淡江大學覺生紀念圖書館 (TKU Library)
進階搜尋


下載電子全文限經由淡江IP使用) 
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
委員-曹銳勤
委員-陳怡妃
委員-康信鴻
委員-王世澤
委員-吳怡芳
委員-王智弘
委員-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 
學科別分類
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。

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。
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
參考文獻 References
Abdollahzadegan, A., Hussin, C., Razak, A., Moshfegh Gohary, M., & Amini, M. (2013). The organizational critical success factors for adopting cloud computing in SMEs. Journal of Information Systems Research and Innovation, 4(1), 67-74.
Almorsy, M., Grundy, J., & Müller, I. (2016). An analysis of the cloud computing security problem. arXiv:1609.01107.
Alhammadi, A., Stanier, C., & Eardley, A. (2015). The determinants of cloud computing adoption in Saudi Arabia. Computer Science & Information Technology, 55-67.
Alismaili, S., Li, M., Shen, J., & He, Q. (2016). A multi perspective approach for understanding the determinants of cloud computing adoption among Australian SMEs. arXiv preprint arXiv:1606.00745.
Alkhater, N., Wills, G., & Walters, R. (2014). Factors influencing an organisation's intention to adopt cloud computing in Saudi Arabia. In Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference, 1040-1044.
Alshamaila, Y., Papagiannidis, S., & Li, F. (2013). Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework. Journal of Enterprise Information Management, 26(3), 250-275.
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. 11-39. Springer Berlin Heidelberg.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., et al., (2009). Above the Clouds: A Berkeley View of Cloud Computing, Retrieved from http://www.cs.columbia.edu/~roxana/teaching/COMS-E6998-7-Fall-2011/papers/armbrust-tr09.pdf
Arpaci, I., Kilicer, K., & Bardakci, S. (2015). Effects of Security and Privacy Concerns on Educational Use of Cloud Services. Computers in Human Behavior, 45, 93-98.
Awa, H. O., Ojiabo, O. U. & Emecheta, B. C. (2015). Integrating TAM, TPB and TOE Frameworks and Expanding Their Characteristic Constructs for E-commerce Adoption by SMEs. Journal of Science & Technology Policy Management, 6, 76-94.
Bagozzi, R. P., & Phillips, L. W. (1982). Representing and testing organizational theories: A holistic construal. Administrative Science Quarterly, 459-489.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94.
Baker, J. (2012). The technology–organization–environment framework. In Information systems theory. 231-245. Springer New York.
Bollen, K.A. (1989). Structural Equations with Latent Variables; John Wiley and Sons, Inc.: New York, NY, USA.
Boomsma, A., & Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. Structural equation models: Present and future. A Festschrift in honor of Karl Jöreskog, 2(3), 139-168.
Borgman, H. P., Bahli, B., Heier, H., & Schewski, F. (2013). Cloudrise: exploring cloud computing adoption and governance with the TOE framework. IEEE 46th Hawaii International Conference, Maui, Hawaii, 4425-4435.
Brodkin, J. (2008). Gartner: Seven cloud-computing security risks. Retrieved from http://www.networkworld.com/news/2008/070208-cloud.html
Brown, E. (2011). Final Version of NIST Cloud Computing Definition Published. Retrieved from http://www.nist.gov/itl/csd/cloud-102511.cfm
Brown, I. (2010). Factors influencing the adoption of the World Wide Web for job-seeking in South Africa. South African Journal of Information Management, 12(1), 1-9.
Browne, M. W., & Cudeck, R. (1989). Single sample cross-validation indices for covariance structures. Multivariate behavioral research, 24(4), 445-455.
BusinessDictionary. (2017). What is entrepreneurship definition and meaning. Retrieved from http://www.businessdictionary.com/definition/entrepreneurship.html
Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599-616.
Casaló, L.V., Flavián, C., & Guinalíu, M. (2011). The generation of trust in the online services and product distribution: the case of Spanish electronic commerce. Journal of Electronic Commerce Research, 12(3), 199-213.
Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students' behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71-83.
