Manufacturing relations: An empirical study of the organization of production across multiple networks

• 成果类型: Article
• 署名作者: Lomi, Alessandro; Pattison, Philippa
• 署名单位: University of Bologna; University of Melbourne
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.1060.0190
• 发表日期: 2006
• 页码: 313-332
• 关键词: INTERORGANIZATIONAL NETWORKS; p-star models; network motifs; RESOURCE DEPENDENCE

摘要

Organizational communities present two generic features that are recurrently documented in empirical studies, but only imperfectly accounted for in current models of interorganizational relations. The first is the tendency of participant organizations to construct observed macrostructure locally, through relational activities that involve only a small subset of possible network ties. The second is the tendency for different types of ties to overlap, concatenate, and induce a variety of local structures-or relational motifs-across network domains. A critical task in the analysis of organizational communities is to specify appropriate local dependence structures across multiple networks, starting from detailed observation of dyadic interaction among participants. In this paper we illustrate one way in which this analytical task might be accomplished in the context of a study of interorganizational networks. We use data that we have collected on different types of relationships among 106 organizations, located in Southern Italy, involved in the production of means of transportation to test hypotheses about patterns of local network ties and paths across multiple networks. Our empirical analysis is guided by the general claim that the formation of network ties is subject to endogenous and exogenous processes. We specify statistical models for random graphs that allow us to examine this claim, and to formulate and test specific hypotheses about the form that such network-based processes might take. The results that we report provide clear empirical support for the relational motifs implied by our hypotheses. We also find strong empirical support for the proposition that interorganizational dependencies extend across multiple networks.