"To infinity and beyond!" - A genre-specific film analysis of movie success mechanisms. Daniel Kaimann

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1 "To infinity and beyond!" - A genre-specific film analysis of movie success mechanisms Daniel Kaimann University of Paderborn Department of Business Administration and Economics Warburger Str. 100, D Paderborn, Germany Daniel.Kaimann@wiwi.upb.de, Phone: Abstract This paper examines key success factors in the motion-pictures industry. We conduct an empirical analysis of the two film genres, computer animation and comic book adaption, using a cross-sectional data set covering the time horizon from 1978 to Using a Seemingly Unrelated Regression analysis, we identify in particular four movie features (Production Costs, Reputation Effects from Book Adaptions, Seasonality and Critical Evaluations from Professionals and Non-Professionals) that have a strongly significant influence on a movie s box office success and return on investment, helping to understand what drives the success of a film in a highly volatile market. Key words Motion-pictures economics, Key success factors, Film genre, Seemingly Unrelated Regression JEL classification C31 L10 L82 Acknowledgment I would like to thank the participants of the Organizational, Media and Sports Economics seminar at the University of Paderborn, Bernd Frick, Claus-Jochen Haake and especially Darlene Chisholm for her useful comments and discussions.

2 1 Introduction Not only did the computer triumph in the second half of the 20th Century in various areas of life, but the film industry also benefited from the use of the digital medium. First, visual effects could be created more cheaply, and with more credible effect, with the help of computer-generated imaging. In addition, it became possible to create complete virtual worlds using computer-based tools, and movies could be produced completely artificial. The new genre of computer-animated movies was born. Due to its quality and improved authenticity relative to traditional animation, computer animated films overtook animated films in popularity within a short time. The first installments of the films Cars, Kung Fu Panda and Happy Feet earned $461 million, $631 million and $384 million, respectively, at the box office worldwide. Thanks primarily to Toy Story 3 and Shrek Forever After and other box office successes like Despicable Me, How to Train Your Dragon and Tangled, computer animation accounted for nearly $1.5 billion of 2010's business, a rise of 16 percent in comparison to In addition to the genre of computer animated films, comic book adaption is a second field in which Hollywood faces significant potential for growth and increased box office revenues. In 2008 the two comic book adaptions The Dark Knight and Iron Man led the genre to a record $1.22 billion in sales 1. In 2011, the two big players in the comic book industry, Marvel Comics and DC Comics, released together four major film titles from their comic book repertoire. Marvel continued its Avengers series with Thor and Captain America: The First Avenger. Thor gained $449 million and Captain America: The First Avenger earned $368 million at the box office worldwide. These two major titles from Marvel build on the box office success of Iron Man, which earned $585 million worldwide. The X-Men series accounted for a worldwide box office of about $1.9 billion in total so far. DC Comics and Warner Bros. finally unleashed Green Lantern in The film adaption of the Green Latern earned $219 million at the box office worldwide. Computer animated and comic booked based films made up 12.5 percent of the total U.S.-Canadian box office in The American market is responsible for approximately 40 percent of a film s total revenue. Therefore, cinema revenues constitute the main source of income for the production studios. The average revenue of computer animated films was $119 million ( ) and $97 million ( ) for comic book based films at the 1 That is about 13 percent of overall box office in

3 American box office. Computer animated and comic book based films generate approximately 28.5 percent of their total revenue in the U.S. market on the first weekend. One important aspect for a successful opening weekend is the number of booked cinema screens. Computer animated and comic book based films achieve 98 percent of their full screen capacity in the first days of their performance. Another important feature of a successful opening is the quarter of release of a movie. As shown by our data, distributors of computer animated films prefer the fourth quarter for a film s premiere, whereas distributors of comic book based films have a preference for the second quarter. Table 1.1 outlines the key economic figures for computer animated and comic book based films in comparison to the All Time Top 50 films from 1995 to Table 1.1: Economic indicators Computer-animated Films ( ) Comic Book Based Films ( ) All Time Top 50 Films ( ) Economic indicators (in Million US$) Average Revenue (USA) Average Revenue (World) Average Production Costs Average Number of Cinema Screens Total Premieres per Quarter Q1 15% 18% 4% Q2 23% 40% 44% Q3 23% 29% 20% Q4 39% 13% 32% MPAA Ratings G 34% 1% 6% PG 60% 30% 28% PG-13 6% 40% 62% R 0% 29% 4% Source: Own calculation based on the data set 2

