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The purpose of this paper is to investigate the relation between average ratings (viewership) and the volume and valence of electronic word of mouth (e-WOM) for early episodes of TV shows.

Linear regression was performed in which the dependent variable is average TV ratings and main independent variables are volume and valence of e-WOM. The study used a Breusch–Pagan test to detect heteroscedasticity. Accordingly, the model is analyzed using heteroscedasticity-consistent standard error estimators.

The results show that the volume of the early e-WOM does not significantly contribute to explaining average ratings, but the valence does.

Because the advertising revenue of television broadcasters is determined according to expected TV ratings, the average ratings should be predicted as early as possible. This study shows that analyzing early e-WOM helps predict average ratings.

Social media (e.g., e-WOM) and traditional media (e.g., media coverage) serve different roles in a firm's marketing activities and also interact with each other, which in turn affect the market outcome. In addition, how market outcome affects the two types of media in turn has not been examined, which brings the need for a holistic framework. The rare study that examines this relation mostly relies on the volume of media rather than the valence. This study examines the interdependent relation between the volume and valence of social media, the volume of traditional media and TV ratings.

Forty-one South Korean TV drama shows from October 2014 to March 2016 were analyzed using the 3SLS estimation to examine the interdependent relation between the variables.

First, the volume of traditional media has a negative effect on the volume of social media. Second, ratings negatively affect the valence of social media. Third, the volume of traditional media is found to have a negative effect on ratings. This is explained by the displacement effect.

This study is one of the very few studies that examine the interdependent relation between various earned media and market outcomes in one framework. In addition, it has originality in that it considers the valence of social media, which is an important dimension in analyzing earned media. Our results show negative effects of news media on TV ratings and e-WOM, which diverge from common intuition.