中國大陸自從鄧小平於1979年4月提倡經濟改革開放並成立經濟特區以來，過去20多年中台灣廠商即在其發展軌跡中扮演著一定重要的角色。其中藉由台灣過去成功的代工經驗複製移植到中國大陸，該套代工生產模式也成功協助將大陸轉型為全球的世界工廠。在此一現象的同時，眾多到大陸投資的台商們也陸續形成許多著名的台商聚落。從最早期的4個經濟特區中廣東省的深圳市、汕頭市、珠海市以及福建省的廈門市，而後擴展至同地理區域的廣州市、東莞市、中山市、福州市。之後更隨著投資類型從傳統產業轉往電子、半導體產業，台商的投資腳步重心也逐漸移往長江三角洲，並形成新的一群台商電腦產業聚落如上海市、江蘇省的蘇州市、吳江市、昆山市等。

而本研究初始欲探討台商在大陸所形成的諸多產業群聚及對企業績效之關聯性時，從過去文獻回顧中發現學者多以探討『群聚效應』下單一群聚對其群聚成員的影響性為主，對於本研究中台商所形成的諸多不同城市、不同產業的產業群聚間之交互影響性這狀況的研究則較為缺乏。因此，本研究即以產業面的觀點來分析台商在大陸所形成的眾多產業群聚，以產業群聚而非群聚內企業為衡量單位的次級資料實證方式，從總體經濟學的產業關聯表以及空間地理學的K函數來建立一整合性架構，進而探討台商的產業群聚規模、群聚內的相關及支援產業以及群聚間的互動網路，對台商的產業群聚績效之影響。

本研究之研究母體為台灣經濟新報資料庫中在台上市(櫃)母企業的739家廠商在2002至2004年三年期間對大陸投資之1571家子(相關)企業，由於後續進行資料處理時將對企業做群聚內與群聚外的進一步分類，因此屬於群聚內的觀察子(相關)企業樣本數僅1466家廠商(比率為93％)。之後本研究即以1466家廠商在中國各城市所形成的313個不同產業的產業群聚城市做產業關聯表的矩陣運算、空間統計的K函數處理，以多元迴歸的方式獲得下列的分析結果：

(1) 總體產業面來看群聚效應的確有助於群聚績效之提升，群聚規模對群聚績效有正面顯著的影響。而從個別產業面來看，對電子業產業群聚來說，群聚效應中的規模經濟對其績效影響則不顯著，需進一步分析。

(2) 群聚內相關及支援產業的規模，對該群聚績效有正面顯著的影響。本研究從對特定產業對的供給面與需求面產業來分析皆有此一結果。

(3) 從空間地理的觀點來探討，單一產業群聚與500Km半徑範圍內之其他相同產業群聚所建構的互動網路關係的確會對該群聚績效有顯著影響。半徑範圍過小網路成員數不足，或是半徑範圍過大涵蓋過多所建構的群聚網路關係其影響則不顯著。

(4) 群聚內相關及支援產業與群聚規模的交互作用對群聚績效具有正面顯著影響。然而群聚內台商相關及支援產業較低之族群績效反而較高者表現更為傑出，此一結果說明了對於『台商』產業群聚而言，隨著企業成長所實行在地化策略後將有利於之後獲得更好之經營績效。

(5) 群聚間網路與群聚規模的交互作用對群聚績效之影響在250Km半徑範圍內為正向顯著影響，呼應了群聚效應中所謂的『地理鄰近性』。然而在500Km半徑範圍外卻為負向顯著影響也表示隨著距離增加，群聚效應將消失轉為區域性競爭的關係。此外，對單一產業群聚而言，在空間上擁有較高的互動網路將有助於該群聚去面對在地與其他外商之競爭而獲得更高的區域經濟商機。反之，低互動網路反而隨著群聚規模成長而開始造成負向的『群聚擁擠』現象影響。

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.

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