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系統識別號 U0026-1403201102502300
論文名稱(中文) 跨越社會網絡:知識網絡的建構
論文名稱(英文) Beyond social networks: Constructing the knowledge networks
校院名稱 成功大學
系所名稱(中) 企業管理學系碩博士班
系所名稱(英) Department of Business Administration
學年度 99
學期 2
出版年 100
研究生(中文) 王明照
研究生(英文) Ming-Chao Wang
學號 r4894113
學位類別 博士
語文別 英文
論文頁數 96頁
口試委員 指導教授-方世杰
口試委員-張元杰
口試委員-陳明惠
口試委員-曾娟娟
口試委員-曾瓊慧
口試委員-蔡惠婷
中文關鍵字 知識網絡  網絡結構  知識異質性  知識認知  創新績效 
英文關鍵字 knowledge network  network structure  knowledge heterogeneity  knowledge cognition  innovative performance 
學科別分類
中文摘要 網絡研究學者詮釋社會網絡著眼於組織間連繫的強度及結構特徵上,甚於強調網絡形態的內容及機制。換言之,網絡研究學者視社會網絡來自於組織的特定互動關係以利於知識的傳送及交換。在本研究中,我們藉討論下列的論點試圖提出對網絡形態內容的了解及知識流於網絡中的機制發展有所貢獻:(1)網絡結構特徵及組織間互動內容建構起知識網絡中的三個構面;(2)個別的知識網絡構面促使組織累積及創造知識;以及(3)組織能夠於競爭的環境中創造及管理知識網絡。我們所呈現的模型來自於上述各論點的組成,以建構本研究於創新產出中,知識網絡不同構面及其機制的各項假設。
以來自於台灣科學園區的164家高科技廠商為基礎,並以滾雪球之方法進行五次的層級延伸,共計有344家廠商納進於知識網絡疆界中。為使問卷項目貼近現實環境,並確保其有效性,藉詢問10位高科技廠商的高階管理團隊成員以檢驗問卷項目的可靠及清晰程度。在收集完整的財務及公司資料後,我們進行三波的問卷發放及電訪。最後,總計有144家廠商回覆,問卷回收率為41.86%。
本研究結果對各項假設提供強而有力的支持。網絡結構及知識異質性對知識認知呈現正向的影響。此外,知識認知對創新績效也呈現正向影響。這些結果指出知識認知在知識網絡中扮演著重要的角色,蓄積知識能量以增加創新的產出。對組織間網絡結構與內容、知識異質性與品質、以及策略與決策的管理意涵在文章後面進行討論。
英文摘要 Scholars in network research have primarily emphasized the tie strength and structural characteristics of what has been called “the social network,” rather than the content and mechanism of network formation. Typically, researchers see social networks as accruing from the particular interactions organizations have for channeling and exchanging knowledge. In this research we seek to contribute to the body of work on networks by discussing the following: (1) the structural characteristics of networks and the content of inter-organizational interactions; (2) the way the three dimensions of knowledge networks facilitate organizations to accumulate and create knowledge; and (3) the ability of an organization to develop and manage the knowledge network in a competitive environment. We present a model that integrates the arguments made in this paper through a series of hypothesized relationships between different dimensions of knowledge networks and the main mechanisms for innovative outcomes.
The research for this study is based on 164 high-technology firms from Taiwan science parks, and 80 additional firms that were generated using the snowball method rounded five times. Thus, 344 firms were investigated in the knowledge network. In order to fit the real environment and ensure the validity of questionnaire items, ten top management team members from high-technology firms were asked to verify the clarity of the measurements. After collecting complete financing data and the company portfolio of participating firms, we mailed a three-wave questionnaire and followed up on the mailings with phone calls. Finally, we received 144 questionnaires back, yielding a 41.86% response rate.
The results of this research provide strong support for all hypotheses. Network structure and knowledge heterogeneity have a positive impact on knowledge cognition. Moreover, knowledge cognition has a positive influence on innovative performance. The findings indicate that knowledge cognition plays a critical role in the knowledge network by storing knowledge capacities that increase innovative outcomes. Managerial implications for inter-organizational network configuration and content, knowledge heterogeneity and quality, and strategy and decision making are also discussed.
論文目次 1. INTRODUCTION 1
1.1. Research Background and Motivation 1
1.2. Objectives of the Research 7
2. LITERATURE REVIEW 9
2.1. Social Networks (SN) 9
2.1.1. The Social Network Defined 9
2.1.2. Research on Social Networks 11
2.2. Knowledge Networks (KN) 14
2.2.1. The Knowledge Network Defined 14
2.2.2. The Architecture of the Knowledge Network 17
2.3. The Difference between SN and KN 36
2.3.1. Perspectives of the Knowledge Network 36
2.3.2. Influences of the Knowledge Network on Performance 38
3. HYPOTHESES DEVELOPMENT 41
3.1. Network Structure 41
3.2. Knowledge Heterogeneity 43
3.3. Knowledge Cognition 44
4. METHODOLOGY 46
4.1. Research Sample 46
4.2. Network Definition 48
4.3. Questionnaire Design and Data Collection 49
4.4. Nonresponse Bias Effect 51
4.5. Measurements 51
4.5.1. Dependent Variables 51
4.5.2. Independent Variables 52
4.5.3. Control Variables 56
5. RESULTS 61
5.1. Descriptive Analysis 61
5.2. Measurement Validity and Reliability 63
5.3. Structural Equation Model (SEM) 65
5.4. Assessment of Model Fit and Path Significance 67
6. CONCLUSION 70
6.1. Discussion 70
6.2. Theoretical Implications 73
6.3. Managerial Implications 76
6.4. Limitations and Suggestions for Future Research 78
6.5. Conclusion 79
7. REFERENCES 81
APPENDIX: QUESTIONNAIRE (IN ENGLISH) 94
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