TerritorioAgricolo/vite.php

Audretsch, D. B. (1998) Agglomeration and the location of innovative activity, Oxford Review of Economic

Policy, 14(2), pp. 18–29.

Audretsch, D. B. & Feldman, M. P. (1996) R&D spillovers and the geography of innovation and production,

American Economic Review, 86(3), pp. 630–640.

Bathelt, H., Malberg, A. & Maskell, P. (2004) Clusters and knowledge: Local buzz, global pipelines and the

process of knowledge creation, Progress in Human Geography, 28(1), pp. 31–56.

Becattini, G. (1990) The Marshallian industrial district as a socio-economic notion, in: F. Pyke, G. Becattini &

W. Sengenberger (Eds) Industrial Districts and Inter-Firm Cooperation in Italy, pp. 37–51 (Geneva: ILO).

Bell, G. G. (2005) Research notes and commentaries clusters, networks, and firm innovativeness, Strategic

Management Journal, 26(3), pp. 287–295.

Belussi, F., Gottardi, G. & Rullani, E. (Eds) (2003) The Technological Evolution of Industrial Districts

(Amsterdam: Kluwer Academic Press).

Bonacich, P. (1987) Power and centrality: A family of measures, American Journal of Sociology, 92(5),

pp. 1170–1182.

Borgatti, S. P. & Everett, M. G. (1999) Models of core/periphery structures, Social Networks, 21(4), pp. 375–395.

Boschma, R. A. (2005) Proximity and innovation: A critical assessment, Regional Studies, 39(1), pp. 1–14.

Boschma, R. A. & Frenken, K. (2006) Why is economic geography not evolutionary science? Towards an evol-

utionary economic geography, Journal of Economic Geography, 6(3), pp. 273–302.

Boschma, R. A. & Lambooy, J. G. (2002) Knowledge, market structure, and economic coordination: Dynamics of

industrial districts, Growth and Change, 33(3), pp. 291–311.

Boschma, R. A. & Ter Wal, A. L. W. (2007) Knowledge networks and innovative performance in an industrial

district. The case of a footwear district in the South of Italy, Industry and Innovation, 14(2), pp. 177–199.

Breschi, S. & Lissoni, F. (2001a) Knowledge spillovers and local innovation systems: A critical survey, Industrial

and Corporate Change, 10(4), pp. 975–1005.

Breschi, S. & Lissoni, F. (2001b) Localised knowledge spillovers vs. innovative milieux: Knowledge “taciteness”

reconsidered, Papers in Regional Science, 80(3), pp. 255–273.

Breschi, S. & Lissoni, F. (2009) Mobility of skilled workers and co-invention networks: An anatomy of localized

knowledge flows, Journal of Economic Geography, (forthcoming).

Brusco, S. (1996) Global systems and local systems, in: F. Cossentino, F. Pyke & W. Sengenberger (Eds) Local

and Regional Response to Global Pressure: The Case of Italy and Its Industrial Districts, pp. 147–158

(Geneva: ILO).

Camagni, R. (1991) Local milieu, uncertainty and innovation networks: Towards a new dynamic theory of

economic space, in: R. Camagni (Ed.) Innovation Networks: Spatial Perspectives, pp. 121–144 (London:

Belhaven-Pinter).

Capello, R. (1999) Spatial transfer of knowledge in high technology milieux: Learning versus collective learning

processes, Regional Studies, 33(4), pp. 353–365.

Capello, R. & Faggian, A. (2005) Collective learning and relational capital in local innovation processes,

Regional Studies, 39(1), pp. 75–87.

Carter, A. P. (1989) Know-how trading as economic exchange, Research Policy, 18(3), pp. 155–163.

Cohen, W. M. & Levinthal, D. A. (1990) Absorptive capacity: A new perspective on learning and innovation,

Administrative Science Quarterly, 35(1), pp. 128–152.

