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。

本研究結合科技接受模型＂創新擴散理論和技術-組織-環境架構＂以及模型簡約原則，發展出四個企業雲服務採用研究競爭模型，並以企業使用意向作為實際行為的代表。研究採問卷調查方式，收集台灣製造業和服務業227家公司的數據。構面間因果關及競爭模型間比較是以結構方程模型(SEM)進行檢驗，而最佳產業客戶排序是以偏好順序評估法(TOPSIS)求解。實證研究結果指出，雖然四個RCMs在兩階段（巢狀和非巢狀結構）比較過程中都具有高資料配適度，但競爭模型A（模型A）優於其他研究模型為最適模型，企業使用雲服務行為意向解釋能力為74.8％。所有六個構面—認知的資訊安全保證、服務相容性、企業家精神、社會影響力、認知的成本節約以及高階管理層支持—都是影響企業採用雲服務的顯著且正向決定因素。其中，高階管理層支持是影響企業使用意願的最大影響因素，而社會影響力是影響高階管理層支持的最關鍵因素。這些因素可以作為TOPSIS方法中分析最佳產業客戶的評估準則。結果還顯示：大型企業傾向於採用比中小企業(SME)更多的創新；此外，服務業型企業比製造業型企業有更高採用雲服務可能性，因此大型服務型企業是採用雲服務的最佳產業客戶。

本研究不僅構建了模型發展策略，並闡明了影響企業使用雲服務意圖的因素和關係，而且還為雲服務供應商確定了解雲服務設計和推廣策略的最佳產業客戶。此外，本文是首次將SEM和TOPSIS方法相結合的研究之一，為解決多準則決策問題（評估準則和權重的獨立性，不完整性和主觀性）提供了一客觀、可行的替代方法。

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).

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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