디지털융복합연구 (Journal of Digital Convergence)

  • 제17권8호
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  • Pages.123-130
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  • 2019
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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클라우드 컴퓨팅 서비스 활성화를 위한 기술적 측면 특성요인의 중요도 우선순위 분석

Analysis of Priority of Technical Factors for Enabling Cloud Computing Services

  • 강다연 (경북대학교 경영학부 BK21플러스) ;
  • 황종호 (동명대학교 경영정보학과)
  • 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)
  • 투고 : 2019.05.14
  • 심사 : 2019.08.20
  • 발행 : 2019.08.28
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초록

본격적인 사물인터넷(IoT) 시대의 도래는 다양한 형태의 정보를 사물인터넷 기기를 통해 수집하게 되고, 수집된 방대한 정보는 분석과정에 의해 새로운 정보로 탄생한다. 이렇게 생성된 정보를 효과적으로 저장하기 위해서는 유연성과 확장성이 뛰어난 클라우드 컴퓨팅 시스템이 유리하다. 따라서 본 연구에서는 효과적인 클라이언트 시스템 수용을 위한 주요 결정요인을 동기요인(경제성, 효율성 등)과 저해요인(전환비용, 보안문제 등)으로 보고, 저해요인을 중심으로 새로운 시스템 수용결정을 함에 있어서 어떤 세부요인이 주요하게 작용하는지에 대한 순위 파악에 연구목적을 두고 있다. 주요우선순위 결정에 필요한 요인은 문헌고찰을 통해 확보된 기술 관점의 시스템 수용결정 요인으로 정하고, 도출된 요인을 중심으로 설문지를 작성한 후, 관련 전문가를 대상으로 설문을 실시하고자 한다. 그리고 AHP분석을 통해 의사결정단위 측정을 위한 요소들 간의 쌍대비교를 수행하여 최종 우선순위를 도출하고자 한다. 나아가 본 연구 결과는 기술 수용(활성화)에 따른 의사결정을 함에 있어서 중요한 판단 근거가 될 것이다.

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.

키워드

참고문헌

  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. https://doi.org/10.13089/JKIISC.2018.28.1.229
  4. S. H. Kim. (2019). Korea Cloud Market of 3 group war. Woman Consumer. http://www.womancs.co.kr/news/articleView.html?idxno=50992.
  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. https://doi.org/10.7838/jsebs.2012.17.1.111
  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. https://doi.org/10.14400/JDC.2014.12.10.271
  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. https://doi.org/10.1016/j.jss.2010.11.890
  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. https://doi.org/10.14400/JDC.2014.12.3.237
  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. https://doi.org/10.1016/j.im.2004.11.007
  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. https://doi.org/10.9728/dcs.2011.12.3.319
  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. https://doi.org/10.1016/j.future.2013.01.010
  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. https://doi.org/10.14329/isr.2014.17.1.117
  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. https://doi.org/10.13089/JKIISC.2017.27.2.353
  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. https://doi.org/10.7232/JKIIE.2018.44.1.069
  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, https://doi.org/10.1016/j.ejor.2004.04.028