Do big data support TV viewing rate forecasting? A case study of a Korean TV drama
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 g...
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| Veröffentlicht in: | Information systems frontiers 2017-04, Vol.19 (2), p.411-420 |
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| creator | Ahn, Jongchang Ma, Kyungran Lee, Ook Sura, Suaini |
| description | 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. |
| doi_str_mv | 10.1007/s10796-016-9659-5 |
| format | Article |
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A case study of a Korean TV drama</title><title>Information systems frontiers</title><addtitle>Inf Syst Front</addtitle><description>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. 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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.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10796-016-9659-5</doi><tpages>10</tpages></addata></record> |
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| subjects | Big Data Broadcasting Business and Management Case studies Complement Consumers Control Customer relationship management Customer satisfaction Data analysis Data management Drama Information systems Internet stocks Investments IT in Business Literature reviews Management of Computing and Information Systems Manufacturing engineering Marketing Media Operations Research/Decision Theory Principal components analysis Ratings Regression analysis Social networks Social research Studies Systems Theory Television programs Television ratings Trends Viewing |
| title | Do big data support TV viewing rate forecasting? A case study of a Korean TV drama |
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