臺灣博碩士論文加值系統

在知識經濟時代，研發創新為企業組織建立競爭優勢的重要途徑之一，同時也是驅動國家經濟發展的基盤。面對動態的競爭環境，如何有效地運用研發資源以提升企業創新能力並帶動國家創新效率，實為重要的管理實務課題之一。雖然過去學者對於研發創新活動如何提升競爭優勢已有深入研究，但多忽略了探究其在不同組織情境下所產生網絡互動關係，如何影響創新績效的展現；因此本研究從巨觀層級到微觀層級進行跨層次管理研究，分別探討不同的網絡互動關係對於知識擴散及創新績效的影響效果。在微觀層級分析，主要從資源基礎論探討組織內及組織間之知識網絡如何影響公司創新績效的表現，本研究用社會資本理論針對組織間創新活動進行實證研究，主要是探討企業供應鏈所構成合作網絡對於綠色創新績效的影響；再者，本研究進一步延伸了過去學者對於社會資本理論對組織創新績效的觀點，分別自群體和員工等不同角度去分析組織內創新活動，以利掌握影響創新績效的組織因素和個人因素。而在巨觀層級的分析，主要從競爭觀點剖析各國的創新網絡對於國家競爭力的影響，本研究應用行動者網絡理論，提出了一個三階段國家創新效率評估架構，分別就基礎研究、技術轉譯、和系統開發等不同研發階段所形成的創新網絡進行效率分析，同時也將不同網絡間互動連結效率一併納入考量，以期能作為國家投入研發資源配置的參考。
從本研究在微觀層次的實證結果可得知，企業應善用組織已累積的社會資本，並將之視為重要的策略性資源進行管理。就組織間創新管理而言，本研究結果也顯示組織間累積形成的結構資本、關係資本和認知資本，皆會直接或間接地影響綠色供應鏈的知識交流進而帶動綠色創新；而就組織內創新管理而言，本研究區別了員工社會資本和群體社會資本的重要性，並強調組織內所形成的社會網絡結構和員工所享有的共同願景是影響團隊成員進行資訊分享的重要因素，皆進一步會影響團隊創新績效的表現。再者從本研究巨觀層次的實證結果可得知，提升國家整體研發效率將有助於帶動國家競爭力的強化。本研究應用行動者網絡理論建構國家創新系統的行動者角色，並進一步以網絡資料包絡分析法進行國家研發績效的評估，研究結果發現連結性創新網絡確實扮演影響國家創新系統的重要關鍵，本研究並針對各國創新效率進行比較分析，以提供作為國家標竿學習之參考。
藉由上述從微觀到巨觀層次的管理實證分析，本研究以資源基礎論與競爭觀點來探討創新網絡關係對於研發創新績效的關鍵影響力。在微觀層次的分析研究中，本研究延伸了社會資本理論的應用並進一步提出影響組織推行創新管理的前置因素，將有助於企業建構研發管理策略之參考。在巨觀層次的分析研究中，有別於過去國家創新績效評估的研究，本研究提出評估連結性創新效率的另一觀點，此將有助於瞭解國家應如何有效配置研發資源以維持競爭力，並可進一步作為國家制定產業發展政策的參考。

