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Influence Factors of Innovation Resistance of Cloud Computing Service: Focus on Small and Medium Enterprises

클라우드 컴퓨팅 서비스의 혁신저항 영향요인: 중소기업을 대상으로

  • Received : 2020.10.19
  • Accepted : 2020.12.20
  • Published : 2020.12.28

Abstract

The purpose of this study is to investigate the factors influencing the innovation resistance of cloud computing services and to suggest policy alternatives to increase the use of domestic cloud computing services. For this, a survey was conducted on 178 SMEs that introduced cloud computing services with government support. As a result, technostress, CEO informatization leadership and organizational structure concentration had a significant influence on innovation resistance. Therefore, in the future, first, it is necessary to provide user-centered cloud computing services in the direction of reducing technostress. Second, it is necessary to apply cloud computing services through a deeper understanding of the organizational characteristics of each small and medium enterprise. Third, there is a need for advanced security authentication and a compensation system. In order to promote the use of cloud computing services, an environment in which users can safely use should be prepared first.

본 연구는 클라우드 컴퓨팅 서비스의 혁신저항에 영향을 주는 요인을 알아보고, 국내 클라우드 컴퓨팅 서비스 사용을 높이기 위한 정책적 대안을 제시하는데 목적이 있다. 이를 위해 정부지원을 받아 클라우드 컴퓨팅 서비스를 도입한 중소기업 178개사를 설문조사하였다. 그 결과, 테크노스트레스, CEO 정보화 리더십과 조직구조 집권성은 혁신저항에 유의미한 영향을 미쳤다. 따라서 향후에는 첫째, 테크노스트레스를 줄일 수 있는 방향으로 이용자 중심의 클라우드 컴퓨팅 서비스 제공이 필요하다. 둘째, 중소기업별 조직특성에 대한 보다 심도 있는 이해를 통한 클라우드 컴퓨팅 서비스의 적용이 필요하다. 셋째, 보안인증 고도화 및 보상제도가 필요하다. 클라우드 컴퓨팅 서비스 이용을 촉진하기 위해서는 이용자가 안전하게 이용할 수 있는 환경이 우선적으로 마련되어야 할 것이다.

