$\require{mediawiki-texvc}$

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.

유튜브 기반 홈 트레이닝 콘텐츠 이용요인에 관한 연구
A Study on the Use Factors of YouTube-based Home Training Content 원문보기

디지털융복합연구 = Journal of digital convergence, v.19 no.2, 2021년, pp.345 - 355  

윤승욱 (전북대학교 문화융복합아카이빙연구소) ,  김건 (전북대학교 기록관리대학원)

초록
AI-Helper 아이콘AI-Helper

본 연구는 기술수용모델과 건강신념모델을 통합, 적용하여 유튜브 기반 홈 트레이닝 콘텐츠의 이용에 영향을 미치는 요인을 살펴보았다. 주요 연구결과를 다음과 같다. 먼저, 개인의 혁신성은 지각된 용이성과 지각된 유용성에 정(+)의 영향을 미치는 것으로 나타났다. 그리고 지각된 민감성은 지각된 유용성에 유의한 영향을 미치지 못하였고, 지각된 이익은 지각된 유용성에 정(+)의 영향을 미친 것으로 나타났다. 마지막으로 지각된 용이성은 지각된 유용성에 정(+)의 영향을 미치는 것으로 나타났고, 지각된 용이성과 지각된 유용성은 모두 지속이용의도에 정(+)의 영향을 미치는 것으로 나타났다. 이를 통해 기술수용모델과 건강신념모델의 통합 가능성을 일정 부분 재확인하였다는 점에서 본 연구의 의의가 있을 것이다.

Abstract ▼ AI-Helper 아이콘AI-Helper

This study examined the factors that influence the use of YouTube-based home training contents by integrating and applying the technology acceptance model and health belief model. The main results are as follows. First of all, it was found that personal innovativeness had a positive (+) effect on pe...

주제어

#유튜브   #홈 트레이닝 콘텐츠   #기술수용모델   #건강신념모델   #코로나19  

표/그림 (4)

참고문헌 (44)

  1. Korea Communications Agency. (2020). Change in the media market due to the COVID-19 outbreak. Korea Communications Agency. 

  2. Korea Press Foundation. (2019). Media users in Korea 2019. 

  3. Aju Business Daily. (2020.05.02.). In march, after corona 19, users flocked to YouTube. https://www.ajunews.com/view/20200502180233529 

  4. Maeil Business News Korea. (2020.07.29.). Lansun training is booming in the era of 'Holmt' untact armed with a content subscription economy. https://www.mk.co.kr/news/culture/view/2020/07/774211/ 

  5. S. C. Jo & Y. J. Han. (2020). A study on the effect of health belief factors on the acceptance of mobile healthcare: Focusing on mediating effects of perceived usefulness. Regional Industry Review, 43(2), 263-280. 

  6. J. S. Lim. (2019). The study on preventive behavior to particulate matter by using smart phone: Focused on extended technology acceptance model and health belief model. Doctoral Dissertation, Kangwon National University. 

  7. F. Davis. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral thesis, MIT Sloan School of Management, Cambridge, MA. 

  8. M. Y. Chuttur. (2009). Overview of the technology acceptance model: Origins, developments and future directions. Sprouts: Working Papers on Information Systems, 9(37), Indiana University, USA. 

  9. M. Vukovic, S. Pivac & D. Kundid. (2019). Technology acceptance model for the internet banking acceptance in split. Business Systems Research, 10(2), 124-140. DOI: 10.2478/bsrj-2019-022. 

    상세보기 crossref

  10. F. D. Davis, R. P. Bogozzi, & P. R. Warshaw. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. 

    상세보기 crossref

  11. V. Venkatesh & F. D. Davis. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. 

    상세보기 crossref

  12. T. A. Sykes, V. Venkatesch, & S. Gosain. (2009). Model of acceptance with peer support: A social network perspective to understand individual-level-system use. MIS Quarterly, 33(2), 371-393. DOI: 10.2307/20650296 

