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유튜브 기반 홈 트레이닝 콘텐츠 이용요인에 관한 연구

A Study on the Use Factors of YouTube-based Home Training Content

  • 윤승욱 (전북대학교 문화융복합아카이빙연구소) ;
  • 김건 (전북대학교 기록관리대학원)
  • Yun, Sung-uk (Institute of Culture Convergence Archiving, Jeonbuk National University) ;
  • Kim, Geon (Graduate School of Archives and Records Management, Jeonbuk National University)
  • 투고 : 2020.11.21
  • 심사 : 2021.02.20
  • 발행 : 2021.02.28

초록

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.

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 perceived ease and perceived usefulness. Perceived susceptibility did not have a significant effect on perceived usefulness, and perceived benefit had a positive (+) effect on perceived usefulness. Finally, it was found that perceived ease had a positive (+) effect on perceived usefulness, Both perceived ease of use and perceived usefulness were found to have a positive (+) effect on continuous intention to use. This study will be meaningful in that it partially reconfirmed the possibility of integrating the technology acceptance model and the health belief model.

키워드

참고문헌

  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. https://doi.org/10.33932/rir.43.2.12
  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.
  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. https://doi.org/10.1287/mnsc.35.8.982
  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. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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. https://doi.org/10.51979/KSSLS.2018.02.71.269
  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. https://doi.org/10.51979/KSSLS.2019.02.75.267
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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
  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. https://doi.org/10.9728/dcs.2018.19.3.547
  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
  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.