IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v19y2017i2d10.1007_s10796-016-9659-5.html
   My bibliography  Save this article

Do big data support TV viewing rate forecasting? A case study of a Korean TV drama

Author

Listed:
  • Jongchang Ahn

    (Hanyang University)

  • Kyungran Ma

    (University of Cologne)

  • Ook Lee

    (Hanyang University)

  • Suaini Sura

    (Hanyang University)

Abstract

This study focuses on big data, including data from social networking sites (SNS), and data that can complement prior researches on TV viewing rate prediction. The paper analyzes the variables, which influence the average minute rating (AMR) and share rating (SHR) through regression analysis after gathering buzz data on a 20-episode drama series in Korea. The R-square value of regression analysis results shows that the consumer-generated media (CGM) variable including SNS items explained 64 % of both AMR and SHR. However, the Media variable is not statistically significant. For SNS items, the Korean SNS me2DAY and DaumYozm are statistically significant for AMR and SHR, but Twitter is not significant. This study contributes to practitioners’ ability to alleviate the hurdles of broadcasting production communities on the difficulty of predicting viewing rate in advance. Thus, it is possible to determine whether to invest production cost persistently or to adjust the broadcasting volume based on viewers’ response.

Suggested Citation

  • Jongchang Ahn & Kyungran Ma & Ook Lee & Suaini Sura, 2017. "Do big data support TV viewing rate forecasting? A case study of a Korean TV drama," Information Systems Frontiers, Springer, vol. 19(2), pages 411-420, April.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:2:d:10.1007_s10796-016-9659-5
    DOI: 10.1007/s10796-016-9659-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-016-9659-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-016-9659-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    2. Qiu-Hong Wang & Viktor Mayer-Schönberger & Xue Yang, 2013. "The determinants of monetary value of virtual goods: An empirical study for a cross-section of MMORPGs," Information Systems Frontiers, Springer, vol. 15(3), pages 481-495, July.
    3. Mitchell, Mark L & Mulherin, J Harold, 1994. "The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-950, July.
    4. Rui Chen & Sushil K. Sharma, 2013. "Self-disclosure at social networking sites: An exploration through relational capitals," Information Systems Frontiers, Springer, vol. 15(2), pages 269-278, April.
    5. Wei-Tsong Wang & Wen-Hung Chang, 2014. "A study of virtual product consumption from the expectancy disconfirmation and symbolic consumption perspectives," Information Systems Frontiers, Springer, vol. 16(5), pages 887-908, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jongchang Ahn & Kyungran Ma & Ook Lee & Suaini Sura, 0. "Do big data support TV viewing rate forecasting? A case study of a Korean TV drama," Information Systems Frontiers, Springer, vol. 0, pages 1-10.
    2. Young Bin Kim & Sang Hyeok Lee & Shin Jin Kang & Myung Jin Choi & Jung Lee & Chang Hun Kim, 2015. "Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    3. Yulei Gavin Zhang & Mandy Yan Dang & Hsinchun Chen, 2020. "An Explorative Study on the Virtual World: Investigating the Avatar Gender and Avatar Age Differences in their Social Interactions for Help-Seeking," Information Systems Frontiers, Springer, vol. 22(4), pages 911-925, August.
    4. E. Mitchell Church & Richelle L. Oakley, 2018. "Etsy and the long-tail: how microenterprises use hyper-differentiation in online handicraft marketplaces," Electronic Commerce Research, Springer, vol. 18(4), pages 883-898, December.
    5. Federico Iannacci & Colm Fearon & Kristine Pole, 2021. "From Acceptance to Adaptive Acceptance of Social Media Policy Change: a Set-Theoretic Analysis of B2B SMEs," Information Systems Frontiers, Springer, vol. 23(3), pages 663-680, June.
    6. Rahman, Shaikh Moksadur, 2020. "Relationship between Job Satisfaction and Turnover Intention: Evidence from Bangladesh," Asian Business Review, Asian Business Consortium, vol. 10(2), pages 99-108.
    7. Wang Kai, 2019. "Towards a Taxonomy of Idea Generation Techniques," Foundations of Management, Sciendo, vol. 11(1), pages 65-80, January.
    8. Bridgelall, Raj & Stubbing, Edward, 2021. "Forecasting the effects of autonomous vehicles on land use," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    9. Christoffersen, Peter & Ghysels, Eric & Swanson, Norman R., 2002. "Let's get "real" about using economic data," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 343-360, August.
    10. Bevilacqua, Maurizio & Ciarapica, Filippo Emanuele, 2018. "Human factor risk management in the process industry: A case study," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 149-159.
    11. Naveena Prakasam & Louisa Huxtable-Thomas, 2021. "Reddit: Affordances as an Enabler for Shifting Loyalties," Information Systems Frontiers, Springer, vol. 23(3), pages 723-751, June.
    12. Colin Jerolmack & Alexandra K. Murphy, 2019. "The Ethical Dilemmas and Social Scientific Trade-offs of Masking in Ethnography," Sociological Methods & Research, , vol. 48(4), pages 801-827, November.
    13. Valeriy Makarov & Albert Bakhtizin, 2014. "The Estimation Of The Regions’ Efficiency Of The Russian Federation Including The Intellectual Capital, The Characteristics Of Readiness For Innovation, Level Of Well-Being, And Quality Of Life," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(4), pages 9-30.
    14. Zhao, Jing & Knoop, Victor L. & Wang, Meng, 2020. "Two-dimensional vehicular movement modelling at intersections based on optimal control," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 1-22.
    15. Brown, William Jr. & Burdekin, Richard C.K. & Weidenmier, Marc D., 2006. "Volatility in an era of reduced uncertainty: Lessons from Pax Britannica," Journal of Financial Economics, Elsevier, vol. 79(3), pages 693-707, March.
    16. Kristine Edgar Danielyan & Samvel Grigoriy Chailyan, 2019. "Delineation of Effectors Impact on The Human Brain Derived Phosphoribosylpyrophosphate Synthetase-1 Activity," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 24(1), pages 17918-17926, December.
    17. Chuan Wang & Yupeng Liu & Wen Hou & Chao Yu & Guorong Wang & Yuyan Zheng, 2021. "Reliability and availability modeling of Subsea Autonomous High Integrity Pressure Protection System with partial stroke test by Dynamic Bayesian," Journal of Risk and Reliability, , vol. 235(2), pages 268-281, April.
    18. Mohammad AL-Zoubi, 2018. "The Role of Technology, Organization, and Environment Factors in Enterprise Resource Planning Implementation Success in Jordan," International Business Research, Canadian Center of Science and Education, vol. 11(8), pages 48-65, August.
    19. Damgaard, Mette Trier & Nielsen, Helena Skyt, 2018. "Nudging in education," Economics of Education Review, Elsevier, vol. 64(C), pages 313-342.
    20. Nicole D. Sintov & P. Wesley Schultz, 2017. "Adjustable Green Defaults Can Help Make Smart Homes More Sustainable," Sustainability, MDPI, vol. 9(4), pages 1-12, April.

    More about this item

    Keywords

    SNS; Big data; TV rating; CGM; AMR; SHR;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:19:y:2017:i:2:d:10.1007_s10796-016-9659-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.