$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

코로나-19의 특징과 전파위험 걱정이 스마트 러닝 수용에 미치는 영향: ISSM과 HBM의 통합 모형 적용을 중심으로
The effect of COVID-19 characteristics and transmission risk concerns on smart learning acceptance: Focusing on the application of the integrated model of ISSM and HBM 원문보기

디지털융복합연구 = Journal of digital convergence, v.19 no.7, 2021년, pp.57 - 70  

표규진 (계명대학교 경영정보학과) ,  김양석 (계명대학교 경영정보학과) ,  노미진 (계명대학교 경영정보학과) ,  한무명초 (동국대학교 경주캠퍼스 파라마타칼리지 디지털기초교육부) ,  (계명대학교 경영정보학과) ,  손재익 (계명대학교 경영정보학과)

초록
AI-Helper 아이콘AI-Helper

코로나-19가 확산하면서 비대면 학습을 할 수 있는 스마트 러닝에 관한 관심이 증가하고 있다. 본 연구는 스마트 러닝의 개념에 대한 이해와 스마트 러닝에 관련된 선행연구를 살펴보고, 코로나-19에 대한 사용자의 생각과 스마트러닝 시스템의 정보 품질 및 시스템 품질이 사용자의 수용에 어떻게 영향을 미치는지 분석한 실증연구이다. 본 연구는 코로나-19에 대한 지각된 민감성과 심각성이 전파위험 걱정을 매개로 하여 스마트 러닝에 대한 만족과 사용에 대한 영향력을 살펴보았고, 콘텐츠 품질과 상호작용 품질로 구성된 정보 품질과 시스템 접근성과 기능성으로 구성된 시스템 품질이 사용자 만족을 매개로 하여 스마트 러닝 사용에 미치는 영향력을 살펴보았다. 제안된 모형을 검증하기 위해 스마트 러닝 사용 경험이 있는 사용자 334명을 대상으로 설문을 실시하였고, Smart PLS 3.0을 이용하여 분석을 수행하였다. 분석 결과에 따르면 정보 품질과 시스템 품질 중에서 기능성만 스마트 러닝의 만족에 양(+)의 영향을 미쳤고, 만족은 사용 행동에 양(+)에 영향을 미쳤다. 그러나 시스템 품질 중 접근성은 만족에 영향을 미치지 않는 것으로 나타났으며 전파위험 걱정은 만족에 부정적인 영향을 미치는 것으로 나타났다. 본 연구는 코로나-19와 같은 새로운 감염병의 위기상황 속에서 학생들의 학습을 지원하기 위한 스마트 러닝을 연구할 때에 연구자들에게 의미 있는 가이드 라인을 제공할 수 있을 뿐만 아니라 교육기관과 스마트 러닝 관련 업체들에게도 유용한 시사점을 제공할 수 있을 것이다.

Abstract AI-Helper 아이콘AI-Helper

As COVID-19 spreads, people's interest in smart learning that can do non-face-to-face learning is increasing nowadays. In this study, we aim to empirically analyze how users' thoughts on COVID-19 and the information quality and system quality of smart learning systems affect users' acceptance of sma...

주제어

표/그림 (11)

참고문헌 (79)

  1. C. Clark, A. Davila, M. Regis & S. Kraus. (2020). Predictors of COVID-19 Voluntary Compliance Behaviors: An International Investigation. Global transitions, 2, 76-82. 

  2. M. Zhang, M. Zhou, F. Tang, Y. Wang, H. Nie, L. Zhang & G. You. (2020). Knowledge, Attitude, and Practice Regarding COVID-19 Among Healthcare Workers in Henan, China. Journal of Hospital Infection, 105(2), 183-187. 

  3. T. T. Le, Z. Andreadakis, A. Kumar, R. G. Roman, S. Tollefsen, M. Saville & S. Mayhew. (2020). The COVID-19 Vaccine Development Landscape. Nat Rev Drug Discov, 19(5), 305-306. 

  4. S. M. Kim. (2020). Analysis of Press Articles in Korean Media on Online Education related to COVID-19. Journal of Digital Contents Society, 21(6), 1091-1100. 

  5. Ministry of Education (2020). Announcement of Academic Management and Support Strategies in Education for COVID-19 Correspondence. 

  6. Laura Bicker.(2020). Coronavirus: How South Korea is Teaching Empty Classrooms. BBC News Services. https://www.bbc.com/news/world-asia-52230371 

  7. T. Muthuprasad, S. Aiswarya, K. S. Aditya & G. K. Jha. (2021). Students' Perception and Preference for Online Education in India During COVID-19 Pandemic. Social Sciences & Humanities Open, 3(1), 100101. 

