Universität zu Köln

Ahn, Jongchang ORCID: 0000-0003-1807-035X, Ma, Kyungran, Lee, Ook and Sura, Suaini (2017). Do big data support TV viewing rate forecasting? A case study of a Korean TV drama. Inf. Syst. Front., 19 (2). S. 411 - 421. DORDRECHT: SPRINGER. ISSN 1572-9419

Full text not available from this repository.

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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Ahn, Jongchang UNSPECIFIED orcid.org/0000-0003-1807-035X UNSPECIFIED Ma, Kyungran UNSPECIFIED UNSPECIFIED UNSPECIFIED Lee, Ook UNSPECIFIED UNSPECIFIED UNSPECIFIED Sura, Suaini UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-235944
DOI:	10.1007/s10796-016-9659-5
Journal or Publication Title:	Inf. Syst. Front.
Volume:	19
Number:	2
Page Range:	S. 411 - 421
Date:	2017
Publisher:	SPRINGER
Place of Publication:	DORDRECHT
ISSN:	1572-9419
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ONLINE CONSUMER REVIEWS; PRODUCT; IMPACT; USERS Multiple languages Computer Science, Information Systems; Computer Science, Theory & Methods Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/23594