Keywords

References

  1. Ministry of Science and Technology Information and Communication. (2018). 2018 Cloud Industry Survey.
  2. S. Ram. (1987). A Model of Innovation Resistance , In NA - Advances in Consumer Research (14), Eds. M. Wallendorf and P. Anderson, Provo, UT : Association for Consumer Research, pp. 208-212.
  3. I. M. Yoo. (2011). An Empirical Study on the Influence of Innovation Characteristics on Users' Resistance and Acceptance in the Proliferation of Intelligent Home Networks. Kyung-Hee University Graduate School Doctoral dissertation.
  4. H. Gatignon. & T. S. Robertson. (1985). A Propositional Inventory for New Diffusion Research. Journal of Consumer Research, 11(4), 849-867. https://doi.org/10.1086/209021
  5. H. Gatignon. & T. S. Robertson. (1989). Technology Diffusion: an Empirical Test of Competitive Effects. Journal of Marketing, 53(1), 35-49. https://doi.org/10.2307/1251523
  6. S. G. Kim. (2018). Analyzing the Influence of Innovation Resistance of Non-face-to-face Fintech Service on Recommendation Intention. Kangwon National University Doctoral dissertation.
  7. J. S. Park. (2018). A Study on the Differences in Consumer Knowledge and Perception of Based Technology for Adoption of Blockchain-based Transaction Authentication Technology: Focused on the Innovation Resistance Model. Chung-Ang University Doctoral dissertation.
  8. S. Ram. (1989). Successful Innovation Using Strategies to Reduce Consumer Resistance : An Empirical Test. Journal of Product Innovation Management, 6(1), 20-34. https://doi.org/10.1111/1540-5885.610020
  9. J. H. Kim. (2018). A Study on the Obstacles to Activating R&D Outsourcing from the Perspective of the Innovation Resistance Model : Focused on the Domestic Automobile Industry. Konkuk University Doctoral Dissertation.
  10. J. N. Sheth. (1981). An Integrative Theory of Patronage Preference and Behavior, College of Commerce and Business Administration. Bureau of Economic and Business Research, University of Illinois, Urbana-Champaign.
  11. Y. S. Yang & C. H. Shin. (2010). Consumer Innovation Resistance in Accepting New Technologies, Archives of Design Research, 23(3), 37-52.
  12. E. M. Rogers. (1995). Lessons for Guidelines from the Diffusion of Innovations. Joint Commission Journal on Quality and Patient Safety, 21(7), 324-328.
  13. E. M. Rogers & F. F. Shoemaker. (1971). Communication of Innovations; A Cross-Cultural Approach. 2nd Edition, The Free Press, New York.
  14. J. W. Alba. & J. W. Hutchinson. (1987). Dimensions of Consumer Expertise. Journal of Consumer Research, 13(4), 411-454. https://doi.org/10.1086/209080
  15. L. G. Schiffman. & L. L. Kanuk. (1991). Communication and Consumer Behavior. Consumer Behavior, 2, 268-306.
  16. E. F. Stone, D.G. Gardner, H.G. Gueutal, & S. McClure. (1983) A Field Experiment Comparing Information-Privacy Values, Beliefs and Attitudes Across Several Types of Organizations, Journal of Applied Psychology, 68(3), 459-468. https://doi.org/10.1037//0021-9010.68.3.459
  17. J. S. Kim. (2004). The effect of perceived security on the intention to use internet shopping malls. Expanded technology acceptance model perspective. Kwangwoon University Graduate School Doctoral dissertation.
  18. J. H. Ahn. (2010). Cloud Computing User Acceptance Intention. Master's Thesis in Konkuk University.
  19. C. Brod. (1984). Technostress: The Human Cost of the Computer Revolution. Addison Wesley Publishing Company.
  20. M. Tarafdar. Q. Tu., B. S. Ragu-Nathan. & T. S. Ragu-Nathan. (2007). The Impact of Technostress on Role Stress and Productivity. Journal of Management Information Systems : JMIS, 24(1), 301-328. https://doi.org/10.2753/MIS0742-1222240109
  21. Q. Tu, K. Wang, & Q. Shu. (2005). Computer-Related Technostress in China. Communications of the ACM, 48(4), 77-81. https://doi.org/10.1145/1053291.1053323
  22. M. H. Jeong. (2013). The Effect of Information System (IS) Users' Technostress on IS Burden and Performance Expectations. Sunchon National University doctoral dissertation.
  23. W. C. Shin. & H. C. Ahn. (2019). Effects of Innovation Characteristics of Cloud Computing Services, Technostress on Innovation Resistance and Acceptance Intention: Focused on Public Sector. Knowledge Management Research, 20(2), 59-86.
  24. K. J. Kim. & K. D. Lee. (2017). The Effect of Technostress on User Resistance and End-User Performance. Information Systems Review, 19(4), 63-85. https://doi.org/10.14329/isr.2017.19.4.063
  25. B. S. Kim. (2000). Directions and Challenges of Government Organizational Reform. Journal of the Korean Society for Public Administration.
  26. I. S. Han. (2006). Comparison of Performance Management System Operation Status of Public Organizations and Private Companies. Korean Public Administration Research, 15(3).
  27. S. O. Yoon. (2005). A Study on the Success Factors of Public Informatization Projects: Focusing on the Perceptions of Public Officials in Charge of Informatization of Ministry. Journal of the Korean Society for Policy Analysis and Evaluation, 15(3).
  28. H. S. Kim. (1996). Organizational Economic Approach to Public Organizations. Seoul: Yonsei Administration Research Association.
  29. T. H. Park. & S. J. Baek. (2001). Influence of Internal Factors on the Use of Basic Local Government Websites for Handling Residents' Living Civil Complaints: Targeting the Seoul Metropolitan Government. Journal of the Korean Society for Policy Studies, 10(2).