    상세보기 crossref

  13. F. Mazhar, M. Rizwan, U. Fiaz, S. Ishrat, M. S. Razzaq, & T. N. Khan. (2014). An investigation of factors affecting usage and adoption on internet and mobile banking in Pakistan. International Journal of Accounting and Financial Reporting, 4(2), 478-501. DOI: 10.5296/ijafr.v4i2.6586 

    상세보기 crossref

  14. A. Chayomchai. (2020). The online technology acceptance model of generation-Z people in Thailand during COVID-19 crisis. Management & Marketing. Challenges for the Knowledge Society, 15, 496-513. DOI: 10.2478/mmcks-2020-0029 

    상세보기 crossref

  15. G. C. Nistor. (2019). An extended technology acceptance model for marketing strategies in social media. Review of Economic and Business Studies, 12(1), 127-136. DOI: 10.1515/rebs-2019-0086 

    상세보기 crossref

  16. V. Venkatesch. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. https://www.jstor.org/stable/23011042 DOI: 10.1287/isre.11.4.342.11872 

    상세보기 crossref

  17. E. T. Lwoga & N. B. Lwoga. (2017). User acceptance of mobile payment: The effects of user-centric security, system characteristics and gender. The Electronic Journal of Information Systems in Developing Countries, 81(3), 1-24. DOI: 10.1002/j.1681-4835.2017.tb00595.x 

    상세보기 crossref

  18. S. A. Sair & R. Q. Danish. (2018). Effect of performance expectancy and effort expectancy on the mobile commerce adoption intention though personal innovativeness among Pakistan consumers. Pakistan Journal of Commerce and Social Sciences, 12(2), 501-520. 

  19. T. Daim, A. Basoglu, D. Gunay, C. Yildiz, & F. Gomez. (2013). Exploring technology acceptance for online food services. International Journal of Business Information Systems, 12(4), 383-403. DOI: 10.1504/IJBIS.2013.053214 

    상세보기 crossref

  20. R. Agarwal & J. Prasad. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Science, 28(3), 557-582 DOI:10.1111/j.1540-5915.1997.tb01322.x 

    상세보기 crossref

  21. J. Lu, J. E. Yao, & C. S. Yu. (2005). Personal innovativeness, social influences and adoption of wireless: Internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245-268. DOI: 10.1016/j.jsis.2005.07.003 

    상세보기 crossref

  22. J. M. Ju & B. G. Park. (2006). A study on factors in adopting the interactive TV from the perspective of technology acceptance model. Korean Journal of Journalism & Communication Studies, 50(1), 332-354. 

  23. S. H. Son, Y.J. Choi, & H. S. Hwang. (2012). Understanding acceptance of smartphone among early adopters using extended technology acceptance model. Korean Journal of Journalism & Communication Studies, 55(2), 227-251. 

  24. D. F. Midgley & G. R. Dowling. (1993). A longitudinal study of product form innovation: The interaction between predispositions and social messages. Journal of Consumer Research, 19(4), 611-625. DOI: 10.1086/209326 

    상세보기 crossref

  25. S. J. Park & J. W. Lee. (2018). The effect of service quality and user innovativeness of VR sports broadcasting on acceptance intention: Focusing on the extended technology acceptance model. Journal of Sport and Leisure Studies, 71, 269-282. 

  26. S. J. Park, K. H. Ko, W. J. Kim, J. H. Choi, C. Park, D. Y. Youn, & D. Y. Yang. (2019). The effect of quality of service of smart machine on user innovation and user intention using technology acceptance model. Journal of Sport and Leisure Studies, 75, 267-278. 

