Movie Revenues Research Articles

The Influence of Movie Release Modes on Movie Revenues: Take Lost in Russia and Lost in Thailand as Examples

In recent years, the competition between China's streaming movies and theatrical movies has intensified. The epidemic has further promoted the development of streaming movies and enhanced the competitiveness of streaming platforms. Traditional theater companies have felt the threat of being "replaced". This article takes the two movies Lost in Russia and Lost in Thailand as examples to analyze the impact of different release modes on the tbox office and concludes that the streaming movie Lost in Russia has a higher box office. Alt-hough there are some accidental factors in the success of Lost in Russia, this study guides the future development of streaming media platforms and theater companies. This article finds that streaming movies have advantages in publicity and cinema chains have ad-vantages in production through SWOT analysis, but they also have inherent disadvantages. Since the streaming media platform and the theater chain cannot replace each other for the time being, and the coexistence of the two release modes is unavoidable, the streaming media platform and the theater chain company should learn from each other's advantages and develop together.

The Effect of Online Reviews on Movie Box Office Sales

Due to the rapidly growth of social media, potential moviegoers always depend on the online reviews to make their purchase decisions. Film companies need to know what aspects of reviews will drive sales up or down. This study proposes a framework which integrates an aspect-based sentiment analysis and econometric modeling to explore the relationships between the information features in online reviews and movie ticket sales. The empirical results indicate that whereas the rating does not matter to moviegoers and does not affect movie revenues, additional textual review, both positive and negative contents, does have a positive impact on moviegoers, and further prompts the movie revenues. These findings have significant implications for movie producers as well as advertisers to target promotions at their audience accordingly.

Green-lighting scripts in the movie pre-production stage: An application of consumption experience carryover theory

In the movie pre-production stage, movie studios have scarce information about the movie in planning to select and edit the right script that can be commercially successful. Given this constraint, we provide a procedure of predicting movie revenues in the movie pre-production stage. To utilize the contents of textual scripts as the primary information source, we theoretically predicate the procedure on the market’s collective consumption experiences with prior movies sharing similar content features with the new movie. We hypothesize that the market wants to enjoy not only certain content features again (as positive carryover effects), but also different content features (as negative carryover effects) in the new movie. Toward this end, we integrate two distinct components into our prediction procedure: (1) LIWC (a text-mining tool) and (2) the auto-Gaussian spatial model. Our empirical application demonstrates that our procedure outperforms select benchmark models in predictive accuracy.

Is everybody an expert? An investigation into the impact of professional versus user reviews on movie revenues

This study is the first attempt to examine the effect of electronic word of mouth (user reviews) relative to expert reviews on moviegoing decisions. For the first time, we use time-varying data on expert reviews. We find that expert ratings matter much more for moviegoing decisions than user ratings and volume. Our data also show that experts tend to be more critical but more consistent in their reviews than users. We find that experts, but not eWOM, affect wide release moviegoing, contrary to industry thinking. Finally, we show that experts’ reviews matter most when consumers and critics are in closer agreement about the quality of the film. The study uses OLS as well as instrumental variables analysis to account for possible endogeneity.

Movie aspects, tweet metrics, and movie revenues: The influence of iOS vs. Android

Microblogging word of mouth (MWOM) using Twitter has been found to impact the success of experiential products such as movies. However, the influence of the type of device or platform used for tweeting (iOS or Android) on the relationship between well-established tweet metrics - valence, volume, and time period of tweeting - and movie performance is not yet known. Furthermore, it is not known if users of these platforms differ in the aspects of movies they discuss and how that may influence tweet metrics. In this study, we investigated these gaps by analyzing more than four million tweets for 29 movies from both iOS and Android users and conducted a robustness check on another 8 movies. Results from mixed model estimations show that valence of tweets on Android before a movie's release and volume of tweets on iOS after the release significantly influence the revenues of a movie. Results also show that mentions of director and script are more important in the case of Android users whereas mentions of production and music are more important in the case of iOS users. Finally, results show that it may be more productive for movie studios and advertisers to reach the more prolific Twitter users on Android but relatively newer Twitter users on iOS. These findings have significant implications for movie studios as well as mobile advertisers to target their promotions to these platform users accordingly.

