Cloud Computing Data Center Adoption Factors Validity By Fuzzy AHP

References

• O. Fatahi Valilai and M. Houshmand, A platform for optimisation in distributed manufacturing enterprises based on cloud manufacturing paradigm. International Journal of Computer Integrated Manufacturing, ahead-of-print. 27(11) (2014), pp. 1–24.
   Google Scholar
• R.L. Grossman, The case for cloud computing. IT professional. IEEE. 11(2) (2009), pp. 23–27.
   Google Scholar
• Q. Zhang, L. Cheng, and R. Boutaba, Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications (JISA). 1(1) (2010), pp. 7–18.
   Google Scholar
• A. Aljabre, Cloud computing for increased business value. International Journal of Business and Social Science (IJBSS). 3(1) (2012), pp. 234–239.
   Google Scholar
• R. Brown, Report to congress on server and data center energy efficiency: Public law. Lawrence Berkeley National Laboratory (2008), pp. 109–431.
   Google Scholar
• M.F. Bari, et al., Data center network virtualization: A survey. Communications Surveys & Tutorials, IEEE. 15(2) (2013), pp. 909–928.
   Web of Science ®Google Scholar
• N. Pereira, S. Tennina, and E. Tovar, Building a microscope for the data center, in Wireless Algorithms, Systems, and Applications (Springer, 2012), pp. 619–630.
   Google Scholar
• A. Marinos and G. Briscoe, Community cloud computing, in Cloud Computing. (Springer, 2009), pp. 472–484.
   Google Scholar
• D. Jayasinghe, et al. Experts: A Generator Approach to Automate Performance Testing in IaaS Clouds. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on (2012), pp. 115–122.
   Google Scholar
• H. Qi and A. Gani. Research on mobile cloud computing: Review, trend and perspectives. in Digital Information and Communication Technology and it's Applications (DICTAP), 2012 Second International Conference on IEEE (2012), pp. 195–202.
   Google Scholar
• S. Lohr, Google and IBM join in ‘cloud computing’ research. 8(New York Times, 2007).
   Google Scholar
• Y.-Y. Cheng and H.-J. Shaw, Cloud-based, service-oriented and knowledge-sharing architecture: its design and application in shipbuilding. International Journal of Computer Integrated Manufacturing, ahead-of-print (2014), pp. 1–18.
   Google Scholar
• W. He and L. Xu, A state-of-the-art survey of cloud manufacturing. International Journal of Computer Integrated Manufacturing 28(2) (2014), pp. 1–12.
   Google Scholar
• W. Kim, et al. Adoption issues for cloud computing, in Proc. 7rd Int. Conf. on Advances in Mobile Computing and Multimedia (7)(ACM. 2009),pp. 2–5.
   Google Scholar
• N.S.A.o. Ireland, Adopting the Cloud - decision support for cloud computing. The business perspective. Decision Support Systems, 51(1) (2012), pp.1–27.
   Google Scholar
• I.R. Reports, Critical Link (2008).
   Google Scholar
• M. Schwartz, Integrating Your Cloud and On-Premises Applications (2008), pp.1–12.
   Google Scholar
• IBM, Defining a framework for cloud adoption (2010), pp.1–12.
   Google Scholar
• Ali Khajeh-Hosseini, et al., The cloud adoption toolkit: supporting cloud adoption decisions in the enterprise. Software: Practice and Experience. 42(4) (2012), pp. 447–465.
   Google Scholar
• C. Low, Y. Chen, and M. Wu, Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems. 111(7) (2011), pp. 1006–1023.
   Web of Science ®Google Scholar
• E. Rogers, Diffusion of innovations (New York, 1983), pp. 313–330.
   Google Scholar
• M.L. To and E.W. Ngai, Predicting the organisational adoption of B2C e-commerce: an empirical study. Industrial Management & Data Systems. 106(8) (2006), pp. 1133–1147.
   Web of Science ®Google Scholar
• T. Oliveira and M.F. Martins, Understanding e-business adoption across industries in European countries. Industrial Management & Data Systems. 110(9) (2010), pp. 1337–1354.