4 Three comic book based films are among the ten most successful films with a MPAA rating PG Among the ten most successful films with a MPAA rating G and PG are four computer animated movies 3. All have a total gross over $300 million. Because of their narrative plot and infantile way of presentation, computer animated films aim directly at families as their primary target group. Thus, they try to appeal to children, adolescents and their parents at the same time. For children computer animated films are easily understandable and entertaining. For juveniles, they keep a rebellious potential ready and for adults they offer the delight to recognize intersexual references, sexual innuendo or subtle meanings (Eder 2007: 290). Computer animated films are not only tailored for children and young people, but also inspire adults by their charm and wit. Examining the percentage distribution of family moviegoers with children (see Figure 1.2), one finds that families with children go to the cinema more often than families without children. In 2007, 26 percent of all moviegoers in the United States were parents or singles with children of 12 to 17 years and 22 percent were households with children of the age of 17 and younger. The finding that especially families and youths regularly 4 go to the movies can be a first explanation for the economic success of computer animated films with their specific ambition for this consumer group. Figure 1.2: Frequent Moviegoers by Household Composition ( ) Frequent Moviegoers by Household Composition No Children With Children (17 and younger) With Teenagers (12-17) Source: MPAA Movie Attendance Study, 2007: 9 The economics literature mainly analyses and quantifies the economic success of American feature films, though, there is little work directly differentiating between real and 2 The Dark Knight ($ ) 2008, Spider-Man ($ ) 2002, Spider-Man 2 ($ ) Shrek 2 ($ ) 2004, Toy Story 3 ($ ) 2010, Finding Nemo ($ ) 2003 and Shrek the Third ($ ) Regularly means at least once a month (12 times / year). 3

5 animated films. However, the direct impact of various genres on box office success has been considered. Studies by Litman (1983), Smith and Smith (1986), Wallace, Steigermann and Holbrook (1993), Prag and Casavant (1994), Sawhney and Eliashberg (1996), Eliashberg and Shugan (1997), Krider and Weinberg (1998), Albert (1998), Ravid (1999), Nelson et al. (2001), Hand (2002), De Vany (2004) and Walls (2005) especially examine the influence of actors, film awards and critics as crucial success factors. The British film market was investigated by Elliott and Simmons (2006), the Italian market by Bagella and Becchitti (1999), the Spanisch film industry by Fernández-Blaco and Prieto-Rodríguez (2003) and Gaitanides (2001) studied the German cinema market. In contrast to previous studies, this paper examines the success determinants of the fulllength computer animated and comic book adaptation movie with a focus on empirical evidence for the following reasons. First, they have not been subjected to econometric analysis until now. And second, the considerable success, especially in computer animated films, has ignited some fear that the traditional film, and in particular, traditional actors, could be replaced. In summary, it can be centralized that these two genres are characterized by high marketability, the interlinking of a star with his film, and the strong focus on commerce instead of art. 2 The analysis framework The basic concept of the marketing mix was first introduced by E. Jerome McCarthy in The marketing mix is based on all factors and mechanisms that a business can manipulate to influence the demand for its products or services (Kotler 1994: 46). "The concept of the mix lays out the areas in which facts should be assembled, these to serve as a guide to management judgment [...] (Borden 1964: 12)." With the help of the marketing mix concept we are able to understand human behavior in response to the stimuli to which they are subjected (Borden 1964). The model differentiates among four different constituents known as the "four Ps" product, place, promotion and price. Product summarizes all properties of the goods and services a business offers to its market. Choices about the product lines, the qualities and design of a product, the production quantities and the research and development program must be made. Place reflects all companies activities to distribute and merchandize its product to target consumers. Businesses must decide about the degree of selectivity among the distribution line. Promotion includes business activities to communicate and market the benefits of the product or service. Decisions about adversiting and communication strategies must be taken. Price is the amount 4