The impact of advertising content on movie revenues

We analyze the contents of print ads in the motion picture industry (e.g., number of reviews quoted in the ad, the presence of a top reviewer, size of the ad, star, director, etc.). We find that external validation (a recommendation by a top reviewer) is more important to revenues than the informative content of the ad.

Piracy and box office movie revenues: Evidence from Megaupload

In this paper we evaluate the heterogeneous effects of online copyright enforcement. We ask whether the unexpected shutdown of the popular file hosting platform Megaupload had a differential effect on box office revenues of wide-release vs. niche movies. Identification comes from a comparison of movies that were available on Megaupload to those that were not. We show that only movies that premiere in a relatively large number of theaters benefitted from the shutdown of Megaupload. The average effect, however, is negative. We provide suggestive evidence that this result is driven by information externalities. The idea is that online piracy acts as a mechanism to spread information about product characteristics across consumers with different valuations for the product. Our results question the effectiveness of blanket public anti-piracy policy, not only from a consumer perspective, but also from a producer perspective.

Mere newness: Decline of movie preference over time

AbstractWe investigate the possibility that an individual‐level decline in preference over time is responsible for the well‐known decline in a movie's revenues after opening. In the first part of this study, using a Canadian panel we show that such a decline does indeed occur in both individual consumers’ anticipated liking of movies, and in actual movie choice. The second part explores the mere newness effect building on the fact that new products carry both private and social values, and proposes that these values decay continuously over time. Capitalizing on the notion that characteristics of market mavens interact differently with private and social values, we develop and show support for hypotheses based on the proposed mechanism. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.

Quantifying Domestic Movie Revenues Using Online Resources in Chin

Quantifying Domestic Movie Revenues Using Online Resources in Chin

Movie Stars and the Volatility of Movie Revenues

Research has found conflicting results regarding the profitability of movies that have big-name stars. Why then, are stars selected to act in movies? Using the concepts of risk, volatility, and brand loyalty, this research proposes that although stars may not guarantee profits, their presence results in lower weekly revenue volatility. Using a database comprising 41 stars and their presence in 467 movies from over a 26-year period, this study finds broad support for this hypothesis.

What drives the market popularity of celebrities? A longitudinal analysis of consumer interest in film stars

The economic value of celebrity brands is heavily influenced by their ability to generate large-scale consumer interest. We develop a comprehensive framework for the drivers of celebrities' market popularity (in terms of consumer interest generated by celebrities) including variables related to actors, movies, and actor–movie fit. To test the framework, Internet search histories are examined for 161 film stars over the course of more than 6years (January 2004–June 2010). In particular, we test three hypotheses. First, with regard to actor-related variables, we do not find support for the postulated inverted U-shaped effect from the frequency of movie appearances on the market popularity of film stars (H1). Rather, the results indicate a monotone and positive relationship between a film star's frequency of movie appearances and consumer interest in the film star. Second, with respect to movie-related factors, the findings indicate that both positive and negative abnormal movie revenues increase the popularity of film stars (H2). Third, concerning variables related to actor–movie fit, the results support the hypothesized U-shaped effect from actor–movie fit on the market popularity of film stars (H3). On a managerial level, this study provides insights for film stars on how to enhance their market popularity by increasing the frequency of their movie appearances and by selecting films that are likely to generate abnormal revenues and have a certain fit with their image.

