Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Exploration on factors to affect continuance intention of collegian subscribing YouTube: Focused on Post-adoption Model

대학생 유튜브 구독 이용자의 지속이용 영향요인에 관한 탐색 : 기술 수용 후 모형(Post-adoption model)을 중심으로

  • Joo, Jihyuk (Dept. of Journalism & Communication, Far East University)
  • 주지혁 (극동대학교 언론홍보학과)
  • Received : 2020.08.19
  • Accepted : 2020.10.20
  • Published : 2020.10.28
Download PDF
⟨ Previous Next ⟩

Abstract

The present study analyzed the effector that has an influence on continuance intention to use YouTube that is a prime platform to distribute visual contents. Whether the dispersion of information communication technology succeeds or not is determined by a more extent which user uses active and continued than adopts fast. From this point of view, we employed partial least square(PLS) path modeling to analyze structural causality among constructs in the post-adoption model(PAM). As a result, all paths given in PAM except for the path 'confirmation-satisfaction' were validated. Especially, confirmation mediated with satisfaction than perceived usefulness has a more effect on continuance intention. Finally, we described the limitation of the present study and the suggestion for future research.

이 연구는 영상콘텐츠 유통의 주요한 플랫폼으로 자리잡은 유튜브의 지속 이용의도에 영향을 미치는 요인들을 규명하였다. 정보통신기술을 얼마나 신속하게 수용하느냐 보다는 얼마나 지속적으로 활발하게 이용하는지가 성공을 가늠하는데 더 중요하다. 이러한 관점에서 기술 수용후 모형(PAM, Post-adoption model)을 차용하여 유튜브 구독이용자를 대상으로 PLS 경로모형분석(partial least square path modeling)을 통해 구조적인 인과관계를 분석하였다. 분석 결과 PAM에서 제안한 경로 중 '확인-만족'을 제외한 경로가 인과관계가 있는 것으로 나타났다. 특히 확인이 지속이 용의도에 대하여 만족을 매개한 경로가 지각된 용이성을 매개한 경로보다 상대적으로 영향력이 큰 것으로 나타났다. 마지막으로 연구의 한계와 미래연구를 위한 제안을 기술하였다.

Keywords

References

  1. C. H. Jung & Y. S. Chung. (2012). Determinants of the user's satisfaction and continued usage intention in IPTV services. Journal of Digital Convergence, 10(4), 137-148. DOI: 10.14400/JDPM.2012.10.4.137
  2. S. Chea & M. M. Luo. (2008). Post-adoption behaviors of e-service customers: The interplay of cognition and emotion. International Journal of Electronic Commerce, 12(3), 29-56. https://doi.org/10.2753/JEC1086-4415120303
  3. A. Bhattacherjee. (2001). Understanding information systems continuance: An expectation confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
  4. R. L. Oliver. (1980). A cognitive model for the antecedents and consequences of satisfaction. Journal of Marketing Research, 17, 760-469. https://doi.org/10.1177/002224378001700405
  5. J. Y. I. Thong, S. Hong & K. Y. Tam. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-computer Studies, 64, 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001
  6. C. S. Lin, S. Wu & R. J. Tsai. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information and Management, 42, 683-693. https://doi.org/10.1016/j.im.2004.04.003
  7. S. S. Kim & N. K. Malhotra. (2005). A longutudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741-755. https://doi.org/10.1287/mnsc.1040.0326
  8. S. K. Lippert & H. Forman. (2005). Utiliztionof information technology: Examining cognitive and experiential factors of post-adoption behavior. IEEE Transations on Engineering Management, 52(3), 513-523.
  9. R. L. Oliver. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 200(December), 418-430. https://doi.org/10.1086/209358
  10. Inteco. (1998 September). Why do people choose ISPs and why do they drop them. Inteco Corporation Press Report.
  11. F. D. Davis, R. P. Bagozzi & 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
  12. K. Mathieson. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
  13. S. Taylor & P. A. Todd. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
  14. E. Karahanna, D. W. Straub & N. L. Chervany. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 183-213.
  15. H. Choi & S. Yu. (2007). The impact of continued behavior on real usage: Focusing on mobile web. The Journal of the Korea Contents Association, 8(1), 27-38. DOI: 10.5392/JKCA.2008.8.1.027
  16. J. Joo. (2019). Uses and Gratifications on Subscribed YouTube Channels : Centered on Motives for Ritualized use and Instrumental Use, Flow, and Satisfaction. Journal of the Korea Convergence Society, 10(12), 223-233. DOI: 10.15207/JKCS.2019.10.12.223
  17. J. Joo. (2016). Exploring Korean Collegians' Smartphone Game Behavior: Focusing on Conciseness, Perceived Ease of Use, Perceived Enjoyment, Flow, and Intent to Use. Journal of Digital Convergence, 14(1), 379-386. DOI: 10.14400/JDC.2016.14.1.379
  18. C. M. Ringle, S. Wende & J. M. Becker. (2015). SmartPLS 3. Retrieved from www.smartpls.com
  19. J. O. Cohen. (1998). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum.