Analysis of Priority of Technical Factors for Enabling Cloud Computing Services

클라우드 컴퓨팅 서비스 활성화를 위한 기술적 측면 특성요인의 중요도 우선순위 분석

  • Kang, Da-Yeon (BK21 PLUS School of Business Administration Kyungpook National University) ;
  • Hwang, Jong-Ho (Dept. of Management Information Systems College of Business Administration Tongmyoung University)
  • 강다연 (경북대학교 경영학부 BK21플러스) ;
  • 황종호 (동명대학교 경영정보학과)
  • Received : 2019.05.14
  • Accepted : 2019.08.20
  • Published : 2019.08.28


The advent of the full-fledged Internet of Things era will bring together various types of information through Internet of Things devices, and the vast amount of information collected will be generated as new information by the analysis process. To effectively store this generated information, a flexible and scalable cloud computing system is advantageous. Therefore, the main determinants for effective client system acceptance are viewed as motivator factor (economics, efficiency, etc.) and hindrance factor (transitional costs, security issues, etc.) and the purpose of this study is to determine which detailed factors play a major role in making new system acceptance decisions around harm. The factors required to determine the major priorities are defined as the system acceptance determinants from the technical point of view obtained through the literature review, and the questionnaire is prepared based on the factors derived, and the survey is conducted on the experts concerned. In addition, the AHP analysis aims to achieve a final priority by performing a bifurcation between components for measuring a decision unit. Furthermore, the results of this study will serve as an important basis for making decisions based on acceptance (enabling) of technology.


  1. T. J. Kim, S. S. Hwang, S.H.Seo & D.H. Kim. (2017). Designing Cloud Computing System for Local Governments: In Pursuit of an Optimal Model Utilizing Case Study and Feasibility Study. Journal of Korean Association for Regional Information Society, 20(4), 73-96.
  2. J. H. Jung. (2017). An Exploratory Study for Activating Cloud Computing: Focusing on Legislative Alternatives. Journal of Korean Association for Regional Information Society, 20(12), 73-96.
  3. S. H. Jung & K. H. Lee. (2018). IP-CCTV Risk Decision Model Using AHP (Cloud Computing Based). Journal of The Korea Institute of Information Security & Cryptology, 28(1), 229-239.
  4. S. H. Kim. (2019). Korea Cloud Market of 3 group war. Woman Consumer.
  5. C. T. Jin & G. M. Rhee. (2017). The Study on IOT Security and International Crime Countermeasure Strategy. Korea Association of Police Science, 19(5), 256-278.
  6. Gartner. (2017). Top 10 Strategic Technology Trends for 2018. Gartner Special Report. 1-34.
  7. Gartner. (2018). Top 10 Strategic Technology Trends for 2019. Gartner Special Report( 2018.10).
  8. K. K. Seo. (2013). Factor Analysis of the Cloud Service Adoption Intension of Korean Firms: Applying the TAM and VAM. The Journal of Digital Policy & Management, 11(12), 155-160.
  9. S. J. Shin & S. U. Park. (2015). Understanding Individual's Switching Intentions to Cloud Computing Service: Based on the Social Exchange Theory. Korea Technology Innovation Society, 18(1), 176-203.
  10. D. H. Kim, J. H. Lee & Y. P. Park. (2012). A Study of Factors Affecting the Adoption of Cloud Computing. Journal of Society for e-Business Studies, 17(1), 111-136.
  11. G. W. Kim, W. J. Lee & C. H. Jeon. (2010). Virtualization technology for cloud computing. KSCI Review, 18(1), 25-33.
  12. S. H. Kim & H. S. Park. (2018). The Relationship between Vender Dependency and Expected Benefits of Cloud Computing: The Moderating Effects of Vendor Trust and Organizational Supports. Business Administration Research, 47(5), 1021-1047.
  13. K. Y. Lee, S. Y. Hyoun & G. Y .Gim. (2010). The study of cloud computing service model based on service science. Journal of Korean Institute of Next Generation Computing, 6(1), 50-57. 2010.
  14. S. H. Park & H. S. Yang. (2014). A Study on the method of existing system migration for Cloud computing. Journal of Digital Convergence, 12(10), 271-282.
  15. W. Wu. (2010). Mining Significant Factors Affecting the Adoption of SaaS Using the Rough Set Approach. Journal of Systems and Software, 84(3), 435-441.
  16. Y. T. Kim & G. C. Park. (2014). Group key management protocol adopt to cloud computing environment. Journal of Digital Convergence, 12(3), 237-242.
  17. M, J. Qingxiong, P. Michael & T. A. Suresh. (2005). An exploratory study into factors of service quality for application service providers. Information & Management, 42, 1067-1080.
  18. M. D. Dikaiakos, D. Katsaros, P. Mehra, G. Pallis & A. Vakali. (2009). Cloud Computing : Distributed Internet Computing for IT and Scientific Research. IEEE INTERNET COMPUTING, 10-13.
  19. J. H. Ra. (2011). Qualitative Study on Service Features for Cloud Computing. Journal of Digital Contents Society, 12(3), 319-327.
  20. C. S. Lim. (2011). SLA-based Multi-tenant Framework Design of Cloud Computing Services. Journal of Korean Instiitue of Next Generation Computing, 7(4), 38-46.
  21. Y. R. Shin & E. N. Huh. (2014). User-Centric Optimization of Service Price in Broker based Cloud Service Environment. Journal of KIISE, 20(8), 472-476.
  22. S. H. Nam, J. H. Ahn & H. D. Yang. (2013). The Effect of IT Service Outsourcing Project Risks on the Intention of Purchasing Real Options based on Transaction Cost Theory. Asia pacific journal of information systems, 23(2), 40-66.
  23. J. Gubbi, R. Buyya, S. Marusic & M Palaniswami.. (2013). Internet of Things (IoT) : A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660.
  24. M.Armbrust et al.(2009). Above the Clouds : A Berkeley View of Cloud Computing. UC Berkeley Reliable Adaptive Distributed Systems Laboratory, 1-23.
  25. J. E. Kim & H. D. Yang. (2015). The Effect of Cloud Service Risks on the Intention of Purchasing Real Options: Focusing on Public Cloud Service of Small and Medium-sized Enterprises. Information Systems Review, 17(1), 117-140.
  26. S. H. Sung. (2017). Key Management for Secure Internet of Things(IoT) Data in Cloud Computing. Journal of The Korea Institute of Information Security & Cryptology, 27(2), 353-360.
  27. J. Y. Moon. (2019). Cloud Computing Trend and Future Directions. The Korea Contents Association, 17(1), 23-26.
  28. J. H. Kang & H. Y. Lee. (2018). Analyzing the Technological Structure of Cloud Computing Based on Patent Information. Journal of the Korean Institute of Industrial Engineers, 44(1), 69-81.
  29. J. G. Yoon. (1996). A Comparison of 3 Statistical Technique for Evaluation MIS Sucess Factor = Application Efeects and Limitations of AHP as a Research Methodology. Journal of the Korean Operations Research and Management Science Society, 21(3), 109-124.
  30. O. S. Vaidya & S. S. Kumar. (2004). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169, 1-29,