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http://dx.doi.org/10.14400/JDC.2021.19.10.265

An Empirical Study of Personalized Thumbnail Curation of Netflix  

Park, Siwon (College of Art & Technology, Chung-Ang University)
Park, Jisu (College of Art & Technology, Chung-Ang University)
Kang, Jisu (Graduate School of Advanced Imageing Science, Multimedia and Film, Chung-Ang University)
Rhee, Boa (College of Art & Technology, Chung-Ang University)
Publication Information
Journal of Digital Convergence / v.19, no.10, 2021 , pp. 265-274 More about this Journal
Abstract
This study empirically analyzed the users' experiences with the Netflix thumbnail curation based on the Technology Acceptance Model(TAM). According to the correlation analysis results, the higher the dependence on the thumbnails, the higher the satisfaction with the thumbnail curation. Both Perceived Informational Usefulness(PIU) and Perceived Ease of Use(PEOU) had correlations with the degree of satisfaction with the thumbnail curation. In particular, the factors of relevance in PEOU had the greatest impact on the degree of satisfaction and this result proved that the suitability factors of the thumbnails had significant correlations with the degree of satisfaction. The degree of satisfaction with the thumbnail curation also positively correlated with Netflix's overall degree of satisfaction and behavioral intention to use the Netflix. This study demonstrated the suitability of the TAM as a UX evaluation tool for the Netflix thumbnail curation.
Keywords
Content Curation; Personalized Thumbnail; TAM; Recommendation Algorithm; Netflix; UX;
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1 E. Fernandez-Manzano, E. Neira & J. Clares-Gavilan. (2016). Data management in audiovisual business: Netflix as a case study. Profesional de la Informacion, 25(4), 568-576.   DOI
2 J. Y. Lee. (2017). The Story of Netflix's Strategy to bankrupt Blockbuster(2). MoneyToday. https://news.mt.co.kr/mtview.php?no=2017121808191955107
3 DMC MEDIA. (2019). 2019 OTT Platform Trend Analysis - part 1. Seoul : DMC REPORT.
4 A. Chandrashekar., F. Amat., J. Basilico, T. Jebara. (2017). Artwork Personalization at Netflix. THE NETFLIX TECH BLOG. https://netflixtechblog.com/artwork-personalization-c589f074ad76
5 I. Bojinov. (2020. March). Avoid the Pitfalls of A/B Testing. Harvard Business Review, 2, 28-53.
6 I. Y. Kang. (2016). Secrets of Netflix's Recommended System: Simple Labor and Machine Learning. IT Donga. https://it.donga.com/23942/.
7 Y. S. Lee. (2021). Over 1 bilion OTT subscribers including Netflix.. Movie theater sales are 72% decreased. ChosunBiz. https://biz.chosun.com/site/data/html_dir/2021/03/21/2021032100227.html
8 S. O. Kang. (2021). Netflix reports first-quarter results in 2021.. Pay-per-view households surpass 208 milion. Datanet. https://www.datanet.co.kr/news/articleView.html?idxno=158627
9 KOCCA. (2019). Content Industry Trend of USA. Naju : KOCCA.
10 K. Macdonald. (2016). The Netflix Effect: Technology and Entertainment in the 21th Century. New York : Bloomsbury Publishing USA.
11 B. W. Seo. (2016). April and May 2016 (vol.05). Seoul : KOCCA.
12 S. Urban. (2016). It's all A/Bout Testing: The Netflix Experimentation Platform. Netflix Tech Blog. https://netflixtechblog.com/its-all-a-bout-testing-the-netflix-experimentation-platform-4e1ca458c15
13 Y. C. Jung. (2019). 2019 Investigation of Broadcasting Media Usage Behavior. Gwacheon : KCC.
14 I. C. UTA. (2020). 8 key factors behind Neflix's success story. Brand Minds. https://brandminds.live/8-key-factors-behind-netflixs-success-story/
15 B. H. Bae. (2013). OTT Service. Naju : KISA.
16 H. J. Kwon. (2021). Netflix has 3.98 milion new subscribers in the first quarter. A slowdown in growth. MediaSR. https://www.mediasr.co.kr/news/articleView.html?idxno=68279
17 Mobile Index. (2020). Korea Mobile App Market Analysis Report in the first half of 2020. Seoul : Mobile Index.
18 J. W. Kim. (2019). A study on the use of Big Data in Film Industry - Focused on 'Netflix' Analytical Tools. The Korean Journal of Arts Studies, 25, 51-64.   DOI
19 H. B. Park, H. S. Lee, D. S. Han. (2020). The Effects of Perceived Service Attributes on Continuance Usage Intention of Netflix. Journal of Cybercommunication Academic Society, 13(1), 5-46.
20 S. Y. Oh., Y. H. Oh, S. H. Han, H. J. Kim. (2012). Broadcast Content Recommender System based on User&s Viewing History. Journal of Broadcast engineering, 17(1), 129-139.   DOI
21 J. H. You. & C. Park. (2010). A Comprehensive Review of Technology Acceptance Model Researches. Entrue Jounal of Information Technology, 9(2), 31-50.
22 E. Y. Kim, J. H. Lee, D. U. Seo. (2013). A Study on the Effect of Organization's Environment on Acceptance Intention for Big Data System. Journal of Information Technology Applications & Management, 20(4), 1-18.   DOI
23 S. Y. Jung. ㄴ(2019). Effects of Artificial Intelligence-Based Content Curation Services on User's Satisfaction: Focused on Netflix and Naver's AiRS service. Mater degree dissertation. Hongik University. Seoul.
24 KOCCA. (2020. June). The Effect of Corona 19 on Content Usage Behavior. Ncontent, 15, 7-73.
25 C. Gomez-Uribe & N Hunt. (2015). Netflix Recommender System Algorithms Business Value and Innovation. ACM Transactions on Management Information Systems. 6(4). 1-19.   DOI
26 G. Krishnan. (2016). Selecting the best artwork for videos through A/B testing. Netflix Tech Blog. https://netflixtechblog.com/selecting-the-best-artwork-for-videos-through-a-b-testing-f6155c4595f6
27 Y. S. Shin. (2019). Information Delivery Effect of Thumbnails on Mobile Video Content. Master degree dissertation. Ewha Womans University : Seoul.
28 E. J. Jo. (2020). Development of A/B Testing Method Considering Data Distribution. Master degree dissertation. Myongji University, Yongin.