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Thesis

French

ID: <

http://hdl.handle.net/20.500.11794/25679

>

Where these data come from
Socio-spatial analysis of business networks of manufacturing SMEs: the case of the Drummondville CRM

Abstract

In the current context of globalisation, manufacturing SMEs need to achieve and maintain a high level of competitiveness in order to perform and grow. A wealth of literature shows that the key to success is the ability of companies to innovate. However, innovation requires resources such as financing, efficient technical and information resources and skilled human resources. Thus, the competitiveness of businesses also depends on networks of economic actors that provide access to the resources needed for innovation and market development. But how do these economic networks influence the innovative and commercial performance of companies? Are there more efficient ways of networking than others (optimal)? These are questions that this research project tries to answer. On the basis of the theories of the New Economic Sociology (Embedding the Economy in the Social Structure), a group of French researchers developed the concept of proximity in the mid-1990s to study the spatial and interpersonal structure of economic networks and their impact on the innovative and commercial performance of businesses. Despite much progress on the topic, recent literature on innovation networks concludes that further research is needed to understand how different types of proximity are co-evolving during the business trajectory. In fact, the main scientific challenge is to develop methods that allow a dynamic analysis of the effect of networks on business performance. 23 in-depth interviews with innovative manufacturing SMEs in the Drummondville region in the heart of Quebec Province (Canada) and the development of an innovative data acquisition and analysis methodology led to successful results. This research demonstrates, among other things, that the best-performing companies have much larger space networks, while maintaining a very strong local relational nucleus. It also shows that the structure and dynamics of networks change significantly with the company’s trajectory or degree of experience. In addition, the methodology developed makes it possible to analyse the combined effect of organisational and spatial proximity on companies’ performance in a convincing manner. This is a promising approach that addresses the scientific challenges proposed in recent literature.

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