Knowledge and Information Networks in an Italian Wine Cluster

Abstract

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

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, Università 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

The literature has proposed many different, sometimes overlapping, terms and definitions of geographical agglomeration of economic activities. While acknowledging the relevance of this debate (see Paniccia (2002) for a review), the analysis of the peculiarity of these different forms of agglomerations goes beyond the aim of this work.

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

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 symbolic knowledge. Nevertheless, in the empirical exercise undertaken in this article the definition of knowledge is simplified for making it operational.

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 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 (Denominazione 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.

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.

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

The Chamber of Commerce database includes 71 firms. We have excluded grape growers, farmers producing 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 representatives 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.

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

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.

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

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”.

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).

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.

Bottles sold at an ex-producer price higher than [euro]8.

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.

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”.

See Wasserman and Faust (1994) for a comprehensive review of measures. Indicators have been computed using UCINET VI and PAJECK.

For details, see Bonacich (1987).