Coleman, J. (1988) Social capital in the creation of human capital, American Journal of Sociology, 94(s1),

pp. 95–120.

Cowan, R. (2004) Network Models of Innovation and Knowledge Diffusion, MERIT-Infonomics, 2004-16

(Maastricht: Maastricht University).

Cowan, R., David, P. & Foray, D. (2000) The explicit economics if knowledge codification and tacitness, Indus-

trial and Corporate Change, 9(2), pp. 211–253.

Cross, R., Borgatti, S. P. & Parker, A. (2001) Beyond answers: Dimensions of the advice network, Social Net-

works, 23(3), pp. 215–235.

Dahl, M. S. & Pedersen, C. O. R. (2004) Knowledge flows through informal contacts in industrial clusters: Myth

or reality? Research Policy, 33(10), pp. 1673–1686.

Dosi, G. (1988) Sources, procedures and microeconomic effects of innovation, Journal of economic literature,

26(3), pp. 1120–1171.

Everett, M. G. & Borgatti, S. P. (1999) Peripheries of cohesive subsets, Social Networks, 21(4), pp. 397–407.

Feldman, M. P. (1999) The new economics of innovation, spillovers and agglomeration: A review of empirical

studies, Economics of Innovation and New Technology, 8(1&2), pp. 5–25.


Dow

nloa

ded

by [

Uni

vers

ite D

e Pa

ris

1] a

t 03:

51 0

8 M

ay 2

013

Giuliani, E. (2007) The selective nature of knowledge networks in clusters: Evidence from the wine industry,

Journal of Economic Geography, 7(2), pp. 139–168.

Giuliani, E. & Bell, M. (2005) The micro-determinants of meso-level learning and innovation: Evidence from a

Chilean wine cluster, Research Policy, 34(1), pp. 47–68.

Graf, H. (2007) Gatekeepers in Regional Networks of Innovators, Jena Economic Research Papers #2007-054,

Friedrich-Schiller-University Jena and Max-Planck-Institute of Economics, pp. 1–31.

Granovetter, M. (1973) The strength of weak ties, American Journal of Sociology, 78(6), pp. 1360–1380.

Hanneman, R. A. (2001) Introduction to Social Network Models, mimeo (Riverside: Department of Sociology,

University of California).

von Hippel, E. (1987) Cooperation between rivals: Informal know-how trading, Research Policy, 16(6),

pp. 291–302.

Jaffe, A. B. (1989) Real effects of academic research, American Economic Review, 79(5), pp. 957–970.

Jaffe, A. B., Trajtenberg, M. & Henderson, R. (1993) Geographic localisation of knowledge spillovers as evi-

dence from patent citations, Quarterly Journal of Economics, 108(3), pp. 577–598.

Kauffeld-Monz, M. & Fritsch, M. (2007) The Impact of Network Structure on Knowledge Transfer: An Empirical

Application of Social Network Analysis in the Context of Regional Networks of Innovation, DIME-Workshop

Network dynamics and the performance of local innovation systems (Jena: University of Jena and the Max

Planck Institute of Economics).

Keeble, D. & Wilkinson, F. (1999) Collective learning and knowledge development in the evolution of regional

clusters of high-technology SMEs in Europe, Regional Studies, 33(4), pp. 295–304.

Lazerson, M. H. & Lorenzoni, G. (1999) The firms feed industrial districts: A return to the Italian source, Indus-

trial and Corporate Change, 8(2), pp. 235–266.

Lissoni, F. (2001) Knowledge codification and the geography of innovation: The case of Brescia mechanical

cluster, Research Policy, 30(9), pp. 1479–1500.

Lissoni, F. & Pagani, M. (2003) How many networks in a local cluster? Textile machine production and inno-

vation in Brescia, in: D. Fornahl & T. Brenner (Eds) Cooperation, Networks and Institutions in Regional

Innovation Systems (Cheltenham: Edward Elgar).

