As Hollywood relies heavily on global markets, it is particularly important for studios to understand how their decisions, including on casts, may affect their movies’ box-office in foreign markets. Anecdotal evidence shows that casting actors with similar facial features may be problematic in foreign markets, often disorienting international audiences.

Predicting a box office gross in the film industry is an important goal. Many works have analyzed the elements of a film making. Previous studies have suggested several methods for predicting box office such as a model for distinguishing people's reactions by using a sentiment analysis, a study on the period of influence of word-of-mouth effect through SNS. These works discover that a word of mouth (WOM) effect through SNS influences customers’ choice of movies. Therefore, this study analyzes correlations between a box office gross and a ratio of people reaction to a certain movie by extracting their feedback on the film from before and after of the film opening. In this work, people’s reactions to the movie are categorized into positive, neutral, and negative opinions by employing sentiment analysis. In order to proceed the research analyses in this work, North American tweets are collected between March 2011 and August 2012. There is no correlation for each analysis that has been conducted in this work, hereby rate of tweets before and after opening of movies does not have relationship between a box office gross.

Forecasting of box office performance after a film release is very important, from the viewpoint of increase profitability by reducing the production cost and the marketing cost. Analysis of psychological factors such as word-of-mouth and expert assessment is essential, but hard to perform due to the difficulties of data collection. Information technology such as web crawling and text mining can help to overcome this situation. For effective text mining, categorization of objects is required. In this perspective, the objective of this study is to provide a framework for classifying films according to their characteristics. Data including psychological factors are collected from Web sites using the web crawling. A clustering analysis is conducted to classify films and a series of one-way ANOVA analysis are conducted to statistically verify the differences of characteristics among groups. The result of the cluster analysis based on the review and revenues shows that the films can be categorized into four distinct groups and the differences of characteristics are statistically significant. The first group is high sales of the box office and the number of clicks on reviews is higher than other groups. The characteristic of the second group is similar with the 1st group, while the length of review is longer and the box office sales are not good. The third group's audiences prefer to documentaries and animations and the number of comments and interests are significantly lower than other groups. The last group prefer to criminal, thriller and suspense genre. Correspondence analysis is also conducted to match the groups and intrinsic characteristics of films such as genre, movie rating and nation.

The availability of digital distribution channels raises many new challenges for managers in the media industries. This is particularly true for movie studios where content can be stolen and released through illegitimate digital distribution channels before, or shortly after, the legitimate release date. In response to this potential threat, movie studios have spent millions of dollars attempting to protect their content from unauthorized release, to prosecute those who might distribute or consume pirated content, and to lobby governments to strengthen anti-piracy laws. However, there has been very little rigorous research to analyze whether, and how much, movie piracy cannibalizes legitimate box-office sales. In this paper, we analyze this question in the context of post-release movie piracy. We also consider whether going to the movies is substitutable by watching a pirated version at home. Even though there is a lag between the release in cinema-theaters and a DVD-release (that is when a pirated copy of a good quality is made available), we consider making decision at the certain moment, so time lag does not make any difference. Our study contributes to the growing literature on piracy and digital media consumption in the online community by presenting evidence of the impact of digital piracy, by differentiating the effect of post-release movie piracy from the other types of piracy that the extant literature has previously considered.

배급사와 극장이 박스오피스 수익을 분배하는 비율을 부율이라고 부른다. 미국 등 다른 나라와 달리 한국의 경우는 모든 극장이 동일한 부율을 적용하여 박스오피스 수익을 배급사에게 분배하고 있다. 나아가, 영화가 외화인가 한국영화인가에 따라 부율을 달리하는데 현재 서울의 경우 한국영화는 50:50, 외화는 60:40으로 분배한다. 이와 같은 차별적 부율은 한국영화와 외화 사이의 관객동원력에 따라 성립된 것으로 알려졌는데, 최근 한국영화의 흥행력이 높아졌음에도 불구하고 외화에 비하여 불리하게 부율이 적용되는 것에 대하여 불만이 많다고 한다. 이러한 획일적인 부율 및 차별적인 부율은 공정거래법 이슈를 제기한다. 먼저 외화와 한국영화를 차별하는 것은 공정거래법상 부당하게 거래상대방에 따라 가격을 차별하는 경우에 해당하는가가 문제로 된다. 외화와 한국영화의 상영비용 등이 차이 나는 것도 아니고, 한국영화의 편당 관객동원력이 외화에 비하여 4배가 높다는 점, 가격차별이 개개 영화의 특성, 상품성 등을 고려하지 않고 한국영화라는 이유만으로 일률적으로 차별이 이루어지고 있다는 점등에 비추어보면 일응 부당한 가격차별에 해당될 가능성이 높다. 한편, 극장들이 획일적인 부율을 적용하는 것이 부당공동행위에 해당하는가도 문제로 된다. 카르텔에 해당하는가 여부를 판단하는데 있어 극장주간의 합의가 있었는가가 가장 중요하고 실제 소송절차에서 가장 어려운 부분이다. 현재와 같은 극장 부율은 의식적인 동조행위로 볼 여지도 있고, 그 유래를 추적할 수 없는 때 시작되어 지속된 관행으로 볼 여지도 있다. 다만 한국 공정거래법상 추정 규정을 적용하면 합의의 성립이 인정되어 부당공동행위로 판단될 가능성도 있다.