  27. M. J. Kim & S. B. Lee. (2017). The effect of the innovativeness of delivery application users on perceived traits, satisfaction, and continuous usage intention: Using the extended technology acceptance model. International Journal of Tourism and Hospitality Research, 31(1), 199-214. DOI: 10.21298/ijthr.2017.01.31.1.199 

    crossref

  28. S. H. Gu, D. W. Kim, C. M. Park, & K. H. Kim. (2013). Influence of LTE characteristic and personal innovativeness on LTE smart phone acceptance. Journal of Digital Contents Society, 14(3), 291-301. DOI: 10.9728/dcs.2013.14.3.291 

    원문보기 상세보기 crossref

  29. C. W. Park & H. J. Jeong. (2012). An empirical study on the effects of personal and systematic characteristics on the acceptance of technologically innovative products: With focus on cloud computing. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 7(2), 63-76. DOI: 10.16972/apjbve.7.2.201207.63 

    원문보기 상세보기 crossref

  30. N. K. Janz & M. H. Becker. (1984). The health belief model: A decade later. Health Education Quarterly, 11, 1-47. DOI: 10.1177/109019818401100101 

    상세보기 crossref

  31. P. Sheeran & C. Abraham. (2001). The health belief model. Predicting health behavior. Buckingham: Open University Press. 

  32. B. K. Lee, Y. K. Sohn, S. O. Lee, M. Y. Yoon, M. H. Kim, & C. R. Kim. (2014). An efficacy of social cognitive theory to predict health behavior: A meta-analysis on the health belief model studies in Korea. Journal of Public Relations, 18(2), 163-206. DOI: 10.15814/jpr.2014.18.2.163 

    crossref

  33. K. Glanz, B. K. Rimer, & K. Viswanath. (2008). Health behavior and health education: Theory, research and practice. San Francisco, CA: Jossey-Bass. 

  34. Y. Reisinger & F. Mavondo. (2005). Travel anxiety and intentions to travel internationally: Implications of travel risk perception. Journal of Travel Research, 43(3), 212-225. DOI: 10.1177/0047287504272017 

    상세보기 crossref

  35. J. A. Harrison, P. D. Mullem, & L. W. Green. (1992). A meta-analysis of studies of the health belief model with adults. Health Education Research, 7, 107-116. DOI: 10.1093/her/7.1.107 

    상세보기 crossref

  36. R. S. Zimmerman & D. Vernberg. (1994). Model of preventive health behaviour: Comparison, critique, and meta-analysis. Advances in Medical Sociology, 4, 45-47. DOI: 10.1080/10410236.2010.521906 

    상세보기 crossref

  37. M, E. Choi, P. K. Seo, M. I. Choi, H. J. Paek. (2014). Factors associated with health-specific TV viewing intention: Application of the technology acceptance model. Korean Journal of Journalism & Communication Studies, 58(6), 362-389. 

  38. Y. W. Kim, H. N. Lee, H. I. Kim, & H. J. Moon. (2017). A study on usage effect and acceptance factors of a particulate matter application (App). Journal of Public Relations, 21(4), 114-142. DOI: 10.15814/jpr.2017.21.4.114 

    상세보기 crossref

  39. A. S. Ahadzadeh, S. P. Sharif, F. S. Ong, & K. W. Khong. (2015). Integrating health belief model and technology acceptance model: An investigation of health-related internet use. Journal of Medical Internet Research, 17(2), e45. DOI: 10.2196/jmir.3564 

    상세보기 crossref

  40. S. O. Lee & S. H. Lee. (2018). A study on the factors influencing acceptance of social media-based smart commerce service through personal innovativeness. Journal of Digital Contents Society, 19(3), 547-559. 

    원문보기 상세보기 crossref

  41. R. A. Sanchez & A. D. Hueros. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26, 1632-1640. DOI: 10.1016/j.chb.2010.06.011 

    상세보기 crossref

  42. J. H. Kim & J. H. Cho. (2019). Investigation of effects of individuals social viewing of fine dust information obtained through social media on behavioral intentions of disease prevention: Application of health beliefs model. Korean Journal of Broadcasting and Telecommunication Studies, 33(4), 37-63. 

  43. H. S. Kwon. (2020). A study on the categorization and acceptance factors of e-book users: Focusing on MZ generation. Doctoral Dissertation, Konkuk University. 

  44. J. F. Engle, R. D. Blackwell, & P. W. Miniard. (1995). Consumer behavior. Illinois: The Dryden Press. 

저자의 다른 논문 :

관련 콘텐츠

오픈액세스(OA) 유형

FREE

Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문

이 논문과 함께 이용한 콘텐츠

저작권 관리 안내
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