   Web of Science ®Google Scholar
• K. Zhu, et al., Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. European Journal of Information Systems. 15(6) (2006), pp. 601–616.
   Web of Science ®Google Scholar
• P.Y. Chau and K.L. Hui, Determinants of small business EDI adoption: an empirical investigation. Journal of Organizational Computing and Electronic Commerce. 11(4) (2001), pp. 229–252.
   Google Scholar
• M.-J. Pan and W.-Y. Jang, Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: taiwan's communications industry. Journal of Computer Information Systems. 48(3) (2008), p. 94.
   Web of Science ®Google Scholar
• B. Dehning and V.J. Richardson, Returns on investments in information technology: A research synthesis. Journal of Information Systems. 16(1) (2002), pp. 7–30.
   Google Scholar
• I. Son and D. Lee, Assessing a new IT service model, cloud computing. PACIS 2011 Proceedings. (2011), p. 179
   Google Scholar
• R.G. Fichman, Going beyond the dominant paradigm for information technology innovation research: Emerging concepts and methods. Journal of Computer Information Systems 5(8) (2004), pp. 314–355.
   Google Scholar
• P.Y. Chau and K.Y. Tam, Factors affecting the adoption of open systems: an exploratory study. Mis Quarterly, (1997), pp. 1–24.
   Web of Science ®Google Scholar
• B. Kogut and U. Zander, Knowledge of the firm and the evolutionary theory of the multinational corporation. Journal of international business studies, 34(6), pp. 516–529.
   Web of Science ®Google Scholar
• A.S. Bharadwaj, A resource-based perspective on information technology capability and firm performance: an empirical investigation. Mis Quarterly, (2000), pp. 169–196.
   Web of Science ®Google Scholar
• L.G. Tornatzky, M. Fleischer, and A.K. Chakrabarti, The processes of technological innovation. 273 (Lexington Books Lexington, MA, 1990), pp. 45–46.
   Google Scholar
• S. Bikhchandani, D. Hirshleifer, and I. Welch, A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of political Economy, 100(5) (1992), pp. 992–1026.
   Web of Science ®Google Scholar
• G. Feuerlicht and N. Margaris. “Cloud Computing Adoption: A comparative study.” Proceedings of the 1st WSEAS International Conference on Cloud Computing (CLC'12), Vienna, Austria. Recent Advances in Computer Engineering Series (WSEAS. 2012), PP.1–8.
   Google Scholar
• N. Badie, A.R.C. Hussin, and E. Yadegaridehkordi, The Policy as Repudiation Factors of Adopting Cloud Computing in University Administration, Journal of information systems research and innovation. 3(1) (2013), pp. 54–63.
   Google Scholar
• B. Adam, Enterprise cloud adoption framework standardization as the road to enterprise cloud. (2012).
   Google Scholar
• M. Mircea and A.I. Andreescu, Using Cloud Computing in Higher Education: A Strategy to Improve Agility in the Current Financial Crisis. Communications of the IBIMA (2011), pp.1–15.
   Google Scholar
• W. Suen, A correlation inequality and a Poisson limit theorem for non overlapping balanced subgraphs of a random graph. Random Structures & Algorithms. 1(2) (1990), pp. 231–242.
   Google Scholar
• J.C. Nunnally and I.H. Bernstein, Psychometric theory. 1994. McGraw, New York, (1991).
   Google Scholar
• S. Messick,. Validity of psychological assessment: validation of inferences from persons' responses and performances as scientific inquiry into score meaning. American psychologist. 50(9) (1995), p.741.
   Web of Science ®Google Scholar
• S.N. Haynes, D. Richard, and E.S. Kubany, Content validity in psychological assessment: A functional approach to concepts and methods. Psychological assessment. 7(3) (1995), pp. 238.
   Web of Science ®Google Scholar
• A. Anastasi and S. Urbina, Psychological testing (Prentice Hall Upper Saddle River, NJ, 1997).
   Google Scholar
• A.E.R. Association, et al., Standards for educational and psychological testing (Amer Educational Research Assn, 1999.