  8. J. Daniel. (2020). Education and the COVID-19 Pandemic. Prospects, 49(1), 91-96. 

  9. N. J. Navimipour & B. Zareie. (2015). A Model for Assessing the Impact of E-Learning Systems on Employees' Satisfaction. Computers in Human Behavior, 53, 475-485. 

  10. P. J. Peng & A. Samah. (2006). Measuring Students' Satisfaction for Quality Education in E-Learning University. Unitar E Journal, 2(1), 11-21. 

  11. J. Burns, J. Clift & J. Duncan. (1991). Understanding of Understanding: Implications for Learning and Teaching. British Journal of Educational Psychology, 61(3), 276-289. 

  12. J. B. Arbaugh. (2000). Virtual Classroom Characteristics and Student Satisfaction with Internet-Based MBA Courses. Journal of Management Education, 24(1), 32-54. 

  13. U. Ehlers, L. Goertz, B. Hildebrandt & J. M. Pawlowski. (2004). Quality in E-Learning. VOCATIONAL TRAINING-BERLIN-CEDEFOP-, (29), 3-15. 

  14. M. G. Moore. (2001). Surviving as a Distance Teacher. American Journal of Distance Education. 15(2) 

  15. A. G. Picciano. (2002). Beyond Student Perceptions: Issues of Interaction, Presence, and Performance in an Online Course. Journal of Asynchronous Learning Networks, 6(1), 21-40. 

  16. M. A. Almaiah & M. Man. (2016). Empirical Investigation to Explore Factors that Achieve High Quality of Mobile Learning System Based on Students' Perspectives. Engineering Science and Technology, An International Journal, 19(3), 1314-1320. 

  17. G. G. You. (2011). Smart-Learning Technology Based on Mixed Reality. Journal of Advanced Information Technology and Convergence, 9(3), 63-73. 

  18. K. S. Noh, S. H. Ju & J. T. Jung. (2011). An Exploratory Study on Concept and Realization Conditions of Smart Learning. Journal of Digital Convergence, 9(2), 79-88. 

  19. Z. Zekun. (2015). User Acceptance of Mobile Healthcare Applications: An Integrated Model of UTAUT and HBM Theory. Journal of Korean Association for Policy Sciences, 19(3), 203-236. 

  20. N. K. Janz & M. H. Becker. (1984). The Health Belief Model: A Decade Later. Health Education Quarterly, 11(1), 1-47. 

  21. 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. 

  22. D. Arney. (2008). Cooperative E-Learning and Other 21st Century Pedagogies. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2461-2466). Las Vegas : AACE. 

  23. C. Dalsgaard. (2006). Social Software: E-Learning Beyond Learning Management Systems. European Journal of Open, Distance and E-Learning, 9(2). 

  24. I. N. Kang, B. R. Lim & J. Y. Park. (2012). Exploring the Theoretical Framework and Teaching & Learning Strategies of Smart Learning: Using Cases of University Classrooms. The Korean Journal of Educational Methodology Studies, 24(2), 283-303. 

  25. E. N. Koh. (2012). A Study on measures to revitalize educational contents in smart learning environments. Seoul : Ewha Womans University Graduate School of Education 

  26. Im Geol. (2011). Research on Developing Instructional Design Models for Enhancing Smart Learning. The Journal of Korean Association of Computer Education, 14(2), 33-45. 

  27. S. H. Bhang. (2012). A Study on Strategies of Self-directed Learning to Promote Smart Learning. The Journal of Lifelong Learning Society, 8(1), 93-112. 

  28. W. H. DeLone & E. R. McLean. E. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60-95. 

  29. I. Ryu & M. Kim. (2002). An Empirical Study on the Success Factors and Performance Model of Hospital Information Systems. Information Systems Review, 12(1), 45-65. 

  30. H. Jo & S. K. Lee (2012). A Study on the Success Factors of Smartphone from the Model Perspective of Technology Acceptance and Systems Success, The Journal of Korean Institute of Information Technology, 10(5), 169-175. 

  31. M. So, E. Y. Ch & H. W. Ki. (2014). An Empirical Study on the Measurement of E-Learning Succes. Knowledge Management Resarch. 15(2), 67-88. 

  32. C. W. Holsapple & A. Lee-Post. (2006). Defining, Assessing, and Promoting E-Learning Success: An Information Systems Perspective. Decision Sciences Journal of Innovative Education, 4(1), 67-85. 