  30. G. Bassellier., B. H. R. Reich & I. Benbasat. (2001). Information Technology Competence of Business Managers: A Definition and Research Model. Journal of Management Information Systems, 17(4).
  31. S. H. Jo. (2003). A Study on the Factors Affecting the Level of Administrative Informatization: For Public Officials of the Intellectual Property Office and Cultural Heritage Administration. Master's Thesis in Seoul National University Graduate School.
  32. J. H. Lim. (2006). The Impact of Urban E-Government on Citizen Participation. Korean Public Administration Review, 40(3).
  33. R. H. Hall, (2002). Organizations: Structures, Processes, and Outcomes. 9th ed. NJ: Prentice Hall.
  34. J. W. Fredrickson. (1984). The Effect of Structure on the Strategic Decision Process. Academy of Management Proceeding, 1, 12-16. https://doi.org/10.5465/ambpp.1984.4978163
  35. Stephen P. Robbins. (1983). Organization Theory: Structures, Designs, and Applications. New Jersey: Englewood Cliffs, Prentice-Hall, Inc.
  36. G. Kim. (2001). Factors Affecting Improving the Utility of Information and Communication Technology in Administration, Korean Public Administration Review, 35(4), 31-53.
  37. H. J. Joo. (2004). A Sudy on the Rlationship between Oganizational Sructure, Oganizational Clture, and Oanizational Efectiveness: Fcusing on Oanizational Cassification by Business Characteristics, Administrative Thesis, 42(2), 29-52.
  38. Kimberly, J. and Evanisko, M. (1981). Organizational Innovation: The Influence of Individual, Organizational, and Contextual Factors on Hospital Adoption of Technological and Administrative Innovations., Academy of Management Journal, 24, 689-713. https://doi.org/10.2307/256170
  39. C. Ranganathan, Jasbir S. Dhaliwal and Thompson S. H. Teo. (2004). Assimilation and Diffusion of Web Technologies in Supply-Chain Management: An Examination of Key Drivers and Performance Impacts, International Journal of Electronic Commerce, 9(1), 127-161. https://doi.org/10.1080/10864415.2004.11044319
  40. C. J. Lee. (2010). An Empirical Study on Public Officials' Use of Information Technology and Influencing Factors: Comparison of Organizational Characteristics, Personal Characteristics, and Technology Characteristics, Korean Journal of Public Administration, 44(2), 221-260.
  41. D. R. Dalton., W. D. Todor., M. J. Spendolini., G. J. Fielding., L. W. Porter. (1980). Organization Structure and Performance : A Critical Review. Academy of Management Review, 5(1), 49-64. https://doi.org/10.5465/amr.1980.4288881
  42. J. R. Kimberly, & M. J. Evanisko., (1981). Organizational Innovation : The Influence of Individual, Organizational, and Contextual Factors on Hospital Adoption of Technological and Administrative Innovations. Academy of Management Journal, 24(4), 689-713. https://doi.org/10.2307/256170
  43. Zmud. (1982). Diffusion of Modern Software Practices : Influence of Centralization and Formalization. Management Science, 28(12), 1421-1431. https://doi.org/10.1287/mnsc.28.12.1421
  44. T. J. Na & S. Y. Choi. (2003). A Study on the Ways to Improve Organizational Trust of Public Organization Members: Focused on the Case of Seoul City. Korean Journal of Public Administration, 37(1).
  45. H. S. So (2020). The Relationship between Organizational Innovation Characteristics, Resistance and Acceptance Intention: Focusing on Cloud Computing Services, Master's thesis in Graduate School of Technology Management, Korea University.
  46. J. G. Shin, S. W. Lee. (2016). A Study of Intention to Use Wrist-worn Wearable Devices Based on Innovation Resistance Model: Focusing on the Relationship between Innovation Characteristics, Consumer Characteristics, and Innovation Resistance, Journal of the Korea Contents Association, 16(6), 123-134. https://doi.org/10.5392/JKCA.2016.16.06.123
  47. J. K. Bae. (2016). The Structural Relationships among Innovation Characteristics, Consumer Characteristics, Innovation Resistance, and Intention to Acceptance of Wearable Device Customers: Based on Innovation Resistance Model and Theory of Perceived Risk, Information System Research, 25(4), 87-104. https://doi.org/10.5859/KAIS.2016.25.4.87
  48. D. Y. Kang, J. H. Hwang. (2019). Analysis of Priority of Technical Factors for Enabling Cloud Computing Services, Journal of Digital Convergence, 17(8), 123-130. https://doi.org/10.14400/JDC.2019.17.8.123
  49. Tarafdar, M., Q. Tu, T. S. Ragu-Nathan, and B. S. Ragu-Nathan. (2011). Crossing to the Dark Side: Examining Creators, Outcomes, and Inhibitors of Technostress, Communications of the ACM, 54(9), 113-120. https://doi.org/10.1145/1995376.1995403
  50. Ragu-Nathan, T. S., M. Tarafdar, B. S. Ragu-Nathan, and Q. Tu. (2008). The Consequences of Technostress for End Users in Organizations: Conceptual Development and Empirical Validation, Information Systems Research, 19(4), 417-433. https://doi.org/10.1287/isre.1070.0165
  51. Y. J. Kim. (2020). A Study on the Influence of Technostress on Job Performance of Public officials : Focusing on Moderating effect of Self-Efficacy, Master's thesis in Public Administration, Pusan National University.
  52. Damanpour, F. (1991) Organizational Innovation: A Meta-Analysis of Effects of Determinants and Moderators. Academy of management Journal, 34, 555-590. https://doi.org/10.2307/256406