Elsevier

Expert Systems with Applications

Volume 36, Issue 9, November 2009, Pages 11895-11906
Expert Systems with Applications

The evaluation of cluster policy by fuzzy MCDM: Empirical evidence from HsinChu Science Park

https://doi.org/10.1016/j.eswa.2009.04.019Get rights and content

Abstract

In the recent years, industrial clusters have received considerable attention from economists and industrial analysts, because they are seen as the main reason for economic growth and success of certain economic region. This study systematically reviews past researches of industrial cluster. The purpose of this paper is to contribute to the understanding of this issue regarding the driving forces for the growth of industrial cluster and find out the priority among these cluster policies. Taiwan HsinChu Science Park is a prime example for this paper, and its connection with the innovative participators. We begin with an examination of the literature on cluster about its driving forces and policies upon which we propose a conceptual framework. In doing so, we explore the cluster-based industrial system. Then this research adopts the Fuzzy Analytic Hierarchy Process as the analytical tool. The Fuzzy Analytic Hierarchy Process method is used to determine the weightings for evaluation dimension among decision makers. From our research results, the Factor Conditions is the most important driving force for advancing the industrial cluster performance. Moreover, the promotion of international linkages policy and broader framework policies rank the first two priorities for cluster policy. Overall, this paper concludes with some simulations of cluster policy alternatives confronting the industry and the Taiwanese government.

Introduction

The increasing competition and globalization of industries, markets, and technologies have raised the demand for outside-in innovation and acquisition of technology through integrated innovation cluster (Becker & Gassmann, 2006). Companies need to develop cluster competence in order to link their organization to other players in the market to allow interactions beyond organizational boundaries (Ritter & Gemunden, 2004). The formation of clusters of innovation is a useful concept to transform both tangible and intangible knowledge into embodied and disembodied technical change (Liyanage, 1995).

Clusters are defined as selected sets of multiple autonomous organizations, which interact directly or indirectly, based on one or more alliance agreements between them. The aim of clusters is to gain a competitive advantage for the individual organizations involved and occasionally for the cluster as a whole as well. Cluster competence enables a company to establish and use relationships with other organization (Ritter & Gemunden, 2004).

On the other hand, the traditional industrial system has often focused on promoting science and technological policies. These system models have typically believed in the science push effect in radical industrial process. Compared with traditional hierarchical systems, the cluster between industries and other research institutions can reduce innovation costs (Clark and Guy, 1998, Gemunden et al., 1996), gain complementary resources or knowledge (Ritter and Gemunden, 2004, Teng et al., 2006, Williams, 2005), receive financial funds (Colombo and Delmastro, 2002, Rothschild and Darr, 2005), and advance competitive positions (Ritter & Gemunden, 2004).

Previous studies also have examined the cluster structure (Clark and Guy, 1998, Gemunden et al., 1996, Ritter and Gemunden, 2004), and some studies addressed the cluster effect (Teng et al., 2006). A number of empirical studies also provide evidences that clusters affect the innovation performance (Colombo & Delmastro, 2002). Particularly, over the past researches, scholars in the field of innovation system have found it most useful to compare the innovation system between different industries or countries (Chang & Shih, 2005).

Some scholars have draw attention to Taiwan innovation system (Hu et al., 2005, Lai and Shyu, 2005, Lee and Tunzelmann, 2005, Tasi and Wang, 2005, Yang et al., 2006). Taiwan is one of the world’s largest manufacturers of high-technology components and products. Taiwan maintains its current competitive position through investment in research and development (Lai and Shyu, 2005, Tasi and Wang, 2005). To this end, the establishment of a business friendly environment and local innovation cluster, and the creation of an environment to enhance innovation capabilities, is a pressing task (Hu et al., 2005). The development of high-tech industry has obviously reached maturity in Taiwan. According to the World Economic Forum’s “2007–2008 Global Competitiveness Report”, Taiwan has again taken first place in the world in the “State of Cluster Development” index (see Appendix A) (Chen, 2007).

The HsinChu Science Park (HSP) of Taiwan is now one of the world’s most significant areas for semiconductor manufacturing.It is home to the world’s top two semiconductor foundries, Taiwan Semiconductor Manufacturing Company (TSMC) and United Microelectronics Corporation (UMC) (Chen, 2007, Lai and Shyu, 2005, Tasi and Wang, 2005). The HSP was established by the government of Taiwan in 1980. It straddles HsinChu City and HsinChu County on the island of Taiwan. Industries in the HSP cover primarily six spheres – semiconductor, computer peripherals, communications, opto-electronics, biotechnology, and precision machinery. Firms in the science parks bring in high-tech industries, and in addition, help transform Taiwan’s labor-intensive industries into technology-intensive industries.

In the literature, there is no fuzzy logic method aimed at prioritizing the cluster policies. The main purpose of this paper is to provide practitioners with a fuzzy point of view to traditional policy research for dealing with imprecision and at obtaining the prioritization of driving forces measurement dimensions. Moreover, we attempt to assist government representatives or industrial analyst in accessing cluster policy. We take the HSP of Taiwan for pursuing our case purposes. This research invites ten experts that evaluate different cluster policy via the proposed fuzzy AHP method. This research looks forward to provide Taiwan industries and government with some strategic recommendations.

The reminder of this paper is as follows: Section 2 briefs the factors drive the growth of industry clusters rooted in important prior researches. Section 3 introduces the cluster policies. Section 4 presents how we adopt the methodology, Fuzzy AHP. Section 5 displays our empirical results along with some discussions relating to managerial implications. Concluding remarks are then given in Section 6.

Section snippets

What factors drive industrial cluster as national competitiveness?

A major breakthrough for the cluster concept was Porter’s Competitive Advantage of Nations (1998) which advocated specialization according to historical strength by emphasizing the power of industrial clusters. Porter highlighted that multiple factors beyond the ones internal to the firm may improve its performance. In his “diamond model”, four sets of interrelated forces are brought forward to explain industrial cluster. These are associated with factor input conditions; local demand

What are industrial cluster policies?

Cluster policy entails a shift of focus from individual firms to local/regional systems of firms and firms’ value adding environment. Cluster policy also means less reliance on large firms and more interest in local agglomerations of SMEs. The notion of clusters also leads to stimulating social processes, e.g. encouraging trust-based interaction to increase the flow of knowledge between local players, rather than intervening, for instance, through financial incentives. Cluster policy should

Fuzzy Analytic Hierarchy Process

Analytic Hierarchy Process (AHP) is a powerful method to solve complex decision problems. Any complex problem can be decomposed into several sub-problems using AHP in terms of hierarchical levels where each level represents a set of criteria or attributes relative to each sub-problem. The AHP method is a multi-criteria method of analysis based on an additive weighting process, in which several relevant attributes are represented through their relative importance. AHP has been extensively

Empirical evidence from HsinChu Science Park

The cluster is focused on linkages and interdependencies among players in the value chain. It emphasizes the role of technological spillovers and cross-sectoral linkages of dissimilar and complementary firms as major sources of long-term growth. Thus it goes beyond the horizontal networks of firms that operate on the same end-product market and belong to the same industry group, and allows cooperation on aspects such as collective marketing and purchasing (Bonita et al., 2002). Clusters take up

Conclusion and further work

Carroll and Reid (2004) said that clustering brings a variety of benefits to firms and the local economy. They believe that cluster-based economic development represents an opportunity for industries in our region to reach unprecedented levels of competitiveness. Industrial cluster provides sourcing companies with a greater depth to their supply chain and allows for the potential of inter-firm learning and co-operation. Clusters also give firms the ability to draw together complementary skills

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