This paper objective is to dimension the effects of structural and spatial embeddedness on the attitudinal similarity of strategic networks agents characterized as local productive system (LPS). The theoretical background highlights the structural and spatial embeddedness on strategic networks and also on the agent's attitudinal similarity. It is argued that the degree of similarity between two agents in terms of structural and spatial embeddedness will be related to the degree of attitudinal similarity in strategic networks as LPS. More specifically, it is argued that the identification of agents with your network and the evaluation that these agents are coordinating the performance of the network in seeking external support for development of the arrangement shown as two important variables in terms of the degree of cooperation among agents and collective and individual performance. As a driver or inhibitor of interactions the spatial embeddedness is the element that has the large potential to complement more readily the analysis of the influence of structural embeddedness on the similarity between social actors regarding the higher degree of attitudinal agreement or behavioral uniformity. The analysis occurred in the dyadic level because it is the micro locus of reproduction and transformation of logics or social patterns, the biggest spectrum e because it affects the network system and societal level as a whole. It was employed the Multiple Regression Quadratic Assignment Procedure (MRQAP) to test the hypothesis. The number of observations was 600 dyads generated by 25 organizations. The results reveal that similarity in structural prestige and clustering coefficient ego relate positively with similarity in agent network identification. Regarding to spatial embeddedness it was proven the hypothesis that geographical proximity between the agents is positively related with similarity in performance evaluation of network coordination concerning to search external support to LPS development.
Structural embeddedness; Spatial embeddedness; Dyadic similarity; Network; MRQAP