Role of Advertising and Consumer Interest in the Motion Picture Industry

AbstractAdvertising is commonly used as a major marketing tool by many firms to support their new product release. Quantifying the effectiveness of pre-release advertising campaigns, however, is both challenging, since no sales data are available, and costly, because of the need to conduct consumer surveys. This is especially true for movie industry for which the majority of advertising dollars are spent before the movie’s release. Using the recent availability of online data on consumer search behaviors on a popular website dedicated to the movie industry, we construct a consumer interest measure to help the decision makers evaluate their advertising effectiveness. We build a dynamic model to show how this cost-efficient measure of consumer interest can be used to capture pre-release advertising dynamics, and the impact of advertising on weekly movie revenues starting with the opening week. Using a procedure based on

Pre-production forecasting of movie revenues with a dynamic artificial neural network

The production of a motion picture is an expensive, risky endeavor. During the five-year period from 2008 through 2012, approximately 90 films were released in the United States with production budgets in excess of $100 million. The majority of these films failed to recoup their production costs via gross domestic box office revenues. Existing decision support systems for pre-production analysis and green-lighting decisions lack sufficient accuracy to meaningfully assist decision makers in the film industry.Established models focus primarily upon post-release and post-production forecasts. These models often rely upon opening weekend data and are reasonably accurate but only if data up until the moment of release is included. A forecast made immediately prior to the debut of a film, however, is of limited value to stakeholders because it can only influence late-stage adjustments to advertising or distribution strategies and little else.In this paper we present the development of a model based upon a dynamic artificial neural network (DAN2) for the forecasting of movie revenues during the pre-production period. We first demonstrate the effectiveness of DAN2 and show that DAN2 improves box-office revenue forecasting accuracy by 32.8% over existing models. Subsequently, we offer an alternative modeling strategy by adding production budgets, pre-release advertising expenditures, runtime, and seasonality to the predictive variables. This alternative model produces excellent forecasting accuracy values of 94.1%.

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

Piracy and Movie Revenues: A Critical Review of a Tale of the Long Tail

A recent study on piracy entitled Piracy and Movie Revenues: Evidence from Megaupload: A Tale of the Long Tail? serves up the counter-intuitive claim that piracy may increase box office sales for some films. To reach this conclusion, Tale of the Long Tail uses as an "experiment" the January 2012 shutdown of what was likely the single largest online distributor of pirated content — the file sharing website Megaupload. Using data on weekend box office revenues for thousands of films shown in about fifty countries, the authors split this data into pre- and post-shutdown periods, and then apply econometric methods to test for a difference between the two samples, attributed any observed change to the shutdown. Upon close inspection the "piracy helps box office sales" result if found to be an artifact of a poorly designed statistical model, which is, in part, a consequence of the study's authors ignoring the basic economics of the box office. The defect in the Tale of the Long Tail's model is revealed in the dubious implication of that model that a "blockbuster" movie (as the authors define it) will earn no more revenue than a "small movie" and far less revenue than a "mid size" movie. In fact, according to the analysis in Tale of the Long Tail, what the authors define to be a "blockbuster" movie makes only one-fourth (1/4) of the revenues of mid-sized movies. The bizarre result about the benefits of piracy is based upon this dubious implication of the statistical model. In this PERSPECTIVE, I detail this problem with Tale of the Long Tail, and outline a few other defects. There are far too many problems with the study's empirical approach to cover them all in detail, including the fundamental question regarding whether the general "before" and "after" approach can identify the effect of piracy generally, and whether the authors have estimated a model capable of capturing the effect. A thorough analysis of the study is unnecessary since the study's central (and most controversial) finding is easily dismissed.

