Abstract

Clustering is one of the key drivers for regional economic growth. Development of clusters is a dynamic process shaped by a variety of internal and external factors such as availability of skilled labor, presence of functioning networks and partnerships, technological changes, and market competition, etc. As a result, the patterns of cluster growth may differ from one another. Although each cluster is unique in some way, previous research has attempted to identify few simplified models of evolution of clusters. In this study, we briefly reviewed the literature on a variety of models of clusters. Based on these models, we investigated 15 hi-performing metropolitan-based clusters in the United States, covering communications equipment manufacturing, information technology, and biopharmaceutical industries, in order to find out the similarities and differences between real-world clusters. Specifically, by examining the composition of these high-tech clusters, we attempted to find out the following: 1) What are the typologies of these technology clusters? 2) Whether different industries tend to support different cluster typologies? and 3) How do clusters change their typologies over time? Our analysis results suggest that the real-world clusters rarely feature any single type of typology; a mixed type of typology is much more prevalent in reality. We also found that different industries tend to support different types of cluster typologies. In other words, an individual cluster's typology is to some extent shaped by the industry group it belongs to. In addition, we note that, as a cluster goes through different stages of its lifecycle, its typology may change significantly.

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