In the knowledge economic, research and development (R&D) innovation becomes critical to organizational strategy for building sustainable competitive advantages. Investment in R&D innovation activities is also a key contributing factor to economic growth in all countries. Since enterprises must consider the overall dynamic environment constructed by external market conditions, how to manage R&D resources effectively to establish corporate innovative capability and national competitive advantages is an important issue that practitioners cannot afford to overlook. Previous studies have pointed out the relationship between R&D innovation activities and competitive advantages. However, they neglected the interactive effects of these activities in different organizational contexts, and thus could not thoroughly evaluate their impact on the overall performance of the innovation network. Hence, this study employed microscopic and macroscopic analysis to investigate the innovation network interconnected by different actors under intra-organizational, inter-organizational and country contexts, and further examined the relative impact of network interaction on knowledge diffusion and innovation performances from a cross-level perspective.
At the micro-level analysis, this study took the resource-based view to analyze how inter- and intra-organizational knowledge networks affect the innovation performance of the firm. This study also took the inter-organizational innovation activities as an example to analyze how the collaboration network with supply chain partners affects green innovation from the social capital perspective. In addition, this study also made the cross-level study on the intra-organizational innovation activities to examine the ways in which social capital constitutes individuals’ or organizational factors for creating innovation value for a wide range of organizational phenomena, and further investigates the relationship between information sharing, group cohesion and innovation performance. At the macro-level analysis, this study took the competition perspective to analyze the influence of the national innovation network on the competitive advantages across the country. We constructed a three-stage R&D production framework by actor-network theory that emphasizes the linking activities between the basic research stage, technology translation stage, and system development stage. By using the network data envelopment analysis method, this study evaluated relative R&D efficiency across twenty-five countries.
The empirical results are as follows. At the micro-level analysis, the social capital approach of this study can be considered as resources for companies, which leads to a new paradigm for business strategies. In the intra-organizational innovation phenomena, the overall configuration of the network ties constitutes an important aspect of social structure and significantly facilitates the sharing of information between team members. Furthermore, another factor affecting team innovation performance through the effects of individual’s perceived social capital was shared vision. In the inter-organizational innovation phenomena, the three kinds of social capitals, structural, relational and cognitive capital, had significant effects on knowledge sharing regarding green management. Companies should leverage their social capital in order to produce additional competitive advantages through environmental collaboration. With the macro-level analysis, the increase in a country’s overall level of R&D efficiency leads to corresponding increases in its competitive advantage. The actor-network theory approach of this study could help each country access the relative R&D efficiency to identify the transformation of innovation context in which actors get involved. The results of this study also screened out specialized efficient countries at each sub-process and further constructed the efficiency group for benchmark learning.
Through empirical analysis of the multidimensional factors of R&D innovation in cross-level contexts, this study enhanced understanding of the relative impact of network interaction on innovation performance. At the micro-level analysis, this study identified the characteristics of organizations that adopt innovation management from the resource-based view. Unlike many other empirical studies, this study made an important contribution to the literature by examining how social capital embedded in the intra-organizational and inter-organizational relationships affects innovation in a detailed manner. At the macro-level analysis, this study enhances understanding of the characterization of the divisional efficiencies of the innovation system that emphasizes the effect of linking activities by an actor-network theory approach. Additionally, this study further provided managerial implications for R&D resource allocation strategies and the government’s industrial policy.

摘 要……i
ABSTRACT……iii
DEDICATION……vi
ACKNOWLEDGMENTS……vii
LIST OF FIGURES……x
LIST OF TABLES……xi
1.Introduction……1
2.Literature Review……10
2.1.Strategic management of innovation: resource-based view and competitive analysis……10
2.2.Social capital theory……13
2.2.1.Social capital and collaborative green innovation…15
2.2.2.The group and individual determinants of social capital……16
2.3.Actor-network theory and R&D performance measurement…18
2.3.1.Actor-network theory……18
2.3.2.R&D performance measurement……22
3.Research framework and methodology……25
3.1.Research framework……25
3.2.Methodology……29
3.2.1.Structural equation modeling method……29
3.2.2.Hierarchical linear modeling method……30
3.2.3.Network data envelopment analysis method……31
4.Approach I : collaborative green innovation in emerging countries by the social capital perspective……34
4.1.Research Design……34
4.1.1.Research model and hypothesis development……34
4.1.2.Data collection and samples……40
4.1.3.Measurements of variables……42
4.2.Results and discussions……45
4.2.1.Measurement model testing……45
4.2.2.Structural model testing……46
4.3.Managerial implications……52
5.Approach II : re-examining the group and individual determinants of social capital in facilitating NPD team’s innovation performance……54
5.1.Research Design……54
5.1.1.Research model and hypothesis development……54
5.1.2.Data collection and samples……68
5.1.3.Measurements of variables……70
5.2.Results and discussions……73
5.2.1.Confirmatory factor analysis……73
5.2.2.Testing the relationship between social capital, information sharing, group cohesion and team performance…74
5.3.Managerial implications……80
6.Approach III: exploring R&D linking efficiency by actor-network theory perspective……85
6.1.Research Design……85
6.1.1.Three-stage transformation model of actor-network innovation system……85
6.1.2.Data selection and description……88
6.2.Results and discussions……93
6.2.1.Overall R&D performance analysis……93
6.2.2.Constructing the efficiency group for benchmark-learning……96
6.2.3.Sensitivity analysis……99
6.3.Managerial implications……103
7.Conclusions……104
7.1.Conclusions……104
7.2.Limitation and suggestions for future research……107
References……110

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