Lundvall, B.-A. & Johnson, B. (1994) The learning economy, Journal of Industrial Studies, 1(2), pp. 23–42.

Malmberg, A. & Maskell, P. (2002) The elusive concept of localization economies: Towards a knowledge based

theory of spatial clustering, Environment and Planning, 34(3), pp. 429–449.

Marshall, A. (1920) Industry and Trade (London: Macmillan).

Maskell, P. (2001) Towards a knowledge-based theory of the geographical cluster, Industrial and Corporate

Change, 10(4), pp. 921–943.

Morrison, A. (2008) Gatekeepers of knowledge within industrial districts: Who they are how they interact,


Morrison, A. & Rabellotti, R. (2007) The role of research in wine: The emergence of a regional research area in

an Italian wine production system, International Journal of Technology and Globalisation, 3(2/3),

pp. 155–178.

Nelson, R. R. & Winter, S. G. (1982) An Evolutionary Theory of Economic Change (Cambridge, MA: Harvard

University Press).

Nooteboom, B. (1999) Trust as a governance device, in: M. Casson & A. Godley (Eds) Cultural Factors in

Economic Growth, pp. 44–68 (Berlin: Springer).

Odorici, V. & Corrado, R. (2004) Between supply and demand: Intermediaries, social networks and the construc-

tion of quality in the Italian wine industry, Journal of Management and Governance, 8(2), pp. 149–171.

Paniccia, I. (2002) Industrial Districts: Evolution and Competitiveness in Italian Firms (Cheltenham: Edward

Elgar).

Polanyi, M. (1962) Personal Knowledge: Towards a Post-Critical Philosophy (New York: Harper Torchbooks).

Powell, W. & Grodal, S. (2005) Networks of innovators, in: J. Fagerberg, D. Mowery & R. Nelson (Eds) The

Oxford Handbook of Innovation, pp. 56–85 (Oxford: Oxford University Press).

Rabellotti, R. (2004) How globalisation affects Italian industrial districts: The case of Brenta, in: H. Schmitz (Ed.)

Local Enterprises in the Global Economy: Issues of Governance and Upgrading, pp. 140–173 (Cheltenham:

Edward Elgar).

Rabellotti, R. & Schmitz, H. (1999) The internal heterogeneity in industrial districts in Italy, Brazil and Mexico,


Rallet, A. & Torre, A. (2005) Proximity and localization, Regional Studies, 39(1), pp. 47–59.

Rauch, J. E. & Casella, A. (Eds) (2001) Networks and Markets (New York: Russell Sage Foundation).


Dow

nloa

ded

by [

Uni

vers

ite D

e Pa

ris

1] a

t 03:

51 0

8 M

ay 2

013

Rogers, E. M. (1982) Information exchange and technological innovation, in: D. Sahal (Ed.) The Transfer and

Utilisation of Technical Knowledge, pp. 105–123 (Lexington, MA: Lexington Books).

Samarra, A. & Biggiero, L. (2008) Heterogeneity and specificity of inter-firm knowledge flows in innovation

networks, Journal of Management Studies, 45(4), pp. 800–828.

Saxenian, A. L. (1994) Regional Advantage: Culture and Competition in Silicon Valley and Route 128

(Cambridge, MA: Harvard University Press).

Schrader, S. (1991) Informal technological transfer between firms: Cooperation through information trading,

Research Policy, 20(2), pp. 153–170.

Sorenson, O., Rivkin, J. W. & Fleming, L. (2006) Complexity, networks and knowledge flow, Research Policy,

35(7), pp. 994–1017.

Staber, U. (2001) Spatial proximity and firm survival in a declining industrial district: The case of knitwear firms

in Baden-Wurttemberg, Regional Studies, 35(4), pp. 329–341.

Stuart, T. E. & Sorenson, O. (2003) The geography of opportunity: Spatial heterogeneity in founding rates and the

performance of biotechnology firms, Research Policy, 32(2), pp. 229–253.

