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
stagnant. Therefore, how to improve the alarmingly decreasing annual output and industrial value of HSIP is a critical and urgent task for industrial practitioners and government officials of Taiwan now and in the future. This point therefore triggers our research motivation for next step.
Therefore, the second part of this research is to contribute to the understanding of the casual and effect factors among those influencing an industrial cluster. Another core viewpoint anchored in this section is that national competitive advantages can be achieved by industrial clusters. That is, we would like to make use of the concepts of national competitiveness proposed by Porter (1998) and cluster drivers toward cluster value to conduct our second part of research.
We try to examine the impacts of and determine the relationships among different driving forces. Hence, we attempt to find out the impact of the major driving forces behind HSIP clustering and to measure the relationships among those forces.
To this end, we adopt the Diamond Model (Porter, 1998) in addition to the concept of culture to give the priority to these driving forces. This research then applies the Decision Making Trial and Evaluation Laboratory (DEMATEL) to address the related issues. Discussing the relationship between different drivers and making a causal map that finds out the causal group and effect group, this research provides Taiwan industries and government with some strategic recommendations. It is because we believe that Taiwan can be viewed as an appropriate case demonstrating how her industrial clustering have resulted in particular national competitiveness, and from this perspective we wish to find out how those drivers associate with each other, finally leading to successful forms of clusters. From our results, we know that the major causal dimensions are factor conditions and local demand conditions. The factors of cure, firm structure, strategy, and rivalry, related and supporting industries,
factor, local demand conditions and factor conditions for the growth of industrial clusters in Taiwan. Finally we draw upon our policy analysis results in order to assist government officials or industrial analysts in improving Taiwan’s industrial cluster policy and fostering the growth of the clusters.
This research reveals that when policy makers are considering how to drive or improve their industrial clusters as a whole, they must take into account the key influential factors and their affects upon the other indirect dimensions. Generally speaking, activating influential factors can more easily result in expected improvement results, but indirect factors can only have limited contributions to stimulating the continual growth of these industrial clusters, from the viewpoint of country comparative advantages. Based on the results of this research, D2 and D1 are strong direct influencers on all other dimensions with Taiwan’s cluster formation/growth, including affecting themselves.
Again, the local demand conditions and factor conditions are the major causal driving forces for the growth of industrial clustering. Other factors are the influenced/effect factors and once the causal factors are greatly improved, the effect factors are hereby advanced as well. In terms of the policy suggestions, as mentioned before, this research proposed that Taiwan’s local demand factor should more and more involve the condition of cooperation and global networking, and that Taiwan’s government should be opening up the domestic monetary market -- This shall help to cultivate an investment environment for foreign businesses to pull together multi-national capital resources for factor conditions.
In the end, we further suggest that the government officials could carry out the demand side policy, broker policy and training policy to improve the competitive advantages of industrial clustering.
The demand side policy aims at increasing openness to new ideas and innovative
solutions. One instrument for demand side policy is public procurement (Edquist, Hommen & Tsipouri, 2000). Cabral, Cozzi, Denicoló, and Zanza (2006) list some aspects that should be taken into account when establishing a policy for procurement for cluster’s growth. To stimulate R&D and innovation in financially constrained sectors, the government officials should increase the current cash flows of innovative firms by buying more at higher prices. To stimulate R&D and innovation in sectors that easily raise external capital, the government officials should commit to a policy that increases innovative firms’ future expected profits, for example by promising to buy future innovative goods more and at higher prices. Government procurement should make prices and quantities demanded responsive to quality ranking modifications: top quality products should be guaranteed immediate profits whereas for obsolete goods, the public buyer should bargain for very competitive prices.
Government expenditure should reduce expected profits in sectors in which the future innovative prospects are low and re-direct R&D towards the more technologically underexploited sectors.
Moreover, how to improve the factor conditions of industrial clustering is an important and major task for the government officials. We furthermore propose that the government officials could execute the broker policies and training policies.
