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研究生:徐聖訓
研究生(外文):Sheng-Hsun Hsu
論文名稱:價值創造與知識管理
論文名稱(外文):Essays on Creating Value through Knowledge Management
指導教授:陳文華陳文華引用關係
指導教授(外文):Wun-Hwa Chen
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:商學研究所
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
畢業學年度:92
語文別:英文
論文頁數:123
中文關鍵詞:價值創造知識管理顧客滿意度
外文關鍵詞:customer satisfactionKnowledge managementvalue creation
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1990年代是一個社會、政治及技術都快速變遷的年代。除此之外,大部份的公司都能獲取相同的材料、資金與人力資源。因此,學術界與實務界普遍認為真正能創造出長久競爭優勢的不再是土地、勞力及資本,取而代之的是知識。在以知識為主要競爭優勢來源的前提下,知識管理強調的是組織如何結合及使用不同的知識,以期能有系統性的創造價值;藉此,組織能不斷的滿足顧客、創造新產品、開發新市場及降低營運成本。雖然,價值創造與知識管理間的關聯對組織績效有深遠的影響,但這方面的研究卻付之闕如。因此,本研究的主要目的就是在探討這兩者之間的關係,以期更進一步了解組織是如何利用知識產生價值的。研究分為三大議題,分別是線性結構模型的研究,組織績效有效性的探討及顧客知識的取得。
在研究方法上,線性結構模型被認為是第二代的統計方法且被普遍應用在社會科學的研究領域中。因此,本研究藉由線性結構模型的方法來了解複雜的知識管理與價值創造間的關聯。但在線性結構模型中,有兩種主要的方法來估計模型的參數 – 分別是以covariance(如:LISREL及EQS)為主及以component (如:PLS) 為主的方法。學術界對於這兩種方法的差異及應用時機並不清楚。為了解釋此一問題,我們作了一系列以模擬資料及實務資料為測試的實驗;藉此,我們提出了線性結構模型選擇之決策流程。除此之外,因理論複雜性的不斷增加(如:非線性關係),學者也呼籲能夠有其他估算線性結構模型的方法。為回應此,我們嘗試用類神經網路來估算理論模型。實驗結果證實了用類神經網路來估算理論模型的可行性。
其次,我們研究知識為主的資源如何去影響組織績效的有效性和其因果關係。更精確地來說,我們認為組織績效的有效性與人力資源、組織學習及外部網絡資源有關。藉由將樣本分為硬體及軟體產業,我們更進一步地來探討每個因果路徑可能所包含的意義。
再者,顧客的知識對組織是一項重要的資產。知道顧客內心對於產品或服務的看法有助於組織來進行不斷的改進,以符合顧客的需要。本研究運用顧客滿意度模型來研究顧客滿意或不滿意的原因、組織是否有效地處理顧客抱怨及與競爭對手相較,顧客滿意度的情況。在高度競爭的環境中,組織必需不斷地了解顧客的需求並滿足他們,才可能永續生存。
整體而言,本研究有以下重要貢獻。第一,本研究探討了不同線性結構模型的優劣;未來研究可基於此,往下發展。再者,我們提出以類神經網絡為架構的線性結構模型,也值得繼續往下研究。因為,類神經網絡特別適合用於估計非線性關係。第二,我們闡釋了人力資本、組織學習及網路資源間的關係並實證之。這有助於釐清如何有效地利用以知識為主的資源在複雜的競爭環境中。第三,本研究探討如何運用顧客滿意度模型來了解顧客的需要並建構管理矩陣。最後,本研究嘗試建議不同理論之間的關聯以其了解知識管理與價值創造之間的關係。
The 1990s is an era characterized by rapid social, political and technological change. Most firms operated within the same industry tend to have access to roughly the same raw materials, financial capital and labor pools. Accordingly, both academics and practitioners believe that what truly provides a unique and sustainable competitive advantage is knowledge. Assuming that knowledge is a critical input to production processes, knowledge management (KM) emphasizes firms’ capability to use and combine various sources of knowledge so as to systematically pursue value creation activities to constantly satisfy their customers, create new products, open up a new market, and lower operation costs. Although the issue of linkages between value creation and KM has a profound impact on firms’ future success, it has received minimal attention in the literature to date. Thus, the goal of this research is to examine the relationship between value creation and KM. By highlighting the important links between them, we seek to contribute to a further understanding of how organizations can better utilize various sources of knowledge. This thesis focuses on three main topics, including structural equation model (SEM) analysis, organizational effectiveness and customer knowledge.
Methodologically, SEM techniques have been viewed as a second-generation statistical method that could be used to explore complicated social science phenomenon. In this study, we employ SEM techniques to help us understand the complex issues regarding to KM and value creation. There are two major methods to estimate the SEM, including the covariance- (e.g., LISREL and EQS) and component-based (e.g., PLS) SEM techniques. To date, academics still have little understanding regarding to their differences and characteristics. To address this issue, we do the robustness testing on all SEM techniques so that the decision can be made for choosing them. Moreover, as the theoretical model is becoming more complicated (e.g., non-linear relationship), researchers have called for different methods to estimate the SEM. We therefore propose a novel ANN-based SEM technique and demonstrate its feasibility for estimating the SEM.
Next, we explore how can the organizational effectiveness be achieved by knowledge-based resources. What are the underlying mechanisms that transform intangible assets into organizational effectiveness? More specifically, we suggest that organizational effectiveness depends on its human capital, organizational learning and external network resources. Using two-group comparison method, we further compare the specific causal links so that broader inferences from the results could be drawn.
Last, customer knowledge has become one of the most important economic assets to a firm. Knowing what customer value most in a company’s products or services helps companies optimize the utilization of resources on areas for continuous improvement on customer’s needs and wants. This study applies customer satisfaction study to explore the issues associated with why customers are satisfied or dissatisfied, whether the company’s complaint handling is effective, how to improve customers’ satisfaction, how effective efforts at improving customer satisfaction have been, and where you stand on customers’ satisfaction relative to competitors. In order to sustain in such high competitive environment, organizations must ascertain that they can learn customers’ needs and wants and then produce the products and services that will satisfy their needs.
Overall, this study has made several important contributions. First, this research empirically examines strengths and weaknesses of covariance- and component-based SEM techniques, to serve as a base for future theory development. In addition, the newly proposed ANN-based SEM technique also deserves further study as an ANN is especially competent in estimating non-linear relations. Second, this research illustrates the relationship between human capital, organizational learning orientation and network resource and empirically verifies how they influence organizational effectiveness. This clarifies how to manage knowledge effectively in complex knowledge environments. Third, this research employed customer satisfaction study to derive customers’ needs and wants. Finally, this research attempts to suggest relationships between different theoretical perspectives of how knowledge-based resources could be transformed into tangible values.
致 辭 一
論文摘要 二
Dissertation Abstract 四