6 of money the business demands for its products or services. Pricing policies, e.g. skimming or penetration strategy, stand in focus in this condition. Applying the marketing mix to the motion-pictures industry, we are able to indentify and analyze the main mechanisms that drive the market successs of a movie. Figure 2.1 summarizes and shows the marketing mix and its different specifications for the motion-pictures industry. The main individual conditions are described in more detail in the next section. Figure 2.1: Marketing mix for the Motion-Pictures Industry Source: Own illustration 3 Data description By focusing our empirical analysis exclusively on computer animated and comic book adaptation movies, we include all computer animated and comic book adaptation movies that have had a US cinema premiere, resulting in the collection of 150 movies from 1978 to 2009 in total. The economic and financial information of the 150 movies was mainly collected from the Internet databases Box Office Mojo, IMDb, TheNumbers.com and the Motion Picture Association of America (MPAA). Due to missing values the dataset diminishs to 109 movies in total. In order to verify movie success mechanisms, we base our analysis on the eight main influential variables Production Costs (without Marketing Expenditures), MPAA Ratings, Reputation Effects from Sequels and Adaptions, Star Popularity Effects of Actors, Seasonality, Movie Reviews from Professionals and Moviegoers as well as Award Nominations and Wins. Litman (1983: 160) describes the level of production budget as a proxy variable for the 5

7 overall technical and artistic quality of a movie. Additionally, the level of production budget is correlated wih the number of booked cinema screens (correlation coefficient of ). De Vany (2004) again found a positive correlation between the number of booked cinema screens and box office return. We could verify a correlation between the production budget and domestic box office return. Up to now, the most expensive computer animated movie is WALL-E (2008) from Pixar/Disney, with overall production costs of $180 million (not inflation adjusted). So far, the most expensive comic book adaption movie is The Dark Knight (2008), which cost $185 million (not inflation adjusted). The production costs represent all negative costs of a movie without marketing expenditures. We assume that the production function is characterized by decreasing economies of scale. Parental Control guidance intends to protect certain age groups from violent, brutal and sexual content in films. While excluding some consumer groups from consumption, ratings also serve as content and quality indexes through their age classification. De Vany and Walls (2002) and Ravid (1999) were able to demonstrate a positive correlation between movies revenues and movies with a MPAA restriction G (general audiences), PG (parental guidance suggested) or PG-13 (parents strongly cautioned). The existence of a restrictive age classification like R (restricted) or NC-17 (no one 17 and under admitted) by the MPAA had a negative influence on box office results. Based on the studies from De Vany and Walls and Ravid we include the MPAA ratings G, PG and PG-13 as dummy variables in our analysis. R rated movies represent the basis variable. Movies represent an experience good. The product characteristics, such as technical and artistic quality are problematic to observe for consumers in advance. The consumption choices are also made difficult for consumers by experience goods. Sequels or book adaptions are able to overcome the uncertainty of decision-making. The pre-release popularity can be ascertained before consumption. Prag and Cassavant (1994) were able to confirm that prerelease success can induce reputation effects that positively affect box office returns. About 21 percent of all movies in the present dataset are represented by sequels. Nearly 63 percent reflect book adaptions. An actor s reputation is evidently a relevant indicator for the artistic and aesthetic quality of a film and thus, a good signal for the overall box office appeal of a film (Rosen 1981). Consequently, studies that consider the influence of actors with ex ante popularity are dominant in Motion-pictures economic analyses (Litman and Kohl 1989, Wallace, Steigermann and Holbrook 1993, Ravid 1999, De Vany and Walls 1999, Elliot and Simmons 2006). De Vany also shows that movies with stars are shown on 20 percent more screens than 6