Broker policies mean that public authorities can support the establishment of linkages between firms through the creation of platforms for dialogue. The platform also provides supports of knowledge-enhancing organization linkages through public-private partnership. In some instances, the brokers are consultants but in most cases brokers worked for agencies already serving small or medium sized enterprises (SMEs). The aim of broker policies is to enable value-enhancing dialogue and collaboration beyond what would be achieved n the absence of initiatives. The broker
support of knowledge-enhancing organizational linkage. The platform can foster cluster development. It not only encourages and facilitates growth of industrial network but also supports to the external connections. In addition, intellectual property reforms may be reformed so as to provide both the institutions and the individual researchers with an incentive to collaborate industry. Furthermore, the linkages of knowledge-enhancing organization through public-private partnership provide release time, and also create potential learning and benchmarking opportunities in the cluster.
Successfully human resource capability is important for the high tech industry.
Therefore, the training policy could be an efficient policy tool for improving professional skills and capabilities. Training policy focuses on upgrading skills and competencies which are essential for effective cluster of SMEs. Apart from catalyzing inter-firm networks and university-industry linkages, cluster processes may strengthen the incentives for SMEs to upgrade their internal competencies. Special programs may be needed to realize and sharpening such effort. Government agencies should develop human resources, developing a more skilled and specialized labor force and establishing cluster skills centers. Cluster skills centers could become the lead entities for surveying industry needs, developing new curricula, staying in touch with cluster councils, updating skills standards, benchmarking practices in other places, and collecting information about cluster occupations and programs.
This research might have some limitations. The HSIP is our research for exploration. As noted, science parks located in different places or countries probably have different clustering features and characteristics. Therefore, an expanded comparison among different science parks can be a meaningful and interesting research topic regarding the aspect of investigating the growth of industrial clustering.
References
[1] Becker, B., and Gassmann, O. (2006). Gaining leverage effects from knowledge modes within corporate incubators. Innovation Management, 36, 1 -16.
[2] Billah, B., King, M. L., Snyder, R. D., and Koehler, A. B. (2006). Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22, 239– 247.
[3] Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw Hill, New York.
[4] Cabral, L., Cozzi, G., Denicoló, V., Spagnolo, G., and Zanza, M. (2006), Procuring Innovations. Cambridge University Press, Cambridge.
[5] Carroll, M. C., and Reid, N. (2004). Cluster based economic development: a comprehensive strategy to secure NW Ohio’s economic future, Cluster-Based Economic Development, Working Paper, pp. 1-5.
[6] Castells, P., and Hall, P. (1994). Technopoles of the world: The making of the 21st century industrial complexes. Routledge, London
[7] Chang, A. K. H. (2004). Applying grey forecasting model on the systematic risk estimation: A study of the Dow Jones industry index component securities.
Journal of Grey System, 7, 113-120.
[8] Chang, A. K. H., and Kung, J. D. (2006). Applying grey forecasting model on the investment performance of Markowitz efficiency frontier: A case of the Taiwan securities markets. In Proceedings of the First International Conference on Innovative Computing, Information and Control (pp. 254-257). Washington, DC: IEEE Computer Society.
[9] Chang, P. L., and Shih, H. Y. (2004). Comparing patterns of intersectoral innovation diffusion in Taiwan and China: a network analysis. Technovation, 25,
[10] Chang, S. C. (2003). The TFT–LCD industry in Taiwan: competitive advantages and future developments. Technology in Society, 27, 199-215.
[11] Chang, S. C., Lai, H. C., and Yu, H. C. (2005). A variable P value rolling Grey forecasting model for Taiwan semiconductor industry production. Technological Forecasting and Social Change, 72, 623–640.
[12] Chen C. J. (2007). White Paper on Science and Technology. National Science Council Republic of China, Executive Yuan.
[13] Cho, S., Moon, H. C., and Kim, M. Y. (2008). Characterizing international competitiveness in international business research: A MASI approach to national competitiveness. Research in International Business and Finance, 22, 175-192.