  33. C. L. Jones, J. D. Jensen, C. L. Scherr, N. R. Brown, K. Christy. & J. Weaver. (2015). The Health Belief Model As An Explanatory Framework in Communication Research: Exploring Parallel, Serial, and Moderated Mediation. Health Communication, 30(6), 566-576. 

  34. M. H. Kim. (1997). Health Belief Model Approach to Health Beliefs, Attitude, and Health Behaviors Concerning HIV/AIDS. Korean Journal of Health Education and Promotion, 14(2), 125-147. 

  35. Y. H. Ku, G. Y. Noh. (2018). A Study of the Effects of Self-efficacy and Optimistic Bias on Breast Cancer Screening Intention - Focusing on the Health Belief Model(HBM). Ewha Journal of Social Sciences, 34(2), 73-109. 

  36. M. Y. Kim & C. G. Kim (1990). A Study on Breast Cancer Self-examination Compliance in the Context of Health Belief Model. Korean Society for Health Education and Promotion, 7(1), 64-71. 

  37. K. Witte & M. Allen. (2000). A Meta-Analysis of Fear Appeals: Implications for Effective Public Health Campaigns. Health Education & Behavior, 27(5), 591-615. 

  38. T. Park, I. Ju, J. E. Ohs & A. Hinsley. (2021). Optimistic Bias and Preventive Behavioral Engagement in the Context of COVID-19. Research in Social and Administrative Pharmacy, 17(1), 1859-1866. 

  39. C. Clark, A. Davila, M. Regis & S. Kraus. (2020). Predictors of COVID-19 Voluntary Compliance Behaviors: An International Investigation. Global transitions, 2, 76-82. 

  40. P. Katerattanakul & K. Siau. (1999). Measuring Information Quality of Web Sites: Development of an Instrument. ICIS 1999 Proceedings, 25. 

  41. V. McKinney, K. Yoon & F. M. Zahedi. (2002). The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach. Information Systems Research, 13(3), 296-315. 

  42. R. Y. Wang & D. M. Strong. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33. 

  43. A. N. Islam (2011). The Determinants of the Post-Adoption Satisfaction of Educators with an E-Learning System. Journal of Information Systems Education, 22(4), 319. 

  44. J. H. Wu, R. D. Tennyson & T. L Hsia. (2010). A Study of Student Satisfaction in a Blended E-Learning System Environment. Computers & Education, 55(1), 155-164. 

  45. T. Ramayah & J. W. C. Lee (2012). System Characteristics, Satisfaction and E-Learning Usage: A Structural Equation Model (SEM). Turkish Online Journal of Educational Technology-TOJET, 11(2), 196-206. 

  46. R. L. Oliver. (1981). Measurement and Evaluation of Satisfaction Processes in Retail Settings. Journal of Retailing. 

  47. M. J. Bitner. (1990). Evaluating Service Encounters: The Effects of Physical Surroundings and Employee Responses. Journal of Marketing, 54(2), 69-82. 

  48. P. A. LaBarbera & D. Mazursky. (1983). A Longitudinal Assessment of Consumer Satisfaction/Dissatisfaction: The Dynamic Aspect of the Cognitive Process. Journal of Marketing Research, 20(4), 393-404. 

  49. A. Bhattacherjee. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 351-370. 

  50. A. Bhattacherjee, A. (2001). An Empirical Analysis of the Antecedents of Electronic Commerce Service Continuance. Decision Support Systems, 32(2), 201-214. 

  51. A. Hayashi, C. Chen, T. Ryan & J. Wu. (2004). The Role of Social Presence and Moderating Role of Computer Self Efficacy in Predicting the Continuance Usage of E-Learning Systems. Journal of Information Systems Education, 15(2), 139-154. 

  52. C. M. Chiu, M. H. Hsu, S. Y. Sun, T. C. Lin & P. C. Sun. (2005). Usability, qUality, Value And E-Learning Continuance Decisions. Computers & Education, 45(4), 399-416. 

  53. C. S. Lin, S. Wu & R. J. Tsai. (2005). Integrating Perceived Playfulness into Expectation-Confirmation Model for Web Portal Context. Information & Management, 42(5), 683-693. 

  54. A. A, Oni, O. J. Adewoye & I. O. Eweoya. (2016). E-Banking Users' Behavior: E-Service Quality, Attitude, and Customer Satisfaction. International Journal of Bank Marketing, 34(3) 

  55. Y. M. Park, D. W. Kim, J. G. Lee & J. H. Lym. (2020). Economic Impacts and Implications from Major Infectious Diseases and Natural Disasters. Seoul : The Bank of KOREA. 