8 films without actors with star potential (De Vany 2004). In computer animated movies, actors inevitably transfer their associated star image to the animated characters by evoking an interface between the animated figure and real person actor (Eder 2007). In order to identify a movie star, we look at the three most important Hollywood star lists: James Ulmer s list of A/ A+ people from 2009/10, Harris Poll s Top Ten Favorite Movie Star list from 2008 and the Actors People Index from Box Office Mojo (Effective Dec 2009). Especially, the Box Office People Index orders actors by their economical and financial attraction by sorting actors by their all-time career and average gross (in millions), their total number of movies and their economical most successful movie. The three lists are aggregated and summarized in a scale that represents the 104 most influential actors in Hollywood. The variable Star Actor reflects the total number of actors that are represented in both the summarized scale and the number of actors in a certain movie. The seasonality of cinema box-office returns reproduces both the demand of movies as well as the supply of movies in number and quality. Studies from Litman (1983), Nelson et al. (2001), Sochay (1994) and Einav (2007) analyze the correlation between the seasonality and demand for movies. Especially, movies that are released in the fourth quarter, so during Thanksgiving and Christmas, show a positive impact on box office success. Underlying the observed seasonality of sales and offers, three dummy variables are included to represent the three quarters Quarter 2 (from April to June), Quarter 3 (from July to September) and Quarter 4 (from October to December). The Quarter 1 (from January to March) reflects the basis variable. As movies represent an experience good, film reviews become important as a preliminary evaluation for the audience as they rate a movie according to its artistic, aesthetic, technical, economic and political aspects. Existing studies give evidence that critic reviews can reduce information asymmetries of moviegoers and ascertain a positive correlation of film reviews and box office appeal (Litman 1983, Litman and Kohl 1989, Wallace et al. 1993, Sochay 1994, Eliashberg and Shugan 1997). However, certain Motion-picture studies also determine no correlation between movie ratings and box office success indeed (Prag and Casavant 1994, Holbrook 1999, Eliashberg and Shugun 1997). In order to verify the influence of film critics to box office gross, the rating scores from professional reviewers and audience critics have been collected. The relevant data was taken from the Internet sources Metacritic.com, RottenTomatoes.com, MRQE.com (Movie Review Query Engine), Box Office Mojo and IMDb. Due to multicollinearity issues, two weighted average factors were created on the basis 7

9 of the principal-components method 5, one for the professional reviews and one for the evaluations of the audience. The results are two numbers that capture the opinions and validations of critics writing online and in print. An award is a recognition of excellence given to a person or a group of people. It is an acknowledgment of artistic and technical quality and achievement. In 2001, Nelson et al. verified that an Academy Award nomination, as well as an Academy Award win, has a significantly positive effect on box office success. In order to analyze the correlation of award nominations and wins with box office appeal, we collected data from the Academy Awards, the Golden Globes, the Annie Awards, the Golden Trailers, the Kid s Choice Awards, the MTV Movie Awards, the Teen Choice Award and the People's Choice Award. Due to multicollinearity, two summed scales were created, one for overall nominations and one for total wins. The predominant interest in Hollywood movie industry is the total number of ticket sales and box office gross indeed. The financial success of former film releases has direct influence for the production and funding of future projects. In order to adjust the gross for inflation, we adopt the Consumer Price Index - All Urban Consumers (CPI-U) from 1978 to 2009 to determine the appropriate total revenues based on the year Besides the calculation of total gross, we also factor the economic rent of each film into our analysis. The economic rent is represented by the Return of Investment (ROI). The Return of Investment is defined as (1) The box office figures in North America are divided into a domestic, i.e. United States and Canada, and a foreign market, meaning all other countries in the world. Gathering the regional distinction information for both domestic and foreign, we are able to control for cultural differences. As a result, we verify our independent variables against four dependent variables: Box Office Gross (USA), Box Office Gross (Rest of World), ROI (USA), ROI (Rest of World). The descriptive statistics of all independent and dependent variables are represented in Table The principal components method reduces the dimensionality of a variable set by decompounding its total variance. In doing so, the method does not differentiate between common variance and unique variance as factor analysis does (Lewis-Beck 1994: 205). 8

10 Table 3.1: Descriptive statistics Variable Obs Mean Std. Dev. Min Max Production Costs e e e+08 MPAA G MPAA PG MPAA PG MPAA R Sequels Book Adaptions Star Actors Quarter Quarter Quarter Quarter Professional Reviews Moviegoers Reviews Award Nominations Award Wins Box Office Gross (USA) e e e+08 Box Office Gross (ROW) e e e+08 ROI (USA) ROI (ROW) Empirical analysis The cross sectional analysis is essentially based on two regression models. The basic regression model (Regression Model 1) represents only the product-inherent film factors, meaning all factors that have a direct influence on the production of a film. The extended regression model (Regression Model 2) extends the product-inherent factors with productinduced factors like Seasonality, Movie Reviews or Award Nominations and Wins. By estimating the regression models under the condition of regional distinctions, it can be assumed that the disturbance terms in different equations are correlated. The application of single-equation least squares estimators to equation-by-equation models would yield to no efficient coefficient estimators. Zellner's (1962) Seemingly unrelated regression (SUR) considers the correlation of the disturbance terms in equation-by-equation models. Seemingly unrelated regression estimates all regression coefficients in all equations by applying general least square estimators. The SUR estimates are represented in Table 4.1 and

