DOI QR코드

DOI QR Code

기업콘텐츠관리시스템의 지속적 이용의도 향상에 대한 연구: 기술수용모델을 중심으로

A Study on the Improvement of the Intention of Continuous Use of Enterprise Content Management System: Focusing on the Technology Acceptance Model

  • Hwang, In-Ho (College of General Education, Kookmin University)
  • 투고 : 2021.06.07
  • 심사 : 2021.08.20
  • 발행 : 2021.08.28

초록

체계적인 정보 보호 및 관리가 조직의 핵심 가치로 인식되면서, 조직들은 개인 중심의 정보 관리 방식에서 조직 중심의 정보 관리 방식으로의 전환을 추진하고 있다. 기업콘텐츠관리시스템은 내부자의 문서 보안과 정보 공유를 지원하는 시스템으로서, 최근 기술의 발전으로 많은 조직이 도입하고 있다. 본 연구는 사용자 관점에서 기업콘텐츠관리시스템의 지속적 활용을 통한 성과 향상의 방안을 제시하는 것을 목적으로 하며, 기술수용모델의 확장을 통해 지속적 이용의도 향상 방안을 제시한다. 연구는 기업콘텐츠관리시스템을 도입한 기업의 근로자들을 대상으로 설문하였으며, 구조방정식 모델링을 통해 선행연구로부터 도출한 연구가설을 검증하였다. 분석 결과, 기업콘텐츠관리시스템의 지속적 이용의도에 유용성과 이용 용이성이 영향을 미쳤으며, 지식공유 문화 환경과 기업콘텐츠관리시스템 품질 요인 기술수용모델 선행요인에 영향을 미쳤다. 본 연구의 결과는 사용자 관점에서 기업콘텐츠관리시스템 활용성 증대방안을 제시하였다는 측면에서 학술적, 실무적 시사점을 가진다.

As systematic information protection and management is recognized as an organization's core value, organizations are pursuing a shift from an individual-centered information management method to an organization-oriented information management method. The Enterprise content management system(ECMS) is a solution that supports document security and information sharing by insiders and is being introduced by many organizations due to recent technological developments. The purpose of this study is to present a method of improving performance through continuous use of the ECMS from the user's point of view and also suggest a method to improve the intention of continuous use through the expansion of the technology acceptance model. This study surveyed the employees of organizations that adopted the ECMS and verified the research hypothesis derived from previous studies through structural equation modeling. As a result of the analysis, usefulness, and ease of use affected on the intention of continuous use of the ECMS, and the knowledge sharing culture and the ECMS quality factors affected the technology acceptance model factors. The results of this study have academic and practical significance in terms of suggesting a plan to increase the usability of the ECMS from the user's point of view.

