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OTT서비스 이용자의 지속사용의도 영향 요인에 관한 연구: 기술수용모델의 확장을 중심으로

A Study on the Factors Influencing Continuous Intention to Use of OTT Service Users: Focused on the Extension of Technology Acceptance Model

  • 이민규 (중앙대학교 미디어커뮤니케이션학부) ;
  • 김원제 (성균관대 문화융합대학원) ;
  • 송민호 (경기대 산학협력단(인문사회))
  • Lee, Min-Kyu (School of Media and Communication, Chung-Ang University) ;
  • Kim, Won-Je (Graduate School of Culture Management, Sungkyunkwan University) ;
  • Song, Min-Ho (Industry-Academic Cooperation Foundation, Kyonggi University)
  • 투고 : 2019.10.05
  • 심사 : 2019.11.20
  • 발행 : 2019.11.28

초록

본 연구는 기술수용모델의 확장을 통해 OTT 서비스 이용자들의 지속사용의도에 영향을 미치는 요인들을 검증하고자 하였다. 이를 위해 SPSS 21.0 프로그램과 AMOS 21.0 프로그램을 활용하여 상관관계분석과 경로분석 등을 통해 주요결과를 도출하였다. 이를 요약 제시하면 다음과 같다. 첫째, OTT 서비스에 대한 인지된 용이성은 인지된 유용성에 통계적으로 유의한 정적 영향을 미치는 것으로 나타났다. 둘째, OTT 서비스에 대한 인지된 용이성은 지속사용의도에 통계적으로 유의한 영향을 미치는 못하는 것으로 나타났다. 셋째, OTT 서비스에 대한 인지된 유용성은 지속사용의도에 통계적으로 유의한 정적 영향을 미치는 것으로 나타났다. 넷째, OTT 서비스에 대한 인지된 혁신성은 인지된 용이성에 통계적으로 유의한 정적 영향을 미치는 것으로 나타났다. 다섯째, OTT 서비스에 대한 인지된 혁신성은 지속사용의도에 통계적으로 유의한 정적 영향을 미치는 것으로 나타났다. 여섯째, OTT 서비스에 대한 인지된 유희성은 지속사용의도에 통계적으로 유의한 정적 영향을 미치는 것으로 나타났다. 이상의 결과는 OTT 서비스의 기술수용모델 확장과 OTT 서비스 수용을 이해할 수 있는 경로를 밝혔다는 점에서 의의가 있을 것이다.

This study verified the factors influencing continuous intention to use of OTT service users through technology acceptance model and extension. For this purpose, the main results were derived through correlation analysis and path analysis using SPSS 21.0 program and AMOS 21.0 program. The summary is as follows. First, perceived ease of use was found to have a statistically significant positive effect on perceived usefulness. Second, perceived ease of use did not have a statistically significant effect on continuous intention to use. Third, the perceived usefulness has a statistically significant positive effect on continuous intention to use. Fourth, perceived innovativeness has a statistically significant positive effect on perceived ease of use. Fifth, perceived innovativeness has a statistically significant positive effect on continuous intention to use. Sixth, perceived playfulness has a statistically significant positive effect on continuous intention to use. The above results will be meaningful in that it has revealed a path to understand the extension of the technology acceptance model of OTT services and acceptance of OTT services.

