Conclusion

The DEMATEL method we proposed is to discover the direct and indirect driving forces for the growth of industrial clusters, using the HSIP as an example for this dissertation. We measure the relationships among these driving forces.

First, this research helps divide all dimensions into causal groups and effect groups in terms of the driving forces behind industrial clustering. According to our research results, factor conditions and local demand conditions are the major causal driving forces for the growth of industrial clusters. As a side note, research results indicate that most of the driving forces form a system and also are self-reinforcing.

This implies that Taiwan’s clustering of industry has led toward vigorous competition that would result in the rapid development of skilled workers, the creation of related technological industries, and development of a specialized infrastructure that gives the cluster a competitive advantage.

Industrial clusters can be seen as a source of national competitiveness, serving to upgrade productivity, new business formation and innovation, and advance marketing/customer relations. Potentially, however, a different cluster form of development may derive from a mix of different participating levels of the driving forces behind the growth of industrial clusters. The case of Taiwan is a decent example used by this research.

The Taiwan’s experience might appear to be a classic case of the benefits of national competitiveness, with Taiwan specializing in IC and PC manufacturing and Silicon Valley leading in more advanced IC design and electronic system definition (Saxenian, 2006). This also observes that the implemented clustering policy in Taiwan involves the importance attached to policies targeting specific industries and

specializations of science parks and strong cluster formations.

The policy implication would be that as a small economy, Taiwan government’s top-down innovation policy at beginning stages might be beneficial in terms of the cluster formation leading to country competitiveness. Particularly, our observation is that the Taiwan government support is an influenced matter; that is, local demands and factor conditions as the causal drivers can result in specified government plans in growing and developing the industrial cluster located in the HSIP although the effect is not so strong. On the other hand, as just noted, in the past Taiwan government has engaged itself in promoting national innovation and cluster development a lot.

Therefore as a driving force, “government support” at present plays a less significant role in view of its lower improvement effects and hence, for all concerned, becomes an indirect contributor to Taiwan’s industrial cluster growth.

As a small summary, the connecting driving forces behind Taiwan’s growth of industrial clusters are meant to be local demands and factor conditions. Other factors such as related and supporting industries, firm structure, strategy, rivalry, and government support are classified as effect group factors based on our research results.

They can also be viewed as indirect forces leading to Taiwan’s cluster development, in contrast to the causal driving forces. They do not have such great room for further improvement so as to trigger extended cluster growth. Therefore the other policy implication lies in that if Taiwan’s policy makers wish to improve its cluster development more efficiently, they can consider these two sides of factors and take appropriate actions according to the mentioned evaluation results.

From an international angle, the Hsinchu industrial system contrasts, for instance, Irish cluster policy with early Foreign Direct Investment (FDI)-led approaches taken by the Irish government, which provide incentives to attract high-tech inward investment in or near Dublin to take advantage of forces of labor and logistics. As also

a small island country, Irish participation in EU’s programmes obtaining various research funds also helps facilitate high-tech spin-offs from university incubators and gradually form clusters in Dublin, Cork, Galway, and other cities in recent years (Grimes and Collins, 2003). This also says about the fact that a combination of different kinds or levels of driving forces may contribute to different effects of cluster development.

Chapter 4 Dissertation Conclusions and Suggestions

This research includes two parts. First, we want to forecast the annual output using the exponential smoothing forecasting model, the GM (1, 1) model, and the Grey-Markov model. The period of this research is from 2001 to 2007. The computer and semiconductor industries are the research examples for estimating model. The first part of this research is to understand and estimate the annual output growth trend.

However in this section we offered an extra contribution which relates to offering an example as to how people in the future can apply a good estimation tool for industrial output efficiency. From the research results, the error rates for the exponential smoothing model are 13.48% and 13.49% for the two industries. The relative percentage errors of the GM (1, 1) model are 6.7116% and 7.20% for our surveyed industries. Notably, after the GM (1, 1) was modified using the Markov chain, the semiconductor industry’s annual output of absolute error decreased to 6.54%, while the computer industry’s annual output of absolute error decreased to 7.01%. Thus, our research results indicate that Grey-Markov estimating model is much more accurate for estimating the annual output of the semiconductor and computer industries in the case of HSIP.

From our estimation results, we also understand that the annual output of the semiconductor industry will slow down in the future while that of the computer industry has a decreasing trend. The annual values of semiconductor industry and computer industry account for over 50% for the HSIP. Industrial clusters can be seen as a main source of national competitiveness, serving to upgrade productivity, new business formation and innovation, and advance marketing/customer relations for Taiwan . On the other hand, we also obtain a small conclusion from here that the cluster growth for these two dominant clustering industries of Taiwan has been