키워드

참고문헌

  1. C. Maican & R. Lixandroiu. (2016). A system architecture based on open source enterprise content management systems for supporting educational institutions. International Journal of Information Management, 36(2), 207-214. DOI : 10.1016/j.ijinfomgt.2015.11.003.
  2. S. Hullavarad, R. O'Hare & A. K. Roy. (2015). Enterprise content management solutions: Roadmap strategy and implementation challenges. International Journal of Information Management, 35(2), 260-265. DOI : 10.1016/j.ijinfomgt.2014.12.008.
  3. J. Hong. (2020). Document centralization solution, is it optional or mandatory for information security?, Institute of Information & Communications Technology Planning & Evaluation.
  4. Markets and Markets. (2020). Enterprise content management market by component, deployment mode, organization size, business function (HR operations, procurement and supply chain management), vertical (BFSI, transportation and logistics), and region - global forecast to 2025. https://www.marketsandmarkets.com.
  5. G. W. Bock, R. W. Zmud, Y. G. Kim & J. N. Lee. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87-111. DOI : 10.2307/25148669.
  6. A. Harr, J. vom Brocke & N. Urbach. (2019). Evaluating the individual and organizational impact of enterprise content management systems. Business Process Management Journal, 25(7), 1413-1440. DOI : 10.1108/BPMJ-05-2017-0117.
  7. I. Arpaci, M. Al-Emran & M. A. Al-Sharafi. (2020). The impact of knowledge management practices on the acceptance of Massive Open Online Courses (MOOCs) by engineering students: A cross-cultural comparison. Telematics and Informatics, 54, 101468. DOI : 10.1016/j.tele.2020.101468.
  8. V. Venkatesh, M. G. Morris, G. B. Davis & F. D. Davis. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540.
  9. F. D. Davis. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. DOI : 10.2307/249008.
  10. V. Venkatesh & F. D. Davis. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. DOI : 10.1287/mnsc.46.2.186.11926.
  11. M. Gong, Y. Xu & Y. Yu. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365-374.
  12. H. C. Lin. (2014). An investigation of the effects of cultural differences on physicians' perceptions of information technology acceptance as they relate to knowledge management systems. Computers in Human Behavior, 38, 368-380. DOI : 10.1016/j.chb.2014.05.001.
  13. M. Choi. (2019). The effect of information seeking style and news literacy of card news users on recommendation intention: Focused on Technology Acceptance Model (TAM), Journal of the Korea Convergence Society, 10(1), 141-148. DOI : 10.15207/JKCS.2019.10.1.141.
  14. S. Chatterjee, R. Chaudhuri, A. Thrassou & D. Vrontis. (2021). Antecedents and consequences of knowledge hiding: The moderating role of knowledge hiders and knowledge seekers in organizations. Journal of Business Research, 128, 303-313. DOI : 10.1016/j.jbusres.2021.02.033.
  15. S. Chatterjee, N. P. Rana & Y. K. Dwivedi. (2020). Social media as a tool of knowledge sharing in academia: An empirical study using valance, instrumentality and expectancy (VIE) approach. Journal of Knowledge Management, 24(10), 2531-2552. DOI : 10.1108/JKM-04-2020-0252
  16. A. Serenko & N. Bontis. (2016). Understanding counterproductive knowledge behavior: antecedents and consequences of intra-organizational knowledge hiding. Journal of Knowledge Management, 20(6), 1199-1224. https://doi.org/10.1108/JKM-05-2016-0203.
  17. I. Hwang. (2021). The effect of information security related stress and person-organization fit on knowledge sharing behavior, Journal of the Korea Convergence Society, 12(2), 247-258. DOI : 10.15207/JKCS.2021.12.2.247.
  18. K. Ruck & M. Welch. (2012). Valuing internal communication; Management and employee perspectives. Public Relations Review, 38(2), 294-302. DOI: 10.1016/j.pubrev.2011.12.016.
  19. M. Welch & P. R. Jackson. (2007). Rethinking internal communication: A stakeholder approach. Corporate Communications: An International Journal, 12(2), 177-198. DOI: 10.1108/13563280710744847.
  20. M. Lee & Y. Seo. (2019). The effect of team efficacy on individual creativity and product development performance through communication competence in technology development projects, Journal of the Korea Convergence Society, 10(11), 375-386, DOI : 10.15207/JKCS.2019.10.11.375.
  21. H. Shin & S. Yun. (2008). A study on relations of internal communication and corporate culture. Journal of Public Relations, 12(2), 44-81. https://doi.org/10.15814/jpr.2008.12.2.44
  22. S. Asif & A. Sargeant. (2000). Modeling internal communications in the financial services sector. European Journal of Marketing, 34(3/4), 299-318. DOI : 10.1108/03090560010311867.
  23. D. Jimenez-Castillo & M. Sanchez-Perez. (2013). Nurturing employee market knowledge absorptive capacity through unified internal communication and integrated information technology. Information & Management, 50(2-3), 76-86. DOI : 10.1016/j.im.2013.01.001.
  24. W. H. DeLone & E. R. McLean. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. DOI : 10.1287/isre.3.1.60.
  25. W. H. DeLone & E. R. McLean. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. DOI : 10.1080/07421222.2003.11045748.
  26. C. Tam & T. Oliveira. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233-244. DOI : 10.1016/j.chb.2016.03.016.
  27. K. M. Wei, Y. T. Tang, Y. C. Kao, L. C. Tseng & H. H. Wu. (2017). Using an updated Delone and McLean model to assess the success of implementing the ward cleaning logistics system in a medical center. Journal of Statistics and Management Systems, 20(5), 965-976. DOI : 10.1080/09720510.2017.1338609
  28. S. Laumer, C. Maier & T. Weitzel. (2017). Information quality, user satisfaction, and the manifestation of workarounds: A qualitative and quantitative study of enterprise content management system users. European Journal of Information Systems, 26(4), 333-360. DOI : 10.1057/s41303-016-0029-7.
  29. M. Al-Emran, V. Mezhuyev & A. Kamaludin. (2020). Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance. Technology in Society, 61, 101247. DOI : 10.1016/j.techsoc.2020.101247.
  30. J. E. Scott. (2011). User perceptions of an enterprise content management system. In 2011 44th Hawaii International Conference on System Sciences (pp. 1-9). IEEE.
  31. I. Arpaci. (2017). Antecedents and consequences of cloud computing adoption in education to achieve knowledge management. Computers in Human Behavior, 70, 382-390. DOI : 10.1016/j.chb.2017.01.024.
  32. S. C. Park & S. Y. Ryoo. (2013). An empirical investigation of end-users' switching toward cloud computing: A two factor theory perspective. Computers in Human Behavior, 29(1), 160-170. DOI : 10.1016/j.chb.2012.07.032.
  33. W. Sardjono, J. Sudirwan, W. Priatna & G. R. Putra. (2021). Application of factor analysis method to support the users acceptance model of ERP systems implementation. In Journal of Physics: Conference Series, (Vol. 1836, No. 1, p. 012032). IOP Publishing. https://doi.org/10.1088/1742-6596/1836/1/012032
  34. Y. S. Wang. (2008). Assessing e-commerce systems success: A respecification and validation of the DeLone and McLean model of IS success. Information Systems Journal, 18(5), 529-557. DOI : 10.1111/j.1365-2575.2007.00268.x.
  35. S. Hong, M. L. Malik & M. K. Lee. (2003). Testing configural, metric, scalar, and latent mean invariance across genders in sociotropy andautonomy using a non-Western sample. Educational and Psychological Measurement, 63(4), 636-654. https://doi.org/10.1177/0013164403251332
  36. J. C. Nunnally. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
  37. B. H. Wixom & H. J. Watson. (2001). An empirical investigation of the factors affecting data warehousing success. MIS Quarterly, 25(1), 17-41. DOI : 10.2307/3250957.
  38. C. Fornell & D. F. Larcker. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. DOI : 10.2307/3151312.
  39. P. M. Podsakoff, S. B. MacKenzie, J. Y. Lee & N. P. Podsakoff. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. DOI : 10.1037/0021-9010.88.5.879.
  40. R. H. Hoyle & D. A. Kenny. (1999). Sample size, reliability, and tests of statistical mediation, Statistical Strategies for Small Sample Research, 1, 195-222.