11 Regression Model 1: (2) Regression Model 2: (3) ( ) ( ) ( ) ( ) 10

12 Table 4.1: Seeming unrelated regression estimates for Box Office Gross ln Box Office Gross (USA) ln Box Office Gross (ROW) Dependent variable Reg 1 Reg 2 Reg 1 Reg 2 ln Production Costs *** *** *** *** ( ) ( ) ( ) ( ) MPAA G ** ( ) ( ) ( ) ( ) MPAA PG * ** ( ) ( ) ( ) ( ) MPAA PG ** ( ) ( ) ( ) ( ) Sequels * ( ) ( ) ( ) ( ) Book Adaptions ** ** * ( ) ( ) ( ) ( ) Star Actors * * ( ) ( ) ( ) ( ) Quarter ** ** ( ) ( ) Quarter * ( ) ( ) Quarter ** ( ) ( ) Professional Reviews ( ) ( ) Moviegoers Reviews ( ) ( ) Award Nominations ** ( ) ( ) Award Wins ( ) ( ) N R Chi Significance: 0.01 '***' 0.05 '**' 0.1 '*' Note: Standard errors in parentheses. The following additional variables represent control variables: Studio size/market share (Mini, Major, Indepent studio), Opening Weekend Performance (Top 5 Chart Position), Linear Time Trend (from 1978 to 2009), Genre and Group Effects (Comic Book Based Films and Computer Animated Films), Directors and Producers with Star Appeal. 11

13 Table 4.2: Seeming unrelated regression estimates for Return On Investment (ROI) ROI (USA) ROI (ROW) Dependent variable Reg 1 Reg 2 Reg 1 Reg 2 ln Production Costs *** *** ** *** ( ) ( ) ( ) ( ) MPAA G ( ) ( ) ( ) ( ) MPAA PG ( ) ( ) ( ) ( ) MPAA PG * ( ) ( ) ( ) ( ) Sequels * ( ) ( ) ( ) ( ) Book Adaptions ** * ** ** ( ) ( ) ( ) ( ) Star Actors * * ( ) ( ) ( ) ( ) Quarter ( ) ( ) Quarter * ( ) ( ) Quarter ** * ( ) ( ) Professional Reviews * ** ( ) ( ) Moviegoers Reviews *** ** ( ) ( ) Award Nominations ( ) ( ) Award Wins ** ( ) ( ) N R Chi Significance: 0.01 '***' 0.05 '**' 0.1 '*' Note: Standard errors in parentheses. The following additional variables represent control variables: Studio size/market share (Mini, Major, Indepent studio), Opening Weekend Performance (Top 5 Chart Position), Linear Time Trend (from 1978 to 2009), Genre and Group Effects (Comic Book Based Films and Computer Animated Films), Directors and Producers with Star Appeal. 12

14 The statistical significant influence of production costs (without marketing expenditures) on box office appeal as well as on Return on Investment can be verified. This finding can be approved across regional distinctions. So, we can confirm Litman results from 1983 and verify the production budget's attribute as a proxy variable for the overall technical and artistic quality of a movie. However, the level of production costs affects the critical quality of a film but it also reduces the economical rent and certeris paribus the Return of Investment. A statistical verification that a restrictive age classification from the Motion Picture Association of America has a negative impact on total gross or Return on Investment cannot be given. The coefficient estimators differ across equations and regions in significance and leading signs. Hence, a confirmation that the exclusion of consumer groups from consumption through a restrictive age classification has a negative effect on economic profits cannot be approved. We cannot verify the findings of Prag and Cassavant (1994) that were able to confirm that sequels can induce reputation effects that positively affect box office revenues. For sequel films, no clear significant influence can be attested. Thus, sequels are no reliable quality sign to overcome the consumer's uncertainties. Book adaptions have a negative significant influence on box office returns and Returns on Investement indeed. Although, the reputation effects of book adaptions can be statiscally verified, it seems that former novel readers are not willing to pay for the cinematic implementation of their reading. Examining the star popularity effects of actors in computer animated and comic book based films, we cannot confirm the findings from studies conducted by Litman and Kohl (1989), Wallace, Steigermann and Holbrook (1993) and Ravid (1999) that verify the influence of actors with ex ante popularity on box office gross. The coefficient estimators of the variable Star Actors are not consistently significant across equations and regions. The role of star actors seem to be fundamentally different in influencing the box office success in computer animated and comic book based movies then across other film genres. Nevertheless, it can be determined that the attractiveness of an actor can be transfered to the attractiveness of a film, but the influence of stars differs across genres. We cannot acknowledge former studies of seasonality and its direct impact on the demand for movies. The coefficient estimators differ across equations and regions in significance and leading signs in almost the same manner as the coefficient estimators of MPAA Ratings. In order to analyze the seasonality of cinema box-office returns more precisely, individual bank holidays like Thanksgiving or Christmas Day should be included in consideration. As a result, the explanatory value of seasonality and its influence on economic 13

