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題名:網路社群成員知識移轉與研發創新績效模式之研究
作者:陳君瑜
作者(外文):Chen Chun-Yu
校院名稱:國立高雄師範大學
系所名稱:工業科技教育學系
指導教授:曾國鴻
方榮爵
學位類別:博士
出版日期:2006
主題關鍵詞:知識移轉研發創新績效網路知識社群結構方程模式Knowledge Transfer (KT)Research Innovation PerformanceKnowledge CommunitySEM(Structural Equation Modeling)
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研發團隊之專業知識是創造競爭優勢的主要來源,透過網路知識社群關係的建立,成員可從合作中學到新知能。故本研究旨在探討企業透過網路知識社群運作,對其成員知識移轉的影響;並驗證其成員知識移轉成效與研發創新績效的相關;最後建立社群之知識移轉與研發創新績效模式。
本研究採文獻探討、深度訪談與專家諮商法,作為發展結構方程模式與問卷的依據,再經調查驗證模式的適配度。其對象為工研院與A公司的網路知識社群成員,有效樣本共310份,回收率為68.1%。其資料以t檢定、ANOVA做差異分析,另以AMOS 5.0分析結構方程模式。
本研究發現:(1)除婚姻與年資外,七個依變項的態度強度會因其他九個不同背景變項而有顯著差異;(2)除知識移轉的主觀規範外,社群知識移轉的態度、行為控制認知、社群環境辯證性均會正向顯著影響知識移轉的行為意向;(3)知識移轉的行為意向與表現均會正向顯著影響研發創新績效;(4)整體理論模式符合可接受的適合度檢定水準,顯示就本研究樣本而言,其理論模型已獲支持,故網路知識社群、知識移轉、研發創新績效三構念間確存有影響關係。
值得注意的是:(1)計畫行為理論中會影響行為意向的主觀規範,以本研究樣本驗證未達顯著,影響力近於零,但研究者加上的環境辯證性對行為意向有較高影響力;(2)知識移轉的中介角色,若沒透過知識移轉,社群對創新績效的影響關係並不顯著,知識移轉可將社群的知識互動做有效的轉化應用,實際提升了績效。故建議企業應善用網路社群與知識移轉策略,鼓勵有效知識移轉、隱性知識流動傳承、累積分享應用及創造知識,以提升企業競爭優勢。
The purposes of this study were to explore the role of knowledge transfer (KT) in innovation and to construct a model of KT and research innovation performance in a cyber-based knowledge community.
A literature review, in-depth interview, and expert consultant were used to construct the conceptual framework. The questionnaire method was to verify the structural model and each hypothesis. Effective samples were from 310 members of ITRI (Industrial Technology Research Institute) and A Inc. knowledge community, which the effective return rate is 68.1%. Quantitative analytical methods, including t-test, ANOVA, and SEM analysis, were applied using SPSS 10.0 and AMOS 5.0.
The major findings were as follows: (1) For the seven dependent variables, there was a significant difference for nine of the background variables for the community members with the exception of marriage and seniority. (2) KT attitude toward behavior (KTAB), KT perceived behavioral control (KTPBC), and environment with critique and introspection (ECI) all had positive and significant impacts on KT behavioral intention (KTBI) with the exception of KT subjective norms (KTSN). (3) KT behavioral intention and behavior both had positive and significant impacts on research innovation performance. (4) The overall model fit and fit of the internal structure were both acceptable. This empirical result supported the relationships among the cyber-based knowledge community, KT, and research innovation performance.
The difference between this model and TPB (Theory of Planned Behavior) model is that ECI was an important variable in the process of KT. In addition, it showed that without a well-designed KT model, innovation performance was significantly stalled or nonexistent. Therefore, it was recommended that enterprises take advantage of information technology and motivation strategies, and that knowledge community members should be encouraged to improve KT in order to share and create knowledge. This will sustain the competitive advantages of innovation performance.
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