Chen, K. Y., & Chang, M. L. (2013). User acceptance of ‘near field communication’mobile phone service: an investigation based on the ‘unified theory of acceptance and use of technology’ model. The Service Industries Journal, 33(6), 609-623.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chong, A. Y. L., & Chan, F. T. (2012). Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry. Expert Systems with Applications, 39(10), 8645-8654.
Chong, A. Y. L., Lin, B., Ooi, K. B., & Raman, M. (2009). Factors affecting the adoption level of c-commerce: An empirical study. Journal of Computer Information Systems, 50(2), 13-22.
Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors. Journal of the academy of marketing science, 30(3), 184-201.
Damanpour, F., & Schneider, M. (2006). Phases of the adoption of innovation in organizations: Effects of environment, organization and top Managers. British Journal of Management, 17(3), 215-236.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of applied social psychology, 22(14), 1111-1132.
Devine, S. M. (2013). Dangers in the Cloud.
Retrieved from http://www.webvivant.com/dangers-in-the-cloud.html
Dikaiakos, M. D., Katsaros, D., Mehra, P., Pallis, G., & Vakali, A. (2009). Cloud computing: Distributed internet computing for IT and scientific research. IEEE Internet computing, 13(5), 10-13.
Dillon, A. and Morris, M. (1996). User acceptance of new information technology: theories and models. In M. Williams (ed.) Annual Review of Information Science and Technology, 31, Medford NJ: Information Today, 3-32.
Doong, H. S., Wang, H. C., & Shih, H. C. (2008). Exploring loyalty intention in the electronic marketplace. Electronic Markets, 18(2), 142-149.
Epstein, R. (1984). The principle of parsimony and some applications in psychology. J. Mind Behav, 5, 119–130.
Fadlelmula, F. K. (2011). Assessing power of structural equation modeling studies: A meta-analysis. Education Research Journal, 1(3), 37-42.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research, Addison-Wesley, Reading.
Forbes. (2017). 2017 Roundup Of Cloud Computing Forecasts, Retrieved from
https://siliconangle.com/blog/2017/02/20/wikibon-report-preview-big-can-amazon-web-services-get/
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008). Cloud computing and grid computing 360-degree compared. In Grid Computing Environments Workshop. GCE'08, 1-10. Austin, TX, USA,. IEEE.
Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107-130.
Ghobakhloo, M., Arias-Aranda, D., & Benitez-Amado, J. (2011). Adoption of e-commerce applications in SMEs. Industrial Management & Data Systems, 111(8), 1238-1269.
Gonzalez, G. C., Sharma, P. N., & Galletta, D. F. (2012). The antecedents of the use of continuous auditing in the internal auditing context. International Journal of Accounting Information Systems, 13(3), 248-262.
Hair, J.F., Tatham, R.L., Anderson, R.E., and Black, W. (1998), Multivariate Data Analysis with Readings 5th ed., Upper Saddle River, New Jersey: Prentice Hall.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis. Seventh Edition, Upper Saddle River, New Jersey: Prentice Hall.
Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management science, 40(4), 440-465.
Hossain, M. A. & Quaddus, M. (2011). The adoption and continued usage intention of RFID: an integrated framework. Information Technology & People, 24 (3), 236-256.
Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65-74.
Hsu, P. F., Ray, S., & Li-Hsieh, Y. Y. (2014). Examining cloud computing adoption intention, pricing mechanism, and deployment model. International Journal of Information Management, 34(4), 474-488.
Huang, J. H., & Peng, K. H. (2012). Fuzzy Rasch model in TOPSIS: A new approach for generating fuzzy numbers to assess the competitiveness of the tourism industries in Asian countries. Tourism Management, 33(2), 456-465.
Huang, S. Y. (2014). Relevance of IT Integration into Teaching to Learning Satisfaction and Learning Effectiveness. World Journal of Education, 4(2), 1.
Huang, S. Y., Huang, Y. C., Chang, W. H., Chang, L. Y., & Kao, P. H. (2013). Exploring the Effects of Teacher Job Satisfaction on Teaching Effectiveness: Using'Teaching Quality Assurance’as the Mediator. International Journal of Modern Education Forum, 2(1), 17-30.