Wasserman, S. & Faust, K. (1994) Social Network Analysis. Methods and Applications (Cambridge: Cambridge

University Press).

Appendix

The structural properties of local networks are analysed using the following indicators:17

(1) Overall network density is the ratio between the total number of ties and the total

number of possible ties. It is a measure of connectivity and shows the presence of

some relational capital in the local system:

D ¼2L

gðg� 1Þ;

where g are nodes and L identifies all lines in a graph. The density ranges from 0

(L ¼ 0, no lines present) to 1 (all possible lines present).

(2) Actor centrality degree indicates the number of nodes to which each node is directly

connected. The higher the degree, the more actors access to knowledge:

CDðniÞ ¼ dðniÞ;

where di identifies the number of lines incident to it. In directional networks we dis-

tinguish between in-degree centrality (i.e. the number of in-going ties), which

measures the flow of information or knowledge each actor receives, and out-degree

centrality (i.e. the number of out-going ties) measuring the flows generated by each

actor.

(3) Average degree is a synthetic measure obtained by averaging the centrality degree:

d ¼

Pgi¼1 dðniÞ

g:

(4) Strength of ties provides a measure of the importance (in terms of frequency and

quality) and efficiency of communication flows. Respondents were asked to rank

their linkages in terms of frequency on a 1–3 scale (respectively, 1 in a year,


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month, week or more) for their information linkages, and in terms of relevance and

contribution to resolving technical problems on a 1–3 scale (respectively, low,

medium, high) for their knowledge linkages. Strong (weak) knowledge linkages

imply key (not useful) advice on technical problems, while strong (weak) information

ties identify frequent (occasional) contacts among local actors.

To investigate the presence of a core–periphery structure the following measures are

used:

(1) Bonacich power index18 is a version of centrality degree. The centrality of actors

depends on how many connections they develop, and how many connections their

neighbours have. The more connections the actors have, and the more connections

their connected actors have, the lower their power. In our context, power is an indi-

cation of an actor’s ability to be a source of knowledge or information and to

diffuse these flows within the local system.

(2) Betweenness degree of centrality identifies those actors on the shortest path between

two other actors, i.e. “in the middle”, and therefore able to control or impede the com-

munication flow to the detriment of others. In other words, this measure reflects

aspects of arbitrage:

CBðniÞ ¼X

j,k

gjkðniÞ

gjk

;

where i is distinct from j and k. The index minimum is 0 and the maximum is

(g21)(g22)/2.

(3) Centralization index of power and betweenness: “expresses the degree of inequality or

variance in the network as a percentage of that of a perfect star network of the same

size” (Hanneman, 2001, p. 65). Higher centralization (i.e. concentration) for both

indexes means that one actor is the leader of the communication network:

Cj ¼

Pgi¼1 Cjðn

�Þ � CjðniÞ� �

ðg� 1Þðg� 2Þ½ �;

where Cj stands for CD or CB, Cj(n�) is the highest observed value.

(4) Heterogeneity index is equivalent to the coefficient of variation (standard deviation

divided by mean times 100). Higher variance implies higher inequality, that is

actors have different abilities to produce, acquire and control flows of information

and knowledge:

S2j ¼

Pgi¼1 ðCjðniÞ � �CjÞ

� �

g;

where Cj is the mean actor degree index.

To analyse the linkages with organizations and external networks three indicators are

used:


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(1) The indicator of openness measures the external relations of firms, to either firms or

organizations located outside the local area. It is equal to 1 if the interviewee has con-

tacts with firms located outside the Province of Novara, 0 otherwise.

(2) The indicator of collaborations with extension services is a dummy variable equal to 1

if the interviewee has contacts with the local extension service, 0 otherwise.

The indicator of relations with local business associations is a dummy variable equal to

1 if the interviewee has contacts with local wine consortia and business associations, 0

otherwise.


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knowledge and information networks in an italian wine cluster

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