製藥產業是高度依賴研究發展之產業，因此技術知識網絡在製藥產業中相當活躍，製藥產業因技術的突破，又可分為傳統製藥與生技醫藥兩個分支。本研究旨在探討我國製藥產業技術知識網路與創新績效之關係，透過文獻之探討，以社會網絡理論之觀點切入，利用網絡分析工具UCINET計算出廠商網絡中心性，藉由網絡中心性的分析與評估，尋找出台灣地區製藥產業技術知識網絡中的關鍵行動者。並且以網絡中心性來衡量個別廠商網絡關係的強弱及區分網絡的型態，研究區域內與跨區域網絡對廠商創新成效之影響。 研究結果發現，生技醫藥廠商跨區域合作互動情形愈高，對於創新績效愈有顯著性差異；傳統製藥產業則不論是區域內的合作或是跨區域的合作，皆能帶給其創新的能力，另外，不同的合作關係亦會影響其創新績效之表現，區域內的網絡較著重於直接網絡所帶來的系統性知識，而跨區域的網絡則著重於間接網絡中多樣化的知識。 / As a knowledge intensive industry, pharmaceutical industry cultivates highly active networks among firms and relative actors. Because of technology revolution, Pharmaceutical is divided into two forms: traditional Pharmaceutical and Pharmaceutical biotechnology. This paper aims to advance our understanding of the technology knowledge and innovation capacity of the pharmaceutical industry in Taiwan. We begin by reviewing literatures regarding network and network position. Using the UCINET, a useful method to describe and measure firms’ centralities in a social network, we find out the key players in Taiwan Pharmaceutical industry and offers an emprical examination by examining the geography of technology knowledge associated with innovation in Taiwan Pharmaceutical industry. The empirical results indicate that there is a significant relationship between intra-regional knowledge and innovation performance in Pharmaceutical biotechnology industry, and both intra-regional knowledge and regional knowledge determine the innovation performance of the pharmaceutical industry. Furthermore, Results of this study indicate that a firm that occupies a central position in regional network of direct ties is more accessible to useful knowledge; in the other hand, firms can innovate successfully in intra-regional network both directly and indirectly.

近幾年在金融風暴及全球競爭等影響下，企業紛紛導入商業智慧平台，提供管理階層可簡易且快速的分析各種可量化管理的關鍵指標。但在後續的推廣上，經常會因商業智慧系統提供的資訊過於豐富，造成使用者在學習階段無法有效的取得所需資訊，導致商業智慧無法發揮預期效果。本論文以使用者在商業智慧平台上的操作相似度進行分析，建立相對於實體部門的凝聚子群，且用中心性計算各節點的關聯加權，整合至所設計的推薦機制，用以提升商業智慧平台成功導入的機率。經模擬實驗的證實，在推薦機制中考慮此因素會較原始的推薦機制擁有更高的精確度。 / In recent years, enterprises are facing financial turmoil, global competition, and shortened business cycle. Under these influences, enterprises usually implement the Business Intelligence platform to help managers get the key indicators of business management quickly and easily. In the promotion stage of such Business Intelligence platforms, users usually give up using the system due to huge amount of information provided by the BI platform. They cannot intuitively obtain the required information in the early stage when they use the system. In this study, we analyze the similarity of users’ operations on the BI platform and try to establish cohesive subgroups in the corresponding organization. In addition, we also integrate the associated weighting factor calculated from the centrality measures into the recommendation mechanism to increase the probability of successful uses of BI platform. From our simulation experiments, we find that the recommendation accuracies are higher when we add the clustering result and the associated weighting factor into the recommendation mechanism.