Academic journal article Innovation: Organization & Management

Towards Continuous Innovation for Regional High-Tech Industrial Clusters

Academic journal article Innovation: Organization & Management

Towards Continuous Innovation for Regional High-Tech Industrial Clusters

Article excerpt

(ProQuest: ... denotes formulae omitted.)

Clusters, as one of the most significant phenomena in economics, economic geography and management have attracted extensive attention of researchers and practitioners during the past decade (Harrison et al. 2004; Lin et al. 2006). Clusters of high-tech firms and synergies with various organizations (e.g., government, research institutions, technology intermediaries and universities) have characterized the development of the Silicon Valley and Route 128 in the USA. Both developed and under-developed countries have tried to mimic the American successful story by encouraging formation of clusters of high-tech firms. Better-known examples of such clusters include Cambridge, UK Sophia- Antipolis of France, Tsukuba in Japan, and Taiwan's Tsinchu Technology Park (Hu 2007). Observers have noted that such concentration of innovative firms in a cluster helps create an innovative environment that can breed a continuous stream of innovations with information sharing and knowledge spillover (Yam et al. 2004).

However, it is not easy for regional clusters to create a context for continuous innovation. A series of issues need to be faced, such as irrational allocation of resources, incomplete innovation networks, the lack of protection for innovation, the lack of entrepreneurial leadership, inadequate venture capital and technology transfer mechanisms (Tan 2006). As the cluster moves through a life cycle to the maturity stage, firms become overwhelmed by undifferentiated competition (Canina et al. 2005). Then, the benefits from cluster diversity are diminished, the required technical and financial resources of the cluster are reduced, and the motivation for innovation declines (Tan 2006). It is worthwhile to explore the dynamic and complex system of industrial clusters, as many factors influencing the industrial clustering effect and interactions among factors exist (Lin et al. 2006).

There is, to our knowledge, a paucity of studies on continuous innovation of high-tech clusters, which is considered to be the 'life-blood' of many competitive organizations (Irani & Sharp 1997). This paper explores the inherent evolutionary mechanism of high-tech industrial clusters by analyzing the life cycle of continuous innovation of clusters, hoping to provide a better understanding on how to improve the continuous innovation capacities of industrial clusters.


Life cycle and cluster evolution

Cluster evolution mainly is derived from the theory of the life cycle of technology (Kim 2003; Meade & Rabelo 2004; Meade et al. 2006; Haupt et al. 2007). A technology evolution follows a life cycle from entering the market, growing, maturing to eventually declining, and will be replaced by the next generation of technology (Godoe 2000). Some researchers stated the specific phases of technology life cycle (Werker 2003; Dalum et al. 2005; Feldman et al. 2005; Romanelli & Khessina 2005; Otsuka 2006). Esposito and Mastroianni (2002) indicated that relational life cycle of technology was characterized by four phases: introduction, development, expansion and maturity. Corresponding to product life cycles, Haupt et al. (2007) differentiated the introduction, growth, maturity and decline as the various stages of technology life cycle. Moreover, some studies concentrate on various strategies for how to cope with shifts in the technological lifecycles, when facing disruption as the actors in the cluster (Dalum et al. 2005). Basically, the theory of technology life cycle contributes to the evolution of industrial clusters behind the efforts to build regional innovation systems (RIS).

Some researchers focus on the life cycle of industrial clusters (Suchman 1995). Pouder and John (1996) distinguished three phases in the evolution of clusters: origination, convergence and reorientation, which resembled phases of industrial evolution. Ahokangas et al. (1999) identified three phases in cluster evolution, including: (1) origination and emergence; (2) growth and convergence; and (3) maturity and reorientation. …

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