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and application; A State-of-the-Art Survey. New York: Springer.
IBM. (2018). What is cloud computing IBM Cloud, Retrieved from https://www.ibm.com/cloud/learn/what-is-cloud-computing
Ifinedo, P. (2011). An empirical analysis of factors influencing Internet/e-business technologies adoption by SMEs in Canada. International Journal of Information Technology & Decision Making, 10(04), 731-766.
Ishizaka, A., Nemery, P., & Lidouh, K. (2013). Location selection for the construction of a casino in the Greater London region: A triple multi-criteria approach. Tourism management, 34, 211-220.
James, J. (2003). Sustainable Internet access for the rural poor? Elements of an emerging Indian model. Futures, 35, 461-472.
Jeyaraj, A., Rottman, J.W., & Lacity, M.C. (2006). A Review of the Predictors, Linkages, and Biases in It Innovation Adoption Research. Journal of Information Technology, 21(1), 1-23.
Jöreskog, K. G., & Sörbom, D. (1986). LISREL VI: Analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods. Scientific Software.
Jwaifell, M., & Gasaymeh, A. M. (2013). Using the Diffusion of Innovation Theory to Explain the Degree of English Teachers' Adoption of Interactive Whiteboards in the Modern Systems School in Jordan: A Case Study. Contemporary Educational Technology, 4(2), 138-149.
Kearns, G. S. (2006). The effect of top management support of SISP on strategic IS management- insights from the US electric power industry. Omega, 34(3), 236-253.
Khademi-Zare, H., Zarei, M., Sadeghieh, A., & Saleh Owlia, M. (2010). Ranking the strategic actions of Iran mobile cellular telecommunication using two models of fuzzy QFD. Telecommunications Policy, 34(11), 747-759.
Kim, H. Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative dentistry & endodontics, 38(1), 52-54.
Kim, W. (2009). Cloud computing: Today and tomorrow. Journal of object technology, 8(1), 65-72.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.
Kyriakidou, V., Michalakelis, C., & Sphicopoulos, T. (2013). Assessment of information and communications technology maturity level. Telecommunications Policy, 37(1), 48-62.
Leavitt, N. (2009). Is cloud computing really ready for prime time?, IEEE Computer Society, 42(1), 15-20.
Levenburg, N., Magal, S. R., & Kosalge, P. (2006). An Exploratory Investigation of Organizational Factors and e‐Business Motivations Among SMFOEs in the US. Electronic Markets, 16(1), 70-84.
Lian, J. W., Yen, D. C., & Wang, Y. T. (2014). An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. International Journal of Information Management, 34(1), 28-36.
Lin, A., & Chen, N.C. (2012). Cloud computing as an innovation: Perception, attitude, and adoption. International Journal of Information Management, 32(6), 533–540.
Lin, C. T., & Tasi, M. C. (2009). Development of an expert selection system to choose ideal cities for medical service ventures. Journal Expert Systems with Applications, 36(2), 2266-2274.
Lin, H. F., & Lee, G. G. (2005). Impact of organizational learning and knowledge management factors on e-business adoption. Management Decision, 43(2), 171-188.
Lippert, S. K., & Govindarajulu, C. (2006). Technological, organizational, and environmental antecedents to web services adoption. Communications of the IIMA, 6(1), 14.
Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems, 111(7), 1006-1023.
Ma, S. (2012). A review on cloud computing development. Journal of Networks, 7(2), 305-310.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), 130.
Market Research Media. (2017). Government Cloud Computing Markets to Thrive in 2018–2023. Retrieved from https://www.marketresearchmedia.com/?p=863
Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176-189.
Martens, B., & Teuteberg, F. (2012). Decision-making in cloud computing environments: A cost and risk based approach. Information Systems Frontiers, 14(4), 871-893.
Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.