  56. M. D. Allo. (2020). Is the Online Learning Good in the Midst of Covid-19 Pandemic? The Case of EFL Learners. Journal Sinestesia, 10(1), 1-10. 

  57. C. M. Ringle, S. Wende, and J. -M. Becker. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. http://www.smartpls.com. 

  58. I. Ajzen. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. 

  59. D. W. Hahn, M. K. Rhee. (2001). Explaining Drinking and Driving : An Application of Theory of Planned Behavior. The Korean Journal of Social and Personality Psychology, 15(2), 141-158. 

  60. M. S. Kim, Y. S. Han. (2001). Understanding Consumer Behavior on Online Shopping : An Application of the Theory of Reasoned Action and the Theory of Planned Behavior. The Korean Journal of Social and Personality Psychology, 15(3), 17-32. 

  61. S. K. Lee, S. T. Yang, J. H. Han. (2012). A Study on The Behavior of the Leisure Aviation Festival Visitors Using the Extended Theory of Planned Behavior. Journal of Tourism and Leisure Research, 24(4), 273-291. 

  62. Lee Jeongae. (2003). A Study on the Influencing Factors on E-Learning Usage Behavior of Organization Members. Seoul : Ewha Womans University Graduate School of Education 

  63. K. Witte. (1996). Predicting Risk behaviors: Development and Validation of a Diagnostic Scale. Journal of Health Communication, 1(4), 317-342. 

  64. A. C. M. Fioravanti-Bastos, E. Cheniaux & J. Landeira-Fernandez. (2011). Development and Validation of a Short-Form Version of the Brazilian State-Trait Anxiety Inventory. Psicologia: Reflexao e Critica, 24(3), 485-494. 

  65. C. D. Spielberger. (1983). State-Trait Anxiety Inventory for Adults. https://doi.org/10.1037/t06496-000 

  66. B.C. Lee, J. O Yoon & I. Lee. (2009). Learners' Acceptance of E-Learning in South Korea: Theories and Results. Computers & Education, 53(4), 1320-1329. 

  67. C. R. Wright. (2003). Criteria for Evaluating the Quality of Online Courses. Alberta Distance Education and Training Association, 16(2), 185-200. 

  68. M. Alkhattabi, D. Neagu & A. Cullen. (2011). Assessing Information Quality of E-Learning Systems: A Web Mining Approach. Computers in Human Behavior, 27(2), 862-873. 

  69. J. Kandari, E. C. Jones, F. F. H. Nah & R. R. Bishu. (2011). Information Quality on the World Wide Web: Development of a Framework. International Journal of Information Quality, 2(4), 324-343. 

  70. H. L. Hsieh, Y. M. Kuo, S. R. Wang, B. K. Chuang & C. H. Tsai. (2017). A Study of Personal Health Record User'S Behavioral Model Based on the PMT and UTAUT Integrative Perspective. International Journal of Environmental Research and Public Health, 14(1), 8. 

  71. J. W. Moon & Y. G. Kim. (2001). Extending the TAM for a World-Wide-Web Context. Information & Management, 38(4), 217-230. 

  72. I. Sentosa & N. K. N. Mat. (2012). Examining a Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) in Internetpurchasing Using Structural Equation Modeling. Researchers World, 3(2), 62. 

  73. S. Y. Baek. (2012). Analyzing Common Method Bias of the Korean Empirical Studies on Technology Acceptance Model. Korea Association of Information Systems, 21(1), 1-17. 

  74. W. W. Chin. (1998). The Partial Least Squares Approach to Structural Equation Modeling. Modern Methods for Business Research, 295(2), 295-336. 

  75. N. Kock. (2015). Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. International Journal of e-Collaboration (ijec), 11(4), 1-10. 

  76. J. F. Hair, J. J. Risher, M. Sarstedt & C, M, Ringle. (2019). When to Use and How to Report the Results of PLS-SEM. European business review, 31(1) 

  77. C. Fornell & D. F. Larcker. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18(3), 382-388 

  78. H. Shin & Y. Kim. (2011). A Study on the Factors Affecting Smart Learning -Focusing on the Moderating Effect of Learning Time-. Journal of Korea Society of Industrial Information Systems, 16(5), 93-105. 

  79. M. J. Noh. (2014). The Effects of Characteristics of Education App on the Trust and Learners' Acceptance in the Smart Learning Environment. Korea Customer Satisfaction Management Association, 16(4), 87-107. 

관련 콘텐츠

오픈액세스(OA) 유형

BRONZE

출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문

이 논문과 함께 이용한 콘텐츠

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로