淡江大學覺生紀念圖書館 (TKU Library)

系統識別號 U0002-2006201816161900
中文論文名稱 台灣企業雲服務採用影響因素及最佳產業客戶之研究
英文論文名稱 Study of Enterprises' Antecedents and Optimal Industrial Customers for Cloud Services Adoption in Taiwan
校院名稱 淡江大學
系所名稱(中) 管理科學學系博士班
系所名稱(英) Doctoral Program, Department of Management Sciences
學年度 106
學期 2
出版年 107
研究生中文姓名 陳俊宏
研究生英文姓名 June-Hong Chen
學號 800620022
學位類別 博士
語文別 英文
口試日期 2018-06-07
論文頁數 81頁
口試委員 指導教授-陳水蓮
中文關鍵字 雲服務  科技接受模型  競爭模型  最佳產業客戶  結構方程模型  偏好順序評估法 
英文關鍵字 cloud services  technology acceptance model  competing model  optimal industrial customer  SEM  TOPSIS 
中文摘要 雲計算不僅是下一代計算,而且是隨需應變資訊科技服務及產品演變的下一步。雲服務市場的迅速發展迫使服務供應商在面對競爭、成本壓力和客戶服務與應用需求下,使用有限的公司資源來確定他們的最佳潛在產業客戶,以設計以客戶為導向和差異化的服務、制定精確的行銷策略、減少重複投資,並創造最大的利潤。許多研究已針對雲服務技術和運作相關的議題提出討論。然而,只有少數研究專注在界定影響組織行為及其對雲服務接受程度的決定因素和其關係的重要議題上,但這些研究並未證明所建構研究模型是否是最適模型,也沒有再進一步地於產業上實務應用研究。本研究目的構建研究競爭模型(RCM)的模型發展策略、找出理解企業接受雲服務的重要決定因素,然後進一步應用研究結果,探索服務供應商雲服務的最佳產業客戶。


英文摘要 Cloud computing is not only the next generation of computing but also the next step in the evolution of on-demand information technology services and products. The rapid flourishing of the cloud service market necessitates service providers to identify their optimal potential industry customers using limited firm resources when facing competition, cost pressure, and demand for services and applications for designing customer-oriented and differentiated services, developing precise marketing strategies, reducing redundant investments, and generating the greatest profitability. Many studies have addressed technical and operational concerns related to cloud services. However, only few have focused on the critical topic of identifying determinants and their relationships that affect organizational behavior and its acceptance of cloud services, but these studies have neither confirmed whether the research model is the best-fitting model nor considered the practical application of cloud computing in society. This study aims to build a model development strategy for constructing research competing models (RCMs), discover significant determinants for understanding industrial organization’s acceptance of cloud services, and then apply the findings to explore optimal industrial customers for service providers further.

This research integrated the technology acceptance model, diffusion of innovations theory, technology–organization–environment framework, and model parsimony principle to develop four cloud service adoption RCMs with enterprise usage intention as a proxy for actual behavior. A questionnaire-based survey was used to collect data from 227 firms in the manufacturing and services industries in Taiwan. Causal relationships and RCMs comparison were tested through structural equation modeling (SEM), and the ordering of optimal industrial customers was evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The empirical results indicated that although all four RCMs had a high goodness-of-fit in the two-stage comparison procedure (nested and nonnested models), research competing model A (Model A) demonstrated superior performance and was the best-fitting model, accounting for 74.8% of the explanatory power, revealed in organizational behavioral intention to use cloud services. All six constructs—namely perceived information security assurance, service compatibility, entrepreneurship, social influence, perceived cost savings, and top management support—were significant positive factors in the decision to adopt cloud services. Moreover, top management support was the most influential factor, affecting enterprise usage intention, whereas social influence was the most crucial factor affecting top management support. These factors can be used as the criteria in TOPSIS method to analyze optimal industrial customers. The results also revealed that large firms tend to adopt more innovations than do small and medium sized enterprises (SMEs); furthermore, service-type organizations have a higher probability of adoption than manufacturing-type firms, and consequently, large service-type companies are the optimal industrial customer for cloud services adoption.

This study not only constructs a model development strategy and clarifies the factors and relationships that considerably affect enterprise intention to use cloud services but also identifies the optimal industrial customers for cloud service providers regarding understanding strategies for the design and promotion of cloud services. Furthermore, this is one of the first studies to combine SEM and TOPSIS method and provide an objective and feasible alternative method for resolving the multiple criteria decision-making problem (independence, incompleteness, and subjectivity of evaluation criteria and weights).
論文目次 Contents
List of Figures Ⅶ
List of Tables Ⅷ
Chapter 1. Introduction 1
1.1 Overview 1
1.2 Research Objectives 6
1.3 Research Structure and Process 6
Chapter 2. Literature Review and Research Hypotheses 9
2.1 Cloud Computing and Services 9
2.2 DOI Theory 11
2.3 TOE Framework 12
2.4 Constructs and Hypotheses Development 13
2.4.1 Top Management Support (TMS) 16
2.4.2 Service Compatibility (SC) 17
2.4.3 Entrepreneurship (ES) 18
2.4.4 Social Influence (SI) 19
2.4.5 Perceived Information Security Assurance (PISA) 21
2.4.6 Perceived Cost Savings (PCS) 22
2.5 Research Competing Models 23
Chapter 3. Research Methodology 26
3.1 Measures 26
3.2 Data Collection 26
3.3 Adequate Sample Size Estimation 28
3.4 Data Normality and Multicollinearity 30
3.5 Common Method Variance 31
3.6 Moderating Effect of Industries 32
3.7 Data Analysis Method 34
3.7.1 Structural Equation Modeling 34
3.7.2 Technique for Order Preference by Similarity to Ideal Solution 36
Chapter 4. Data Analysis and Results 39
4.1 Measurement Model 39
4.2 Structural Models 41
4.3 Comparison of the RCMs 45
4.3.1 First Stage: Nested Model Comparison Between Models A and B 46
4.3.2 Second Stage: Nonnested Model Comparison Among Models A, C, and D 46
4.3.3 Best-Fitting Model: Model A 47
4.4 Analysis of Optimal Industrial Customers 48
4.4.1 Transfer of Criteria and Relative Weights 48
4.4.2 Alternative Identification 49
4.4.3 Empirical Case Analysis 50
Chapter 5. Conclusions 53
5.1 Discussion 53
5.2 Managerial and Practical Implications 56
5.3 Theoretical Contributions 60
5.4 Research Limitations and Future Research 63
References 65
Appendix: Survey Questionnaire 78

List of Figures
Figure 1.1 Research process 7
Figure 2.1 Four RCMs 25
Figure 4.1 Structural model analysis results of the RCMs 45

List of Tables
Table 2.1 Definitions of cloud computing 10
Table 2.2 Model constructs from DOI theory and TOE framework on cloud computing adoption in peer-reviewed journals 15
Table 3.1 Constructs and measurement items 27
Table 3.2 Demographic characteristics of the respondents 28
Table 3.3 Analysis of data normality and multicollinearity 31
Table 3.4 CMV test results 32
Table 3.5 Invariance test results across industries 33
Table 4.1 Analysis of measurement accuracy 40
Table 4.2 Analysis of confidence intervals 41
Table 4.3 Results of RCM structural model analysis 42
Table 4.4 Results of structural model analysis of Model A 48
Table 4.5 Sample structure of customer alternatives 49
Table 4.6 Calculated data and weighted normalized decision matrix 50
Table 4.7 Euclidean distance of each alternative 51
Table 4.8 Relative closeness and ranking result for each alternative 52
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