Mathwick, C., Malhotra, N. K., & Rigdon, E. (2002). The effect of dynamic retail experiences on experiential perceptions of value: an Internet and catalog comparison. Journal of retailing, 78(1), 51-60.
Merkle, E.C., You, D. & Preacher, K.J. (2015). Testing Nonnested Structural Equation Models. Retrieved from http://arxiv.org/pdf/1402.6720.pdf
Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. Journal of marketing, 69(2), 61-83.
Nedbal, D., Stieninger, M., Erskine, M., Wagner, G., & Wetzlinger, W. (2014). The Adoption of Cloud Services in the Context of Organizations: an examination of drivers and barriers. Proceedings of the 20th Americas Conference on Information Systems (AMCIS 2014 Proceedings), Savannah, Georgia, Vereinigte Staaten von Amerika, 11.
Newsom. (2017). Nested Models, Model Modifications, and Correlated Errors. Retrieved from http://web.pdx.edu/~newsomj/semclass/ho_nested.pdf
Nguyen, T. D., Nguyen, D. T., & Cao, T. H. (2014). Acceptance and Use of Information System: E-Learning Based on Cloud Computing in Vietnam. In ICT-EurAsia Conference, 139-149.
Nkhoma, M. Z., Dang, D. P., & De Souza-Daw, A. (2013). Contributing factors of cloud computing adoption: a technology-organisation-environment framework approach. In Proceedings of the European Conference on Information Management & Evaluation, 180-189.
Nuryanto, M., & Afiah, N. N. (2013). The Impact of Apparatus Competence, Information Technology Utilization and Internal Control on Financial Statement Quality (Study on Local Government of Jakarta Province-Indonesia). World Review of Business Research, 3(4), 157-171.
O'Cass, A., & Weerawardena, J. (2009). Examining the role of international entrepreneurship, innovation and international market performance in SME internationalisation. European Journal of Marketing, 43(11/12), 1325-1348.
OECD-Organisation for Economic Corporation and Development. (2000). Enhancing the Competitiveness of SMEs in the Global Economy: Strategies and Policies. Retrieved from
http://www.oecd.org/cfe/smes/enhancingsmecompetitivenesstheoecdbolognaministerialconferencebologna14-15june2000.htm
Oliveira, T., & Martins, M. F. (2010). Firms Patterns of e-Business Adoption: Evidence for the European Union-27. The Electronic Journal Information Systems, 13(1), 47-56.
Oliveira, T., & Martins, M. F. (2011). Literature Review of Information Technology Adoption Models at Firm Level. The Electronic Journal Information Systems, 14(1), 110-121.
Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497-510.
Pan, M. J., & Jang, W. Y. (2008). Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan’s communications industry. The Journal of Computer Information Systems, 48(3), 94-102.
Park, S. C., & Ryoo, S. Y. (2013). An Empirical Investigation of End-users’ Switching toward Cloud Computing: A Two Factor Theory Perspective. Computers in Human Behavior, 29, 160-170.
Pinheiro, A. B. (2010). How Do Managers Control Technology-Intensive Work?. Journal of technology management & innovation, 5(2), 1-12.
Podsakfoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
Preacher, K. J., & Coffman, D. L. (2006). Computing power and minimum sample size for RMSEA [Computer software]. Retrieved from http://quantpsy. org.
Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega, 27(4), 467-484.
Primer, A. P. (1992). Quantitative methods in psychology. Psychological Bulletin, 112(1,155-159).
Puaschunder, J. M. (2015). Intergenerational transfer mode (Working paper).
Ragu-Nathan, B. S., Apigian, C. H., Ragu-Nathan, T.S., & Tu, Q. (2004). A path analytic study of the effect of top management support for information systems performance. Omega, 32(6), 459-471.
Ratten, V. (2008). Technological innovations in the m-commerce industry: A conceptual model of mobile banking intentions. Journal of High Technology Management Research, 18(2), 111–117.
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of research in Marketing, 26(4), 332-344.
Ro, H. (2012). Moderator and mediator effects in hospitality research. International Journal of Hospitality Management, 31(3), 952-961.