키워드

참고문헌

  1. CJ ENM. (2019). 2019 OTT service trend report.
  2. J. S. Kang. (2019). Competitive environment and business strategy change in global OTT market due to Disney +, Apple TV +, etc. KISDI Premium Report, Korea Information Society Development Institute.
  3. Korea Communications Commission. (2019). 2018 Broadcasting media usage survey.
  4. Y. J. Kim. (2015). Impact of OTT service on the content creation, distribution and consumption. Studies of Broadcasting Culture, 27(1), 75-102. http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE06366055
  5. J. Y. Yoo & J. Y. Park. (2018). A study on the factors influencing continuous usage intension based on OTT service user. Korean Journal of Broadcasting and Telecommunications Research, 46-79.
  6. C. Morosan. (2010). Theoretical and empirical consideration of guests' perceptions of biometric systems in hotels: Extending the technology acceptance model. Journal of Hospitality & Tourism Research, 36(1), 52-84. DOI: 10.1177/1096348010380601
  7. F. Davis. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  8. S. H. Oh, Y. M. Kim, C. W. Lee, G. Y. Shim, & M. S. Park. (2009). Consumer adoption of virtual stores in Korea: Focusing on the role of trust and playfulness. Psychology & Marketing, 26, 652-668. https://doi.org/10.1002/mar.20293
  9. A. George & G. S. G. Kumar. (2013). Antecedents of customer satisfaction in internet banking: Technology acceptance model (TAM) redefined. Global Business Review, 14(4), 627-638. DOI: 10.1177/0972150913501602
  10. J. Lu, C. Liu, C. S. Yu, & K. Wang. (2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45, 52-64. DOI: 10.1016/j.im.2007.11.002
  11. 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
  12. V. Venkatesh & M. G. Morris. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139. DOI: 10.2307/3250981
  13. A. Shukla & S. K. Sharma. (2018). Evaluating consumers' adoption of mobile technology for grocery shopping: An application of technology acceptance model. Vision, 22(2), 185-198. DOI: 10.1177/0972262918766136
  14. L. Gao & X. Bai. (2014). A unified perspective on the factors influencing consumer acceptance of internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2), 211-231. DOI: 10.1108/APJML-06-2013-0061
  15. P. Ketikidis, T. Dimitrovski, L. Lazuras, & P. A. Bath. (2012). Acceptance of health information technology in health professionals: An application of the revised technology acceptance model. Health Informatics Journal, 18(2), 124-134. DOI: 10.1177/1460458211435425
  16. S. A. Raza, W. Qzai, & A. Umer. (2016). Facebook is a source of social capital building among university students: Evidence from a developing country. Journal of Educational Computing Research, 55(3), 295-322. DOI: 10.1177/0735633116667357
  17. M. Yeou. (2016). An investigation of students' acceptance of Moodle in a blended learning setting using technology acceptance model. Journal of Educational Technology systems, 44(3), 300-318. DOI: 10.1177/0047239515618464
  18. 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://www.jstor.org/stable/23010882 https://doi.org/10.1287/isre.2.3.173
  19. J. Schepers & M. Wetzels. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44, 90-103. https://doi.org/10.1016/j.im.2006.10.007
  20. W. R. King & J. He. (2006). A meta analysis of the technology acceptance model. Information & Management, 43(6), 740-755. DOI: 10.1016/j.im.2006.05.003
  21. D. Gefen, E. Karahanna, & D. W. Straub. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. http://www.jstor.org/stable/30036519 https://doi.org/10.2307/30036519
  22. R. Agarwal & J. Prasad. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9, 204-215. DOI:10.1287/isre.9.2.204
  23. E. Karahanna, D. W. Straub, & N. L. Chervany. (1998). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23, 183-213. https://doi.org/10.2307/249751
  24. E. M. Rogers. (1983). Diffusion of innovations (3rd ed.). New York, NY: Free Press.
  25. R. Walczuch, J. Lemmink, & S. Streukens. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44, 206-215. DOI:10.1016/j.im.2006.12.005
  26. 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 System, 14, 245-268. DOI:10.1016/j.jsis.2005.07.003
  27. W. Lewis, R. Agarwal, & V. Sambamurthy. (2003). Sources of influence on belief about information technology use: An empirical study of knowledge workers. MIS Quarterly, 27, 657-679. DOI: 10.2307/30036552
  28. C. Jin. (2013). The perspective of revised tram on social capital building: The case of facebook usage. Information & Management, 50, 162-168. DOI:10.1016/j.im.2013.03.002
  29. H. W. Kim, S. Gupta, & J. Koh. (2011). Investigating the intention to purchase digital items in social networking communities: A customer value perspective. Information & Management, 48, 228-234. DOI:10.1016/j.im.2011.05.004
  30. A. Padilla-Melendez, A. R. D. Aguila-Obra, & A. Garrido-Moreno. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317. DOI:10.1016/j.compedu.2012.12.014
  31. P. Zhang & N. Li. (2005). The importance of affective quality. Communications of the ACM, 48(9), 105-108. DOI:10.1145/1081992.1081997
  32. S. Y. Hung, J. C. A. Tsai, & S. T. Chou. (2016). Decomposing perceived playfulness: A contextual examination of two social networking sites. Information & Management, 53, 698-716. DOI:10.1016/j.im.2016.02.005
  33. C. S. Lin, S. Wu, & R. J. Tsai. (2005). Integrating perceived playfulness into expectation confirmation model for web portal context. Information & Management, 42, 683-693. DOI:10.1016/j.im.2004.04.003
  34. H. H. Chang. (2010). Task-technology fit and user acceptance of online auction. International Journal of Human-Computer Studies, 68, 69-89. DOI:10.1016/j.ijhcs.2009.09.010
  35. H. J. Woo. (2009). Exploring the influence on technology acceptance factors and perceived brand qualities affecting the internet radio player usage: Focusing on KBS Kong, MBC Mini, SBS Gorilla. Journal of Media Economics & Culture, 7(4), 7-45.
  36. J. S. Lee & M. Y. Lee. (2006). Examining factors affecting the intention to use IP-TV with the extended technology acceptance. Broadcasting & Communication, 7(1), 100-131.
  37. D. Y. Shim. (2018). The effect of OTT service's quality characteristics, users' characteristics on user satisfaction, loyalty, and continuous use intention. Master's Thesis, Hanyang University.
  38. H. G. Baek, B. S. Chon, & J. K. Lee. (2013). Determinants of intention to use N-Screen service among college students. Korean Journal of Broadcasting and Telecommunication Studies, 27(1), 94-130.
  39. G. J. Kim. (2009). A study on acceptance factor of digital multimedia broadcasting. Korean Journal of Journalism & Communication Studies, 53(3), 296-323.
  40. S. H. Sohn, Y. J. Choi, & H. S. Hwang. (2011). Understanding acceptance of smartphone among early adopters using extended technology acceptance model. Korean Journal of Journalism & Communication Studies, 55(2), 227-251.
  41. H. S. Lee. (2016). A study on factor influencing on user's satisfaction and loyalty of OTT service. Master's Thesis, Dongguk University.