The Effects of Cluster Characteristics on Cluster Performance - An Empirical Study of Taiwanese Firms in China

• Main Authors: Chien-Ming Tung, 董建明
• Other Authors: Chung-Jen Chen
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/43650883741882595630

碩士 === 國立成功大學 === 企業管理學系碩博士班 === 94 === Since Deng Xiaoping, the general designer of the special economic zone of China, started to macroeconomic reform in April of 1979, Taiwanese manufacturers were sure acting an important role during its reform process in past decades. By duplicate and transplantation the past successful ODM/OEM model of Taiwan to China, this model of production make China the transition for the world global factory successfully, too. In this phenomenon, numerous Taiwanese enterprisers who went made the investment in China gathered at. designate special economic zones of Shenzhen, Shantou, Zhuhai of Guangdong Province and Xiamen of Fujian Province at early stage , had expanded the scope to Guangzhou , Dongguan , ZhongShan, Fuzhou which are adjacent to these special economic zone area at next stage. Later as the type of investment was transferred from the traditional industry to the electron, semiconductor industry, the investment focus of the Taiwanese firms is moved to the Changjiang Delta gradually too, and form a series of new computer industry agglomeration in Wujiang , Kunshan , Suzhou of Jiangsu Province and Shanghai ,etc.. This research initially intend to find the relation between these Taiwanese industry agglomeration and firm performance in China ,but while reviewing from the past scholar’s papers, we found that scholars one-sidedly emphasized on member of single industry cluster as the main factor for " Cluster Effect ", discussion for the mutual influence of multi-industry cluster of different city ,different industry that the Taiwanese manufacturers formed which this research has noticed is comparatively scarce .So this research try to analyze with the view of macroeconomics , use secondary date of these numerous Taiwanese industry clusters in China by cluster but not enterprise level as analytic unit , combining economic Input-Output Table and spatial geography theoretic Ripley’s K function as research structure for the assumption of related and support industry within a cluster and interactive network among different industry clusters, to analyze the influence for the performance of Taiwanese industry clusters. The population of this research is listed in the Taiwan Economic Journal database of 1571 subsidiary companies in China which invested or hold by 739 Taiwanese public companies during three years from 2002 to 2004, because follow-up classification for secondary data, deduct these enterprises located outside the industry cluster, so belong to observation samples within cluster is only counted in 1466 enterprises (93%). Then ran the Input-Output Table matrix and K function for 313 different industry clusters formed by 1466 enterprises, using the multiple regression method, we obtain the following analysis results as below: (1) Clustering effect really help improvement of the cluster performance in the overall industry, there is a positive influence of cluster scale for the cluster performance. Separately discuss the specific industry, the electron industry clusters, it is not apparent that the scale economic of production in the cluster effect related to its performance, and need to further analyze. (2) The scale of related and supporting industry for a specific industry cluster within the same city area, there is a strongly positive influence to this cluster performance. This research for specific industry from both its supply side and demand side analysis indicates the same result. (3) From the view of spatial geography, the network constructed by one and other same industry clusters, 500Km of radius network have apparent influence on the performance for each observed center cluster really. Smaller radius which covered little number of members as insufficient network, or radius too large to contain many unnecessary clusters of network both leads to the influence of network be unapparent. (4) The interaction of the scale of related and supporting industry to the scale of a specific cluster have strongly positive influence on cluster performance. But with a comparatively lower related and supporting industry, on the contrary, have a better cluster performance than the higher one. The result proves that when "Taiwanese" industry cluster grew up, with lower related and supporting industry, have to adopt “Localization” to maintain its business function in tactics, and then feedback with better performance. (5) The interaction of cluster network to the scale of cluster within the range of 250Km radius has a strongly positive influence on cluster performance, this result properly responds to the so-called "Geographic closeness" of cluster effect. But when distance over the 500Km, the relation between clusters transferred to regional competition without cluster effect. In addition, a single “Taiwanese industry cluster with higher cluster network will help it to face the competition from local and other foreign competitors on regional area . On the contrary, when a Taiwanese cluster with lower cluster network grow up, it begins to suffer from the “Cluster crowding effect” with worse performance.