[14] Clark J., and Guy, K. (1998). Innovation and competitiveness: a review.
Technology Analysis & Strategic Management, 10, 363-395.
[15] Colombo, M. G., and Delmastro, M. (2002). How effective are technology incubators? Evidence from Italy. Research Policy, 31, 1103–1122.
[16] Colombo, M. G., and Delmastro, M. (2002). How effective are technology incubators? Evidence from Italy. Research Policy, 31, 1103-1122.
[17] Cooke, P. (2000). Clusters as key determinants of economic growth: the example of biotechnology. Nordregio Report, 23-38.
[18] Cortright, J. (2002). The economic importance of being different: regional variations in tastes, increasing returns and the dynamics of development.
Economic Development Quarterly, 16, 3-16
[19] Cortright, J. (2006). Making Sense of Clusters: Regional Competitiveness and Economic Development. Washington, DC, Brookings Institution.
[20] Deng, J. L. (1986). Grey prediction and decision. Wuhan, China: Huazhong University of Science and Technology.
[21] Edquist, C., Hommen, L., and Tsipouri, L. (2000), Public Technology
Procurement and Innovation. Kluwer Academic.
[22] Enright, M. (1992). Why local clusters are the way to win the game. World Link, 5, 24-25.
[23] Enright, M. (1993). Stuck in the region? Changing scales of regional identity.
Utrecht: KNAG/ Netherlands Geographical Studies.
[24] Furman, J. L., Porter, M. E., and Stern, S. (2002). The determinants of national innovations capacity. Research Policy, 31, 899-933.
[25] Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4, 1-28.
[26] Gemunden, H. G., Ritter T., and Heydebreck, P. (1996). An empirical analysis in German high-tech industries. International Journal of Research in Marketing, 13, 449-462.
[27] Grimes, S., and Collins, P. (2003). Building a knowledge economy in Ireland through European research networks. European Planning Studies, 11, 395-413.
[28] Hall, E.T. (1976). Beyond Culture. NY, Garden City Anchor Books/Doubleday.
[29] Hicks, J. R. (1946), Value and Capital, Oxford University Press.
[30] Holt, C. C. (1957). Forecasting trends and seasonal by exponentially weighted averages. Carnegie Institute of Technology, Pittsburgh, PA.
[31] Hsu, C. C., and Chen, C. Y. (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management, 44, 2241-2249.
[32] Hsu, C. I., and Wen, Y. H. (1998). Improved grey prediction models for the trans-pacific air passenger market. Transportation Planning and Technology, 22, 87-107.
[33] Hu, T. S., Lin, C. Y., and Chang, S. L. (2005). Technology-based regional
study of HSIP, Taiwan. Technovation, 25, 367-380.
[34] Huang, C. Y., Shyu, J. Z., and Tzeng, G.. H. (2007). Reconfiguring the innovation policy portfolios for Taiwan’s SIP Mall industry. Technovation, 27, 744-765.
[35] Huang, H. (2008). Challenges and Opportunities for Infrastructure Development in Taiwan. Taipei, Taiwan Institute of Economic research.
[36] Hung, S. C. (2000). Institutions and systems of innovation: An empirical analysis of Taiwan’s personal computer competitiveness. Technology in Society, 22, 175-187.
[37] Isbasoiu, G. M. (2006). Industrial clusters and regional development: the case of Timisoara and Montebelluna. Brussels, Conference Proceeding, pp.1-30.
[38] Kao, C., Wu, W. Y., Hsieh, W. J., Wang, T. Y., Lin, C., and Chen, L. H. (2008).
Measuring the national competitiveness of Southeast Asian countries. European Journal of Operational Research, 187, 613-628.
[39] Ketels, H. M. (2003). The development of the cluster concept – present experiences and further developments, at NRW conference on clusters.
Conference Paper, Duisburg, Germany.