   Google Scholar
• R. Sartori and M. Pasini, Quality and Quantity in Test Validity: How can we be sure that Psychological Tests Measure what they have to? Quality & quantity. 41(3) (2007), pp. 359–374.
   Web of Science ®Google Scholar
• R. Sartori, Face validity in personality tests: psychometric instruments and projective techniques in comparison. Quality & quantity. 44(4) (2010), pp. 749–759.
   Web of Science ®Google Scholar
• M. Ekmekçioğlu and A. Can Kutlu, A Fuzzy Hybrid Approach for Fuzzy Process FMEA: An Application to a Spindle Manufacturing Process, International Journal of Computational Intelligence Systems (ijcis). 5(4) (2012), pp. 611–626.
   Web of Science ®Google Scholar
• A.H. Lashkari, A.A. Manaf, and M. Masrom. Graphical Password Security Evaluation by Fuzzy AHP, world academy of science, Engineering and technology, 6(6) (WASET, 2012), 267–272.
   Google Scholar
• G. Büyüközkan, C. Kahraman, and D. Ruan, A fuzzy multi-criteria decision approach for software development strategy selection. International Journal of General Systems. 33(2–3) (2004), pp. 259–280.
   Web of Science ®Google Scholar
• A.T. Gumus, Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology, Expert Systems with Applications. 36(2) (2009), pp. 4067–4074.
   Web of Science ®Google Scholar
• A.T. Gumus, Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology, Expert Systems with Applications. 36(2) (2009), pp. 4067–4074.
   Web of Science ®Google Scholar
• Flynn, B.B., et al., Empirical research methods in operations management. Journal of Operations Management, 9(2) (1990), pp. 250–284.
   Google Scholar
• Lawshe, C.H., A quantitative approach to content validity1. Personnel psychology. 28 (4) (1975), pp. 563–575.
   Web of Science ®Google Scholar
• F. Robert Wilson, W. P., and Donald A. Schumsky Measurement and Evaluation in Counseling and Development .2(4) (2012), pp. 296–307.
   Google Scholar
• Saaty, T.L., How to make a decision: the analytic hierarchy process, European journal of operational research. 48(1) (1990), pp. 9–26.
   Web of Science ®Google Scholar
• Yang, C.-C. and B.-S. Chen, Key quality performance evaluation using fuzzy AHP, Journal of the Chinese Institute of Industrial Engineers. 21(6) (2004), pp. 543–550.
   Google Scholar
• Yang, J. L., H. N. Chiu, et al. “Vendor selection by integrated fuzzy MCDM techniques with independent and interdependent relationships”, Information Sciences. 178(21) (2008), pp.4166–4183.
   Web of Science ®Google Scholar
• Wyld, D.C., the Cloudy future of government IT: Cloud computing and the public sector around the world, International Journal of Web & Semantic Technology, 1(1) (2010), pp. 1–20.
   Google Scholar
• Lee, G. and W. Xia. “Organizational size and IT innovation adoption: A meta-analysis.”, Information & Management, 43(8) (2006),pp. 975–985.
   Web of Science ®Google Scholar
• Delmar, F., P. Davidsson, and W.B. Gartner, Arriving at the high-growth firm, Journal of Business Venturing. 18(2) (2003), pp. 189–216.
   Web of Science ®Google Scholar
• Wagner, J., Exports and productivity: A survey of the evidence from firm level data. The World Economy, 2007. 30(1), pp. 60–82.
   Web of Science ®Google Scholar
• Bibi, S., D. Katsaros, and P. Bozanis, Business application acquisition: on-premise or SaaS-based solutions? Software, IEEE. 29(3) (2012), pp. 86–93.
   Google Scholar
• Wang, H., W. He, and F.-K. Wang, Enterprise cloud service architectures. Information Technology and Management, 13(4) (2012), pp. 445–454.
   Web of Science ®Google Scholar
• Dholakia, R. R. and N. Kshetri, Factors impacting the adoption of the Internet among SMEs, Small Business Economics. 23(4) (2004), pp. 311–322.
   Web of Science ®Google Scholar