大學智慧資本與技術移轉辦公室對技術移轉績效之影響分析

The influencing relationship of intellectual capital and universities’ technology transfer offices on technology transfer performance

馮輝毅(Hui-I Feng)

指導教授 ： 陳家聲

國立臺灣大學/管理學院/商學研究所/博士(2011年)

https://doi.org/10.6342/NTU.2011.10688

摘要

本研究旨在建立一概念模型，檢驗技術移轉創新過程中智慧資本的重要性，以及驗證大學專業技轉室、高教與技職體系、園區大學及校齡在此過程中所扮演之影響角色為何，並以台灣地區之大專院校為研究系絡，進行模型之實證。在觀念架構上，本研究整合技術移轉、智慧資本、大學技轉室、產業群聚等文獻，提出大學研究成果與技轉績效之影響模式。
根據上述研究架構，本研究探討三個問題：大學在進行技轉活動時，其績效會受到哪些因素之影響？大學在進行技轉活動時，如何透過技轉室來強化其績效？大學在進行技轉活動時，高教與技職體系、園區大學及校齡是否是其績效強化之因素？針對第一個問題，本研究以智慧資本理論，提出6項路徑假設，包括H1: 人力資本正向影響研究成果；H2: 結構資本正向影響研究成果；H3: 關係資本正向影響研究成果；H4: 人力資本正向影響技轉績效；H5: 關係資本正向影響技轉績效；及H6: 研究成果正向影響技轉績效。而針對後兩個問題，則進一步以大學技轉室、高教與技職體系與產業群聚觀點為基礎，提出4大項調節假設，包括H7: 大學技轉室在人力資本、關係資本與研究成果對技轉績效的正向關係上有強化作用；H8: 高教體系大學在人力資本與研究成果對技轉績效的正向關係上有強化作用，而技職體系大學在關係資本對技轉績效的正向關係上有強化作用；H9: 近園區大學在人力資本、關係資本與研究成果對技轉績效的正向關係上有強化作用；H10: 高校齡大學在人力資本、關係資本與研究成果對技轉績效的正向關係上有強化作用。
在研究方法上，本研究以95至98學年度間的台灣地區129所大專院校為分析樣本，並蒐集、統整與計算教育部統計處、國科會、財團法人高等教育評鑑基金會以及WOS等資料庫之資訊，運用逐步複迴歸及結構方程模式等統計分析方法，進行模型的檢定，得到下列研究結論：
1、 在路徑效果上，H2、H3、H5與H6等4個假設達顯著並獲得支持，僅H1與H4未獲顯著支持，其中H1甚至呈現出負向的顯著效果。再以總效果觀之，不論是在研究成果或是技轉績效的表現上，關係資本皆佔有最重要之影響地位，而以結構資本次之，人力資本則皆出現負向之效果。H1的結果，本研究推論與大學成長速度過快、研究人才生涯規劃及教育部所訂定之評鑑計畫與升等規定有關。
2、 在調節效果方面，具專業化技轉室之大學正向調節了人力資本對技轉績效之影響效果；技職體系大學與近園區大學則正向調節了關係資本對技轉績效之影響效果；高校齡大學則正向調節了研究成果對技轉績效之影響效果。

關鍵字

技術移轉 ； 智慧資本 ； 產業群聚 ； 結構方程模式

並列摘要

This study examines the importance of intellectual capital in the process of technology transfer innovation. As well, this study also tests the moderating roles of a university’s technology transfer offices (UTTOs), the type of learning institution in question (e.g., traditional university or technology and vocational institute), the proximity of a university to scientific parks, and the age of a university in the process of technology transfer innovation. The theoretical model is evidenced by using Taiwanese universities as research samples. Also, this study integrates the literature on technology transfer, intellectual capital, UTTOs, and industrial clusters, and presents the influencing model among universities’ intellectual capital, research outcomes, and technology transfer performance.
According to the research framework, as mentioned above, this study will attempt to discuss the following three research questions: What factors will influence a university’s performance as it proceeds with technology transfer activities? How do universities strengthen their performance through UTTOs as they proceed with technology transfer activities? Are the type, location, and age of universities strengthening factors as universities proceed with technology transfer activities? With regard to the first question, this study proposes six path-effect hypotheses incorporating the theorem of intellectual capital. These hypotheses include the following: H1: Human capital (HC) positively affects research outcomes (RO). H2: Structural capital (SC) positively affects RO. H3: Relational capital (RC) positively affects RO. H4: HC positively affects technology transfer performance (TTP). H5: RC positively affects TTP. H6: RO positively affects TTP. With regard to the last two questions, this study further proposes four moderating-effect hypotheses incorporating the concepts of UTTOs, of the traditional university and the technology and vocational institute types of learning institutions, and of industrial clusters. These hypotheses are as follows: H7: The positive influence of HC, RC, and RO on TTP will increase when a university has a specialized UTTO. H8: The positive influence of HC and RO on TTP will increase when a university is a traditional university, while the positive influence of RC on TTP will increase when a university is a technology and vocational institute. H9: The positive influence of HC, RC, and RO on TTP will increase when a university is near scientific parks. H10: The positive influence of HC, RC, and RO on TTP will increase when a university is an older university.
With regard to the research methodology, this study targets 129 Taiwanese universities during the Taiwanese academic years ‘95 to ‘98, sampling, collecting, arranging, and calculating data from the following datasets: Department of Statistics at the Ministry of Education in Taiwan, National Science Council, Higher Education Evaluation & Accreditation Council of Taiwan, and Web of Science. Using the statistical techniques of stepwise multiple regression and structural equation modeling, this study tests the hypothesized model and concludes that:
1. With regard to the path-effect hypotheses, H2, H3, H5, and H6 are all significantly supported, but H1 and H4 are not. In particular, H1 is even negatively significant. Furthermore, from the perspective of total effects, both RO and TTP are positively affected by RC most strongly and by SC second-most strongly, while they are negatively affected by HC. With respect to the results of H1, this study infers that the reasons may be the fast growth of Taiwanese universities, career planning of research talents, and the appraisal and promotion rules for research talents legislated by the Ministry of Education in Taiwan.
2. With regard to the moderating-effect hypotheses, universities with specialized UTTOs positively moderate the influence of HC on TTP. Additionally, technology and vocational institutes and universities near scientific parks positively moderate the influence of RC on TTP. Finally, older universities positively moderate the influence of RO on TTP.

並列關鍵字

technology transfer ； intellectual capital ； industrial clusters ； structural equation modeling

參考文獻

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