[40] Kim, S., and Tunzelmann, N. (1998). Aligning internal and external networks:
Taiwan’s specialization in IT, Brighton University of Sussex, SPRU Working Paper, 1-40.
[41] Kraemer, K. L., Dedrick, J., Hwang, C. Y., Tu, T. C., and Yap, C. S. (1996).
Entrepreneurship, flexibility and policy coordination: Taiwan’s computer industry. The Information Society, 12, 215–249.
[42] Krugman, P. (1981). Strategic Trade Policy and the New International Economics. Cambridge, MIT Press.
[43] Lai, H. C., and Shyu, J. Z. (2005). A comparison of innovation capacity at
science parks across the Taiwan Strait: The case of Zhangjiang High-Tech Park and Hsinchu Science-based Industrial Park. Technovation, 25, 805–813.
[44] Lai, H. C., Chiu Y. C., and Leu, H. D. (2005). Innovation capacity comparison of China’s information technology industrial clusters: The case of Shanghai, Kunshan, Shenzhen and Dongguan. Technology Analysis & Strategic Management, 17, pp. 293–315.
[45] Lee, T. L. (2006). Action strategies for strengthening industrial clusters in southern Taiwan. Technology in Society, 28, 533-552.
[46] Lee, T. L., and Tunzelmann, N. Von. (2005). A dynamic analytic approach to national innovation systems: the IC industry in Taiwan. Research Policy, 34, 425-440.
[47] Lewis, A. (1955). The Theory of Economic Growth. London: George Allen &
Unwin.
[48] Liang, M. T., Zhao, G. F., Chang, C. W., and Liang, C. H. (2001). Evaluating the carbonation damage to concrete bridges using a grey forecasting model with a statistical method. Journal of the Chinese Institute of Engineers, 24, 85-94.
[49] Lin, C. T., and Yang, S. Y. (2003). Forecast of the output value of Taiwan's opto-electronics industry using the grey forecasting model. Technological Forecasting & Social Change, 70, 177-186.
[50] Lin, H., Tung C. M., and Huang, C. T. (2006). Elucidating the industrial cluster effect from a system dynamics perspective. Technovation, 26, 473–482.
[51] Lin, Y. H., and Lee, P. C. (2007). Novel high-precision grey forecasting model.
Automation in Construction, 16, 771-777.
[52] Liou, J. J. H., Yen, L., and Tzeng, G. H. (2007). Building an effective safety management system for airlines. Journal of Air Transport Management, 14,
[53] Liu, A. (2004), Sources of Structural Change and Output Growth of China's Economy: 1987-92, Economics of Planning, 31(2/3), 95-116.
[54] Liyanage, S. (1995). Breeding innovation clusters through collaborative research networks. Technovation, 5, 553-567.
[55] Mao, M., and Chirwa, E. C. (2006). Application of grey model GM (1, 1) to vehicle fatality risk estimation. Technological Forecasting & Social Change, 73, 588–605.
[56] Marshall, A. (1920). Principles of Economics, London, Macmillan.
[57] Mills, K. G., Reynolds, E. B., and Reamer, A. (2008). Clusters and competitiveness: a new federal role for stimulating regional economies, Metropolitan Policy Program, 1-12.
[58] Monck, C. S. P., Porter, R. B., Quintas, P., Storey, D. J., and Wynarczyk, P.
(1988). Science parks and the growth of high technology firms. London: Croom Helm.
[59] Moore, J. H. (1978), A Measure of Structural Change in Output, The Review of Income and Wealth, Mar. pp. 105-117.
[60] Olie, R. (1994). Shades of culture and institutions in international mergers.
Organizational Studies, 15, 381-405.
[61] Önsel, Ş., Ülengin, F., Ulusoy, G., Aktaş, E., Kabak, Ö., and Topcu, Y. İ. (2008).
A new perspective on the competitiveness of nations. Socio-Economic Planning Sciences, 42, 221-246.
