This paper investigates Formal Collaboration Agreements (FCAs) in Italy, focusing on their spatial and sectoral dimen-sions as facilitated through contracts between businesses ("con-tratti di rete"). Initially designed to support small and medium-sized enterprises (SMEs), FCAs often involve firms of varying sizes. Using a dataset from the Italian Chambers of Commerce, this study examines the geographic distribution and sectoral specialization patterns among of participating firms. Network analysis framework, such as bipartite graphs and backbone ex-traction techniques, help to identify clusters of municipalities. The findings demonstrate a clear relationship between geo-graphic proximity and economic specialization, highlighting re-gional clusters defined by specific industrial activities and cen-trality metrics identify municipalities and sectors functioning as pivotal nodes, crucial for information dissemination and eco-nomic integration. The community detection analysis emphasizes a strong align-ment between economic specialization and geographic cluster-ing, revealing notable patterns in the territorial distribution of economic activities. These insights provide policymakers with practical evidence to promote targeted regional development ini-tiatives, thereby supporting SMEs' competitiveness through strengthened inter-firm collaborations.

Business Alliances and Spatial Network Backbones: A National-Scale Analysis of Formal Collaborations

AE Vurro
;
A Bumbea;A Giuffrida;A Mazzitelli;
2026-01-01

Abstract

This paper investigates Formal Collaboration Agreements (FCAs) in Italy, focusing on their spatial and sectoral dimen-sions as facilitated through contracts between businesses ("con-tratti di rete"). Initially designed to support small and medium-sized enterprises (SMEs), FCAs often involve firms of varying sizes. Using a dataset from the Italian Chambers of Commerce, this study examines the geographic distribution and sectoral specialization patterns among of participating firms. Network analysis framework, such as bipartite graphs and backbone ex-traction techniques, help to identify clusters of municipalities. The findings demonstrate a clear relationship between geo-graphic proximity and economic specialization, highlighting re-gional clusters defined by specific industrial activities and cen-trality metrics identify municipalities and sectors functioning as pivotal nodes, crucial for information dissemination and eco-nomic integration. The community detection analysis emphasizes a strong align-ment between economic specialization and geographic cluster-ing, revealing notable patterns in the territorial distribution of economic activities. These insights provide policymakers with practical evidence to promote targeted regional development ini-tiatives, thereby supporting SMEs' competitiveness through strengthened inter-firm collaborations.
2026
978-88-9326-284-2
Bipartite network, Formal collaborations, Community detection, Spatial clustering, Backbone analysis, Leiden algorithm.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12606/45405
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