Chapter 1 Introduction 1
1.1 Research framework 2
1.2 Knowledge management 6
1.2.1 Can knowledge be managed? 7
1.2.2 What is ‘knowledge’ in KM? 8
1.3 Schumpeter’s view of innovation 11
1.4 Knowledge-based view of the firm 12
1.5 Strategic network perspective 14
1.6 Customer satisfaction index 16

Chapter 2 Human Capital, Organizational Learning Orientation, Network Resources and Organizational Effectiveness 21
2.1 Introduction 21
2.2 Conceptual framework 23
2.2.1 Strategic network resources 24
2.2.2 Human capital 27
2.2.3 Organizational learning orientation 29
2.3 Data and measures 35
2.3.1 Measures 39
2.4 Results 40
2.4.1 Structural model 42
2.5 Discussion and implication 45
2.5.1 Implications for hardware and software industry 47
2.5.2 Implications for practice 49
2.5.3 Limitations and areas for future research 50

Chapter 3 Application of Customer Satisfaction Study to Derive Customer Knowledge 51
3.1 Introduction 51
3.2 CSI model 53
3.2.1 CSI antecedents 55
3.2.2 CSI consequences 57
3.3 Methods 59
3.3.1 Samples 59
3.3.2 Measures 60
3.4 Results 62
3.4.1 Structural equation model 65
3.4.2 Strategic management map 68
3.4.3 Comparison of female and male CSI models 70
3.5 Conclusion and future research directions 72

Chapter 4 Robustness Testing of PLS, LISREL, EQS and ANN 76
4.1 Introduction 76
4.2 Structural equation model overview 76
4.2.1 Covariance-based techniques 78
4.2.2 Component-based techniques 78
4.2.3 ANN-based SEM technique 80
4.2.4 Summary of differences 83
4.3 The simulation study 85
4.3.1 Data generating process 85
4.3.2 Design of the simulation study 86
4.3.3 Results 87
4.4 Chapter 2’s model 92
4.4.1 Assessment of model fit 93
4.4.2 Path coefficients 94
4.5 Chapter 3’s model 94
4.5.1 Assessment of model fit 95
4.5.2 Path coefficients 96
4.6 Conclusion 96

Chapter 5 Conclusions and future research 100
5.1 Findings 100
5.2 Future research 104

Reference 106
Appendix A: Questionnaire for Chapter 2 116
Appendix B: Questionnaire for CSI (Chapter 3) 120

作者簡歷 123
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