The existence of Internet has changed our way of living. It has been a huge part of our life, one we simply cannot live without. We rely on it in almost every aspect of our lives, including when we seek for information. This also applies when we’re deciding what movies to watch. Before go to the cinema and watch a particular movie, some people usually checked the movie’s online reviews first. These movies’ reviews are online user reviews, and it is a form of electronic word-of-mouth (eWOM). According to Duan, Gu, and Whinston (2008), eWOM influences consumer purchase behaviour while it’s also the outcome of consumer purchases. But then, how these online user reviews actually impact the offline purchase?

There are three measures of online user reviews, the volume (Liu 2006, Duan et al. 2008), the valence or the average (Liu 2006, Duan et al. 2008, Chevalier and Mayzlin 2006), and the variance in reviews (Godes and Mayzlin 2004). Chintagunta, Gopinath and Venkataraman (2010) measured the impact (valence, volume, and variance) of national online user reviews on designated market area (DMA)-level local geographic box office performance of movies in the United States. What’s different about their study is they used local geographic data instead of national-level data (used by previous studies) and the ‘when’ and ‘where’ a movie is released are taken into account. Thus, they measured user reviews when a movie was released in a market and those were written by users where the movie was previously released. The impact was measured by combining data from daily box office ticket sales on 148 movies released from November 2003 to February 2005 with user ratings from the Yahoo! Movies website. They found that the overall movie revenues is greatly affected by the opening day gross. As it was conducted on DMA, movie and market fixed effects were included thus taking into account their differences including movie genre and market size, and some other variables was also controlled such as advertising level and number of theaters. In their first study, using the local data, they found that the average user ratings influenced the box office performance the most. This finding is interesting since most previous studies found that it is the volume of reviews which matters the most to box office revenues. But when the national-level data was used, they arrived at the same results as previous studies. And at the last part of the study, they attempted to explain these results difference by using national-level models with market-level controls. This method gave the same result as the first study, the average of user ratings has the greatest impact on the box office revenues. It concluded that it is important to determine where the movie was played, whether on “new markets” or “old markets”, and only then the “true” effect of user ratings can be measured.

As for us the movie goers, what the paper discovered is that we’re mostly affected by the average of ratings in deciding what movies to watch. Yet, how many people rated the movies (volume) is also an important aspect, as I would believe a slightly lower rating with much higher volume rather than a higher rating with much lower volume. In other words, volume and variance make a rating/review more trustworthy. Which one would you prefer?

Source : IMDB

References

Chevalier, J. A., and Mayzlin, D. 2006. ‘The effect of word of mouth on sales: Online book reviews.’ Journal of Marketing Research, 43(3), 345-354.

Chintagunta, P.K., Gopinath, S. and Venkataraman S. (2010). ‘The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets.’ Marketing Science, 29(5), 944–957.

Duan, W., Gu, B., and Whinston, A.B. (2008), ‘The Dynamics of Online Word-of-Mouth and Product Sales — An Empirical Investigation of the Movie Industry,’ Journal of Retailing, 84(2) 233-242.

Godes, D., and Mayzlin, D. 2004. ‘Using online conversations to study word-of-mouth communication.’ Marketing Science, 23(4), 545–560.

Liu, Y. 2006. ‘Word of mouth for movies: Its dynamics and impact on box office revenue.’ J. Marketing, 70(3), 74-89.