Rogers, E.M. (1962). Diffusion of Innovations, New York: The Free Press.
Rogers, E.M. (1983). Diffusion of Innovations (3rd ed.), New York: The Free Press.
Rogers, E.M. (1995). Diffusion of Innovations (4th ed.), New York: The Free Press.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.), New York: The Free Press.
RUI, G. (2007). Information Systems Innovation Adoption among Organizations-a Match-Based Framework and Empirical Studies. (Doctoral dissertation).
Rust, R.T., Lee, C., and Valente, E., 1995. Comparing covariance structure models: A general methodology, International Journal of Research in Marketing, 12(4), 279–291.
Schreiber, J. B., Nora, A., Stage, F. K., & Barlow, E. A. (2006). J. King, Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review. The Journal of Educational Research, 99 (6), 323-338.
Sebora, T. C., Theerapatvong, T., & Lee, S. M. (2010). Corporate entrepreneurship in the face of changing competition: A case analysis of six Thai manufacturing firms. Journal of Organizational Change Management, 23(4), 453-470.
Shah Alam, S. (2009). Adoption of internet in Malaysian SMEs. Journal of Small Business and Enterprise Development, 16(2), 240-255.
Shaukat, K., & Hassan, M. U. (2017). Cloud Security through Encryption Techniques. Transylv. XXV, 4037–4042.
Shukla, A. (2017). What is Entrepreneurship?.
Retrieved from http://www.paggu.com/entrepreneurship/what-is-entrepreneurship/
SMEA. (2018). Small and Medium Enterprise Administration, Ministry of Economic Affairs. Retrieved from https://www.moeasmea.gov.tw/mp.asp?mp=2
STAMFORD, Conn. (2009). Gartner Highlights Five Attributes of Cloud Computing.
Retrieved from https://www.gartner.com/newsroom/id/1035013
Staten, J. (2008). Is Cloud Computing Ready for the Enterprise?
Retrieved from
http://www.forrester.com/Is+Cloud+Computing+Ready+For+The+Enterprise/fulltext/-/E-RES44229
Stieninger, M., & Nedbal, D. (2014). Diffusion and acceptance of cloud computing in SMEs: towards a valence model of relevant factors. IEEE 47th Hawaii International Conference, 3307-3316, Hilton Waikoloa.
Stieninger, M., Nedbal, D., Wetzlinger, W., Wagner, G., & Erskine, M. A. (2014). Impacts on the organizational adoption of cloud computing: A reconceptualization of influencing factors. Procedia Technology, 16, 85-93.
Subashini, S., & Kavitha, V. (2011). A survey on security issues in service delivery models of cloud computing. Journal of network and computer application, 34(1), 1-111.
Subramanianls (2011). Entrepreneurship and Cloud Computing. Retrieved from http://cloudbus.blogspot.com/2011/05/entrepreneurship-and-cloud-computing.html#!/2011/05/entrepreneurship-and-cloud-computing.html
Sultan, N. (2010). Cloud computing for education: a new dawn?. International Journal of Information Management, 30, 109-116.
Swink, M. (2000). Technological Innovativeness as a Moderator of New Product Design Integration and Top Management Support. Journal of Product Innovation Management, 17(3), 208-220.
Tan, K. S., & Eze, U. C. (2008). An Empirical Study of Internet-Based ICT Adoption Among Malaysian SMEs. Communications of the International Business Information Management Association, 1, 1-12.
Tashkandi, A. N., & Al-Jabri, I. M. (2015). Cloud computing adoption by higher education institutions in Saudi Arabia: an exploratory study. Cluster Computing, 18(4), 1527-1537.
Taylor, S. & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6, 144-176.
Teo, T. S., Srivastava, S. C., & Jiang, L. (2008). Trust and electronic government success: an empirical study. Journal of Management Information Systems, 25(3), 99-132.
Tehrani, S. R., & Shirazi, F. (2014). Factors influencing the adoption of cloud computing by small and medium size enterprises (SMEs). In International Conference on Human Interface and the Management of Information, 631-642. Springer, Cham.
