Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Examining Factors Affecting the Binge-Watching Behaviors of OTT Services

OTT(Over-the-Top) 서비스의 몰아보기 시청행위 영향 요인 탐색

  • 황경호 (경남대학교 자유전공학부) ;
  • 김경애 (한양대학교 경영학과)
  • Received : 2020.01.28
  • Accepted : 2020.03.20
  • Published : 2020.03.28
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Abstract

The purpose of this study is to empirically examine the factors affecting the binge-watching behaviors of OTT service users by using a multi-layer perceptron (MLP) artificial neural network. All samples (n=1,000) were collected from 'A survey on user awareness in OTT service' published by a Media Research Center of the Korea Press Foundation in 2018. Our research model includes one dependent variable which is binge-watching behaviors on OTT service and five independent variables such as gender, age, frequency of service usage, users' satisfaction with content recommendation algorithm, and content types mainly consumed. Our findings demonstrate that age, frequency of service usage, users' satisfaction with content recommendation algorithms, and certain types of contents (e.g., Korean dramas, Korean films, and foreign dramas) were found to be highly related to binge-watching behavior on OTT services.

본 연구는 온라인동영상서비스 OTT(Over-the-Top) 이용자의 몰아보기(Binge-watching) 시청행위에 영향을 미치는 요인을 실증적으로 탐색하였다. 이를 위해 2018년 한국언론진흥재단 미디어연구센터의 'OTT 서비스 이용자 인식조사'에 참여한 OTT 이용 경험자 1,000명의 자료를 수집하여 분석하였다. 종속변수는 OTT 서비스 몰아보기로 설정하였으며, 독립변수는 성별, 연령, OTT 서비스 이용 빈도, OTT 콘텐츠 프로그램 추천 알고리즘 만족도, OTT에서 주로 이용하는 콘텐츠 유형을 포함하였다. OTT 몰아보기 시청행위의 예측 요인은 다층 퍼셉트론(MLP) 인공신경망 알고리즘을 이용하여 분석하였다. 연구결과, 연령, OTT 콘텐츠 프로그램 추천 알고리즘 만족도, OTT 서비스 이용 빈도, OTT에서 주로 이용하는 콘텐츠 유형 중 국내드라마, 국내영화, 해외드라마 등이 OTT 몰아보기 시청행위에 중요도가 높은 요인으로 밝혀졌다.

Keywords

References

  1. J. H. Yoo & J. Y. Park. (2018). A Study on the Factors Influencing Continuous Usage Intension based on OTT Service User. Journal of Broadcasting and Telecommunications Research, 102, 46-79.
  2. S. Matrix. (2014). The Netflix Effect: Teens, Binge Watching, and On-Demand Digital Media Trends. Jeunesse: Young People, Texts, Cultures, 6(1), 119-138. DOI : 10.1353/jeu.2014.0002
  3. S. S. Han, H. S. Yu & D. H. Shin. (2017). Explicating Motivations and Attitudes Affecting the Persistent Intention to Adopt Binge-Watching. Journal of the Korea Contents Association, 7(2), 521-534. DOI : 10.5392/JKCA.2017.17.02.521
  4. S. Lee. (2018). Analysis of Psychological and Structural Determinants that Affect Binge-watching of Drama Series. Journal of the Korea Contents Association, 18(2), 405-418. DOI : 10.5392/JKCA.2018.18.02.405
  5. R. Tukachinsky & K. Eyal. (2018). The Psychology of Marathon Television Viewing: Antecedents and Viewer Involvement. Mass Communication and Society, 21(3), 275-295. DOI : 10.1080/15205436.2017.1422765
  6. M. Flayelle, P. Maurage & J. Billieux. (2017). Toward a Qualitative Understanding of Binge-watching Behaviors: A Focus Group Approach. Journal of Behavioral Addictions, 6(4), 457-471. DOI : 10.1556/2006.6.2017.060
  7. H. Kim, S. Kwon & B. Chang. (2019). Investigating Factors Affecting Consumer Segment of Cord-Cutting: Focusing on Korean Subscribers. Korean Journal of Broadcasting and Telecommunication Studies, 33(2), 67-103.
  8. K. H. Jung & Y. J. Choi. (2019). A Study on Explicating Binge-watching as an Active Behavior of Audiences: Binge-watching Model Applying to Theory of Planned Behavior. Korean Journal of Broadcasting and Telecommunication Studies, 33(3), 141-179.
  9. M. Jenner. (2016). Is this TVIV? On Netflix, TVIII and Binge-watching. New Media & Society, 18(2), 257-273. DOI : 10.1177/1461444814541523
  10. Y. C. Chung. (2019). One-person Households: Active Media User. KISDI STAT Report, 19-03.
  11. Limelight Networks. (2019). The State of Online Video [Online]. https://www.limelight.com/resources/white-paper/state-of-online-video-2019/
  12. A. A. M. Ahmed. (2017). New Era of TV-watching Behavior: Binge-watching and Its Psychological Effects. Media Watch, 8(2), 192-207. DOI : 10.15655/mw/2017/v8i2/49006
  13. A. E. Moore. (2015). Binge Watching: Exploring the Relationship of Binge Watched Television Genres and Colleges at Clemson University. Graduate Research and Discovery Symposium, paper 138.
  14. H. J. Shim, S. H. Ju, S. H. Lim & H. Lee. (2015). A Study on the Change of Media Use Behavior of Audiences and Its Ripple Effects in Nonlinear TV Viewing Environment. KISDI, 15-10.
  15. M. J. Choi. (2018). A Survey on User Awareness in OTT Service. Media Issue, 4(12), 1-12.
  16. I. A. Basheer & M. Hajmeer. (2000). Artificial Neural Networks: Fundamentals, Computing, Design, and Application. Journal of Microbiological Methods, 43(3), 3-31. https://doi.org/10.1016/S0167-7012(00)00201-3
  17. H. Byeon. (2017). Exploring Influence Factors for Peer Attachment in Korean Youth Based on Multi-Layer Perceptron Artificial Neural Networks. Journal of the Korea Convergence Society, 8(10), 209-214. DOI : 10.15207/JKCS.2017.8.10.209
  18. S. H. Kwon, K. W. Park & B. H. Chang. (2017). A Comparison of Predicting Movie Success between Artificial Neural Network and Decision Tree. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 7(4), 593-601. DOI : 10.14257/AJMAHS.2017.04.37
  19. K. Jeong. (2018). Application of Artificial Neural Network model to an Analysis of the Factors Affecting the Intention of the Vulnerable Class to move to Hangbok Housing in Incheon. Housing Studies Review, 26(3), 55-78. DOI : 10.24957/hsr.2018.26.3.55
  20. J. Na, S. Park, H. Ju, I. Kim & H. Lee. (2016). Caries Prediction Using the Caries Activity Test with a Sulfisomidine Mixture: A 3-year Follow-up Study. Journal of Korean Academy of Oral Health, 40(3), 183-189. DOI : 10.11149/jkaoh.2016.40.3.183
  21. S. W. Song. (2018). Assess the Accuracy of Diagnostic Tools. Korean Journal of Family Practice, 8(1), 1-2. DOI : 10.21215/kjfp.2018.8.1.1