A two-stage proxy variable approach to estimating movie box office receipts

This paper advances the ongoing discussion of methods for predicting movie box office revenues with two contributions to the methodology and an out-of-sample test of the model. The first innovation is the development of a two-stage model using publicly available pre-release indicators to predict (1) initial week and (2) subsequent run box office revenues. To incorporate the experience-good nature of movies, the second stage is estimated by incorporating a proxy variable for box office success during the first week relative to predicted first week success. The second contribution is an empirical test of De Vany and Walls’ (J Econ Dyn Control 28:1035–1057, 2004) finding that the distribution of movie revenues has “heavy tails” and follows a non-Gaussian stable distribution with infinite variance. We estimate the two-stage model of a movie’s box office success on all general release movies in 1 year with both the Gaussian and stable distribution with heavy tails and infinite variance and find no evidence for the stable distribution in either stage of the estimation. This two-stage model is validated by comparing all general release movies in 3 future years (out-of-sample data) to the model’s predictions.

Piracy and Movie Revenues: Evidence from Megaupload

In this paper we make use of a quasi-experiment in the market for illegal downloading to study movie box office revenues. The sudden shutdown of the popular file hosting platform Megaupload.com on January 19, 2012 significantly (and unexpectedly) changed the availability of illegal movie downloads overnight. We compare box office revenues of movies that were available on Megaupload to those that were not pre- and post-shutdown and find that box office revenues of a majority of movies did not increase. The average effect is even negative. We show that only movies that premiere in a relatively large number of theaters benefitted from the closure of Megaupload. This is consistent with word-of-mouth effects, where online piracy can act as a mechanism to spread information about a good from consumers with low willingness to pay to consumers with high willingness to pay. This information-spreading effect of illegal downloads seems to be especially important for movies with smaller audiences, while for movies aimed at large audiences, the substitution effect between free and paid consumption weighs more heavily.

Accounting for latent classes in movie box office modeling

This article addresses the issue of unobserved heterogeneity in film characteristics influence on box-office. We argue that the analysis of pooled samples, most common among researchers, does not shed light on underlying segmentations and leads to significantly different estimates obtained by researchers running similar regressions for movie success modeling. For instance, it may be expected that a restrictive MPAA rating is a box office poison for a family comedy, whereas it insignificantly influences an action movie's revenues. Using a finite mixture model we extract two latent groups, the differences between that can be explained in part by the movie genre, the source, the creative type and the production method. On the basis of this result, the authors recommend developing separate movie success models for different segments, rather than adopting an approach, that was commonly used in previous research, when one explanatory or predictive model is developed for the whole sample of movies.

Accounting for Latent Classes in Movie Box Office Modeling

This paper addresses the issue of unobserved heterogeneity in film characteristics influence on box-office. We argue that the analysis of pooled samples, most common among researchers, does not shed light on underlying segmentations and leads to significantly different estimates obtained by researchers running similar regressions for movie success modeling. For instance, it may be expected that a restrictive MPAA rating is a box office poison for a family comedy, while it insignificantly influences an action movie's revenues. Using a finite mixture model we extract two latent groups, the differences between which can be explained in part by the movie genre, the source, the creative type and the production method. Based on this result, the authors recommend developing separate movie success models for different segments, rather than adopting an approach, that was commonly used in previous research, when one explanatory or predictive model is developed for the whole sample of movies.

Dynamic Effects among Movie Ratings, Movie Revenues, and Viewer Satisfaction

This research investigates how movie ratings from professional critics, amateur communities, and viewers themselves influence key movie performance measures (i.e., movie revenues and new movie ratings). Using movie-level data, the authors find that high early movie revenues enhance subsequent movie ratings. They also find that high advertising spending on movies supported by high ratings maximizes the movie's revenues. Furthermore, they empirically show that sequel movies tend to reap more revenues but receive lower ratings than originals. Using individual viewer–level data, this research highlights how viewers’ own viewing and rating histories and movie communities’ collective opinions explain viewer satisfaction. The authors find that various aspects of these ratings explain viewers’ new movie ratings as a measure of viewer satisfaction, after controlling for movie characteristics. Furthermore, they find that viewers’ movie experiences can cause them to become more critical in ratings over time. Finally, they find a U-shaped relationship between viewers’ genre preferences and genre-specific movie ratings for heavy viewers.