Thiesse, F., Staake, T., Schmitt, P., & Fleisch, E. (2011). The rise of the “next-generation bar code”: an international RFID adoption study. Supply Chain Management: An International Journal, 16(5), 328-345.
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: testing a conceptual model. Journal of management information systems, 167-187.
Tim, A. (2017). Connectivity Made Simple How the Cloud Can Benefit Your SME, Retrieved from
http://www.telegraph.co.uk/connect/small-business/business-solutions/how-the-cloud-can-benefit-smes
Tornatzky, L., & Fleischer, M. (1990). The process of technology innovation. New York: Lexington Books.
Trigueros-Preciado, S., Pérez-González, D., & Solana-González, P. (2013). Cloud computing in industrial SMEs: identification of the barriers to its adoption and effects of its application. Electronic Markets, 23(2), 105-114.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Verkasalo, H., López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2010). Analysis of users and non-users of smartphone applications. Telematics and Informatics, 27(3), 242-255.
Wang, Y. M., Wang, Y. S., & Yang, Y. F. (2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change, 77(5), 803-815.
Walterbusch, M., Martens, B., & Teuteberg, F. (2013). Evaluating cloud computing services from a total cost of ownership perspective. Management Research Review, 36(6), 613-638.
Wei, L., Zhu, H., Cao, Z., Dong, X., Jia, W., Chen, Y., & Vasilakos, A.V. (2014). Security and privacy for storage and computation in cloud computing. Inf. Sci, 258, 371–386.
Werne, C. & Schermelleh-Enge, K. (2010). Deciding between Competing Models-Chi-Square Difference Tests. Retrieved from
https://www.researchgate.net/publication/241278052_Deciding_Between_Competing_Models_Chi-Square_Difference_Tests
Wikipedia. (2016). Information security,
Retrieved from http://en.wikipedia.org/wiki/Information_security
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.
Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5(3), 161–215.
Wu, C. S., Lin, C. T., & Lee, C. (2010). Optimal marketing strategy: A decision-making with ANP and TOPSIS. International Journal of Production Economics, 127(1), 190-196.
Wu, J. H., Wang, S. C. & Lin, L. M. (2007). Mobile Computing Acceptance Factors in the Healthcare Industry: A Structural Equation Model. International Journal of Medical Informatics, 76, 66-77.
Wu, L., Li, J. Y. & Fu, C. Y. (2011). The Adoption of Mobile Healthcare by Hospital's Professionals: An Integrative Perspective. Decision Support Systems, 51, 587-596.
Wu, Y., Cegielski, C. G., Hazen, B. T., & Hall, D. J. (2013). Cloud computing in support of supply chain information system infrastructure: understanding when to go to the cloud. Journal of Supply Chain Management, 49(3), 25-41.
Xu, S., Zhu, K., & Gibbs, J. (2004). Global technology, local adoption: A Cross‐Country investigation of internet adoption by companies in the united states and china. Electronic Markets, 14(1), 13-24.
Yang, H., & Tate, M. (2012). A descriptive literature review and classification of cloud computing research. Communications of the Association for Information Systems, 31, 35–60.
Yeung, H. Y., Shim, J. P., & Lai, Y. K. (2003). Current Progress of E-Commerce Adoption: Small and Medium Enterprises in Hong Kong. Communication of the ACM, 46(9), 226-232.
Zhu, K., Kraemer, K. L., & Xu, S. (2006). The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business. Management Science, 52(10), 1557, 1576.
Zhu, K., Kraemer, K. L., Xu, S., & Dedrick, J. (2004). Information technology payoff in e-business environments: an international perspective on value creation of e-business in the financial services industry. Journal of Management Information Systems, 21(1), 17-54.
論文使用權限
  • 同意紙本無償授權給館內讀者為學術之目的重製使用,於2018-07-18公開。
  • 同意授權瀏覽/列印電子全文服務,於2018-07-18起公開。


  • 若您有任何疑問,請與我們聯絡!
    圖書館: 請來電 (02)2621-5656 轉 2486 或 來信