15 revenues would be improved. Testing for the economical effect of movie reviews from professional critics, we can confirm both a significant negative correlation between professional reviews and Return on Investment and no significant negative correlation between professional reviews and box office revenues. The coefficient estimators of movie reviews from moviegoers are consistently positive but only statistically significant in the regression analysis for Return on Investment. Since our dataset goes back to the pre-internet time period, the findings of this estimators should be verified in future studies. In summary, results of former studies can be validated and the condition that film reviews represent a preliminary evaluation source for the audience can be affirmed. Although, awards like the Academy Awards or the Golden Globes have a high media impact, their economical statistical confirmation cannot be given in our study. Neither the nomination for a film award nor the actual win of an award can lead to a clear significant effect on box office or Return on Investment. This finding stands in contrast to the previous study from Nelson et al. (2001). A circumstance that is even more surprising considering the sales promotion effects of awards for tickets at the box office as well as for DVD rentals and sales. The consolidated results are shown in Table 4.3. They are separated by independent variables and their significant effect on the two tested dependent variables. Table 4.3: Summary of findings Dependent variables Independent variables Significant positive No significant positive No clear conclusion (negative) effect (negative) effect Box Office Production Costs, (Book (Professional Reviews), MPAA G, MPAA PG, Adaptions), Moviegoers Reviews, MPAA PG-13, Sequels, (Quarter 2) Award Wins Star Actors, Quarter 3, Quarter 4, Award Nominations Return on Investment (Production Costs), (Book MPAA G, MPAA PG, MPAA PG-13, Sequels, (ROI) Adaptions), (Quarter 4), (Quarter 2), Award Star Actors, Quarter 3, (Professional Reviews), Nominations Award Wins Moviegoers Reviews 14

16 5 Conclusion The emphasis of the present study is the analysis of movie success mechanisms of the two film genres of computer animated and comic book based films. Despite the examination of these particular genres, the results possess a universal validity. Via an empirical analysis of cross-sectional data with a time horizon of about 30 years, we produce strong evidence that especially Production Costs, Reputation Effects from Book Adaptions, Seasonality and Movie Reviews from Professionals and Non-Professionals do have statistically robust significant influence on box office appeal within this class of films. In particular, critical statements from consumers and users about the quality of a product or service can have a significant impact on sales figures. Nowadays, customers are able to voice their opinion and share their experience about products and services in diverse discussion forums, assessment portals or online blogs. Thus, it is possible for them to enter into a dialogue and to share beliefs and positions. Due to the greater credibility and stronger social control, social online word-of-mouth does not only have the capability to affect product sales, but also to influence the business sales stratgies (Liu 2006, Chen and Xie 2008). From a business perspective, it is of vital importance to forecast product sales efficiently, in particular those of new products that stand at the beginning of their product life cycle. Thus, it is critical to control and analyze how the information contained in the online word-of-mouth can be measured and evaluated in order to optimize managerial decisions and to strenghten competitive advantages. So far, studies mainly examined sales portals, such as ebay or Amazon. They verified a correlation of customer reviews and product sales (Dellarocas 2003, Liu (2006), Chen and Xie 2008). However, these studies considered mostly either the average ratings of the sellers or the products. Future studies in Motion-pictures economics should work on the transcription and intergration of word-of-mouth. As a result, the explanatory value of prospective analyses would be increased and the uncertainty in this highly volatile market would be comprehended more efficiently. 15

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