[62] Pillai, J. (2006). Importance of clusters in industry development: a case of Singapore’s petrochemical industry. Asian Journal of Technology Innovation, 14, 1-27.
[63] Porter, M. E. (1998). The Competitive Advantage of Nations. New York, The Free Press.
[64] Porter, M. E. (2000). Location, competition and economic development: Local clusters in the global economy. Economic Development Quarterly, 14, 15-31.
[65] Potworowski, J. A. (2002). Creating and growing technology clusters:
Observations and best practices from NRC managers engaged in the creation and development of technology based clusters. Canada: National Research Council.
[66] Ritter, T., and Gemunden, H. G. (2004). The impact of a company’s business strategy on its technology competence: network competence and innovation success. Journal of Business Research, 57, 548-556.
[67] Rojas, T. D. (2007). National forest economic clusters: A new model for assessing national-forest-based natural resources products and services. Pacific Northwest Research Station Series Paper, 1-40.
[68] Rostow, W. W. (1960). The stages of economic growth, a non-Communist manifesto, Cambridge, University Press.
[69] Saxenian A. (2006). The New Argonauts: Regional Advantage in a Global Economy. MA, Harvard University Press.
[70] Saxenian, A. (2001). Taiwan’s Hsinchu region: imitator and partner for Silicon Valley, Stanford Institute for Policy Research. Working Paper, pp. 0-44.
[71] Sun, C. C., and Lin, G. T. R. (2007). Combining grey relationship with entropy for evaluating notebook ODM companies’ performance. Management Science Research, 14, 35-55.
[72] Tasi, K. H., and Wang, J. C. (2005). An examination of Taiwan’s innovation policy measures and their effects. International Journal of Technology and Globalization, 1, 239-257.
[73] Teng, C. I., Tseng F. C., and Chiang, D. M. (2006). Customer-capturing strategies:
Technovation, 26, 1348-1389.
[74] Tien, T. L. (2005). A research on the prediction of machining accuracy by the deterministic grey dynamic model DGDM (1, 1, 1). Applied Mathematics and Computation, 161, 923-945.
[75] Wade, R. (1990). Governing the Market: Economic Theory and the Role of Government in East Asian Industrialization. N.J., Princeton University Press.
[76] Wang, C. H. (2004). Predicting tourism demand using fuzzy time series and hybrid grey theory. Tourism Management, 25, 367-374.
[77] Wang, M. H., Chiu, C. C., and Chen, W. Y. (2003). An empirical study of global logistic management for Taiwan computer industry. Journal of Da-Yeh University, 12, 31-44.
[78] Wang, T. Y., Chien, S. C., and Kao, C. (2007). The role of technology development in national competitiveness-evidence from Southeast Asian countries. Technological Forecasting and Social Change, 74, 1357-1373.
[79] Weber, Y., Shenkar, O., and Raveh, A. (1996). National and corporate cultural fit in mergers/acquisitions: an exploratory study. Management Science, 42, 1215-1227.
[80] Williams, T. (2005). Cooperation by design: structure and cooperation in interorganizational networks. Journal of Business Research, 58, 223-231.
[81] Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324–342.
[82] Wong, W. H. (2007). Taiwan’s investment environment ranks the 6th in world.
Council for Economic Planning and Development, Taiwan, 77, pp. 12.
[83] Wonglimpiyarat, J. (2008). Building effective research evaluation systems to assist R&D investment decisions. International Journal of Business Innovation and Research, 2, 123-140.
[84] Woodward, D. (2004). Porter’s cluster strategy versus industrial targeting.
Orlando, FL, Conference Proceeding, 1-18.
[85] Wu, W. W. (2008). Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Systems with Applications, 35, 828-835.
[86] Wu, W. W., and Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32, 499-507.
[87] Yao, A. W. L., Chi, S. C., and Chen, J. H. (2003). An improved grey-based approach for electricity demand forecasting. Electric Power Systems Research, 67, 217-224.